Intelligent fan control method for monitoring wind pressure in real time

Abstract

An intelligent fan control method for real-time wind pressure monitoring comprises the steps that a smoke exhaust ventilator and a pan are provided, the smoke exhaust ventilator is arranged on the pan, two sound detection modules are arranged on the smoke exhaust ventilator, and an image acquisition module is further arranged on the smoke exhaust ventilator; the range hood operates at the initial rotating speed; the two sound detection modules simultaneously acquire sound characteristics emitted from the pan in real time, and acquire the sound intensity A1 of the farthest distance between the fried object in the pan and the image acquisition module according to the sound characteristics; the frying degree of the food is inferred according to the sound intensity A1, so that a first frying gear is determined; the image acquisition module acquires edge image information of the pan shielded by the oil smoke; deducing the oil smoke concentration N1 above the pan according to the collected edge image information so as to determine a second frying gear; comparing the first frying gear with the second frying gear to determine an actual frying gear; and adjusting the gear of the range hood and the rotating speed of the fan according to the actual frying gear.

Description

Technical Field

[0001] The present invention relates to the field of smart home appliances, and particularly to an intelligent fan control method for real-time air pressure monitoring. Background Art

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

[0003] Traditional range hood designs often operate based on fixed suction parameters, ignoring several key variables during deep-frying cooking. Fumes generated from deep-frying food at a relatively far distance from the range hood are often more difficult to be effectively extracted due to the longer path, resulting in fume escape and affecting the fume extraction effect. As a type of cookware, frying pans come in different sizes on the market. The change in the size of the frying pan directly leads to an unfixed distance range between the fried food and the range hood, posing challenges to the precise regulation of the range hood's suction. Existing range hoods usually adopt a unified suction setting, making it difficult to flexibly adjust according to the cookware size, the position of the fried food, and the actual fume generation situation, thus causing problems of excessive energy consumption (when the fan speed is too high) or low 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 an intelligent fan control method for real-time air pressure monitoring that can effectively extract the fumes generated by fried food in a frying pan and improve the accuracy of the range hood fan speed setting. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent fan control method for real-time air pressure monitoring that can effectively extract the fumes generated by fried food in a frying pan and improve the accuracy of the range hood fan speed setting.

[0006] According to one aspect of the present application, there is provided an intelligent fan control method for real-time air pressure monitoring, which is used to extract the fumes within the range of a frying pan by a range hood during deep-frying. The method includes the steps of:

[0007] Providing a range hood and a frying pan, wherein the range hood is vertically arranged above the frying pan, and two sound detection modules are respectively arranged on both sides of the range hood, and an image acquisition module is also arranged on the range hood;

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

[0009] Two sound detection modules simultaneously and real-time acquire at least one sound feature emitted from the frying pan, and calculate and obtain 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 according to the sound feature;

[0010] Infer the frying degree of the food according to the sound intensity A1 to determine the first frying gear;

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

[0012] Infer the oil fume concentration N1 above the frying pan according to the acquired edge image information to determine the second frying gear;

[0013] Compare the first frying gear and the second frying gear to determine the actual frying gear;

[0014] Adjust the gear and the fan speed of the range hood according to the actual frying gear.

[0015] Preferably, in the step "Two sound detection modules simultaneously and real-time acquire at least one sound feature emitted from the frying pan, and calculate and obtain 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 according to the sound feature",

[0016] The sound feature includes sound intensity. 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 obtained according to the time of receiving the sound feature;

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

[0018] Obtain the first distance D at which the fried food is farthest from the image acquisition module in the distance set;

[0019] Obtain the sound intensity A1 of the fried food at the image acquisition module according to the sound feature, the first sound source distance L1, the second sound source distance L2 and the first distance D obtained by the two sound detection modules.

[0020] 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 sub-microphone with a distance d from the main microphone;

[0022] The main microphone acquires the sound feature of the fried food after a time t1, and the sub-microphone acquires the sound feature of the fried food after a time t2;

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

[0024] Based on the time interval t and the distance d, the sound detection module calculates and obtains the sound source distance between the explosive and the main microphone.

[0025] Preferably, in the step of "inferring the frying degree of the food based on the sound intensity A1 to determine the first frying gear",

[0026] Based on the historical data of the frying sound within the range of the frying pan during frying, the range hood presets a frying degree model corresponding to several sound intensity ranges and a first frying gear model corresponding to several frying degrees;

[0027] Put the collected sound intensity A1 into the frying degree model and match a sound intensity range in the frying degree model;

[0028] Obtain the corresponding frying degree in the frying degree model according to the matched sound intensity range;

[0029] Put the frying degree into the first frying gear model and match one of the frying degree data in the first frying gear model;

[0030] Obtain the corresponding first frying gear in the first frying gear model according to the matched frying degree data.

[0031] 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",

[0032] The camera unit captures images in the first area directly below the image acquisition module in real time;

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

[0034] Identify the edge contour of the frying pan in the image, extract the edge information and form the edge image information;

[0035] Transmit the edge image information to the processing unit.

[0036] Preferably, 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 gear",

[0037] The processing unit identifies and obtains the oil fume area composed of pixels in the edge image information;

[0038] Identify the pixel with the maximum brightness value in the oil fume area and record the brightness value I;

[0039] Based on the historical image data of the frying pan after being blocked by cooking fumes during frying, the range hood preset a cooking fume concentration model corresponding to a number of pixel brightnesses and a second frying gear model corresponding to a number of cooking fume concentration data;

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

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

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

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

[0044] Preferably, the range hood is also provided with a cooking fume sensor, the cooking fume sensor is located at the edge of the frying pan, and the cooking fume sensor detects the real-time cooking fume concentration N2,

[0045] If the relational expression N2 > N1 is satisfied, then the cooking fume concentration N2 is put into the second frying gear model, and a new second frying gear is obtained.

[0046] Preferably, in the step of "comparing the first frying gear and the second frying gear to determine the actual frying gear",

[0047] The range hood presets a sound intensity threshold A, a cooking fume concentration threshold N, and a gear threshold S;

[0048] When the relational expression: |first frying gear - second frying gear| ≥ S is satisfied,

[0049] If the relational expression: A1 ≥ A is satisfied, then the first frying gear is the actual frying gear;

[0050] If the relational expression: A1 < A is satisfied, then the second frying gear is the actual frying gear.

[0051] Preferably,

[0052] When the relational expression: |first frying gear - second frying gear| < S is satisfied,

[0053] If the relational expression: first frying gear ≥ second frying gear, and N1 ≥ N or N2 ≥ N is satisfied, then the second frying gear is the actual frying gear;

[0054] If the relational expression: first frying gear ≥ second frying gear, and N1 < N or N2 < N is satisfied, then the first frying gear is the actual frying gear;

[0055] If the relational expression is satisfied: the second frying gear ≥ the first frying gear, then the second frying gear is the actual frying gear.

[0056] More preferably, in the step of "adjusting the gear and the fan speed of the range hood according to the actual frying gear",

[0057] The range hood is preset with a fan gear model corresponding to a plurality of frying gears;

[0058] According to the actual frying gear, match the frying gear in the fan gear model, and the range hood obtains the corresponding fan gear in the fan gear model;

[0059] The range hood fan operates at the preset fan speed V0;

[0060] According to the first distance D and the oil fume concentration N1 or the oil fume concentration N2, calculate and obtain the ideal speed V, and the range hood adjusts the fan speed to operate at the ideal speed V.

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

[0062] By using the sound detection module to obtain the sound intensity A1 of the fried food in the frying pan and the image acquisition module to obtain the oil fume concentration N1 above the frying pan, the range hood obtains real-time effective data, avoids the escape of the oil fume generated by the fried food at a relatively far distance from the range hood, and improves the accuracy of the fan speed setting. And by determining the actual frying gear and adjusting the gear and the fan speed of the range hood according to the actual frying gear, the range hood can effectively extract the oil fume generated by the fried food when frying food in the frying pan. By using the sound detection module to obtain the sound intensity A1 of the first distance D between the fried food in the frying pan and the image acquisition module at the farthest distance, the range hood can detect the distance between the fried food at the farthest distance from it, avoiding the escape of the oil fume at a relatively far distance. By comparing the first frying gear and the second frying gear to determine the actual frying gear, the range hood can obtain a more suitable frying gear according to the actual situation, avoiding incorrect judgments when only relying on the oil fume concentration and the sound intensity to determine the actual frying gear. Description of the Drawings

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0064] Figure 1 It is a flowchart of a method for intelligent fan control with real-time air pressure monitoring described in an embodiment of the present application;

[0065] Figure 2 This is the schematic diagram of an intelligent fan control method for real-time wind pressure monitoring described in an embodiment of the present application.

[0066] Explanation of the reference numerals in the accompanying drawings: 100, range hood; 11, first sound detection module; 12, second sound detection module; 20, image acquisition module; 200, frying pan; 300, fried food. Detailed implementation manners

[0067] To facilitate the understanding of the present application, the present application will be described more comprehensively below with reference to the relevant accompanying drawings. The preferred embodiments of the present application are shown in the accompanying drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the understanding of 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 also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

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

[0071] S10 Provide a range hood 100 and a frying pan 200. The range hood 100 is arranged vertically above the frying pan 200, and two sound detection modules are respectively arranged on both sides of the range hood 100. An image acquisition module 20 is also arranged on the range hood 100.

[0072] S20 The range hood 100 operates at an initial speed.

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

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

[0075] S50 The image acquisition module 20 acquires the edge image information of the frying pan 200 after being blocked by cooking fumes;

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

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

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

[0079] Wherein, a range hood 100 and a frying pan 200 are provided. The range hood 100 is arranged on the frying pan 200 in the vertical direction. Two sound detection modules are respectively arranged on both sides of the range hood 100 for capturing the sound features in the frying pan 200. An image acquisition module 20 is further arranged on the range hood 100 for acquiring the edge image information of the frying pan 200 after being blocked by cooking fumes. The range hood 100 operates at an initial speed to provide a basic cooking fume extraction ability for the frying process. The two sound detection modules simultaneously and real-time acquire the sound features emitted from the frying pan 200. According to the sound features (such as sound intensity, propagation time, etc.), combined with the positions of the sound detection modules, calculate and acquire the sound intensity A1 of the first distance D from the fried food 300 in the frying pan 200 to the image acquisition module 20 being the farthest. According to the sound intensity A1, combined with the corresponding relationship between the preset frying degree and the sound intensity, infer the frying degree of the food. According to the frying degree, determine the first frying gear to adjust the extraction ability of the range hood 100. The image acquisition module 20 acquires the edge image information of the frying pan 200 after being blocked by cooking fumes. By means of image processing technology, analyze the cooking fume blocking situation in the edge image information to infer the cooking fume concentration N1 above the frying pan 200. According to the cooking fume concentration N1, determine the second frying gear. Compare the first frying gear and the second frying gear to determine the actual frying gear. According to the actual frying gear, adjust the gear and the fan speed of the range hood 100 to achieve more precise cooking 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 comes into contact with the hot oil during frying. By analyzing the sound characteristics, the frying degree of the food can be inferred, and then the speed of the range hood 100 can be adjusted. Setting two sound detection modules can improve the accuracy of sound localization and more accurately calculate the distance between the fried food 300 and the image acquisition module 20. The image acquisition module 20 can collect the edge image information of the frying pan 200 after being blocked by the cooking fumes. Through image processing technology, the concentration and distribution of the cooking fumes can be analyzed. The combination of the image acquisition module 20 and the sound detection module can achieve a comprehensive monitoring of the frying process. By integrating the sound characteristics and the image information, the frying degree and the cooking fume concentration can be inferred more accurately. Determining the actual frying speed according to the frying degree and the cooking fume concentration can achieve precise adjustment of the speed of the range hood 100 and the fan speed. This adjustment method can ensure that the range hood 100 maintains the best cooking fume extraction effect at different frying stages. Although the scheme does not directly mention the wind pressure monitoring, the real-time wind pressure monitoring is an important part of the intelligent fan control method. By monitoring the wind pressure, the cooking fume extraction effect of the range hood 100 can be understood in real time and adjusted as needed. The combination of real-time wind pressure monitoring, sound detection, and image acquisition can achieve a more comprehensive monitoring and control of the frying process.

[0081] More preferably, in the step S30,

[0082] The sound characteristics include the sound intensity. 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] The distance set between the fried food 300 and the image acquisition module 20 is calculated and obtained according to the first sound source distance L1 and the second sound source distance L2.

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

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

[0086] Among them, in step S30, two sound detection modules (respectively located on both sides of the image recognition module) capture the sound characteristics emitted from the frying pan 200 in real time, mainly focusing on the sound intensity. The sound intensity reflects the energy magnitude of the sound and is closely related to the frying activity of the fried food 300. Since the sound propagation speed is known (about 343 m / s in the 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 the sound characteristics. 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 sound propagation speed 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, the second sound source distance L2, and the relative position relationship between the sound detection module and the image acquisition module 20, the distance between the fried food 300 and the image acquisition module 20 can be calculated by trigonometric functions. The distance set here actually refers to the set of multiple possible distances between multiple identical fried foods 300 and the image acquisition module 20 (i.e., near the mouth of the range hood 100) during the frying process. When there are multiple fried foods 300, each fried food 300 will emit sounds, 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 between the fried food 300 and the image acquisition module 20 (i.e., the mouth of the range hood 100). This is because the farthest distance corresponds to the position of the oil fume that is most difficult to be extracted by the range 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 range hood 100. Placing the two sound detection modules on both sides of the image recognition module respectively 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 the positioning and helps to construct an accurate distance set. Selecting the farthest distance D between the fried food 300 and the image acquisition module 20 (i.e., the mouth of the range 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 range hood 100 and adjust the gear and fan speed of the range hood 100 accordingly. The sound intensity A1 not only reflects the frying activity of the fried food 300 but is also closely related to the amount of oil fume generated. By monitoring the sound intensity A1, we can indirectly understand the generation situation of the oil fume and adjust the gear and fan speed of the range hood 100 accordingly to achieve more efficient oil fume extraction.

[0087] More preferably,

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

[0089] The main microphone obtains the sound characteristics of the explosive 300 after time t1, and the secondary microphone obtains the sound characteristics of the explosive 300 after time t2;

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

[0091] Based on the time interval t and the distance d, the sound detection module calculates and obtains the sound source distance between the explosive 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 and is usually located near the image recognition module for synchronous processing with the image data. The secondary microphone is kept at a certain distance d from the main microphone, and this distance is determined according to the actual application scenario and the required positioning accuracy. The role of the secondary microphone is to provide an additional sound signal for comparison with the signal received by the main microphone, so as to calculate the time difference of sound propagation. When the fried food 300 is being fried in the frying pan 200, it will emit sounds. These sound signals are respectively received by the main microphone and the secondary microphone. The main microphone obtains the sound characteristics of the fried food 300 after time t1, while the secondary microphone obtains the same sound characteristics after time t2. Since the speed of sound propagation is constant (about 343 m / s in air, and the specific value is affected by factors such as temperature and humidity), the time difference t (t = t2 - t1) reflects the time difference of sound propagation from the fried food 300 to the main microphone and the secondary 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 propagates from the fried food 300 to the two microphones, the actually 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 point of the position of the main microphone). To obtain the exact distance from the fried food 300 to the main microphone, certain corrections may need to be made to L, but this usually depends on the specific configuration of the microphone array and the positioning algorithm. Advantages of the microphone array: Using a microphone array for sound localization has multiple advantages. First, it provides an additional sound signal source, making the localization more accurate and reliable. Second, by adjusting the distance d and the number of microphones, it can flexibly adapt 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 localization. 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, and high positioning accuracy. In the intelligent fan control method, by using the time difference principle for sound localization, the position information of the fried food 300 can be obtained in real time, providing an important reference basis for subsequent oil fume extraction and fan control. By optimizing the configuration of the microphone array and the positioning algorithm, the accuracy of sound localization can be further improved. For example, the number of secondary microphones can be increased to provide more sound signal sources; more advanced signal processing algorithms can be adopted to reduce errors and interference; or the position information provided by the image recognition module can be used to assist sound localization, etc. These measures all contribute to improving the accuracy and reliability of sound localization, providing better support for the intelligent fan control method.

[0093] Preferably, in the step S40,

[0094] According to the historical data of the frying sound within the range of 200 of the frying pan during frying, the range hood 100 presets a frying degree model corresponding to a number of sound intensity ranges and a first frying gear model corresponding to a number of frying degrees;

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

[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 gear model, and one of the frying degree data in the first frying gear model is matched;

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

[0099] Among them, the model is constructed based on the historical data of the sound intensity within 200 of the frying pan during frying. It divides the sound intensity into several ranges, and each range corresponds to a specific frying degree, such as light frying, medium frying, and heavy frying, etc. These sound intensity ranges are obtained by analyzing the sound characteristics of a large number of frying activities and can accurately reflect the intensity of the frying activity and the amount of oil fume generated. The model is associated with the frying degree model and presets several frying gears according to the frying degree. Each frying gear corresponds to a specific fan speed and oil fume extraction efficiency to meet the oil fume extraction requirements under different frying degrees. 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 effective oil fume extraction. 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 the frying activity. Once the frying degree is determined, the system will put this information into the first frying gear model and automatically select and determine the appropriate frying gear by matching the frying degree data in the model. This process is also real-time and can ensure that the range hood 100 quickly adjusts to the best working state when the frying activity occurs. Through the above steps, the range hood 100 can automatically adjust the gear according to the real-time sound intensity of the frying activity, realizing intelligent oil fume control and energy consumption management. This can not only improve the oil fume extraction efficiency, reduce the impact of oil fume on the kitchen environment, but also reduce energy consumption and extend the service life of the range hood 100. The sound intensity is one of the important indicators reflecting the intensity of the frying activity. Through historical data analysis, we can establish an association model between the sound intensity and the frying degree. This model enables the range hood 100 to accurately judge the frying degree by monitoring the sound intensity and then select the appropriate gear for oil fume extraction. The traditional gear selection of the range hood 100 usually relies on the user's manual operation or a preset fixed mode. However, this method often cannot accurately reflect the real-time changes of the frying activity, resulting in low oil fume 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 the frying activity, realizing more accurate and efficient oil fume control. The optimized step S40 not only improves the oil fume extraction efficiency and the energy consumption management level, but also significantly enhances the user experience. The user can enjoy the intelligent oil fume control service without manually adjusting the gear. At the same time, by reducing unnecessary energy consumption, this solution also helps to achieve the goal of energy conservation and emission reduction, meeting the current green and environmental protection development trend.

[0100] More preferably, in the step S50,

[0101] The imaging unit captures images in the first area directly below the image acquisition module 20 in real time;

[0102] Preprocess the image to obtain the boundary between the fume and the edge contour of the frying pan 200;

[0103] Identify the edge contour of the frying pan 200 in the image, extract the edge information and form edge image information;

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

[0105] Among them, in step S50, the imaging unit captures images in the first area directly below the image acquisition module 20 in real time. This area usually includes the frying pan 200 that is being fried and the oil fume around it. Subsequently, the system preprocesses the images to obtain the boundary between the oil fume and the edge contour of the frying pan 200, identifies the edge contour of the frying pan 200, and extracts the edge information to form edge image information. Finally, this edge image information is transmitted to the processing unit for further analysis and processing. The imaging unit is configured to capture images in the first area directly below the image acquisition module 20 in real time. The selection of this area is based on the expected positions of the oil fume and the frying pan 200 during the frying process to ensure that key information can be captured. The imaging unit uses a high-resolution camera to capture clear image details, providing an accurate data basis for subsequent edge detection and oil fume identification. The main purpose of the preprocessing stage is to enhance the image quality, reduce noise and interference, so as to more accurately identify the edge contours of the oil fume and the frying pan 200. Common preprocessing techniques include image denoising, grayscale conversion, binarization, etc. These techniques help to highlight the key features in the image while reducing unnecessary computational complexity. In the preprocessed image, the system uses an edge detection algorithm to identify the boundary between the oil fume and the edge contour of the frying pan 200. This step is crucial for subsequent edge information extraction. The edge detection algorithm can identify edges based on information such as gray-scale changes and texture features in the image. At the junction of the oil fume and the frying pan 200, due to the fuzziness of the oil fume and the clear edge of the frying pan 200, the algorithm can accurately capture this boundary. Once the boundary between the oil fume and the edge contour of the frying pan 200 is identified, the system further identifies the complete edge contour 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 includes 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. Capturing images in real time ensures that the system can timely capture key information during the frying process, while preprocessing improves the image quality, providing a reliable data basis for subsequent edge detection and oil fume identification. The edge detection algorithm can accurately capture the boundary between the oil fume and the edge contour 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 positions of the oil fume and the frying pan 200. Transmitting the edge image information to the processing unit for further analysis helps the system to more accurately identify the concentration and diffusion range of the oil fume, thereby providing a basis for oil fume control and blower gear adjustment.

[0106] Preferably, in step S60,

[0107] The processing unit identifies and obtains 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] Based on the historical image data of the frying pan 200 after being blocked by oil fume during frying, the range hood 100 presets an oil fume concentration model corresponding to a number of pixel brightnesses and a second frying gear model corresponding to a number of oil fume concentration data;

[0110] Put the obtained brightness value I into the oil fume concentration model and match one of the pixel brightnesses in the oil fume concentration model;

[0111] Obtain the corresponding oil fume concentration N1 in the oil fume concentration model according to the matched pixel brightness;

[0112] Put the oil fume concentration N1 into the second frying gear model and match one of the oil fume concentration data in the second frying gear model;

[0113] Obtain the corresponding second frying gear in the second frying gear model according to the matched oil fume concentration data.

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

[0115] Preferably, the range hood 100 is further provided with a soot sensor, and the soot sensor is located at the edge of the frying pan 200. The soot sensor detects the real-time soot concentration N2.

[0116] If the relational expression N2 > N1 is satisfied, then the soot concentration N2 is put into the second frying gear model, and a new second frying gear is obtained.

[0117] Among them, the oil fume sensor is ingeniously installed at the edge of the frying pan 200. This position can ensure that the sensor directly contacts the oil fume generated during the cooking process, thereby providing more accurate oil fume concentration data. The oil fume sensor adopts advanced detection technology, 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 simultaneously uses two methods, namely image processing technology and the oil fume sensor, to detect the oil fume concentration. The image processing technology indirectly reflects the oil fume concentration by identifying the pixel with the maximum brightness value in the oil fume area (denoted as N1), while the oil fume sensor directly detects the oil fume concentration (denoted as N2). The system compares N1 and N2. 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 gear model for matching. The second frying gear model presets corresponding frying gears according to different levels of oil fume concentration. The system automatically selects and determines a new frying gear 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 gear to adapt to the change of oil fume concentration during the cooking process. The addition of the oil fume sensor provides more accurate and direct oil fume concentration data, complements the image processing technology, and jointly constitutes 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 intelligent level of oil fume control.

[0118] More preferably, in the step S70,

[0119] The range hood 100 presets a sound intensity threshold A, an oil fume concentration threshold N, and a gear threshold S;

[0120] When the relational expression: ∣the first frying gear - the second frying gear∣≥S is satisfied,

[0121] If the relational expression: A1≥A is satisfied, then the first frying gear is the actual frying gear;

[0122] If the relational expression: A1<A is satisfied, then the second frying gear is the actual frying gear.

[0123] Among them, the system first calculates the difference between the first frying gear and the second frying gear (|first frying gear - second frying gear|) and compares it with the preset gear threshold S. If the difference is less than S, it means the two gears are similar. The system may select one gear as the actual frying gear according to other factors (such as default settings, user preferences, etc.) or keep the current gear unchanged. However, in this solution, we mainly focus on the case where the difference is greater than or equal to S. When the gear 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, the running sound of the range hood, etc. during the cooking process) and compares it with the preset sound intensity threshold A. Case 1: If A1 is greater than or equal to A, it means the current cooking activity may be relatively intense and the generated sound intensity is large. At this time, the system tends to select the first frying gear as the actual frying gear because the first frying gear may be obtained based on image processing technology and can better reflect the instantaneous change of the oil fume concentration. If A1 is less than A, it means the current cooking activity is relatively stable and the sound intensity is small. At this time, the system tends to select the second frying gear as the actual frying gear because the second frying gear may be determined based on more direct and accurate oil fume concentration data obtained by the oil fume sensor. The sound intensity can be an indirect indicator of the intensity of the cooking activity. By introducing the sound intensity threshold A, the system can more comprehensively consider various factors during the cooking process and thus make a more reasonable choice of the frying gear. Only when the difference between the two frying gears reaches or exceeds the preset gear threshold S does the system perform the sound intensity judgment. This is to avoid frequent gear adjustments when the gears are similar, resulting in unnecessary energy consumption and a decline in the user experience. The association between the sound intensity and the frying gear selection is based on the actual situation of the cooking activity. During the cooking process, a larger sound intensity is often accompanied by a rapid change in the oil fume concentration. Therefore, the system tends to select the first frying gear that can better reflect the instantaneous oil fume concentration; while a smaller sound intensity may mean that the cooking activity is relatively stable, and at this time the system is more inclined to select the second frying gear determined based on more direct and accurate oil fume concentration data.

[0124] More preferably,

[0125] When the relational expression: |first frying gear - second frying gear| < S is satisfied,

[0126] If the relational expressions: first frying gear ≥ second frying gear, and N1 ≥ N or N2 ≥ N are satisfied, then the second frying gear is the actual frying gear;

[0127] If the relational expressions: first frying gear ≥ second frying gear, and N1 < N or N2 < N are satisfied, then the first frying gear is the actual frying gear;

[0128] If the relational expression is satisfied: the second frying gear ≥ the first frying gear, then the second frying gear is the actual frying gear.

[0129] Among them, when |the first frying gear - the second frying gear| < S, it indicates that the two frying gears are similar, and the system needs to make a choice based on other factors. Case 1: If the first frying gear ≥ the second frying gear, and either the oil fume concentration N1 (obtained based on image processing technology) or N2 (obtained based on an oil fume sensor) is greater than or equal to the preset oil fume concentration threshold N, the system selects the second frying gear as the actual frying gear. This is because when the oil fume concentration is high, the system tends to select a lower gear to reduce the generation and diffusion of oil fume while ensuring the cooking effect. Case 2: If the first frying gear ≥ the second frying gear, but both the oil fume concentrations N1 and N2 are less than the preset oil fume concentration threshold N, the system selects the first frying gear as the actual frying gear. This is because when the oil fume concentration is low, the system can allow a higher gear to improve cooking efficiency. If the second frying gear ≥ the first frying gear, regardless of the oil fume concentration, the system selects the second frying gear as the actual frying gear. This is because when the two gears are similar but the second gear is higher, the system tends to select a higher gear to meet possible cooking requirements, and at the same time, considering that the influence of the oil fume concentration has been comprehensively considered in the previous judgment. The frying gear and the oil fume concentration are key factors affecting the cooking effect and oil fume control. By comprehensively considering these two factors, the system can make a more reasonable and intelligent choice of the frying gear. When the two frying gears are similar, the system needs to make a choice based on the actual situation of the oil fume concentration. This logic avoids blindly adjusting the gear when the gears are similar, ensuring the stability and continuity of the cooking process. The oil fume concentration, as one of the judgment bases, can reflect the oil fume generation situation during the cooking process. The system adjusts the frying gear according to the level of the oil fume concentration, aiming to minimize the oil fume and optimize the cooking effect. In the solution, when specific conditions are met (such as the second frying gear is higher than or equal to the first frying gear, or a lower gear is selected when the oil fume concentration is high), the system has a clear priority setting. This setting ensures that a reasonable choice of the frying gear can be made in different situations.

[0130] More preferably, in the step S80,

[0131] The range hood 100 presets a fan gear model corresponding to several frying gears;

[0132] According to the actual frying gear, match the frying gear in the fan gear model, and the range hood 100 obtains the corresponding fan gear in the fan gear model;

[0133] The fan of the range hood 100 operates at the preset rotational speed V0 of the fan gear;

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

[0135] Among them, a fan speed model corresponding to several frying gears is preset inside 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 rotation speed under different frying gears. When the actual frying gear is determined, the system will search for the corresponding fan speed in the fan speed model. This step ensures the matching between the fan speed and the actual cooking requirements. After the fan speed is matched, the fan of the range hood 100 starts to operate at the preset rotation speed V0. This preset rotation speed V0 is a basic value to ensure a certain oil fume control ability at the beginning of cooking. The system will calculate and obtain the ideal rotation 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 the ideal rotation speed V is calculated, the system will automatically adjust the fan rotation 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 speed model is the core part of the solution. It is established based on a large amount of experimental data and user feedback, and can ensure that the fan can operate at the optimal rotation speed under different frying gears. This greatly improves the accuracy and efficiency of oil fume control. The preset rotation speed V0 ensures a certain oil fume control ability at the beginning of cooking, avoiding the problem of oil fume diffusion caused by the delayed start of the fan. Considering factors such as the distance between the oil fume source and the range hood 100 and the oil fume concentration to calculate the ideal rotation speed V realizes the intelligent adjustment of the fan rotation speed. This adjustment method is more flexible and accurate, and can make the optimal adjustment according to different situations. The entire solution not only improves the accuracy of oil fume control through intelligent fan rotation speed adjustment, but also reduces the operation burden of users. Users can enjoy the best cooking environment without manually adjusting the fan speed.

[0136] According to the steps S10 - S80, this specific embodiment provides a specific example to simulate the process of the intelligent fan control system adjusting the range hood 100 gear and fan rotation speed when frying food in the frying pan 200:

[0137] Example parameter settings:

[0138] Dimensions of the frying pan 200: maximum diameter 30 cm, minimum diameter 20 cm, height 6 cm;

[0139] Fried food: meatballs;

[0140] Food size: diameter 4 cm;

[0141] Oil fume concentration threshold: 180 mg / m3 ;

[0142] Sound intensity threshold: 85 dB;

[0143] Spacing between two sound detection modules: 0.5 m;

[0144] Spacing between the sound detection module and the image acquisition module 20: 0.25 m;

[0145] Sound propagation speed: 343 m / s;

[0146] Time t1 for the A sound detection module to obtain the sound of the explosive 300: 0.028 s;

[0147] Time t2 for the B sound detection module to obtain the sound of the explosive 300: 0.032 s;

[0148] Gear threshold: 1;

[0149] The corresponding frying gear obtained according to the first frying gear model is gear 3;

[0150] The corresponding frying gear obtained according to the second frying gear model is gear 4;

[0151] Example process:

[0152] The oil fume concentration N2 detected by the oil fume sensor is 200. Since N2 (200) is already greater than the oil fume concentration threshold N (180), N2 is used to re-determine the second frying gear as gear 5.

[0153] Based on the time difference between the two sound detection modules to obtain the sound of the explosive 300 (t2 - t1 = 0.032 - 0.028 = 0.004 seconds) and the sound propagation speed (343 m / s), the distance corresponding to half of the distance difference between the explosive 300 and the two sound detection modules can be calculated (i.e., half of the distance corresponding to the time difference of sound propagation):

[0154] The calculation method is as follows:

[0155] (0.04 * 343) / 2 = 0.7 m;

[0156] Since the spacing between the two sound detection modules is 0.5 meters, and assuming that the sound detection module and the frying pan 200 are on the same vertical line (horizontal spacing is 0), the horizontal distance from the explosive 300 to the image acquisition module 20 (or assumed to be the midpoint of the connection line of the two sound detection modules) can be calculated through geometric relationships. However, in this case, we mainly focus on the sound intensity A1 and the oil fume concentration N2, so there is no need to further calculate the specific position of the explosive 300.

[0157] When the gear threshold S is 1, the first frying gear is gear 3, and the second frying gear (after considering N2) is gear 5, satisfying |first frying gear - second frying gear| ≥ S.

[0158] The sound intensity A1 is 80 dB, satisfying A1 < A.

[0159] Therefore, it is determined that the actual frying gear is the second frying gear, that is, gear 5.

[0160] Thereby, the sound intensity A1 of the fried food 300 in the frying pan 200 is obtained through the sound detection module, and the oil fume concentration N1 above the frying pan 200 is obtained through the image acquisition module 20, so that the range hood 100 obtains real-time effective data, avoiding the escape of the oil fume generated by the frying of food at a relatively far distance from the range hood 100, and improving the accuracy of the fan speed setting. And by determining the actual frying gear and adjusting the gear and fan speed of the range hood 100 according to the actual frying gear, the range hood 100 can effectively extract the oil fume generated by the fried food 300 when frying food in the frying pan 200. By obtaining the sound intensity A1 of the first distance D between the fried food 300 in the frying pan 200 and the image acquisition module 20 through the sound detection module, the range hood 100 can detect the distance between the fried food 300 farthest from it, avoiding the escape of the oil fume at a relatively far distance. By comparing the first frying gear and the second frying gear to determine the actual frying gear, the range hood 100 can obtain a more suitable frying gear according to the actual situation, avoiding incorrect judgments when only relying on the oil fume concentration and sound intensity to determine the actual frying gear.

[0161] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application.

Claims

1. An intelligent fan control method with real-time wind pressure monitoring, used for extracting oil smoke from a frying pan when frying, characterized in that: The method comprises the steps of: A range hood and a frying pan are provided, wherein the range hood is arranged on the frying pan in a vertical direction, two sound detection modules are respectively arranged on both sides of the range hood, and an image acquisition module is also arranged on the range hood; The range hood operates at an initial rotation speed; The two sound detection modules simultaneously obtain at least one sound feature emitted from the pan in real time, and calculate and obtain the sound intensity A1 of the fried food in the pan at the first distance D farthest from the image acquisition module according to the sound feature; Inferring the frying degree of the food according to the sound intensity A1 to determine the first frying gear; The image acquisition module acquires edge image information of the pan after being blocked by oil smoke; The fume concentration N1 above the pan is inferred based on the collected edge image information to determine the second frying gear; Compare the first frying gear and the second frying gear to determine the actual frying gear; The gear position and fan speed of the range hood are adjusted according to the actual frying gear position.

2. According to the intelligent wind turbine control method for real-time wind pressure monitoring as claimed in claim 1, it is characterized in that: In the step of "the two sound detection modules simultaneously and in real time acquire at least one sound feature emitted from the pan, and calculate and acquire the sound intensity A1 of the first distance D at which the fried object in the pan is farthest from the image acquisition module based on the sound feature", The sound feature includes sound intensity. 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 object and the two sound detection modules are obtained according to the time of receiving the sound feature. Calculate and obtain a distance set between the explosive object and the image acquisition module according to the first sound source distance L1 and the second sound source distance L2; Obtain the first distance D of the explosive object farthest from the image acquisition module in the distance set; According to the sound features 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.

3. According to the intelligent wind turbine control method for real-time wind pressure monitoring as claimed in claim 2, it is characterized in that: The sound detection module is composed of a microphone array, and the microphone array is composed of a main microphone and at least one secondary microphone with a distance d from the main microphone; The main microphone acquires the sound characteristics of fried food after time t1, and the auxiliary microphone acquires the sound characteristics of fried food after time t2; Get the time interval t between time t1 and time t2; According to the time interval t and the distance d, the sound detection module calculates and obtains the sound source distance between the explosive and the main microphone.

4. According to the intelligent wind turbine control method for real-time wind pressure monitoring as claimed in claim 1, it is characterized in that: In the step of "inferring the frying degree of the food according to the sound intensity A1 to determine the first frying gear", According to the historical data of frying sound within the pan during frying, the range hood is preset with frying degree models corresponding to several sound intensity ranges and first frying gear models corresponding to several frying degrees; The collected sound intensity A1 is put into the deep-frying degree model and matched with a sound intensity range in the deep-frying degree model; Acquire the corresponding degree of frying in the frying degree model according to the matched sound intensity range; The frying degree is put into the first frying gear model and matched with one of the frying degree data in the first frying gear model; The first frying gear corresponding to the first frying gear model is obtained according to the matched frying degree data.

5. According to the intelligent wind turbine control method for real-time wind pressure monitoring as claimed in claim 1, it is characterized in that: In the step "the image acquisition module acquires edge image information of the pan after being blocked by oil smoke", The camera unit captures the image in the first area vertically below the image acquisition module in real time; Preprocess the image to obtain the boundary between the oil smoke and the edge of the 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 according to claim 1 is characterized in that: In the step of "inferring the oil smoke concentration N1 above the pan based on the collected edge image information to determine the second frying gear", The processing unit identifies and obtains the oil smoke area composed of pixels in the edge image information; Identify the pixel with the largest brightness value in the oil smoke area and record the brightness value I; According to the historical image data of the frying pan after being blocked by oil smoke during frying, the range hood is preset with an oil smoke concentration model corresponding to a number of pixel brightnesses and a second frying gear model corresponding to a number of oil smoke concentration data; The obtained brightness value I is put into the oil fume concentration model and matched with the brightness of one pixel in the oil fume concentration model; Obtaining the corresponding oil fume concentration N1 in the oil fume concentration model according to the matched pixel brightness; The oil fume concentration N1 is put into the second frying gear model and matched with one of the oil fume concentration data in the second frying gear model; The corresponding second frying gear in the second frying gear model is obtained according to the matched oil smoke concentration data.

7. The intelligent wind turbine control method for real-time wind pressure monitoring according to claim 1 is characterized in that: The range hood is also provided with an oil fume sensor, which is located at the edge of the pan and detects the real-time oil fume concentration N2. If the relationship N2>N1 is satisfied, the oil smoke concentration N2 is put into the second frying gear model, and a new second frying gear is obtained.

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

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

10. The intelligent wind turbine control method for real-time wind pressure monitoring according to claim 1, characterized in that: In the step of "adjusting the gear position and fan speed of the range hood according to the actual frying gear position", The range hood is preset with fan gear models corresponding to several frying gears; According to the actual frying gear, the frying gear in the fan gear model is matched, and the range hood obtains the corresponding fan gear in the fan gear model; The range hood fan runs at the preset speed V0 of the fan gear; The ideal rotation speed V is calculated according to the first distance D and the oil fume concentration N1 or the oil fume concentration N2, and the range hood adjusts the fan speed to the ideal rotation speed V for operation.

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