Method and device for oil smoke data analysis and computer readable storage medium

Abstract

The invention relates to the technical field of intelligent household electrical appliances, and discloses a method for oil smoke data analysis, which comprises the following steps: acquiring an initial speed and a particle size parameter of oil smoke particulate matters in a target space; and determining a smoke source in the target space according to the initial speed and the particle size parameter of the lampblack particles. According to the method, oil smoke particulate matter parameters are collected to obtain the initial speed and particle size parameters of the oil smoke particulate matter. And determining the type of a smoke source based on the obtained initial speed and particle size parameters of the oil smoke particulate matters, namely determining a cooking scene. In this way, the mapping relation between the initial speed and the particle size parameter of the oil smoke particulate matter and the smoke source can be determined. Therefore, the smoke suction effect of the range hood in various cooking states can be accurately simulated based on the smoke source information. The invention further discloses a device for oil smoke data analysis and a computer readable storage medium.

Description

Technical Field

[0001] This application relates to the field of range hood technology, and for example to a method, apparatus and computer-readable storage medium for analyzing oil fume data. Background Technology

[0002] As a kitchen exhaust system, range hoods reduce the contact between users and oily fumes during cooking, while also improving air quality in the kitchen. The exhaust efficiency of a range hood is crucial for improving and optimizing its performance; and reliable acquisition of fume data is a key factor in analyzing its exhaust effectiveness. Specifically, the source information in the fume data directly affects the range hood's fume extraction efficiency. Therefore, evaluating the effectiveness of a range hood requires accurate source information to arrive at objective and reliable conclusions. This, in turn, provides a scientific basis for the design and performance optimization of range hoods.

[0003] A testing method and apparatus for evaluating the performance of a range hood are disclosed in related technologies. The testing method includes: establishing a three-dimensional structural model of the range hood based on its structural parameters; setting fluid dynamics simulation parameters, including suspended particle parameters and airflow parameters; performing fluid dynamics simulation on the model of the range hood using these parameters to obtain particulate matter escape simulation results and airflow / pressure simulation results; and determining the simulated performance of the range hood by combining the particulate matter escape simulation results and the airflow / pressure simulation results.

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

[0005] The relevant technologies collect parameters of suspended particles (such as particle generation rate, particle mass, particle size, etc.) in the performance testing of range hoods, but do not involve how to determine the smoke source information.

[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 oil fume data, which can accurately obtain smoke source information.

[0009] In some embodiments, the method includes: acquiring the initial velocity and particle size parameters of oil fume particles in a target space; and determining the smoke source in the target space based on the initial velocity and particle size parameters of the oil fume particles.

[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 oil fume data.

[0011] In some embodiments, the computer-readable storage medium stores program instructions that, when executed, cause the computer to perform the aforementioned method for analyzing oil fume data.

[0012] The method, apparatus, and computer-readable storage medium for oil fume data analysis provided in this disclosure can achieve the following technical effects:

[0013] The system collects particulate matter parameters from cooking scenarios to obtain the initial velocity and particle size parameters of the particulate matter. Based on the obtained initial velocity and particle size parameters, the smoke source information is determined to define the cooking scenario. This establishes the mapping relationship between the initial velocity and particle size parameters and the smoke source. Consequently, the range hood's smoke extraction effect under various cooking conditions can be accurately simulated based on the smoke source information.

[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 oil fume data provided in an embodiment of this disclosure;

[0017] Figure 2 This is a flowchart illustrating the process of obtaining the initial velocity of oil fume particles in the target space in the method provided in this embodiment of the disclosure;

[0018] Figure 3 This is a schematic flowchart illustrating the process of processing the acquired oil fume images in the method provided in this embodiment of the disclosure;

[0019] Figure 4 This is a schematic diagram of another method for analyzing oil fume data provided in an embodiment of this disclosure;

[0020] Figure 5This is a schematic diagram of the image after grayscale conversion in the method of this embodiment of the disclosure;

[0021] Figure 6 This is a schematic diagram of an apparatus for analyzing oil fume data 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 this embodiment of the disclosure, the generation and diffusion of cooking fumes under various cooking scenarios, as well as the changes in the velocity field of the fumes under the action of a range hood, are simulated in an experimental test space. The experimental equipment includes a range hood, a heating source, and cookware; the heating source includes a stove or other device capable of heating the cookware. Image acquisition devices are provided on both sides of the cookware to acquire the velocity parameters of the cooking fume particles. For example, one image acquisition device is provided on the front and one on the side of the cookware. A fume sampler is provided above the cookware to collect 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] Combination Figure 1 As shown in the embodiments of this disclosure, a method for analyzing oil fume data is provided, including:

[0031] S101, the processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0032] S102, 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] The method for analyzing cooking fume data provided in this disclosure collects particulate matter parameters of cooking fumes under specific cooking scenarios to obtain the initial velocity and particle size parameters of the particulate matter. Based on the obtained initial velocity and particle size parameters, the smoke source is determined to define the cooking scenario. 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, in step S101, the processor obtains the initial velocity of the oil fume particles in 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] Step S101, the processor obtains the initial velocity of the oil fume particles in the target space, including:

[0039] S111, the processor uses an image acquisition device to acquire images of oil fume particles generated in the target space; wherein, the oil fume images include multiple frames.

[0040] S112, the processor processes the acquired oil fume image to obtain the grayscale peak distribution of the image; 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, in step S112, the processor processes the acquired oil fume image to obtain the grayscale peak distribution of the image, including:

[0044] S1121, the processor performs grayscale conversion on the acquired oil fume image before dividing it into grids.

[0045] S1122, the processor calculates the gray value of each grid cell, and the gray value of each grid cell is a gray value sequence that changes over time.

[0046] S1123, the processor performs a Fourier transform on the gray values ​​to obtain the gray 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 (Open Source Computer Vision Library) to convert RGB images to grayscale. The grayscale image is then discretized, i.e., divided into grids. The grayscale value of each grid is calculated, such as by calculating the average value of each grid. This processing is performed on each frame, so that the grayscale values ​​of the corresponding 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, and 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, in step S112, 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, in step S1122, the processor calculates the grayscale value for each grid cell by:

[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, in step S1121, the processor performs grayscale conversion on the acquired oil fume image, 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 oil fume image is converted to a grayscale image, where each grid in the image is represented by a two-dimensional array. Thus, each image is a two-dimensional matrix. For example, combining... Figure 5 As shown, the coordinates of a certain grid in an 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-smoke areas from the image. For example, if the grayscale value of a non-smoke area is small, a suitable grayscale threshold can be set to delete that area. This reduces computational load and improves processing speed.

[0063] Optionally, in step S101, the processor obtains 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 μm to 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] Combination Figure 4 As shown in the embodiments of this disclosure, a method for analyzing oil fume data is provided, including:

[0069] S101, the processor acquires the initial velocity and particle size parameters of the oil fume particles in the target space.

[0070] S121, if 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] S122, 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 the initial velocity and particle size parameters. In this embodiment, the smoke source mainly includes two types: stir-frying and frying. The smoke source is determined based on the particle size parameters and initial velocity. Specifically, the particle size ratio in the smoke source information for stir-frying is significantly higher than that for frying, and the initial velocity of the oil fume particles in stir-frying is significantly higher than that in frying. Therefore, velocity conditions and particle size conditions are set to distinguish between these two types of smoke sources. The velocity condition includes a velocity threshold, and the particle size condition includes particle size ratio thresholds for PM1.0 and PM2.5 particles. As an example, the first particle size condition includes a PM2.5 particle size ratio greater than or equal to 90% and a PM1.0 particle size ratio greater than or equal to 80%. The second particle size condition includes a PM2.5 particle size ratio greater than or equal to 85%, and a PM1.0 particle size ratio range of (68%, 80%). The first velocity condition is greater than or equal to 0.8 m / s, and the velocity range of 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, information on the particulate matter in the smoke source can be obtained. This allows for the simulation of the range hood's smoke extraction effect under various cooking conditions based on accurate smoke source information, improving the accuracy of effect evaluation.

[0073] After determining the smoke source, i.e., the mapping relationship between the particle size parameters, initial velocity, and smoke source of the oily fumes, it can be used to analyze the smoke extraction effect of range hoods or the grease separation degree of oily fumes.

[0074] Methods for analyzing the smoke extraction effect of range hoods include:

[0075] The processor identifies the target smoke source to obtain the corresponding particulate matter parameters and cooking temperature parameters of the target smoke source.

[0076] The processor inputs the parameters of oil fume particulate matter and cooking temperature into the target simulation model to simulate the smoke extraction effect and obtain the evaluation results of the smoke extraction effect of the range hood.

[0077] 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.

[0078] This process involves obtaining the initial velocity and particle size parameters of the oil fume particles, as well as the temperatures of the cookware and stove, based on the target smoke source. For example, when the target smoke source is the primary smoke source, the initial velocity can be set to 1 m / s, and the cookware temperature can range from 20℃ to 350℃. This allows for the acquisition of accurate parameters that truly reflect the actual environment, resulting in accurate and reasonable data.

[0079] The parameters of oil fume particulate matter 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 range hood's smoke extraction evaluation results. The evaluation of the range hood's smoke extraction effectiveness is primarily based on its oil fume particulate matter capture efficiency. The target simulation model includes a fluid dynamics model and a discrete model; the fluid dynamics model simulates the oil fume flow field, while the discrete model performs calculations. The parameters input to the target simulation model include not only oil fume particulate matter parameters and cooking temperature parameters, but also other factors affecting oil fume diffusion, such as airflow parameters, range hood parameters, and operating parameters. This makes the simulation more closely resemble the actual scenario.

[0080] The method for analyzing the smoke extraction effect 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 of particulate matter, smoke generation 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, a more accurate evaluation of the range hood's smoke extraction effect is obtained, which is helpful for optimizing range hood performance.

[0081] Optionally, the parameters of oil fume particulate matter include the initial velocity, particle size distribution, and particle size percentage of the oil fume particles.

[0082] Among them, the parameters of oil fume particulate matter and the target smoke source have a mapping relationship.

[0083] 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 particles.

[0084] Optionally, the cooking temperature parameters include stove temperature and cookware temperature;

[0085] 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.

[0086] 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 of the cookware. Cookware temperature comprises multiple annular temperature zones centered on the center of the cookware (see [link to relevant documentation]). Figure 2 The temperature of the annular temperature zone closer to the center is higher. Generally, the temperature of the outer surface of the cookware is slightly higher than that of the inner surface. Furthermore, the cookware temperature is related to the stove temperature. The more annular temperature zones there are, the more accurate the cookware temperature readings will be. 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 resembles the actual cooking process.

[0087] 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.

[0088] Before the processor inputs the particulate matter parameters and cooking temperature parameters into the target simulation model, it also includes:

[0089] The processor builds a kitchen model and imports it into the target simulation model to simulate the flow field of cooking fumes.

[0090] The processor uses a static mesh to divide the oil fume flow field into a grid.

[0091] 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, in this embodiment, the kitchen model size is not limited to this and can be set according to requirements.

[0092] 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.

[0093] Optionally, the processor inputs the particulate matter parameters and cooking temperature parameters into the target simulation model to simulate the smoke extraction effect and obtain the evaluation results of the range hood's smoke extraction effect, including:

[0094] The processor inputs the parameters of particulate matter in cooking fumes and the parameters of cooking temperature into the discrete model.

[0095] The processor sets and initializes the boundary conditions of the discrete model to calculate the simulated oil fume flow field.

[0096] The processor calculates the capture efficiency of oil fume particles after the simulated oil fume flow field converges.

[0097] 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 such as 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, the oil fume particle data is statistically analyzed to calculate the oil fume particle capture efficiency. This ensures data accuracy, obtains a stable flow field, and avoids errors in calculating the capture efficiency. Thus, by controlling the model parameters and boundary conditions, the reliability of the range hood effect simulation is ensured.

[0098] It should be noted that the input parameters of the discrete model also include the operating parameters of the range hood and other parameters. Furthermore, when calculating the simulated oil fume flow field, an appropriate calculation model or equation can be selected to improve computational efficiency and accuracy. For example, energy equations, thermal radiation models, and turbulence models can be used for calculation.

[0099] Optionally, the processor inputs the particulate matter parameters of the cooking fumes into the discrete model, including:

[0100] The processor sets the composition of oil fume particles as heavy oil particles and the particle source as a surface jet source.

[0101] 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.

[0102] The processor inputs the average particle size and distribution coefficient of the oil fume particles into the discrete model.

[0103] 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 d is the particle size. The total number of smoke-generating particles is a set value. After the total number of smoke-generating 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.

[0104] 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.

[0105] Optionally, the processor sets the boundary conditions for the discrete model, including:

[0106] 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.

[0107] 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 locations 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. Then, the above boundary conditions are initialized. For example, the gas outlet velocity range is [0m / s~1m / s], and the gas temperature range is [100℃~600℃], etc.

[0108] Optionally, the processor calculates the collection efficiency of oil fume particles after the simulated oil fume flow field converges, including:

[0109] The processor counts the particulate matter from the primary and secondary capture of cooking fumes.

[0110] The processor calculates the primary capture efficiency and the secondary capture efficiency based on the primary capture of oil fume particles and the secondary capture of oil fume particles, respectively.

[0111] The processor uses the sum of the primary and secondary capture efficiencies as the capture efficiency of oil fume particles.

[0112] Here, primary and secondary capture of oil fume particles refer to the number of oil fume particles captured on the capture surface at different time intervals. Then, based on the total number of primary and secondary capture oil fume particles and the total number of smoke-generating particles, the primary capture rate and secondary capture efficiency can be calculated. The sum of the two capture rates is the oil fume particle capture efficiency. Specifically, by statistically analyzing the residence time x corresponding to the peak of the normal distribution of the number of particles on the capture surface, the minimum residence time s, and the maximum residence time m, particles within the residence time interval [s, 2x-s] are primary capture particles, and particles within the residence time interval [2x-s, m] are secondary capture particles. Thus, the oil fume particle capture efficiency is obtained to evaluate the smoke extraction effect of the range hood. Understandably, the higher the capture efficiency, the better the smoke extraction effect of the range hood. Furthermore, the capture surface of the range hood includes key areas or components that actively capture and intercept oil fumes during operation, such as the oil filter, centrifugal device, and baffle plate.

[0113] In addition, after calculating the collection efficiency of oil fume particles, the processor also includes:

[0114] The processor stores the correspondence between the particulate matter collection efficiency and the range hood parameters and operating parameters. Preferably, when the range hood parameters are the same, it stores the correspondence between the highest particulate matter collection efficiency and the range hood operating parameters. In this way, during the application of the range hood, the operating parameters can be controlled based on the stored relationship to achieve better smoke extraction effect.

[0115] Application Scenario 1: Fan Control of Range Hoods

[0116] When the range hood is turned on, the processor obtains the installation height of the range hood and the installation position of the cooktop.

[0117] The processor determines the target fan parameters corresponding to the range hood installation height and the cooktop installation position based on the correspondence between the range hood installation height, the cooktop installation position, and the fan parameters.

[0118] The processor determines the target fan parameters as the initial fan parameters.

[0119] The fan control method for range hoods provided in this disclosure, when the range hood is started, obtains the range hood installation height and the cooktop installation position, and determines the target fan parameters corresponding to the range hood installation height and cooktop installation position based on the correspondence between the range hood installation height, cooktop installation position, and fan parameters (this correspondence can be obtained based on the above content). The range hood configures its initial fan parameters according to the target fan parameters. This disclosure combines multiple installation parameters reflecting the specific installation structure of the range hood, such as the range hood installation height and cooktop installation position, to configure the initial fan parameters of the range hood. Compared with simply adjusting the opening area of ​​the smoke inlet, this achieves height adaptation of the initial fan parameters to the range hood installation structure, which is beneficial to improving the accuracy of fan airflow control and thus improving the oil fume capture efficiency of the range hood.

[0120] Optionally, the processor determines the target fan parameters corresponding to the range hood installation height and the cooktop installation position based on the correspondence between the range hood installation height, the cooktop installation position, and the fan parameters, including:

[0121] The processor obtains the smoke source from the range hood. When the smoke source is for stewing or cooking, the processor obtains the first correspondence between the range hood installation height, the cooktop installation position, and the fan parameters under the stewing or cooking smoke source, and determines the target fan parameters corresponding to the range hood installation height and the cooktop installation position based on the first correspondence.

[0122] When the smoke source is a stir-fry smoke source, the processor obtains a second correspondence between the range hood installation height, the stove installation position, and the fan parameters under the stir-fry smoke source, and determines the target fan parameters corresponding to the range hood installation height and the stove installation position based on the second correspondence.

[0123] Thus, during cooking, users typically select the low heat setting on the stove for stewing or simmering, and the medium or high heat setting for stir-frying. Compared to stewing, stir-frying produces more fumes, requiring higher fan parameters. Therefore, this embodiment configures a first and a second correspondence for different smoke sources, and determines the correspondence based on the determined correspondence to configure the target fan parameters. This embodiment comprehensively considers the specific installation structure of the range hood, the stove installation location, and the smoke source to configure the target fan parameters, further improving the accuracy of fan airflow control and effectively enhancing the range hood's fume collection efficiency.

[0124] As an example, the first correspondence between the installation height of the range hood under the cooking smoke source and the installation position of the stove and the air volume of the fan is shown in Table 1. Here, the stove installation position is the distance between the center of the stove burner and the wall where the range hood is located, i.e., the shortest distance between the center of the stove burner and the wall where the range hood is located.

[0125] Table 1

[0126]

[0127] Table 2 provides a second reference for the correspondence between the installation height of the range hood under the source of stir-frying smoke and the installation position of the stove and the air volume of the fan:

[0128] Table 2

[0129]

[0130] Optionally, the processor obtains the smoke source from the range hood in the following manner:

[0131] The processor obtains the stove setting and the height of the pot on the stove.

[0132] When the stove setting indicates a stewing / cooking setting and / or the pot height matches the steaming / cooking pot height, the processor determines the smoke source as a stewing / cooking smoke source.

[0133] When the stove setting indicates the stir-fry setting and / or the pot height matches the wok height, the processor determines that the smoke source is a stir-fry smoke source.

[0134] In this context, the pot height refers to the vertical distance between the highest and lowest points of the pot. Thus, if the user is stewing or simmering, a steaming pot, such as a steamer or earthenware pot, is typically used. If the user is stir-frying, a stir-frying pot, such as a wok, is typically used. The height of steaming / simmering pots is greater than that of stir-frying pots. Furthermore, the stove setting corresponding to stewing / simmering is lower than the stove setting corresponding to stir-frying. Based on this, the embodiments of this disclosure combine stove setting and / or pot height to determine the smoke source, ensuring the accuracy of smoke source identification and improving the precision of fan airflow control.

[0135] After the processor determines that the target wind turbine parameters are the initial wind turbine parameters, it also includes:

[0136] The processor obtains user-sensitive types.

[0137] The processor determines the target control method for the initial wind turbine parameters based on the user's sensitivity type.

[0138] The processor adjusts the initial fan parameters according to the target control method so that the adjusted fan parameters are adapted to the user's sensitivity type.

[0139] The fan control method for range hoods provided in this disclosure involves obtaining the range hood's installation height and the cooktop's installation position when the range hood is started. Based on the correspondence between the range hood's installation height, the cooktop's installation position, and the fan parameters, target fan parameters corresponding to these positions are determined. Initial fan parameters are then configured according to these target parameters. This disclosure combines multiple installation parameters reflecting the specific installation structure of the range hood, such as the range hood's installation height and the cooktop's installation position, to configure the initial fan parameters. Compared to simply adjusting the opening area of ​​the smoke inlet, this allows for a higher degree of adaptation between the initial fan parameters and the range hood's installation structure, improving the accuracy of fan airflow control and thus enhancing the range hood's fume collection efficiency. After configuring the initial fan parameters, this disclosure further adjusts them based on the user's sensitivity to noise or fumes, ensuring the adjusted parameters match the user's sensitivity type. This avoids excessively high initial fan parameters causing discomfort and enables personalized fan control.

[0140] Optionally, the processor determines the initial fan parameter adjustment method based on the user's sensitivity type, including:

[0141] When the user's sensitivity type indicates noise sensitivity, the processor determines that the target adjustment method for the initial fan parameters is to decrease the initial fan parameters. When the user's sensitivity type indicates oil fume sensitivity, the processor determines that the target adjustment method for the initial fan parameters is to increase the initial fan parameters.

[0142] Thus, when the user's sensitivity type indicates noise sensitivity, meaning the user is highly sensitive to fan noise, the range hood will adjust by reducing the initial fan parameters. Conversely, when the user's sensitivity type indicates smoke sensitivity, meaning the user is highly sensitive to cooking fumes, the fan parameters need to be increased beyond the original settings to quickly remove cooking fumes. In this case, the range hood will adjust by increasing the initial fan parameters. This embodiment of the present disclosure, after configuring the initial fan parameters, further adjusts them based on the user's sensitivity to fan noise or smoke, ensuring the adjusted fan parameters match the user's sensitivity type and achieving personalized fan control.

[0143] Optionally, reducing the initial fan parameters of the range hood includes: reducing the initial fan parameters according to a first periodic threshold and a first fan parameter change amount. Increasing the initial fan parameters of the range hood includes: increasing the initial fan parameters according to a second periodic threshold and a second fan parameter change amount. Thus, this embodiment of the present disclosure can periodically perform step-like adjustments to the fan parameters based on the initial fan parameters, improving the reliability of the range hood's fan parameter adjustment. It should be noted that the first periodic threshold and the second periodic threshold can be the same or different. The first periodic threshold can be determined based on the user's sensitivity to noise; the higher the user's sensitivity to noise, the smaller the first periodic threshold. The second periodic threshold can be determined based on the user's sensitivity to cooking fumes; the higher the user's sensitivity to cooking fumes, the smaller the second periodic threshold. The first fan parameter change amount and the second fan parameter change amount can be the same or different. The first fan parameter change amount can be determined based on the user's sensitivity to noise; the higher the user's sensitivity to noise, the larger the first fan parameter change amount. The variation in the parameters of the second fan can be determined based on the user's sensitivity to cooking fumes. The higher the user's sensitivity to cooking fumes, the greater the variation in the parameters of the second fan.

[0144] Optionally, the processor obtains user-sensitive types in the following manner:

[0145] The processor receives the fan control command sent by the user, which indicates the historical initial fan parameters. If the fan control command indicates a decrease in the historical initial fan parameters, the range hood determines the user is a noise-sensitive user. If the fan control command indicates an increase in the historical initial fan parameters, the range hood determines the user is a smoke-sensitive user.

[0146] Thus, this embodiment of the present disclosure can determine the user's sensitivity type based on the user's operation of the fan obtained during the historical operation phase of the range hood, ensuring the accuracy of the user sensitivity type determination, thereby providing data support for subsequent personalized fan control.

[0147] Application Scenario 2: Control of the smoke baffle in a range hood

[0148] After the range hood is started, the processor obtains the correlation between the oil fume particulate matter capture efficiency and the rotation angle of the baffle. The rotation angle of the baffle represents the angle of the baffle relative to the reference plane when it rotates, with the plane containing the oil filter as the reference plane.

[0149] The processor determines the optimal smoke baffle rotation angle based on the correlation between the smoke particulate matter capture efficiency and the smoke baffle rotation angle.

[0150] The processor controls the smoke baffle to rotate according to the smoke baffle's rotation angle.

[0151] This embodiment of the invention obtains the correspondence between the particulate matter capture efficiency and the rotation angle of the baffle plate after the range hood is started. Based on this correspondence, it determines the baffle plate rotation angle corresponding to the optimal particulate matter capture efficiency of the range hood and controls the baffle plate to rotate according to this rotation angle. This embodiment of the invention can predict the impact of the baffle plate rotation angle on the particulate matter capture efficiency based on the correspondence between the particulate matter capture efficiency and the baffle plate rotation angle. Before the cooking fumes rise, the baffle plate has already rotated according to the baffle plate rotation angle corresponding to the optimal particulate matter capture effect, which can prevent the escape of fumes during cooking, improve the baffle plate's fume capture efficiency after the range hood is started, and thus improve the range hood's fume extraction effect.

[0152] After the processor controls the smoke baffle to rotate according to the smoke baffle's rotation angle, it also includes:

[0153] The processor obtains the rotation status of the smoke baffle. When the smoke baffle is in a rotating state, indicating that it is obstructing the line of sight, the processor controls the smoke baffle to rotate according to the target angle rotation strategy.

[0154] The processor compensates for the airflow of the fan based on the change in the rotation angle of the smoke baffle, thereby increasing the intensity of smoke extraction by increasing the fan parameters.

[0155] The target angle rotation strategy refers to a rotation angle that is within the range of the smoke baffle rotation angle corresponding to the optimal capture efficiency and is greater than the current smoke baffle rotation angle.

[0156] Thus, this embodiment of the present disclosure can predict the impact of the baffle plate rotation angle on the oil fume particulate matter collection efficiency based on the correspondence between the oil fume particulate matter collection efficiency and the baffle plate rotation angle. Before the oil fumes generated during cooking rise, the baffle plate has already rotated according to the baffle plate rotation angle corresponding to the optimal oil fume particulate matter collection effect, which can prevent oil fumes from escaping during cooking, improve the baffle plate's oil fume collection efficiency after the range hood is turned on, and thus improve the range hood's oil fume extraction effect. In addition, this embodiment of the present disclosure can also take into account the user's line of sight needs in the cooking scenario, optimize the range hood's oil fume extraction effect to the greatest extent, and improve the user's cooking experience in actual cooking scenarios.

[0157] After the smoke baffle rotates, the range hood's smoke extraction effect decreases. However, the sacrifice in smoke extraction effect due to the rotation of the smoke baffle can be compensated for by increasing the fan parameters. Simultaneously, the rotating smoke baffle may obstruct the user's view. Therefore, this embodiment first obtains the rotation state of the smoke baffle after controlling its rotation according to the specified angle. If this rotation state indicates that the smoke baffle obstructs the view, the fan parameters are adjusted to compensate for the airflow based on the change in the smoke baffle rotation angle, thereby increasing the smoke extraction intensity. In this way, this embodiment can comprehensively adjust the range hood fan parameters based on both the smoke baffle rotation angle and the user's real-time needs for the cooking environment, improving the accuracy of range hood control and smoke extraction effect while achieving personalized control of the range hood fan.

[0158] Optionally, the processor obtains the rotation state of the smoke baffle, including:

[0159] The processor receives a smoke baffle update command sent by the user, and if the smoke baffle update command indicates a reduction in the smoke baffle rotation angle, determines that the smoke baffle rotation state is a reduction in the smoke baffle rotation angle. Alternatively, the processor obtains the initial smoke baffle rotation angle before rotation and the smoke baffle control command sent by the user based on the initial smoke baffle rotation angle during the historical operation of the range hood, and if the smoke baffle control command indicates a reduction in the initial smoke baffle rotation angle, determines that the smoke baffle rotation state is a reduction in the smoke baffle rotation angle. The smoke baffle rotation state includes either a first smoke baffle rotation state or a second smoke baffle rotation state.

[0160] Thus, this embodiment of the present disclosure can determine whether the smoke baffle should reduce its rotation angle based on the user's adjustment needs for the smoke baffle in actual cooking scenarios, or it can determine whether the smoke baffle should reduce its rotation angle based on the user's adjustment habits for the smoke baffle angle in historical cooking scenarios. This can accurately understand the user's needs for the cooking environment and take into account both the personalized control of the range hood fan and the accuracy of the range hood control.

[0161] Optionally, the processor performs airflow compensation on the fan parameters based on the change in the rotation angle of the smoke baffle, including: the processor obtaining the correspondence between the change in the rotation angle of the smoke baffle and the change in fan parameters. Based on this correspondence, the processor determines the target change in fan parameters corresponding to the reduction in the smoke baffle rotation angle. It should be noted that the correspondence between the change in the smoke baffle rotation angle and the change in fan parameters can be obtained using the method described above for analyzing the smoke extraction effect of the range hood.

[0162] The range hood compensates for the airflow by adjusting the fan parameters based on the changes in the target fan parameters.

[0163] Thus, the embodiments of this disclosure can determine the target change in fan parameters corresponding to the reduction in the rotation angle of the smoke baffle based on the correspondence between the change in the rotation angle of the smoke baffle and the change in the fan parameters, thereby achieving precise control of the range hood fan.

[0164] Application Scenario 3: Control of range hoods with top-suction and side-suction structures

[0165] The processor obtains the installation height of the range hood and the height of the cookware.

[0166] The processor determines the range hood's fume extraction strategy based on the installation height of the range hood and the height of the cookware.

[0167] The processor determines the target structure corresponding to the fume extraction strategy based on the fume extraction strategy.

[0168] The processor controls the target structure to perform fume extraction via the top-suction structure and / or the side-suction structure.

[0169] The control method for a range hood provided in this disclosure includes both a top-mounted suction structure and a side-mounted suction structure. Based on this configuration, after obtaining the installation height and cookware height, the range hood first determines its fume extraction strategy according to these dimensions, and then determines the target structure corresponding to the fume extraction strategy. Finally, the range hood controls the target structure to perform fume extraction through the top-mounted suction structure and / or the side-mounted suction structure. This disclosure, based on a hybrid fume extraction structure configuration, matches a suitable fume extraction strategy to the cooking conditions reflected by the range hood's installation height and cookware height, and performs corresponding actions through the target structure. This approach takes into account the fume escape situation under different cooking types, ensuring effective fume extraction for various cooking methods.

[0170] Optionally, the installation height of the range hood refers to the distance between the lowest point of the range hood and the cooktop. The processor determines the range hood's fume extraction strategy based on the installation height of the range hood and the height of the cookware, including:

[0171] If both the installation height is greater than or equal to the lower limit threshold and the cookware height is greater than the lower limit threshold and less than or equal to the upper limit threshold, the processor determines the fume extraction strategy as side-suction. Alternatively,

[0172] If the installation height is less than the lower limit threshold and the cookware height is greater than the lower limit threshold but less than or equal to the upper limit threshold, the processor determines the fume extraction strategy as top fume extraction.

[0173] Thus, when both the installation height is greater than or equal to the lower threshold and the cookware height is greater than the lower threshold and less than or equal to the upper threshold, it indicates that the range hood is installed at a relatively high height and the cookware height is moderate. In this case, a side-suction fume extraction strategy can achieve powerful bottom suction, thus quickly gathering smoke at close range. When both the installation height is less than the lower threshold and the cookware height is greater than the lower threshold and less than or equal to the upper threshold, it indicates that the range hood is installed at a relatively low height and the cookware height is moderate. If a side-suction fume extraction strategy is used, it is highly likely to obstruct the user's view. Furthermore, if the side-suction structure is equipped with a side-suction flap, the flap's movement can easily interfere with the cookware (e.g., a steamer). To avoid obstructing the user's view and avoiding interference with the cookware, a top-suction fume extraction strategy is used in this case. The embodiments disclosed herein can adaptively adjust the fume extraction strategy according to the installation height of the range hood and the size of the cookware, thereby taking into account the fume escape situation of different cooking types and ensuring the fume extraction effect under different cooking types. At the same time, it provides users with a high-quality cooking experience.

[0174] Optionally, the processor determines the range hood's fume extraction strategy based on the range hood's installation height and the cookware height, and further includes:

[0175] When the cookware height exceeds the upper limit threshold, the processor determines the fume extraction strategy as top-suction. When the cookware height is less than or equal to the lower limit threshold, the processor determines the fume extraction strategy as side-suction.

[0176] Thus, when the pot height exceeds the upper limit threshold, it indicates that the user is performing steaming or boiling cooking. In steaming or boiling, most of the cooking fumes diffuse upwards. Therefore, this embodiment selects a top-suction fume extraction strategy to prevent the fumes from escaping upwards. Conversely, when the pot height is less than or equal to the lower limit threshold, it indicates that the user is performing stir-frying. In stir-frying, most of the cooking fumes diffuse towards the vicinity of the pot. Therefore, this embodiment selects a side-suction fume extraction strategy to rapidly gather the fumes at close range near the pot, ensuring effective fume extraction in stir-frying scenarios.

[0177] Optionally, after the processor controls the target structure to perform fume extraction through the top-suction structure and / or side-suction structure, it further includes determining the target fan parameters corresponding to the range hood installation height and the stove installation position based on the correspondence between the range hood installation height and the stove installation position and the fan parameters of the target structure.

[0178] The processor determines the target wind turbine parameters as the initial wind turbine parameters of the target structure.

[0179] The range hood provided in this embodiment is equipped with both a top-mounted suction structure and a side-mounted suction structure. Based on this configuration, after obtaining the installation height and cookware height, the range hood first determines its fume extraction strategy according to these dimensions, and then determines the target structure corresponding to the fume extraction strategy. Finally, the range hood controls the target structure to perform fume extraction through the top-mounted suction structure and / or the side-mounted suction structure. This embodiment is based on a hybrid fume extraction structure configuration. It matches a suitable fume extraction strategy to the cooking conditions reflected by the range hood's installation height and cookware height, and performs corresponding actions through the target structure, thereby taking into account the fume escape situation of different cooking types and ensuring effective fume extraction under different cooking methods. When the range hood is started, it obtains the range hood's installation height and cookware installation position, and determines the target fan parameters corresponding to the range hood's installation height and cookware installation position based on the correspondence between the range hood's installation height, cookware installation position, and the fan parameters of the target structure. The range hood configures the initial fan parameters of the target structure according to the target fan parameters. This embodiment combines multiple installation parameters, including the range hood installation height and the stove installation position, which reflect the specific installation structure of the range hood, to configure the initial fan parameters of the range hood. Compared with simply adjusting the opening area of ​​the smoke inlet, this configuration can achieve a height match between the initial fan parameters of the target structure and the range hood installation structure, which helps to improve the accuracy of the fan airflow control of the target structure and thus improve the oil fume capture efficiency of the range hood.

[0180] Optionally, the range hood determines the target fan parameters corresponding to the range hood installation height and the cooktop installation position based on the correspondence between the range hood installation height and the cooktop installation position, and the fan parameters of the target structure. This includes:

[0181] The processor obtains the smoke source from the range hood. When the smoke source is for stewing or simmering, the processor obtains a first correspondence between the range hood's installation height, the cooktop's installation position, and the fan parameters of the target structure under the stewing / simmering smoke source, and determines the target fan parameters corresponding to the range hood's installation height and the cooktop's installation position based on the first correspondence. When the smoke source is for stir-frying, the processor obtains a second correspondence between the range hood's installation height, the cooktop's installation position, and the fan parameters of the target structure under the stir-frying smoke source, and determines the target fan parameters corresponding to the range hood's installation height and the cooktop's installation position based on the second correspondence.

[0182] Thus, during cooking, users typically select the low heat setting on the stove for stewing and simmering, and the medium or high heat setting for stir-frying. Compared to stewing, stir-frying produces more fumes, requiring higher fan parameters. Therefore, this embodiment configures a first and a second correspondence for different smoke sources, and determines the correspondence based on the determined correspondence to configure the target fan parameters. This embodiment can comprehensively consider the specific installation structure of the range hood, the installation location of the stove, and the target fan parameters configured for the smoke source, further improving the accuracy of fan airflow control and effectively enhancing the fume collection efficiency of the range hood.

[0183] Scenario 4: Range Hood Control Based on User Attributes

[0184] After the range hood is started, the processor obtains the user's attributes and the operating parameters of the range hood corresponding to the optimal smoke extraction effect. The processor adjusts the operating parameters of the range hood according to the user's attributes.

[0185] User attributes include user sensitivity types. Range hood operating parameters include fan parameters or baffle rotation angle. Fan parameters include fan speed or fan airflow. The baffle rotation angle represents the angle of the baffle relative to the plane containing the grease filter when it rotates.

[0186] The control method for a range hood provided in this disclosure, when the range hood is started, obtains user attributes and operating parameters corresponding to the optimal smoke extraction effect, and adjusts these parameters based on the user attributes. This disclosure allows for the control of the range hood by combining the user's needs for the cooking environment with the operating parameters corresponding to the optimal smoke extraction effect. Compared to simply adjusting the range hood speed manually by the user, this method improves the control effect, satisfying the user's needs for the cooking environment while maintaining effective smoke extraction.

[0187] Optionally, the processor adjusts the range hood's operating parameters based on user attributes, including: decreasing the fan parameters when the user's sensitivity type indicates noise sensitivity, and increasing the fan parameters when the user's sensitivity type indicates oil fume sensitivity.

[0188] Thus, when the user's sensitivity type indicates noise sensitivity, it means the user is highly sensitive to fan noise. In this case, it is not advisable to output excessively high fan parameters to users with noise sensitivity. Therefore, when the user's sensitivity type is determined to be noise sensitivity, this embodiment of the disclosure lowers the fan parameters. When the user's sensitivity type indicates oil fume sensitivity, it means the user is highly sensitive to oil fumes, and the fan parameters need to be further increased based on the original fan parameters to quickly remove cooking fumes in a short time. In this way, in a cooking scenario, this embodiment of the disclosure can dynamically adjust the range hood fan parameters based on the user's own sensitivity to fan noise or oil fumes and the range hood operating parameters corresponding to the optimal smoke extraction effect. The range hood's adjustment effect is improved, meeting the user's needs for the cooking environment while taking into account the smoke extraction effect.

[0189] Optionally, the processor obtains the user's sensitivity type as follows: The processor obtains the fan control commands sent by the user during the historical operation phase of the range hood. These fan control commands instruct the user to update historical fan parameters. If the fan control command indicates a decrease in historical fan parameters, the processor determines the user to be a noise-sensitive user. If the fan control command indicates an increase in historical fan parameters, the processor determines the user to be a fume-sensitive user.

[0190] Thus, the embodiments of this disclosure can determine the user's sensitivity type based on the user's operating habits of the fan during the historical operation of the range hood, ensuring the accuracy of the user sensitivity type determination, thereby providing data support for the subsequent personalized fan control of the range hood.

[0191] Optionally, user attributes also include the user's line-of-sight obstruction status. Based on the user attributes, the range hood adjusts its operating parameters, including: reducing fan parameters when the user's sensitivity type indicates noise sensitivity; increasing fan parameters when the user's sensitivity type indicates oil fume sensitivity. After increasing the fan parameters, when the user's line-of-sight obstruction status indicates that the baffle plate obstructs their view, the processor obtains the correspondence between the change in the baffle plate rotation angle and the change in fan parameters. It should be noted that the correspondence between the change in the baffle plate rotation angle and the change in fan parameters can be obtained based on the method described above for analyzing the range hood's smoke extraction effect. Based on the correspondence between the change in the baffle plate rotation angle and the change in fan parameters, the processor determines the target change in fan parameters corresponding to the reduction in the baffle plate rotation angle. The processor compensates for the airflow of the adjusted fan parameters based on the target change in fan parameters to enhance the smoke extraction intensity by increasing the fan parameters.

[0192] The relationship between the change in the smoke baffle rotation angle and the change in fan parameters can be obtained using the method described above for analyzing the smoke extraction effect of a range hood. As an example, the change in the smoke baffle rotation angle is -1°, and the change in fan parameters is xm. 3 / min (square meters per minute). The change in the smoke baffle's rotation angle is α. When α is greater than zero, it indicates that the smoke baffle's rotation angle increases by α. When α is less than zero, it indicates that the smoke baffle's rotation angle decreases by |α|. The unit of α is degrees, and the symbol is °.

[0193] Thus, after the smoke baffle rotates, the adjusted fan parameters will weaken the range hood's smoke extraction effect before the baffle rotates. The sacrificed smoke extraction effect after the baffle rotates can be compensated for by increasing the fan parameters. However, increasing the fan parameters can also result in the rotated baffle obstructing the user's view. Therefore, this embodiment first determines whether the baffle obstructs the user's view after increasing the fan parameters. If it does, the correspondence between the change in the baffle rotation angle and the change in fan parameters is obtained. Then, based on this correspondence, the target fan parameter change corresponding to the reduction in the baffle rotation angle is determined, and the adjusted fan parameters are compensated for based on this target fan parameter change to enhance smoke extraction intensity. In this way, this embodiment can adjust the range hood fan parameters based on the real-time needs of smoke-sensitive users and the optimal smoke extraction parameters, improving the accuracy of range hood control while maintaining smoke extraction effectiveness.

[0194] It should be noted that adjusting the operating parameters of the range hood based on user attributes also includes: when the user's sensitivity type indicates noise sensitivity, the range hood reduces the fan parameters but maintains the adjusted fan parameters unchanged. Thus, based on the above method for analyzing the smoke extraction effect of the range hood, simulation tests show that for every 1-2 m³ / h increase in fan airflow... 3 The fan noise will increase by 1-2 dB or more per minute. To avoid discomfort for noise-sensitive users due to increased fan parameters, the adjusted fan parameters will be maintained and no airflow compensation will be performed when the user's sensitivity type is indicated as noise-sensitive.

[0195] Optionally, the user attributes also include the user's line-of-sight obstruction status. Based on the user attributes, the processor adjusts the range hood's operating parameters, including controlling the rotation of the baffle according to the target baffle rotation angle. The target baffle rotation angle is determined by the correlation between the oil fume particulate matter capture efficiency and the baffle rotation angle. After the baffle rotation is complete, the processor obtains the user's line-of-sight obstruction status. If the user's line-of-sight obstruction status indicates that the baffle obstructs their view, the processor controls the baffle to reduce the target baffle rotation angle and, after reducing the target baffle rotation angle, compensates for the fan parameters by increasing the airflow to enhance the oil fume extraction intensity.

[0196] In this way, since the reduced smoke extraction effect of the range hood after the smoke baffle rotates can be compensated for by increasing the fan parameters, and since the smoke baffle may obstruct the user's view after rotation, this embodiment of the disclosure obtains the user's view obstruction status after the smoke baffle finishes rotating. If the user's view obstruction status indicates that the smoke baffle is obstructing the view, the target smoke baffle rotation angle is reduced, and after reducing the target smoke baffle rotation angle, the fan parameters are adjusted to compensate for the airflow, thereby increasing the smoke extraction intensity. Thus, this embodiment of the disclosure can comprehensively adjust the range hood fan parameters by considering both the optimal smoke extraction effect corresponding to the smoke baffle rotation angle and the user's real-time needs for the cooking environment, optimizing the smoke extraction effect of the range hood during cooking while achieving personalized control of the range hood fan.

[0197] Optionally, the processor determines the target smoke baffle rotation angle in the following manner: the processor obtains the correspondence between the oil fume particulate matter collection efficiency and the smoke baffle rotation angle. Based on this correspondence, the processor determines the target smoke baffle rotation angle corresponding to the optimal oil fume particulate matter collection efficiency. It should be noted that the correspondence between the oil fume particulate matter collection efficiency and the smoke baffle rotation angle can be obtained based on the aforementioned method for analyzing the smoke extraction effect of the range hood.

[0198] Thus, after obtaining the correspondence between the capture efficiency and the rotation angle of the smoke baffle in this embodiment of the present disclosure, the target rotation angle of the smoke baffle corresponding to the optimal oil fume particulate matter capture efficiency is determined according to the correspondence. After the range hood is started, the smoke baffle is controlled to rotate to the smoke baffle rotation angle that achieves the optimal oil fume particulate matter capture efficiency, thereby maximizing the smoke extraction effect of the range hood during cooking.

[0199] Optionally, the processor obtains the user's line-of-sight occlusion status in the following manner:

[0200] The processor receives a smoke baffle update command from the user, and if the smoke baffle update command indicates a reduction in the smoke baffle rotation angle, it determines that the user's line of sight is obstructed by the smoke baffle. Alternatively, the processor obtains the initial smoke baffle rotation angle before rotation and the smoke baffle adjustment commands sent by the user based on the initial smoke baffle rotation angle during the range hood's historical operation, and if the smoke baffle adjustment commands indicate a reduction in the initial smoke baffle rotation angle, it determines that the user's line of sight is obstructed by the smoke baffle.

[0201] In this way, the embodiments of this disclosure can determine whether the smoke baffle constitutes a visual obstruction based on the user's adjustment needs for the smoke baffle in actual cooking scenarios, or based on the user's adjustment habits of the smoke baffle angle in historical cooking scenarios, thereby knowing the user's needs for the cooking environment, so as to meet the user's needs for the cooking environment through the adjustment of the range hood.

[0202] In addition, methods for analyzing the grease separation efficiency of range hoods include:

[0203] The processor identifies the target smoke source to obtain the corresponding particulate matter parameters and cooking temperature parameters of the target smoke source.

[0204] The processor inputs the particulate matter parameters of the oil fume and the cooking temperature parameters into the target simulation model to perform oil fume capture simulation and obtain the oil separation degree of the range hood.

[0205] 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.

[0206] 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 by the inner surface of the range hood; that is, the oil fume particles adhere to the inner wall surface after impacting it. Specifically, the total number of smoke-generating particles is determined; this total number of smoke-generating 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 by the inner surface and the total number of smoke-generating particles. The more oil fume particles captured by 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.

[0207] 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.

[0208] 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.

[0209] Optionally, the processor inputs the particulate matter parameters and cooking temperature parameters into the target simulation model to perform oil fume capture simulation and obtain the grease separation degree of the range hood, including:

[0210] The processor inputs the parameters of particulate matter in cooking fumes and the parameters of cooking temperature into the discrete model.

[0211] The processor sets and initializes the boundary conditions of the discrete model to calculate the simulated oil fume flow field.

[0212] The processor calculates the grease separation degree of the range hood after the simulated oil fume flow field converges.

[0213] 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.

[0214] The input of 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. After determining the corresponding oil fume particulate matter parameters and cookware temperature, the total number of smoke-generating particles is set based on these parameters and input into the discrete model. Specifically, under the same target smoke source, the higher the values ​​of the oil fume particulate matter parameters and cookware temperature, the greater the total number of smoke-generating particles.

[0215] The processor sets the boundary conditions for the discrete model, including:

[0216] 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.

[0217] The processor sets the inner wall surface of the range hood as the collection surface for oil fume particles.

[0218] Here, 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, wherein 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, cause grease particles to be thrown out, collide with the collection surface, and are adsorbed to obtain grease. Therefore, the oil fume particles on the collection surface can characterize the grease interception situation.

[0219] Optionally, the processor calculates the grease separation degree of the range hood after the simulated oil fume flow field converges, including:

[0220] The processor counts the number of oil fume particles captured by the oil fume particle collection surface.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] Optionally, after obtaining the grease separation degree, the processor further includes: during the operation of the range hood, adjusting the fan and / or cooling module of the range hood according to the grease separation degree of the range hood to optimize the grease separation degree.

[0225] The cooling module is located inside the range hood and is used to regulate the temperature of the inner wall of the range hood.

[0226] 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.

[0227] Combination Figure 6As shown in the figure, this disclosure provides an apparatus 100 for oil fume data analysis, including a processor 101 and a memory 102. Optionally, the apparatus 100 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 oil fume data analysis described in the above embodiment.

[0228] 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.

[0229] 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 oil fume data analysis in the above embodiments.

[0230] 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.

[0231] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for analyzing oil fume data.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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 oil fume data, characterized in that, include: Obtain the initial velocity and particle size parameters of oil fume particles in the target space; Based on the initial velocity and particle size parameters of the oil fume particles, the smoke source in the target space is determined.

2. The method according to claim 1, characterized in that, The initial velocity of the oil fume particles in the target space is obtained by: The velocity field of oil fume particles in the target space is calculated using particle image velocimetry to obtain the initial velocity of the oil fume particles in the target space; or... Acquire images of oil fume particles generated within the target space; these images include multiple frames. The acquired images of cooking fumes are processed to obtain the grayscale peak distribution of the images; and the time-averaged velocity field is calculated based on the grayscale peak distribution to obtain the initial velocity of the cooking fume particles in the target space.

3. The method according to claim 2, characterized in that, Processing the acquired oil fume images to obtain the grayscale peak distribution includes: The acquired oil fume images are converted to grayscale before being divided into grids; Calculate the gray value of each grid cell, where the gray value of each grid cell is a gray value sequence that changes over time; Perform a Fourier transform on the gray values ​​to obtain the gray peak distribution of the image.

4. The method according to claim 2, characterized in that, The time-averaged velocity field is calculated based on the gray-scale peak distribution to obtain the initial velocity of oil fume particles in the target space, including: The instantaneous velocity field is obtained by measuring the displacement of grayscale peaks in adjacent frames. The instantaneous velocity field within a preset time period is averaged to obtain the time-averaged velocity field, which is then used as the initial velocity of oil fume particles in the target space.

5. The method according to claim 3, characterized in that, The oil fume images include oil fume images from a first-view perspective and oil fume images from a second-view perspective; calculating the grayscale value of each grid cell includes: Map the two-dimensional grids corresponding to the oil fume images from two perspectives at the same moment to the same three-dimensional grid; In a 3D mesh, gray values ​​at the same mesh location are fused to calculate the gray value of each mesh. The higher the gray value, the higher the concentration of oil fume particles.

6. The method according to claim 3, characterized in that, The grayscale conversion of the acquired oil fume images includes: Convert each pixel in the colored image of cooking fumes to a grayscale value; and, Non-oil smoke areas in the image are removed based on grayscale thresholding to obtain the grayscale converted image.

7. The method according to claim 1, characterized in that, Obtain the particle size parameters of oil fume particles in the target space, including: Obtain the particle size distribution and particle size percentage of oil fume particles at multiple locations within the target space; The average values ​​of particle size distribution and particle size percentage were calculated separately, and the average values ​​of particle size distribution and particle size percentage were used as the particle size parameters of oil fume particles.

8. The method according to any one of claims 1 to 7, characterized in that, Based on the initial velocity and particle size parameters of the oil fume particles, the smoke source within the target space is determined, including: If the initial velocity meets the first velocity condition and the particle size parameter meets the first particle size condition, the smoke source in the target space is determined as the first smoke source. If the initial velocity satisfies the second velocity condition and the particle size parameter satisfies the second particle size condition, the smoke source in the target space is determined as the second smoke source.

9. An apparatus for analyzing oil fume data, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to execute, when running the program instructions, the method for analyzing oil fume data 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 oil fume data as described in any one of claims 1 to 8.

Images