Intelligent fan control method for real-time wind pressure monitoring

Abstract

A kind of intelligent fan control method of real-time wind pressure monitoring, method includes the following steps: providing a range hood and frying pan, the range hood is located frying pan, and range hood is equipped with two sound detection modules, range hood is also equipped with image acquisition module;Range hood runs at initial speed;Two sound detection modules simultaneously real-time obtain the sound characteristics emitted in the frying pan, and obtain the sound intensity A1 of the farthest frying pan in the fried food from image acquisition module according to sound characteristics;According to sound intensity A1, the degree of food frying is inferred, to determine the first frying position;Image acquisition module collects the edge image information of frying pan after being obscured by oil smoke;Inference oil smoke concentration N1 above frying pan according to the edge image information collected, to determine the second frying position;Compare first frying position and second frying position to determine actual frying position;According to actual frying position, adjust the range of range hood and fan speed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent household appliances, in particular to a real-time wind pressure monitoring intelligent fan control method. BACKGROUND

[0002] In modern kitchen environments, deep-frying cooking, as a widely adopted cooking method, not only can produce unique-flavored delicacies, but also faces the challenge of oil fume management. During deep-frying, food is rapidly heated in hot oil, releasing a large amount of oil fume. These oil fumes not only contain harmful particles and gases, affecting indoor air quality, but also adhere to kitchen walls, furniture, and cooking utensils, increasing cleaning difficulty. Therefore, efficient and intelligent oil fume management has become a key factor in improving kitchen environment quality and user experience.

[0003] Traditional range hood designs often work based on fixed suction parameters, ignoring several key variables in the deep-frying cooking process. Oil fume generated by food deep-frying far from the range hood is often more difficult to be effectively extracted due to the longer path, leading to oil fume escape and affecting the range hood effect. As one of the types of pots, there are different sizes of flat-bottomed pots on the market. The change in the size of the flat-bottomed pot directly leads to the range of distance between the fried food and the range hood, which brings challenges to the precise control of the suction of the range hood. The existing range hood usually adopts a unified suction setting, which is difficult to flexibly adjust according to the size of the pot, the position of the fried food, and the actual generation of oil fume, thereby causing problems such as excessive energy consumption (when the fan speed is too high) or low oil fume extraction efficiency (when the fan speed is insufficient). This not only increases household energy consumption, but also may affect the health and comfort of the kitchen environment.

[0004] Therefore, it is necessary to provide a real-time wind pressure monitoring intelligent fan control method which can effectively extract the oil fume generated by the fried food when the flat-bottomed pot is used for deep-frying, and can improve the accuracy of the fan speed setting of the range hood. SUMMARY

[0005] The purpose of the present application is to provide a real-time wind pressure monitoring intelligent fan control method which can effectively extract the oil fume generated by the fried food when the flat-bottomed pot is used for deep-frying, and can improve the accuracy of the fan speed setting of the range hood.

[0006] According to an aspect of the present application, a real-time wind pressure monitoring intelligent fan control method is provided for the range hood to extract oil fume within the range of the flat-bottomed pot during deep-frying, the method comprising the steps of:

[0007] A range hood and a flat-bottomed pot are provided, the range hood is arranged on the flat-bottomed pot along the vertical direction, and two sound detection modules are arranged on the two sides of the range hood respectively, and an image acquisition module is further arranged on the range hood;

[0008] The range hood operates at an initial rotation speed;

[0009] Two sound detection modules simultaneously acquire at least one sound characteristic emitted from the frying pan in real time, and calculate a sound intensity A1 of the first distance D of the fried food from the image acquisition module according to the sound characteristic;

[0010] According to the sound intensity A1, the degree of frying of the food is inferred to determine the first frying gear;

[0011] The image acquisition module acquires the edge image information of the frying pan after being shielded by the oil fume;

[0012] According to the acquired edge image information, the oil fume concentration N1 above the frying pan is inferred to determine the second frying gear;

[0013] The first frying gear and the second frying gear are compared to determine the actual frying gear;

[0014] The range hood gear and the fan rotation speed are adjusted according to the actual frying gear.

[0015] More preferably, in the step "two sound detection modules simultaneously acquire at least one sound characteristic emitted from the frying pan in real time, and calculate a sound intensity A1 of the first distance D of the fried food from the image acquisition module according to the sound characteristic",

[0016] The sound characteristic includes sound intensity, and the two sound detection modules are respectively located on both sides of the image recognition module, and the first sound source distance L1 and the second sound source distance L2 between the fried food and the two sound detection modules are respectively acquired according to the time of receiving the sound characteristic;

[0017] The distance set between the fried food and the image acquisition module is calculated according to the first sound source distance L1 and the second sound source distance L2;

[0018] The first distance D of the fried food from the image acquisition module is acquired in the distance set;

[0019] According to the sound characteristic acquired by the two sound detection modules, the first sound source distance L1, the second sound source distance L2 and the first distance D, the sound intensity A1 of the fried food at the image acquisition module is acquired.

[0020] More preferably,

[0021] The sound detection module is composed of a microphone array, and the microphone array is composed of a main microphone and at least one auxiliary microphone with a distance d from the main microphone;

[0022] The main microphone acquires the sound characteristic of the fried food for a time t1, and the auxiliary microphone acquires the sound characteristic of the fried food for a time t2;

[0023] acquiring a time interval t between the time t1 and the time t2;

[0024] According to the time interval t and the distance d, the sound detection module calculates the sound source distance between the fried food and the main microphone.

[0025] More preferably, in the step of "inferred the degree of frying of the food according to the sound intensity A1 to determine the first frying position", the sound intensity A1 is put into the frying degree model, and one of the sound intensity ranges in the frying degree model is matched.

[0026] According to the historical data of the frying sound in the frying pan range during frying, the range hood is pre-set with a frying degree model corresponding to a plurality of sound intensity ranges and a first frying position model corresponding to a plurality of frying degrees;

[0027] The collected sound intensity A1 is put into the frying degree model, and one of the sound intensity ranges in the frying degree model is matched.

[0028] According to the matched sound intensity range, the corresponding frying degree in the frying degree model is obtained;

[0029] The frying degree is put into the first frying position model, and one of the frying degree data in the first frying position model is matched.

[0030] According to the matched frying degree data, the corresponding first frying position in the first frying position model is obtained.

[0031] More preferably, in the step of "the image acquisition module acquires the edge image information of the frying pan after being blocked by the oil fume", the image acquisition module acquires the edge image information of the frying pan after being blocked by the oil fume.

[0032] The camera unit shoots the image in the first area vertically below the image acquisition module in real time;

[0033] The image is preprocessed to obtain the boundary between the oil fume and the edge contour of the frying pan;

[0034] The edge contour of the frying pan in the image is recognized, the edge information is extracted, and the edge image information is formed;

[0035] The edge image information is transmitted to the processing unit.

[0036] More preferably, in the step of "inferred the oil fume concentration N1 above the frying pan according to the collected edge image information to determine the second frying position", the processing unit identifies the oil fume area composed of pixels in the acquired edge image information.

[0037] The processing unit identifies the oil fume area composed of pixels in the acquired edge image information.

[0038] The pixel with the maximum brightness value in the oil fume area is identified, and the brightness value I is recorded.

[0039] According to the historical data of the image of the frying pan after being shielded by the oil fume during frying, the range hood is preset with an oil fume concentration model corresponding to a plurality of pixel brightness and a second frying range model corresponding to a plurality of oil fume concentration data;

[0040] The obtained brightness value I is put into the oil fume concentration model, and one of the pixel brightness in the oil fume concentration model is matched;

[0041] According to the matched pixel brightness, the corresponding oil fume concentration N1 in the oil fume concentration model is obtained;

[0042] The oil fume concentration N1 is put into the second frying range model, and one of the oil fume concentration data in the second frying range model is matched;

[0043] According to the matched oil fume concentration data, the corresponding second frying range in the second frying range model is obtained.

[0044] More preferably, the range hood is further provided with an oil fume sensor, which is located at the edge of the frying pan, and the oil fume sensor detects the real-time oil fume concentration N2,

[0045] If the relationship N2>N1 is satisfied, the oil fume concentration N2 is put into the second frying range model, and a new second frying range is obtained.

[0046] More preferably, in the step of "comparing the first frying range and the second frying range to determine the actual frying range",

[0047] The range hood is preset with a sound intensity threshold A, an oil fume concentration threshold N and a range threshold S;

[0048] When the relationship |first frying range-second frying range|≥S is satisfied,

[0049] If the relationship A1≥A is satisfied, the first frying range is the actual frying range;

[0050] If the relationship A1

[0051] More preferably,

[0052] When the relationship |first frying range-second frying range|<S is satisfied,

[0053] If the relationship first frying range≥second frying range and N1≥N or N2≥N is satisfied, the second frying range is the actual frying range;

[0054] If the relationship first frying range≥second frying range and N1

[0055] If the relationship: the second frying level >= the first frying level is satisfied, the second frying level is the actual frying level.

[0056] More preferably, in the step of "adjusting the level of the range hood and the fan speed according to the actual frying level", the actual frying level is determined by comparing the first frying level and the second frying level.

[0057] The range hood is pre-set with a fan level model corresponding to a plurality of frying levels;

[0058] According to the actual frying level matching the frying level in the fan level model, the range hood obtains the corresponding fan level in the fan level model;

[0059] The fan of the range hood runs at the fan level preset speed V0;

[0060] According to the first distance D and the oil smoke concentration N1 or the oil smoke concentration N2, the ideal speed V is calculated and obtained, and the range hood adjusts the fan speed to the ideal speed V to run.

[0061] The present application has the following beneficial effects:

[0062] The sound intensity A1 of the fried food in the frying pan is obtained by the sound detection module, and the oil smoke concentration N1 above the frying pan is obtained by the image acquisition module, so that the range hood obtains real-time effective data, avoids the escape of oil smoke generated by food frying far away from the range hood, and improves the accuracy of the fan speed setting. And the way of determining the actual frying level and adjusting the level of the range hood and the fan speed according to the actual frying level makes the range hood able to effectively extract the oil smoke generated by the fried food in the frying pan. The sound intensity A1 of the first distance D of the fried food in the frying pan farthest from the image acquisition module is obtained, so that the range hood can detect the distance between the fried food farthest from it, and avoid the escape of oil smoke far away. By comparing the first frying level and the second frying level to determine the actual frying level, the range hood can obtain a more suitable frying level according to the actual situation, and avoid incorrect judgment when only relying on oil smoke concentration and sound intensity to determine the actual frying level. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0064] Figure 1 It is a flow chart of the intelligent fan control method for real-time wind pressure monitoring in an embodiment of the present application.

[0065] Figure 2 The schematic diagram of the intelligent fan control method for real-time wind pressure monitoring according to an embodiment of the present application;

[0066] The figure caption is explained: 100, the extractor hood; 11, the first sound detection module; 12, the second sound detection module; 20, the image acquisition module; 200, the pan; 300, the fried food. DETAILED DESCRIPTION

[0067] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0068] It should be noted that when an element is referred to as being "fixed" to another element, it can be directly on the other element or there can be an intervening element. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or there can be an intervening element. The terms "vertical", "horizontal", "left", "right", and similar expressions used herein are for illustrative purposes only.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0070] Please refer to Figure 1 - Figure 2 The embodiment of the present application provides an intelligent fan control method for real-time wind pressure monitoring, which is used for extracting the oil fume in the range of the pan 200 by the extractor hood 100 during frying. The method comprises the following steps:

[0071] S10 provides an extractor hood 100 and a pan 200, the extractor hood 100 is arranged on the pan 200 along the vertical direction, and two sound detection modules are arranged on the two sides of the extractor hood 100 respectively, and an image acquisition module 20 is further arranged on the extractor hood 100;

[0072] S20, the extractor hood 100 operates at an initial rotating speed;

[0073] S30 two sound detection modules simultaneously and in real time acquire at least one sound characteristic emitted in the frying pan 200, and calculate a sound intensity A1 of a first distance D of the fried food 300 in the frying pan 200 from the image acquisition module 20 according to the sound characteristic;

[0074] S40 infer the degree of frying of the food according to the sound intensity A1 to determine a first frying gear;

[0075] S50 the image acquisition module 20 acquires edge image information of the frying pan 200 after being shielded by the oil fume;

[0076] S60 infer the oil fume concentration N1 above the frying pan 200 according to the acquired edge image information to determine a second frying gear;

[0077] S70 compare the first frying gear and the second frying gear to determine an actual frying gear;

[0078] S80 adjust the gear and the fan speed of the range hood 100 according to the actual frying gear.

[0079] Among them, a range hood 100 and a frying pan 200 are provided, and the range hood 100 is arranged on the frying pan 200 along the vertical direction. Two sound detection modules are arranged on both sides of the range hood 100 respectively, which are used to capture the sound characteristics in the frying pan 200. An image acquisition module 20 is also arranged on the range hood 100, which is used to acquire the edge image information of the frying pan 200 after being shielded by the oil fume. The range hood 100 operates at an initial speed to provide a basic oil fume extraction capacity for the frying process. Two sound detection modules simultaneously and in real time acquire the sound characteristics emitted in the frying pan 200. According to the sound characteristics (such as sound intensity, propagation time, etc.), combined with the position of the sound detection module, the sound intensity A1 of the first distance D of the fried food 300 in the frying pan 200 from the image acquisition module 20 is calculated and acquired. According to the sound intensity A1, combined with the corresponding relationship between the preset degree of frying and the sound intensity, the degree of frying of the food is inferred. According to the degree of frying, the first frying gear is determined to adjust the extraction capacity of the range hood 100. The image acquisition module 20 acquires the edge image information of the frying pan 200 after being shielded by the oil fume. Through image processing technology, the oil fume shielding condition in the edge image information is analyzed, and the oil fume concentration N1 above the frying pan 200 is inferred. According to the oil fume concentration N1, the second frying gear is determined. The first frying gear and the second frying gear are compared to determine the actual frying gear. According to the actual frying gear, the gear and the fan speed of the range hood 100 are adjusted to realize more accurate oil fume extraction.

[0080] The sound detection module can capture the sound characteristics in the frying pan 200 in real time, such as the sound generated when the food material contacts the hot oil during frying. By analyzing the sound characteristics, the degree of frying of the food material can be inferred, and the gear of the range hood 100 can be adjusted. Setting two sound detection modules can improve the accuracy of sound positioning and more accurately calculate the distance between the fried food 300 and the image acquisition module 20. The image acquisition module 20 can capture the edge image information of the frying pan 200 after being blocked by the oil smoke. Through image processing technology, the concentration and distribution of the oil smoke can be analyzed. The combination of the image acquisition module 20 and the sound detection module can achieve comprehensive monitoring of the frying process. By integrating sound characteristics and image information, the degree of frying and the concentration of oil smoke can be more accurately inferred. According to the degree of frying and the concentration of oil smoke, the actual frying gear can be determined, and the range hood 100 gear and fan speed can be accurately adjusted. This adjustment method can ensure that the range hood 100 can maintain the best oil smoke extraction effect at different frying stages. Although the scheme does not directly mention wind pressure monitoring, real-time wind pressure monitoring is an important part of the intelligent fan control method. By monitoring the wind pressure, the oil smoke extraction effect of the range hood 100 can be understood in real time, and adjustments can be made as needed. Real-time wind pressure monitoring combined with sound detection and image acquisition can achieve more comprehensive monitoring and control of the frying process.

[0081] More preferably, in the step S30,

[0082] The sound characteristics include sound intensity, and the two sound detection modules are respectively located on both sides of the image recognition module, and the first sound source distance L1 and the second sound source distance L2 between the fried food 300 and the two sound detection modules are obtained according to the time of receiving the sound characteristics;

[0083] According to the first sound source distance L1 and the second sound source distance L2, the distance set between the fried food 300 and the image acquisition module 20 is calculated and obtained;

[0084] In the distance set, the first distance D between the fried food 300 and the image acquisition module 20 is obtained.

[0085] According to the sound characteristics obtained by the two sound detection modules, the first sound source distance L1, the second sound source distance L2 and the first distance D, the sound intensity A1 of the fried food 300 at the image acquisition module 20 is obtained.

[0086] In step S30, two sound detection modules (located on both sides of the image recognition module) capture the sound features emitted in the frying pan 200 in real time, mainly focusing on sound intensity. Sound intensity reflects the energy size of the sound, which is closely related to the frying activity of the fried food 300. Since the speed of sound is known (about 343 meters per second in air), the sound detection module can calculate the distance between the fried food 300 and each sound detection module according to the time difference of receiving sound features. Let the time when the first sound detection module 11 receives the sound be t1, the time when the second sound detection module 12 receives the sound be t2, and the speed of sound be v, then the first sound source distance L1 and the second sound source distance L2 can be calculated by the formulas L1 = v * (t1-t0) and L2 = v * (t2-t0) respectively, where t0 is the time when the sound is emitted. According to the first sound source distance L1 and the second sound source distance L2, and the relative position relationship between the sound detection module and the image acquisition module 20, the distance of the fried food 300 from the image acquisition module 20 can be calculated by trigonometric functions. Here, the distance set actually refers to the set of multiple possible distances of multiple identical fried foods 300 from the image acquisition module 20 (i.e. near the hood 100) during the frying process. When there are multiple fried foods 300, each fried food 300 will emit sound, and the sound detection module will receive these sounds respectively, and calculate the distance between each fried food 300 and the sound detection module according to the time difference of sound propagation. In this distance set, we need to find the farthest distance D of the fried food 300 from the image acquisition module 20 (i.e. the hood 100). This is because the farthest distance corresponds to the position where the oil fume is most difficult to be extracted by the hood 100. Since the oil fume is more difficult to be extracted as the distance of the fried food 300 increases, finding the farthest distance D is crucial for evaluating the efficiency of the hood 100. Placing the two sound detection modules on both sides of the image recognition module can maximize the use of the time difference of sound propagation to locate the position of the fried food 300. This layout improves the accuracy and reliability of positioning, which helps to build an accurate distance set. Selecting the farthest distance D of the fried food 300 from the image acquisition module 20 (i.e. the hood 100) to estimate the sound intensity A1 is because at this position, the oil fume is most difficult to be extracted. By considering this factor, we can more accurately evaluate the efficiency of the hood 100 and adjust the gear and fan speed of the hood 100 accordingly. The sound intensity A1 not only reflects the frying activity of the fried food 300, but also is closely related to the generation amount of oil fume. By monitoring the sound intensity A1, we can indirectly understand the generation of oil fume and adjust the gear and fan speed of the hood 100 accordingly to achieve more efficient oil fume extraction.

[0087] More preferably,

[0088] The sound detection module is composed of a microphone array, which is composed of a main microphone and at least one auxiliary microphone with a distance d from the main microphone;

[0089] The main microphone acquires the sound characteristics of the fried food 300 through time t1, and the auxiliary microphone acquires the sound characteristics of the fried food 300 through time t2;

[0090] Obtain the time interval t between time t1 and time t2;

[0091] According to the time interval t and the distance d, the sound detection module calculates the sound source distance between the fried food 300 and the main microphone.

[0092] Among them, as the core of the sound detection module, the main microphone is responsible for receiving the sound signal from the fried food 300. Its position is fixed, usually near the image recognition module, in order to process synchronously with the image data. The auxiliary microphone is kept at a certain distance d from the main microphone, which is determined according to the actual application scene and the required positioning accuracy. The role of the auxiliary microphone is to provide additional sound signals for comparison with the signals received by the main microphone, so as to calculate the time difference of sound propagation. When the fried food 300 is frying in the pan 200, it will emit sound. These sound signals are received by the main microphone and the auxiliary microphone respectively. The main microphone obtains the sound characteristics of the fried food 300 after time t1, while the auxiliary microphone obtains the same sound characteristics after time t2. Since the speed of sound propagation is constant (about 343 meters per second in air, the specific value is affected by factors such as temperature and humidity), the time difference t (t = t2 - t1) reflects the difference in time between the sound propagating from the fried food 300 to the main microphone and the auxiliary microphone. According to the time difference t of sound propagation and the distance d between the microphones, we can use the formula L = v * t / 2 to calculate the sound source distance L between the fried food 300 and the main microphone (where v is the speed of sound propagation in air). It should be noted that since the sound is propagated from the fried food 300 to the two microphones, the actual calculated sound source distance L is the distance from the fried food 300 to the midpoint of the two microphones (i.e. a virtual extension of the main microphone position). In order to get the exact distance from the fried food 300 to the main microphone, L may need to be corrected, but this usually depends on the specific configuration of the microphone array and the positioning algorithm. Advantages of microphone array: Using a microphone array for sound positioning has multiple advantages. First, it provides additional sound signal sources, making positioning more accurate and reliable. Second, by adjusting the distance d and number of microphones, it can be flexibly adapted to different application scenarios and positioning accuracy requirements. Finally, the microphone array can also be used to suppress noise and interference, improving the signal-to-noise ratio of the sound signal. The time difference principle is one of the commonly used methods in sound positioning. It uses the time difference of sound propagation to different positions to calculate the position of the sound source. This method has the advantages of low cost, simple implementation, high positioning accuracy, etc. In the intelligent fan control method, by using the time difference principle for sound positioning, the position information of the fried food 300 can be obtained in real time, providing important reference for subsequent oil smoke extraction and fan control. By optimizing the configuration of the microphone array and the positioning algorithm, the accuracy of sound positioning can be further improved. For example, the number of auxiliary microphones can be increased to provide more sound signal sources; more advanced signal processing algorithms can be used to reduce errors and interference; or the position information provided by the image recognition module can be used to assist sound positioning, etc. These measures help improve the accuracy and reliability of sound positioning, providing better support for the intelligent fan control method.

[0093] More preferably, in the step S40,

[0094] According to historical data of frying sound in the range of the frying pan 200, the range hood 100 is preset with a frying degree model corresponding to a plurality of sound intensity ranges and a first frying level model corresponding to a plurality of frying degrees;

[0095] The collected sound intensity A1 is put into the frying degree model and matched with a sound intensity range in the frying degree model;

[0096] According to the matched sound intensity range, the corresponding frying degree in the frying degree model is obtained;

[0097] The frying degree is put into the first frying level model and matched with one of the frying degree data in the first frying level model;

[0098] According to the matched frying degree data, the corresponding first frying level in the first frying level model is obtained.

[0099] The model is based on historical data of sound intensity in the range of the frying pan 200 during frying. It divides the sound intensity into several ranges, each corresponding to a specific degree of frying, such as light frying, medium frying, and heavy frying, etc. These sound intensity ranges are derived by analyzing the sound characteristics of a large number of frying activities, which can accurately reflect the intensity of frying activities and the amount of oil smoke generated. The model is associated with the frying degree model, which presets several frying gears according to the degree of frying. Each frying gear corresponds to a specific fan speed and oil smoke extraction efficiency to meet the oil smoke extraction needs under different degrees of frying. For example, during light frying, a lower gear can be selected to reduce energy consumption; while during heavy frying, the gear needs to be increased to ensure that the oil smoke is effectively extracted. The real-time collected sound intensity A1 is put into the frying degree model, and the system automatically matches and determines the current frying degree by comparing A1 with the preset sound intensity ranges in the model. This process is real-time and can quickly respond to changes in frying activities. Once the frying degree is determined, the system will put this information into the first frying gear model, and by matching the frying degree data in the model, it will automatically select and determine the appropriate frying gear. This process is also real-time and can ensure that the range hood 100 quickly adjusts to the best working state when frying activities occur. Through the above steps, the range hood 100 can automatically adjust the gear according to the real-time sound intensity of frying activities, achieving intelligent oil smoke control and energy consumption management. This not only improves the efficiency of oil smoke extraction and reduces the impact of oil smoke on the kitchen environment, but also reduces energy consumption and prolongs the service life of the range hood 100. Sound intensity is one of the important indicators reflecting the intensity of frying activities. Through historical data analysis, we can establish a correlation model between sound intensity and frying degree. This model enables the range hood 100 to accurately determine the frying degree by monitoring the sound intensity, and then select the appropriate gear for oil smoke extraction. Traditional range hood 100 gear selection usually relies on user manual operation or preset fixed mode. However, this method often cannot accurately reflect the real-time changes of frying activities, resulting in low oil smoke extraction efficiency or excessive energy consumption. By introducing an intelligent gear selection mechanism, the range hood 100 can automatically adjust the gear according to the actual situation of frying activities, achieving more accurate and efficient oil smoke control. The optimized step S40 not only improves the efficiency of oil smoke extraction and energy consumption management, but also significantly improves the user experience. Users do not need to manually adjust the gear to enjoy intelligent oil smoke control services. At the same time, by reducing unnecessary energy consumption, this scheme also helps to achieve the goal of energy saving and emission reduction, in line with the current green and environmentally friendly development trend.

[0100] More preferably, in the step S50,

[0101] The image acquisition module 20 vertically below the first area in real time;

[0102] preprocessing the image to obtain the boundary between the oil fume and the edge profile of the pan 200;

[0103] identifying the edge profile of the pan 200 in the image, extracting the edge information and forming the edge image information;

[0104] transmitting the edge image information to the processing unit.

[0105] In step S50, the image capturing unit captures images in the first area vertically below the image acquisition module 20 in real time. This area usually contains the frying pan 200 and the surrounding oil fumes. Then, the system pre-processes the images to obtain the boundary between the oil fumes and the edge profile of the frying pan 200, identifies the edge profile of the frying pan 200, extracts the edge information to form edge image information. Finally, the edge image information is transmitted to the processing unit for further analysis and processing. The image capturing unit is configured to capture images in the first area vertically below the image acquisition module 20 in real time. The selection of this area is based on the expected position of the oil fumes and the frying pan 200 during frying to ensure that key information can be captured. The image capturing unit uses a high-resolution camera to capture clear image details, providing an accurate data basis for subsequent edge detection and oil fume recognition. The main purpose of the pre-processing stage is to enhance image quality, reduce noise and interference, so as to more accurately identify the edge profile of the oil fumes and the frying pan 200. Common preprocessing techniques include image denoising, grayscale, binarization, etc. These techniques help highlight key features in the image while reducing unnecessary computational load. In the pre-processed image, the system uses edge detection algorithms to identify the boundary between the oil fumes and the edge profile of the frying pan 200. This step is crucial for subsequent edge information extraction. Edge detection algorithms can identify edges based on grayscale changes, texture features, etc. in the image. At the junction of the oil fumes and the frying pan 200, due to the ambiguity of the oil fumes and the clear edge of the frying pan 200, the algorithm can accurately capture this boundary. Once the boundary between the oil fumes and the edge profile of the frying pan 200 is identified, the system further identifies the complete edge profile of the frying pan 200. Through the contour tracking algorithm, the system can extract the precise information of the edge of the frying pan 200 and form edge image information. This information contains key parameters such as the shape, size, and position of the frying pan 200. The extracted edge image information is transmitted to the processing unit for further analysis and processing. Real-time shooting ensures that the system can capture key information in the frying process in a timely manner, while pre-processing improves image quality, providing a reliable data basis for subsequent edge detection and oil fume recognition. The edge detection algorithm can accurately capture the boundary between the oil fumes and the edge profile of the frying pan 200, and the contour recognition algorithm can extract the complete edge information of the frying pan 200. These techniques together form the basis for the system to identify the position of the oil fumes and the frying pan 200. Transmitting the edge image information to the processing unit for further analysis helps the system more accurately identify the concentration and diffusion range of the oil fumes, thereby providing a basis for oil fume control and fan speed adjustment.

[0106] More preferably, in step S60,

[0107] The processing unit identifies the oil fume area composed of pixels in the edge image information;

[0108] Identify the pixel with the maximum brightness value in the oil fume area and record the brightness value I;

[0109] According to the historical data of the image of the frying pan 200 after being blocked by the oil fume, the extractor hood 100 is pre-set with an oil fume concentration model corresponding to a number of pixel brightness and a second frying level model corresponding to a number of oil fume concentration data;

[0110] The obtained brightness value I is put into the oil fume concentration model and matched with one of the pixel brightness in the oil fume concentration model;

[0111] According to the matched pixel brightness, the corresponding oil fume concentration N1 in the oil fume concentration model is obtained;

[0112] The oil fume concentration N1 is put into the second frying level model and matched with one of the oil fume concentration data in the second frying level model;

[0113] According to the matched oil fume concentration data, the corresponding second frying level in the second frying level model is obtained.

[0114] The processing unit analyzes the edge image information and identifies the oil fume region composed of pixels. This region is usually represented as a part of the image with high brightness and irregular shape, which can reflect the diffusion of oil fume during cooking. Within the oil fume region, the system further identifies the pixel with the maximum brightness value. The brightness value of this pixel can be used as an indicator of oil fume concentration, as the thicker the oil fume, the more obvious the effect of blocking light, resulting in a higher brightness value in the corresponding region of the image. Based on the historical data of the image of the frying pan 200 blocked by oil fume during frying, the range hood 100 is pre-set with an oil fume concentration model. This model divides the pixel brightness into several ranges, each range corresponding to a specific oil fume concentration. At the same time, the system also pre-sets a second frying position model corresponding to several oil fume concentrations. This model divides the oil fume concentration into different levels and pre-sets a suitable frying position for each level to achieve intelligent oil fume control. The system puts the recorded brightness value I into the oil fume concentration model for matching to find the corresponding pixel brightness range. Then, according to the range, the current oil fume concentration N1 is determined. Once the oil fume concentration N1 is determined, the system will put it into the second frying position model for matching. By comparing N1 with the pre-set oil fume concentration data in the model, the system can automatically select and determine the appropriate frying position. The range hood 100 can automatically adjust the position according to the real-time change of the oil fume concentration to achieve intelligent oil fume control. This not only improves the efficiency of oil fume extraction and reduces the impact of oil fume on the kitchen environment, but also reduces energy consumption and improves user experience. By identifying the oil fume region and analyzing the pixel with the maximum brightness value, the system can indirectly reflect the concentration of oil fume. This method is based on the blocking effect of oil fume on light and has the characteristics of intuitiveness and easy implementation. Through the model pre-set based on historical data, the system can accurately select the appropriate frying position according to the oil fume concentration. This method improves the accuracy and intelligence level of oil fume control. By monitoring the oil fume concentration in real time and automatically adjusting the position, the system can achieve intelligent oil fume control. This not only improves the efficiency of oil fume extraction, but also reduces energy consumption and prolongs the service life of the range hood 100.

[0115] More preferably, the range hood 100 is also provided with an oil fume sensor, which is located at the edge of the frying pan 200, and the oil fume sensor detects the real-time oil fume concentration N2,

[0116] If the relationship N2 > N1 is satisfied, the oil fume concentration N2 is put into the second frying position model to obtain a new second frying position.

[0117] The oil fume sensor is ingeniously installed at the edge of the flat-bottomed pot 200, which can ensure that the sensor directly contacts the oil fume generated during cooking, thereby providing more accurate oil fume concentration data. The oil fume sensor adopts advanced detection technology, which can monitor the change of oil fume concentration in real time and continuously, and transmit the data to the processing unit for analysis in real time. The system uses both image processing technology and oil fume sensor to detect the oil fume concentration. The image processing technology indirectly reflects the oil fume concentration (denoted as N1) by identifying the pixel with the maximum brightness value in the oil fume area, while the oil fume sensor directly detects the oil fume concentration (denoted as N2). The system compares N1 and N2, and if N2 is greater than N1, it is considered that the data of the oil fume sensor is more accurate, because the sensor directly contacts the oil fume and is less affected by factors such as light and image quality. When it is determined to use N2 as the input value of the oil fume concentration, the system puts it into the second frying position model for matching. The second frying position model presets corresponding frying positions according to different levels of oil fume concentration. The system automatically selects and determines the new frying position by comparing N2 with the preset oil fume concentration data in the model. According to the real-time detected oil fume concentration N2, the system can automatically and quickly adjust the frying position to adapt to the change of oil fume concentration during cooking. The addition of the oil fume sensor provides more accurate and direct oil fume concentration data, which complements the image processing technology and together forms the intelligent oil fume detection system of the range hood 100. By comparing the oil fume concentration data obtained by the image processing technology and the oil fume sensor, the system can select more accurate data as the input value, thereby improving the accuracy and intelligence level of oil fume control.

[0118] More preferably, in the step S70,

[0119] The range hood 100 is preset with a sound intensity threshold A, an oil fume concentration threshold N and a position threshold S.

[0120] When the relationship ∣first frying position-second frying position∣≥S is met,

[0121] If the relationship A1≥A is met, the first frying position is the actual frying position.

[0122] If the relationship A1<A is met, the second frying position is the actual frying position.

[0123] In this scheme, the system first calculates the difference between the first and second frying levels (|first frying level - second frying level|) and compares it with a preset level threshold S. If the difference is less than S, it means that the two levels are close, and the system may choose one level as the actual frying level based on other factors (such as default settings, user preferences, etc.) or keep the current level unchanged. However, in this scheme, we mainly focus on the case where the difference is greater than or equal to S. When the level difference is greater than or equal to S, the system enters the sound intensity judgment stage. The system detects the current sound intensity A1 (which may be composed of frying sounds, exhaust fan operation sounds, etc. during cooking) and compares it with a preset sound intensity threshold A. Case one: if A1 is greater than or equal to A, it means that the current cooking activity may be more intense, and the sound intensity is larger. At this time, the system tends to choose the first frying level as the actual frying level, because the first frying level may be obtained based on image processing technology, which can better reflect the instantaneous change of oil smoke concentration. If A1 is less than A, it means that the current cooking activity is relatively stable, and the sound intensity is small. At this time, the system tends to choose the second frying level as the actual frying level, because the second frying level may be determined based on more direct and accurate oil smoke concentration data obtained by the oil smoke sensor. Sound intensity can be used as an indirect indicator of the intensity of cooking activity. By introducing the sound intensity threshold A, the system can more comprehensively consider various factors in the cooking process, thereby making more reasonable frying level selection. Only when the difference between the two frying levels reaches or exceeds the preset level threshold S, does the system perform sound intensity judgment. This is to avoid frequent adjustment of the level when the levels are close, causing unnecessary energy consumption and user experience degradation. The relationship between sound intensity and frying level selection is based on the actual situation of cooking activity. In the cooking process, a larger sound intensity often accompanies a rapid change in oil smoke concentration, so the system tends to choose the first frying level which can better reflect the instantaneous oil smoke concentration; while a smaller sound intensity may mean that the cooking activity is relatively stable, at which time the system tends to choose the second frying level determined based on more direct and accurate oil smoke concentration data.

[0124] More preferably,

[0125] When the relationship |first frying level - second frying level| < S is satisfied,

[0126] If the relationship first frying level ≥ second frying level and N1 ≥ N or N2 ≥ N is satisfied, the second frying level is the actual frying level;

[0127] If the relationship first frying level ≥ second frying level and N1 < N or N2 < N is satisfied, the first frying level is the actual frying level;

[0128] If the relationship is satisfied: the second frying level ≥ the first frying level, then the second frying level is the actual frying level.

[0129] When |first frying level-second frying level| < S, it means that the two frying levels are close, and the system needs to make a choice based on other factors. Case one: if the first frying level ≥ the second frying level, and any one of the oil fume concentrations N1 (obtained based on image processing technology) or N2 (obtained based on oil fume sensor) is greater than or equal to the preset oil fume concentration threshold N, then the system selects the second frying level as the actual frying level. This is because when the oil fume concentration is high, the system tends to choose a lower level to reduce the generation and diffusion of oil fume, while ensuring the cooking effect. Case two: if the first frying level ≥ the second frying level, but the oil fume concentrations N1 and N2 are both less than the preset oil fume concentration threshold N, then the system selects the first frying level as the actual frying level. This is because when the oil fume concentration is low, the system can allow a higher level to improve cooking efficiency. If the second frying level ≥ the first frying level, regardless of the oil fume concentration, the system selects the second frying level as the actual frying level. This is because when the two levels are close but the second level is higher, the system tends to choose a higher level to meet the possible cooking needs, while considering that the influence of oil fume concentration has been comprehensively considered in the previous judgment. Frying level and oil fume concentration are key factors affecting cooking effect and oil fume control. By considering these two factors comprehensively, the system can make more reasonable and intelligent frying level selection. When the two frying levels are close, the system needs to make a choice based on the actual situation of the oil fume concentration. This logic avoids blindly adjusting the level when the levels are close, ensuring the stability and continuity of the cooking process. Oil fume concentration as one of the judgment bases can reflect the oil fume generation in the cooking process. The system adjusts the frying level according to the oil fume concentration, aiming to minimize the oil fume and optimize the cooking effect. In the scheme, when certain conditions are met (such as the second frying level being higher than or equal to the first frying level, or choosing a lower level when the oil fume concentration is high), the system has clear priority setting. This setting ensures that reasonable frying level selection can be made in different situations.

[0130] More preferably, in the step S80,

[0131] The range hood 100 is pre-set with a fan level model corresponding to a plurality of frying levels;

[0132] According to the actual frying level, the range hood 100 matches the frying level in the fan level model to obtain the corresponding fan level in the fan level model;

[0133] The fan of the range hood 100 runs at the fan level preset speed V0;

[0134] According to the first distance D and the oil fume concentration N1 or the oil fume concentration N2, the ideal rotating speed V is calculated and obtained, and the range hood 100 adjusts the fan rotating speed to the ideal rotating speed V for operation.

[0135] The fan range model corresponding to several frying ranges is preset in the range hood 100. This model is established based on a large amount of experimental data and user feedback, aiming to ensure that the fan can operate at the optimal rotating speed under different frying ranges. After the actual frying range is determined, the system will search for the fan range corresponding to the frying range in the fan range model. This step ensures the matching between the fan range and the actual cooking demand. After matching the fan range, the fan of the range hood 100 will start operating at the preset rotating speed V0. This preset rotating speed V0 is a basic value, ensuring a certain oil fume control ability at the beginning of cooking. The system will calculate the ideal rotating speed V according to the first distance D (the distance between the oil fume source and the range hood 100) and the oil fume concentration N1 or N2. After calculating the ideal rotating speed V, the system will automatically adjust the fan rotating speed to this value for operation. This step ensures that the fan can make intelligent adjustments according to the actual cooking situation to achieve the best oil fume control effect. The fan range model is the core part of the scheme, which is established based on a large amount of experimental data and user feedback, ensuring that the fan can operate at the optimal rotating speed under different frying ranges. This greatly improves the accuracy and efficiency of oil fume control. The preset rotating speed V0 ensures a certain oil fume control ability at the beginning of cooking, avoiding the problem of oil fume diffusion due to fan start-up delay. The ideal rotating speed V is calculated by considering the distance between the oil fume source and the range hood 100, the oil fume concentration, etc., realizing the intelligent adjustment of the fan rotating speed. This adjustment method is more flexible and accurate, and can make the best adjustment according to different situations. The whole scheme realizes intelligent fan rotating speed adjustment, not only improving the accuracy of oil fume control, but also reducing the user's operation burden. Users do not need to manually adjust the fan range to enjoy the best cooking environment.

[0136] The above-described embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the protection scope of the present application.

Claims

1. A smart fan control method with real-time wind pressure monitoring, used for a range hood to extract fumes from within the range of a frying pan during frying, characterized in that, The method includes the following steps: A range hood and a frying pan are provided. The range hood is vertically mounted on the frying pan, and two sound detection modules are respectively provided on both sides of the range hood. The range hood is also provided with an image acquisition module. The range hood operates at its initial speed. Two sound detection modules simultaneously acquire at least one sound feature emitted from the frying pan in real time, and calculate the sound intensity A1 of the first distance D at which the fried food in the frying pan is farthest from the image acquisition module based on the sound feature; The degree of frying of the food is inferred based on the sound intensity A1 to determine the first frying level; The image acquisition module captures image information of the edge of the frying pan after it has been covered by cooking fumes; The oil fume concentration N1 above the frying pan is inferred based on the collected edge image information to determine the second frying level; Compare the first frying setting with the second frying setting to determine the actual frying setting; Adjust the range hood setting and fan speed according to the actual frying setting.

2. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step "two sound detection modules simultaneously acquire at least one sound feature emitted from the frying pan in real time, and calculate the sound intensity A1 at the first distance D furthest from the image acquisition module in the frying pan based on the sound feature", The sound features include sound intensity. Two sound detection modules are located on both sides of the image recognition module, and the first sound source distance L1 and the second sound source distance L2 between the explosive and the two sound detection modules are obtained according to the time of receiving the sound features. The distance set between the explosive and the image acquisition module is calculated based on the distance L1 from the first sound source and the distance L2 from the second sound source. Obtain the first distance D between the explosive and the image acquisition module from the distance set; Based on the sound features obtained from the two sound detection modules, the distance L1 from the first sound source, the distance L2 from the second sound source, and the first distance D, the sound intensity A1 of the explosive at the image acquisition module is obtained.

3. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 2, characterized in that, The sound detection module consists of a microphone array, which comprises a main microphone and at least one secondary microphone located at a distance d from the main microphone. The main microphone acquires the sound characteristics of the explosive after time t1, and the secondary microphone acquires the sound characteristics of the explosive after time t2. Obtain the time interval t between time t1 and time t2; Based on the time interval t and the distance d, the sound detection module calculates the sound source distance between the explosive and the main microphone.

4. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step of "inferring the degree of frying of the food based on the sound intensity A1 to determine the first frying level", Based on historical data of frying sounds within the pan during frying, the range hood has preset frying degree models corresponding to several sound intensity ranges and first frying level models corresponding to several frying degrees. The collected sound intensity A1 is placed into the frying degree model and matched with a sound intensity range in the frying degree model; The degree of frying in the frying degree model is obtained based on the matched sound intensity range; The degree of frying is entered into the first frying level model and matched with one of the frying degree data in the first frying level model; The first frying level is obtained from the first frying level model based on the matched frying level data.

5. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step "the image acquisition module acquires the edge image information of the frying pan after it has been covered by oil fumes", The camera unit captures images in real time within the first area vertically below the image acquisition module; Preprocess the image to obtain the boundary between the oil fumes and the edge contour of the frying pan; Identify the edge contour of the pan in the image, extract edge information, and form edge image information; Transmit edge image information to the processing unit.

6. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step of "inferring the oil fume concentration N1 above the frying pan based on the collected edge image information to determine the second frying setting", The processing unit identifies and acquires oil fume regions composed of pixels in the edge image information; Identify the pixel with the highest brightness value in the oil fume area and record the brightness value I; Based on historical image data of the pan after being covered by oil fumes during frying, the range hood has a preset oil fume concentration model corresponding to a certain number of pixel brightness and a second frying mode model corresponding to a certain number of oil fume concentration data. The obtained brightness value I is put into the oil fume concentration model and matched with the brightness of one of the pixels in the oil fume concentration model; The corresponding oil fume concentration N1 in the oil fume concentration model is obtained based on the matched pixel brightness; The oil fume concentration N1 is input into the second frying level model and matched with one of the oil fume concentration data in the second frying level model; The second frying level is obtained from the matching oil fume concentration data.

7. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, The range hood is also equipped with a fume sensor, which is located on the edge of the pan. The fume sensor detects the real-time fume concentration N2. If the relationship is satisfied, N2 > N1, then the oil fume concentration N2 is added to the second frying level model, and a new second frying level is obtained.

8. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step of "comparing the first frying setting and the second frying setting to determine the actual frying setting", The range hood has preset sound intensity threshold A, oil fume concentration threshold N, and speed setting threshold S; When the relationship is satisfied: |First frying level - Second frying level| ≥ S If the relation A1≥A is satisfied, then the first frying setting is the actual frying setting; If the relationship is satisfied: A1 < A, then the second frying setting is the actual frying setting.

9. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 7, characterized in that, When the relationship is satisfied: |First frying setting - Second frying setting| < S If the following relationship is satisfied: first frying level ≥ second frying level, and N1 ≥ N or N2 ≥ N, then the second frying level is the actual frying level; If the following relationship is satisfied: first frying level ≥ second frying level, and N1 < N or N2 < N, then the first frying level is the actual frying level; If the following relationship is satisfied: the second frying level ≥ the first frying level, then the second frying level is the actual frying level.

10. The intelligent wind turbine control method for real-time wind pressure monitoring as described in claim 1, characterized in that, In the step of "adjusting the range hood setting and fan speed according to the actual frying setting", The range hood has preset fan speed models corresponding to several frying settings; The range hood obtains the corresponding fan speed from the fan speed model by matching the actual frying speed with the frying speed in the fan speed model. The range hood fan operates at the preset speed V0. The ideal rotational speed V is calculated based on the first distance D and the oil fume concentration N1 or N2. The range hood then adjusts its fan speed to the ideal rotational speed V.

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