Power adjusting method and device of extractor hood, extractor hood, medium and product

Abstract

The invention discloses a power adjusting method and device of a range hood, the range hood, a medium and a product, and relates to the technical field of intelligent kitchen appliances. The method comprises the steps that environment parameters of a kitchen where a target extractor hood is located and target working parameters of the target extractor hood are obtained; based on the environment parameters, the target working parameters and a target regression model, the current oil smoke suction efficiency of the target extractor hood is determined; increasing a set power threshold value to the current power of the target extractor hood corresponding to the current oil smoke suction efficiency to obtain second power, and determining second oil smoke suction efficiency matched with the second power based on the target regression model; and determining the second power as the target power of the target extractor hood under the condition that the second oil smoke suction efficiency is determined to be within the preset oil smoke suction efficiency range. According to the scheme, the power of the range hood can be quickly, automatically and accurately adjusted, so that the working state of the range hood can be dynamically adjusted according to the actual oil smoke concentration, and the excellent oil smoke suction efficiency is always maintained.

Description

Technical Field

[0001] This invention relates to the field of intelligent kitchen appliance technology, and in particular to a power adjustment method, device, range hood, medium, and product for a range hood. Background Technology

[0002] With modern families increasingly demanding higher levels of comfort and health in their living environments, the air quality in the kitchen, as the core area of ​​home cooking, directly impacts users' health and experience. Cooking fumes contain various harmful substances; prolonged exposure to high concentrations of these fumes not only pollutes kitchen appliances and the indoor environment but can also damage the respiratory system. Therefore, range hoods, which can efficiently and effectively remove cooking fumes, have become indispensable appliances in the kitchen.

[0003] Currently, before or during cooking, users manually select "low," "medium," "high," or other subdivided fan speeds based on their experience or subjective perception of the fume concentration (e.g., visually observing the degree of fume rising or smelling the fumes). This method relies heavily on user judgment, is slow, and is significantly influenced by user subjectivity. It often results in situations where the kitchen fume concentration is high, but the range hood's power is low and its fume extraction efficiency is low.

[0004] How to achieve rapid, automatic, and precise adjustment of the power of a range hood, enabling it to dynamically adjust its working state according to the actual concentration of cooking fumes and always maintain optimal fume extraction efficiency, is a key research issue in the industry. Summary of the Invention

[0005] This invention provides a power adjustment method, device, range hood, medium, and product for a range hood, so as to achieve rapid, automatic, and precise power adjustment of the range hood, enabling it to dynamically adjust its working state according to the actual oil fume concentration and always maintain optimal oil fume extraction efficiency.

[0006] According to one aspect of the present invention, a power adjustment method for a range hood is provided, the method comprising:

[0007] During the use of the target range hood, obtain the environmental parameters of the target user's kitchen where the target range hood is located, as well as the target operating parameters of the target range hood.

[0008] The current fume extraction efficiency of the target range hood is determined based on the environmental parameters of the target user's kitchen, the target operating parameters, and the target regression model.

[0009] The current power of the target range hood corresponding to the current fume extraction efficiency is increased by a set power threshold to obtain a second power. Based on the target regression model, a second fume extraction efficiency matching the second power is determined.

[0010] If the second fume extraction efficiency is determined to be within the preset fume extraction efficiency range, the second power is determined as the target power of the target range hood.

[0011] According to another aspect of the present invention, a power regulating device for a range hood is provided, the device comprising:

[0012] The parameter acquisition module is used to acquire the environmental parameters of the target user's kitchen where the target range hood is located and the target operating parameters of the target range hood during the use of the target range hood.

[0013] The first determining module is used to determine the current fume extraction efficiency of the target range hood based on the environmental parameters of the target user's kitchen, the target operating parameters, and the target regression model.

[0014] The second determining module is used to increase the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power, and to determine a second fume extraction efficiency that matches the second power based on the target regression model;

[0015] The target power determination module is used to determine the second power as the target power of the target range hood when it is determined that the second fume extraction efficiency is within a preset fume extraction efficiency range.

[0016] According to another aspect of the present invention, a range hood is provided, the range hood comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power adjustment method of the range hood according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the power adjustment method of the range hood according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the power adjustment method for a range hood as described in any embodiment of the present invention.

[0022] The technical solution of this invention involves acquiring environmental parameters of the target user's kitchen and target operating parameters of the target range hood during its use; determining the current fume extraction efficiency of the target range hood based on the environmental parameters, target operating parameters, and a target regression model; determining the current fume extraction efficiency of the range hood based on the acquired parameters; increasing the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power; determining a second fume extraction efficiency matching the second power based on the target regression model; determining the second fume extraction efficiency based on the current fume extraction efficiency; determining the second fume extraction efficiency if the second fume extraction efficiency is within a preset fume extraction efficiency range; verifying the second fume extraction efficiency; and determining the optimal target power of the target range hood at the current moment if preset conditions are met; enabling rapid, automatic, and precise adjustment of the range hood power, allowing it to dynamically adjust its working state according to the actual fume concentration and always maintain a superior fume extraction efficiency.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a power adjustment method for a range hood according to Embodiment 1 of the present invention;

[0026] Figure 2 This is a schematic diagram of the training process of a target regression model according to Embodiment 2 of the present invention;

[0027] Figure 3 This is a flowchart of a power adjustment method for a range hood according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a flowchart of a power adjustment method for a range hood according to Embodiment 4 of the present invention;

[0029] Figure 5This is a flowchart of another power adjustment method for a range hood according to Embodiment 4 of the present invention;

[0030] Figure 6 This is a schematic diagram of the power adjustment device for a range hood according to Embodiment 5 of the present invention;

[0031] Figure 7 This is a schematic diagram of the structure of a range hood that implements the power adjustment method of the range hood according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a power adjustment method for a range hood according to Embodiment 1 of the present invention. This embodiment is applicable to situations where the operating power of a range hood is intelligently adjusted. The method can be executed by a power adjustment device for the range hood, which can be implemented in hardware and / or software and can be configured within the range hood. Specifically, refer to... Figure 1 The method includes:

[0036] Step 110: During the use of the target range hood, obtain the environmental parameters of the target user's kitchen where the target range hood is located, as well as the target operating parameters of the target range hood.

[0037] The target range hood can be any range hood installed in the kitchen of a family, canteen, or restaurant, and is not limited to it in this embodiment; the target range hood can be a top-mounted range hood or a side-mounted range hood, and is not limited to it in this embodiment; it should be noted that in this embodiment, the kitchen where the target range hood is installed is referred to as the target user's kitchen.

[0038] Optionally, in this embodiment, during the use of the target range hood, that is, after the target range hood is detected to be working, the environmental parameters of the target user's kitchen where the target range hood is located and the target working parameters of the target range hood can be obtained in real time.

[0039] In this embodiment, the environmental parameters of the target user's kitchen may include at least one of the following: temperature, humidity, pressure, and oil fume concentration; the target operating parameters of the target range hood may include at least one of the following: relative position to the stove, power, and oil fume concentration at the outlet.

[0040] Optionally, in this embodiment, obtaining the environmental parameters of the target user's kitchen where the target range hood is located and the target operating parameters of the target range hood may include: obtaining the temperature of the target user's kitchen through a temperature sensor separately deployed in the target user's kitchen, obtaining the humidity of the target user's kitchen through a humidity sensor, obtaining the pressure of the target user's kitchen through a pressure gauge, and obtaining the oil fume concentration of the target user's kitchen through a particulate matter sensor; or, determining the temperature, humidity, pressure, or oil fume concentration of the target user's kitchen through other electrical equipment; determining the vertical distance between the target range hood and the stove, and / or the horizontal coverage area of ​​the target range hood, and determining the relative position of the target range hood and the stove based on the vertical distance and / or the horizontal coverage area; determining the current operating level of the target range hood, and determining the power of the target range hood based on the current operating level and the user manual of the target range hood; and determining the oil fume concentration at the outlet of the target range hood through a particulate matter sensor deployed at the outlet of the target range hood.

[0041] In one optional implementation of this embodiment, sensors such as temperature sensors, humidity sensors, pressure gauges, or particulate matter sensors may be deployed in the target user's kitchen. In this embodiment, the installation location of each sensor is not specifically limited.

[0042] Optionally, in this embodiment, the temperature of the target user's kitchen can be measured by a temperature sensor deployed in the target user's kitchen; the air humidity in the target user's kitchen can be measured by a humidity sensor; the air pressure in the target user's kitchen can be measured by a pressure gauge; and the concentration of cooking fumes in the target user's kitchen can be detected by a particulate matter sensor.

[0043] In another optional implementation of this embodiment, temperature sensors, humidity sensors, pressure gauges, or particulate matter sensors may not be deployed in the target user's kitchen. Instead, environmental parameters of the target user's kitchen can be obtained through sensors built into the range hood or other electrical appliances (e.g., gas water heaters, refrigerators, or exhaust fans). For example, the concentration of cooking fumes in the target user's kitchen can be detected by the range hood; the air humidity and pressure in the target user's kitchen can be detected by the gas water heater; and the temperature in the target user's kitchen can be detected by the exhaust fan.

[0044] Optionally, in this embodiment, the vertical distance between the target range hood and the stovetop can be determined using an infrared sensor in the target fume extractor, and the relative position of the target range hood and the stovetop can be determined based on this vertical distance. Alternatively, the width of the range hood's air inlet or filter section can be measured using a measuring tape. This represents the physical dimension of the target range hood in the horizontal direction. Furthermore, the overall width of the stovetop (gas stove or electric stove), including all burners or heating areas, can be measured using a measuring tape. Further, the horizontal coverage area of ​​the target range hood on the stovetop can be determined based on the target range hood's physical dimensions in the horizontal direction, the overall width of the stovetop, and the vertical distance between the target range hood and the stovetop. Further, the relative position of the target range hood and the stovetop can be determined based on the vertical distance, the horizontal coverage area, or both. In this embodiment, the relative position of the target range hood and the stovetop can be represented using coordinates or vectors.

[0045] Optionally, in this embodiment, the current operating level of the target range hood (e.g., low, medium, or high) can be determined. Furthermore, the power of the target range hood that matches the current operating level can be found in the user manual of the target range hood. Furthermore, the concentration of oil fumes at the outlet of the target range hood can be detected by a particulate matter sensor deployed at the outlet of the target range hood.

[0046] Step 120: Determine the current fume extraction efficiency of the target range hood based on the environmental parameters of the target user's kitchen, the target operating parameters, and the target regression model.

[0047] Optionally, in this embodiment, during the use of the target range hood, after obtaining the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood, the current smoke extraction efficiency of the target range hood can be further determined based on the environmental parameters of the target user's kitchen, the target operating parameters of the target range hood, and the target regression model.

[0048] Among them, the target regression model is a pre-trained deep learning model that matches the target range hood, such as a deep regression model.

[0049] In an optional implementation of this embodiment, after obtaining the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood, the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood can be further input into the target regression model to output the current smoke extraction efficiency of the target range hood.

[0050] Step 130: Increase the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power, and determine a second fume extraction efficiency that matches the second power based on the target regression model.

[0051] The power threshold can be set to 0.5W, 1W, or 3W, etc., and is not limited to this embodiment.

[0052] Optionally, in this embodiment, after determining the current fume extraction efficiency of the target range hood based on the target regression model, the current power of the target range hood corresponding to the current fume extraction efficiency (i.e., the power in the target operating parameters obtained in step 110, for example, 200W) can be further increased by a set power threshold to obtain a second power; for example, if the current power is 200W and the set power threshold is 1W, then the second power is 201W.

[0053] Furthermore, a second fume extraction efficiency matching the second power can be determined based on a target regression model. For example, the second power, the environmental parameters of the target user's kitchen obtained in step 110, the relative position to the stove in the target working parameters, and the fume concentration at the outlet can be input into the target regression model to output the second fume extraction efficiency.

[0054] Step 140: If the second fume extraction efficiency is determined to be within the preset fume extraction efficiency range, the second power is determined as the target power of the target range hood.

[0055] The preset fume extraction efficiency can be set according to user requirements and the parameters of the target range hood. For example, it can be 80%~100%, 80%~99%, or 85%~100%, etc., but it is not limited in this embodiment.

[0056] Optionally, in this embodiment, after determining the second fume extraction efficiency, it can be further determined whether the second fume extraction efficiency is within a preset fume extraction efficiency range; for example, if the second fume extraction efficiency is 90%, which is within the preset fume extraction efficiency (80%~99%), then the second power corresponding to the second fume extraction efficiency can be determined as the target power of the target range hood.

[0057] In this embodiment, after determining the current power of the range hood, the solution does not directly set the power as the optimal power of the target range hood. Instead, it adjusts the power and determines the final target power based on the adjustment result, which ensures that the determined target power is optimal.

[0058] The technical solution of this embodiment obtains the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood during its use; determines the current fume extraction efficiency of the target range hood based on the environmental parameters, target operating parameters, and a target regression model; determines the current fume extraction efficiency of the range hood based on the obtained parameters; increases the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power; determines a second fume extraction efficiency matching the second power based on the target regression model; determines the second fume extraction efficiency based on the current fume extraction efficiency; if the second fume extraction efficiency is determined to be within a preset fume extraction efficiency range, the second power is determined as the target power of the target range hood; verifies the efficiency of the second range hood, and determines the optimal target power of the target range hood at the current moment if preset conditions are met; enables rapid, automatic, and precise adjustment of the range hood power, allowing it to dynamically adjust its working state according to the actual fume concentration and always maintain a relatively optimal fume extraction efficiency.

[0059] Example 2

[0060] Figure 2 This is a schematic diagram illustrating the training process of a target regression model according to Embodiment 2 of the present invention. This embodiment further refines the process of determining the target regression model in the above-described technical solution. The technical solution in this embodiment can be combined with various optional solutions in one or more of the above-described embodiments. Figure 2 As shown, the training steps for a target regression model may include:

[0061] Step 210: Obtain the sample dataset.

[0062] The sample dataset includes environmental parameters of each reference user's kitchen and reference operating parameters of each range hood during use.

[0063] Optionally, in this embodiment, the environmental parameters of the reference user's kitchen and the reference operating parameters of each range hood can be obtained during the use of multiple range hoods (e.g., 100, 1000, or 10000, etc., which are not limited in this embodiment) at different times.

[0064] For example, the temperature, humidity, pressure, and oil fume concentration of each reference user's kitchen during the use of each range hood can be obtained to obtain the environmental parameters of each reference user's kitchen; the relative position of each range hood to the stove, its power, and the oil fume concentration at the outlet can also be obtained to obtain the reference operating parameters of each range hood.

[0065] It should be noted that the method of obtaining the environmental parameters of the user's kitchen and the reference operating parameters of each range hood is the same as the method of obtaining the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood. Therefore, it will not be described in detail here, as it is not intended to limit this embodiment.

[0066] In this embodiment, since the dimensions of the parameters in the obtained sample dataset are inconsistent, the sample dataset can be further cleaned and normalized after it is obtained.

[0067] Step 220: Divide the sample dataset into at least two sample data groups, designate one of the sample data groups as the first test set, and designate the remaining sample data groups as the first training set.

[0068] Optionally, in this embodiment, after obtaining the sample dataset and preprocessing each data point in the sample dataset, the sample dataset can be directly divided into at least two sample data groups, such as 10, 20, or 100, etc., which are not limited in this embodiment. For example, the sample dataset can be grouped according to the number of range hoods in the sample dataset. For example, each group can contain the same number of reference user kitchen environmental parameters and reference operating parameters. Alternatively, the sample dataset can be grouped according to the collection time of each reference user kitchen environmental parameter and reference operating parameter. For example, the collection time of each parameter in each group is within a time range.

[0069] Furthermore, any one of the sample data groups can be designated as the first test set, and the remaining sample data groups can be designated as the first training set. For example, if the sample dataset is divided into k sample data groups, then any one of the sample data groups can be designated as the first test set, and the remaining k-1 sample data groups can be designated as the first training set.

[0070] Step 230: Iteratively train the initial regression model based on the first training set, and validate the trained first regression model when the iteration stopping condition is met.

[0071] The iteration stopping condition can be either an accuracy condition or an iteration count condition; this embodiment does not impose any limitation on it.

[0072] For example, in this embodiment, the initial regression model can be a neural network model with 1 layer of neurons and 32 neurons per layer, an optimizer learning rate (e.g., SGD: 1e−2, 1e−1, Adagrad: 1e−3, 1e−2), and a Dropout regularization rate of 0.5.

[0073] Optionally, in this embodiment, the initial regression model can be iteratively trained based on the first training set. If the iteration stopping condition is met, the first regression model can be obtained. Furthermore, the first regression model can be validated based on the first test set to determine the validation accuracy of the first regression model on the first test set. If the validation accuracy meets certain conditions (e.g., the accuracy reaches more than 90%), subsequent operations can continue.

[0074] Step 240: Continue to determine one of the sample data groups as the second test set, and the remaining sample data groups as the second training set.

[0075] Optionally, in this embodiment, one sample data group (any sample data group other than the sample data group corresponding to the first test set determined in the above steps) can be further determined as the second test set, and the remaining sample data groups can be determined as the second training set.

[0076] Step 250: Iteratively train the initial regression model based on the first training set, and validate the trained second regression model when the iteration stopping condition is met, until all sample data groups are used as a test set.

[0077] Optionally, in this embodiment, the initial regression model can be iteratively trained based on the second training set. If the iteration stopping condition is met, a second regression model can be obtained. Furthermore, the second regression model can be validated based on the second test set to determine the validation accuracy of the second regression model on the second test set. If the validation accuracy meets certain conditions (e.g., the accuracy reaches more than 90%), subsequent operations can continue.

[0078] Step 260: Determine the standard regression model based on the regression models obtained from the validation.

[0079] Optionally, in this embodiment, after different regression models are trained based on different training sets, a standard regression model can be further determined based on each regression model.

[0080] Optionally, in this embodiment, after obtaining each regression model, the regression model with the best performance (e.g., the smallest mean squared error on the corresponding validation set) can be selected and determined as the final standard regression model; or the regression models can be weighted and averaged to obtain the final standard regression model; or the regression models can be validated using the same test set, and the regression model with the highest output accuracy can be determined as the final standard regression model.

[0081] Step 270: Determine the target regression model based on the standard regression model, the environmental parameters of the target user's kitchen, and the target operating parameters of the target range hood.

[0082] Optionally, in this embodiment, after obtaining the standard regression model, a target regression model adapted to the target range hood can be further determined based on the standard regression model, the environmental parameters of the target user's kitchen, and the target operating parameters.

[0083] In one optional implementation of this embodiment, determining the target regression model based on the standard regression model, the environmental parameters of the target user's kitchen, and the target operating parameters of the target range hood may include: standardizing the environmental parameters and target operating parameters of each target user's kitchen respectively; and adjusting the weights of each parameter in the standard regression model based on the standardization results to obtain the target regression model.

[0084] Optionally, in this embodiment, the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood can be standardized separately; for example, each parameter can be standardized using the following formula:

[0085] ;

[0086] Where i represents the environmental parameters of any target user's kitchen or the target operating parameters of the target range hood; μ i Let σ be the mean of the parameters. i The standard deviation is denoted as .

[0087] Furthermore, the weights of each parameter in the standardized regression model can be adjusted based on the standardization results to obtain the target regression model; it can be understood that, based on the above standardization results, the following can be obtained:

[0088] ;

[0089] Furthermore, in the objective regression model, the final weights of each parameter can be:

[0090] .

[0091] In this embodiment, regression models are trained on different training sets, and a standard regression model is determined based on each regression model. Furthermore, a target regression model adapted to the target range hood is determined based on the relevant parameters of the target range hood. This allows for a more accurate determination of the fume extraction efficiency corresponding to each parameter related to the target range hood, providing a basis for quickly and accurately adjusting the power of the range hood.

[0092] Example 3

[0093] Figure 3 This is a flowchart of a power adjustment method for a range hood according to Embodiment 3 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 3 As shown, the method includes:

[0094] Step 310: During the use of the target range hood, obtain the environmental parameters of the target user's kitchen where the target range hood is located, as well as the target operating parameters of the target range hood.

[0095] Step 320: Input the environmental parameters of the target user's kitchen and the target operating parameters into the pre-trained target regression model to obtain the current smoke extraction efficiency of the target range hood.

[0096] Optionally, in this embodiment, after obtaining the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood, the obtained environmental parameters and target operating parameters can be further input into the target regression model trained through the above embodiment. The target regression model processes the input environmental parameters and target operating parameters to obtain the current fume extraction efficiency of the target range hood. For example, the current fume extraction efficiency output by the target regression model can be 88%.

[0097] Step 330: Increase the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power, and determine a second fume extraction efficiency that matches the second power based on the target regression model.

[0098] Optionally, in this embodiment, after determining the current fume extraction efficiency of the target range hood based on the target regression model, the current power of the target range hood corresponding to the current fume extraction efficiency (i.e., the power in the target operating parameters obtained through the above steps, for example, 190W) can be further increased by a set power threshold to obtain a second power; for example, if the current power is 190W and the set power threshold is 5W, then the second power is 195W.

[0099] Furthermore, a second fume extraction efficiency matching the second power can be determined based on the target regression model. Optionally, in this embodiment, determining the second fume extraction efficiency matching the second power based on the target regression model may include: determining the second environmental parameters of the target user's kitchen corresponding to the generation time of the second power and the second fume concentration at the outlet of the target range hood; inputting the second environmental parameters, the second power, the second fume concentration, and the relative position of the target range hood and the stove into the target regression model to obtain the second fume extraction efficiency.

[0100] In an optional implementation of this embodiment, after determining the second power, the second environmental parameters of the target user's kitchen corresponding to the time the second power is generated and the second oil fume concentration at the outlet of the target range hood can be further determined. Furthermore, the second environmental parameters, the second power, the second oil fume concentration, and the relative position of the target range hood and the stove can be input into the target regression model for calculation to obtain the second oil fume extraction efficiency.

[0101] In another optional implementation of this embodiment, after determining the second power, the second power can be directly input into the target regression model for calculation to obtain the second fume extraction efficiency; in this way, the second fume extraction efficiency corresponding to the second power can be obtained quickly without determining other parameters, thus improving the execution efficiency of the algorithm.

[0102] Step 340: If the second fume extraction efficiency is determined to be within the preset fume extraction efficiency range, the second power is determined as the target power of the target range hood.

[0103] In this embodiment, the environmental parameters of the target user's kitchen and the target operating parameters can be directly input into a pre-trained target regression model to obtain the current fume extraction efficiency of the target range hood. Furthermore, the current power of the target range hood corresponding to the current fume extraction efficiency can be increased by a set power threshold to obtain a second power. Based on the target regression model, a second fume extraction efficiency matching the second power can be determined. The fume extraction efficiency of the range hood can be quickly determined based on the target regression model, providing a basis for subsequent power adjustment of the range hood.

[0104] Example 4

[0105] Figure 4 This is a flowchart of a power adjustment method for a range hood according to Embodiment 3 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 4 As shown, the method includes:

[0106] Step 410: During the use of the target range hood, obtain the environmental parameters of the target user's kitchen where the target range hood is located, as well as the target operating parameters of the target range hood.

[0107] Step 420: Determine the current fume extraction efficiency of the target range hood based on the environmental parameters of the target user's kitchen, the target operating parameters, and the target regression model.

[0108] Step 430: Increase the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power, and determine a second fume extraction efficiency that matches the second power based on the target regression model.

[0109] Step 440: Determine that the second fume extraction efficiency is not within the preset fume extraction efficiency range.

[0110] Optionally, in this embodiment, after determining the second fume extraction efficiency, it can be further determined whether the second fume extraction efficiency is within a preset fume extraction efficiency range; for example, if the second fume extraction efficiency is 79%, which is within the preset fume extraction efficiency (80%~99%), it can be determined that the second fume extraction efficiency is not within the preset fume extraction efficiency range.

[0111] Step 450: Continue to determine whether the difference between the second fume extraction efficiency and the first fume extraction efficiency is greater than the preset parameter. If so, continue to increase the second power by the set power threshold to obtain the third power, and determine the third power as the target power of the target range hood.

[0112] The preset parameter can be the target value / the target range hood's maximum power; the target value can be 100, 90, or 120, etc., and is not limited in this embodiment; for example, the preset parameter can be: 100 / the target range hood's maximum power.

[0113] The first fume extraction efficiency is matched with the current power of the target range hood.

[0114] Optionally, in this embodiment, if it is determined that the second fume extraction efficiency is not within the preset fume extraction efficiency range, then it can be further determined whether the difference between the second fume extraction efficiency and the first fume extraction efficiency is greater than a preset parameter; if it is determined that the difference between the second fume extraction efficiency and the first fume extraction efficiency is greater than the preset parameter (100 / maximum power of the target range hood), then the second power can be further increased by a set power threshold to obtain a third power (for example, if the current fume extraction power is 200W, the set threshold is 1W, the second power is 201W, then the third power is 202W), and the third power is determined as the target power of the target range hood.

[0115] Step 451: If it is determined that the difference between the second fume extraction efficiency and the first fume extraction efficiency is less than or equal to a preset parameter, then the current power of the target range hood corresponding to the current fume extraction efficiency is reduced by a set power threshold to obtain a fourth power; a fourth fume extraction efficiency matching the fourth power is determined based on the target regression model; until it is determined that the fourth fume extraction efficiency is within the preset fume extraction efficiency range.

[0116] Optionally, in this embodiment, if it is determined that the second fume extraction efficiency is not within the preset fume extraction efficiency range, and it is determined that the difference between the second fume extraction efficiency and the first fume extraction efficiency is less than or equal to a preset parameter (100 / maximum power of the target range hood), then the current power of the target range hood corresponding to the current fume extraction efficiency can be reduced by a set power threshold to obtain a fourth power (for example, if the current fume extraction power is 200W and the set threshold is 1W, then the fourth power is 199W).

[0117] Furthermore, a fourth fume extraction efficiency that matches the fourth power can be determined based on the target regression model, and it can be determined whether the fourth fume extraction efficiency is within the preset fume extraction efficiency range. If so, the fourth power can be determined as the optimal power of the target range hood.

[0118] In this embodiment, if it is determined that the second fume extraction efficiency is not within the preset fume extraction efficiency range, it can further determine whether the difference between the second fume extraction efficiency and the first fume extraction efficiency is greater than a preset parameter. If so, the second power is increased by a set power threshold to obtain a third power, and the third power is determined as the target power of the target range hood. If it is determined that the difference between the second fume extraction efficiency and the first fume extraction efficiency is less than or equal to a preset parameter, the current power of the target range hood corresponding to the current fume extraction efficiency is decreased by a set power threshold to obtain a fourth power. A fourth fume extraction efficiency matching the fourth power is determined based on the target regression model. This process continues until the fourth fume extraction efficiency is determined to be within the preset fume extraction efficiency range. Different power levels of the range hood can be determined based on different fume extraction efficiencies, providing a basis for accurate adjustment of the range hood power.

[0119] To better understand the power adjustment method of the range hood involved in this embodiment, a specific example is used for explanation:

[0120] Optionally, in this embodiment, data collection can be performed first. Specifically, environmental parameters such as temperature T, humidity S, and pressure P in the user's kitchen, the relative position K of the range hood and the stove, the power W of the range hood, the oil fume concentration C1 in the room, and the oil fume concentration C2 at the range hood outlet can be collected. This data is then organized into a dataset. Each parameter in the dataset is cleaned and normalized, and divided into k subsets. Further, a neural network model is initialized with one layer of neurons, 32 neurons per layer, and an optimizer learning rate (e.g., SGD: 1e−2, 1e−1, Adagrad: 1e−3, 1e−2) and a Dropout regularization rate of 0.5.

[0121] Further, iterative training of the model is performed. After k iterations of training, the i-th subset of the dataset is designated as the test set, and the remaining subsets are used as the training set. The training set is used to train the model, and the test set is used to verify the model's accuracy and determine its stability. If the model is unstable, the parameters of the neuron model are adjusted (for example, the number of layers and neurons can be increased, the optimizer and Dropout regularization can be changed), and the iterative training in step two is repeated. The parameters are adjusted until a suitable regression prediction model is found.

[0122] Furthermore, the range hood's smoke extraction efficiency range (e.g., 80%~100%) and power range (e.g., 100W-400W) can be set.

[0123] For example, during the operation of the range hood, real-time data is collected on environmental parameters such as temperature (26 degrees Celsius), humidity (50%), pressure (101200 Pa), range hood power (200W), oil fume concentration at the range hood outlet (60%), and oil fume concentration in the room (70%), forming a dataset for that specific moment. For example, the resulting dataset can be shown in Table 1.

[0124] Table 1

[0125]

[0126] Furthermore, the dataset can be substituted into the regression model to calculate the fume extraction efficiency under the current parameters. By adjusting the power in the dataset, such as increasing the power by 1W, it can be determined whether the range hood power is within the set range. If it is, the optimal power point is output; otherwise, the power in the dataset is changed to 201W, and the fume extraction efficiency (e.g., 85%) is calculated again by substituting it into the regression model. 201W∈[100,400] is within the range, which satisfies the performance of the range hood. When the increase in fume extraction efficiency is 5%>0.25% (the average rate of change of fume extraction efficiency with power), it indicates that the increase in power significantly increases the fume extraction efficiency, and the power can be further increased until the optimal power point is reached.

[0127] Conversely, if the power is not increased, it indicates that increasing the power further will not significantly improve the fume extraction efficiency, resulting in energy waste. To find the optimal point, the power should be reduced by 1W to check if the range hood power is within the set range. If it is, output the optimal power point. Otherwise, check if the reduction in fume extraction efficiency is less than 0.25% (the average rate of change of fume extraction efficiency with power). If it is, the reduction in power will not significantly reduce the fume extraction efficiency, indicating that the power is too high. Continue to reduce the power to complete the subsequent process.

[0128] Furthermore, if the reduction in fume extraction efficiency is greater than or equal to 0.25% (the average rate of change of fume extraction efficiency with power), then a 1W reduction in power will have a significant impact on fume extraction efficiency. Therefore, the power should not be reduced further, and the optimal power should be output in the end.

[0129] Figure 5 This is a flowchart of another power adjustment method for a range hood according to Embodiment 4 of the present invention, see reference. Figure 5 It mainly includes:

[0130] Step 510: Set the efficiency range and power range of the range hood to obtain the relative position of the range hood and the stove.

[0131] Step 511: Collect environmental parameters such as temperature, humidity, and pressure in real time to obtain the range hood power, range hood outlet oil fume concentration, and room oil fume concentration.

[0132] Step 512: Substitute the collected data into the regression model to calculate the smoking efficiency.

[0133] Step 513: Increase the power in the collected data by 1W and call the regression model to calculate its fume extraction efficiency.

[0134] Step 514: Is the range hood efficiency within the set range?

[0135] If so, proceed to step 515;

[0136] Otherwise, proceed to step 516.

[0137] Step 515: Output the optimal power point.

[0138] Step 516: Does the increase in fume extraction efficiency exceed 100 / maximum power of the range hood?

[0139] If so, proceed to step 511;

[0140] Otherwise, proceed to step 517.

[0141] Step 517: Reduce the power by 1W in the collected data and call the regression model to calculate its oil fume extraction efficiency.

[0142] Step 518: Is the fume extraction efficiency within the acceptable range?

[0143] If so, proceed to step 515;

[0144] Otherwise, proceed to step 519.

[0145] Step 519: Does the increase in fume extraction efficiency exceed 100 / maximum power of the range hood?

[0146] If so, proceed to step 515;

[0147] Otherwise, proceed to step 517.

[0148] Step 520: Collect environmental parameters such as temperature, humidity, and pressure to obtain the range hood power, range hood outlet oil fume concentration, and room oil fume concentration.

[0149] Step 521: Perform dataset cleaning and normalization.

[0150] Step 522: Divide the dataset into k sub-datasets.

[0151] Step 523: Initialize the number of neuron layers and the number of inner neurons, the number of layers, and the learning rate of the optimizer.

[0152] Step 524: Has k iterations been completed?

[0153] If so, proceed to step 527;

[0154] Otherwise, proceed to step 525.

[0155] Step 525: The i-th subset is the test set, and the remaining subsets are the training set.

[0156] Step 526: Training set: Train data; Model: Test set: Verify model accuracy.

[0157] Step 527: Compare models trained on different training and test sets to verify the model's generalization ability (stability).

[0158] Step 528: Is the model stable?

[0159] If so, proceed to step 529;

[0160] Otherwise, proceed to step 524.

[0161] Step 529: Obtain a regression model with smoking efficiency as the output and other parameters as the input.

[0162] The solution of this invention establishes a regression model with smoke extraction efficiency as the output by using machine learning technology and environmental parameters such as smoke extraction efficiency, temperature and humidity monitored in real time by the user's range hood. This model can intelligently match the optimal working point and operating power according to the user's actual working conditions; it can quickly respond to the matching of power and smoke extraction efficiency, reduce energy waste, and improve the comfort of residents.

[0163] Example 5

[0164] Figure 6 This is a schematic diagram of the power adjustment device for a range hood according to Embodiment 5 of the present invention. Figure 6 As shown, the device includes: a parameter acquisition module 610, a first determination module 620, a second determination module 630, and a target power determination module 640.

[0165] Among them, the parameter acquisition module 610 is used to acquire the environmental parameters of the target user's kitchen where the target range hood is located and the target operating parameters of the target range hood during the use of the target range hood.

[0166] The first determining module 620 is used to determine the current fume extraction efficiency of the target range hood based on the environmental parameters of the target user's kitchen, the target operating parameters, and the target regression model.

[0167] The second determining module 630 is used to increase the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power, and to determine a second fume extraction efficiency that matches the second power based on the target regression model;

[0168] The target power determination module 640 is used to determine the second power as the target power of the target range hood when it is determined that the second fume extraction efficiency is within a preset fume extraction efficiency range.

[0169] In this embodiment, the parameter acquisition module acquires the environmental parameters of the target user's kitchen and the target operating parameters of the target range hood during its use. A first determination module determines the current fume extraction efficiency of the target range hood based on the environmental parameters, target operating parameters, and a target regression model. A second determination module increases the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power. A second fume extraction efficiency matching the second power is determined based on the target regression model. Finally, the target power determination module, upon determining that the second fume extraction efficiency is within a preset range, determines the second power as the target power of the target range hood. This enables rapid, automatic, and precise adjustment of the range hood power, allowing it to dynamically adjust its operating state according to the actual fume concentration and consistently maintain optimal fume extraction efficiency.

[0170] In an optional implementation of this embodiment, the environmental parameters of the target user's kitchen include at least one of the following: temperature, humidity, pressure, and oil fume concentration;

[0171] The target operating parameters of the target range hood include at least one of the following: its relative position to the stove, power, and the concentration of oil fumes at the outlet;

[0172] Correspondingly, the parameter acquisition module 610 is specifically used to acquire the temperature of the target user's kitchen through a temperature sensor deployed separately in the target user's kitchen, the humidity of the target user's kitchen through a humidity sensor, the pressure of the target user's kitchen through a pressure gauge, and the concentration of cooking fumes in the target user's kitchen through a particulate matter sensor; or,

[0173] Determine the target user's kitchen temperature, humidity, pressure, or oil fume concentration using other electrical devices;

[0174] Determine the vertical distance between the target range hood and the stove, and / or the horizontal coverage area of ​​the target range hood; and determine the relative position of the target range hood and the stove based on the vertical distance and / or the horizontal coverage area.

[0175] Determine the current operating level of the target range hood, and based on the current operating level and the user manual of the target range hood, determine the power of the target range hood;

[0176] The concentration of cooking fumes at the target range hood outlet is determined by a particulate sensor deployed at the target range hood outlet.

[0177] In an optional implementation of this embodiment, the power adjustment device of the range hood further includes a training module, used for:

[0178] Obtain a sample dataset; the sample dataset contains environmental parameters of each reference user's kitchen where each range hood is located during use, as well as reference operating parameters of each range hood;

[0179] The sample dataset is divided into at least two sample data groups, one of which is designated as the first test set, and the remaining sample data groups are designated as the first training set.

[0180] The initial regression model is iteratively trained based on the first training set, and the trained first regression model is validated when the iteration stopping condition is met.

[0181] Continue to designate one sample data group as the second test set, and the remaining sample data groups as the second training set;

[0182] The initial regression model is iteratively trained based on the first training set, and the second regression model is validated when the iteration stopping condition is met, until all sample data groups are used as a test set.

[0183] Based on the validation results of each regression model, the standard regression model is determined.

[0184] The target regression model is determined based on the standard regression model, the environmental parameters of the target user's kitchen, and the target operating parameters of the target range hood.

[0185] In an optional implementation of this embodiment, the training module is further configured to:

[0186] The environmental parameters and target operating parameters of each target user's kitchen were standardized.

[0187] The weights of each parameter in the standard regression model are adjusted based on the standardization results to obtain the target regression model.

[0188] In an optional implementation of this embodiment, the first determining module 620 is specifically used for:

[0189] The environmental parameters of the target user's kitchen and the target operating parameters are input into a pre-trained target regression model to obtain the current smoke extraction efficiency of the target range hood.

[0190] In an optional implementation of this embodiment, the second determining module 630 is specifically used for:

[0191] Determine the second environmental parameters of the target user's kitchen corresponding to the generation time of the second power and the second oil fume concentration at the outlet of the target range hood;

[0192] The second environmental parameter, the second power, the second oil fume concentration, and the relative position of the target range hood and the stove are input into the target regression model to obtain the second oil fume extraction efficiency.

[0193] In an optional implementation of this embodiment, the power adjustment device of the range hood further includes: a third determining module, used for:

[0194] Determine whether the second fume extraction efficiency is within the preset fume extraction efficiency range;

[0195] If so, then the second power is determined as the target power of the target range hood;

[0196] Otherwise, continue to determine whether the difference between the second fume extraction efficiency and the first fume extraction efficiency is greater than the preset parameter. If so, continue to increase the second power by the set power threshold to obtain the third power, and determine the third power as the target power of the target range hood.

[0197] The first fume extraction efficiency is matched with the current power of the target range hood.

[0198] In an optional implementation of this embodiment, the power adjustment device of the range hood further includes: a fourth determining module, used for:

[0199] If it is determined that the difference between the second fume extraction efficiency and the first fume extraction efficiency is less than or equal to a preset parameter, then the current power of the target range hood corresponding to the current fume extraction efficiency is reduced by a set power threshold to obtain a fourth power.

[0200] The fourth fume extraction efficiency that matches the fourth power is determined based on the target regression model;

[0201] This continues until the fourth fume extraction efficiency is determined to be within the preset fume extraction efficiency range.

[0202] In one optional implementation of this embodiment, the preset parameter is the target value / the maximum power of the target range hood.

[0203] The power adjustment device for the range hood provided in this embodiment of the invention can execute the power adjustment method for the range hood provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0204] Example 6

[0205] Figure 7 A schematic diagram of the structure of a range hood 10 that can be used to implement embodiments of the present invention is shown, as follows: Figure 7As shown, the range hood 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the range hood 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0206] Multiple components in the range hood 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the range hood 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0207] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a power adjustment method for a range hood, which includes: during the use of the target range hood, acquiring environmental parameters of the target user's kitchen where the target range hood is located and target operating parameters of the target range hood; determining the current fume extraction efficiency of the target range hood based on the environmental parameters of the target user's kitchen, the target operating parameters, and a target regression model; increasing the current power of the target range hood corresponding to the current fume extraction efficiency by a set power threshold to obtain a second power; determining a second fume extraction efficiency matching the second power based on the target regression model; and determining the second power as the target power of the target range hood if the second fume extraction efficiency is determined to be within a preset fume extraction efficiency range.

[0208] In some embodiments, the power regulation method of the range hood may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the range hood 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power regulation method of the range hood described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the power regulation method of the range hood by any other suitable means (e.g., by means of firmware).

[0209] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0210] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0211] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0212] To provide user interaction, the systems and techniques described herein can be implemented on a range hood that includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the range hood. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0213] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0214] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0215] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0216] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0217] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a database detection method as provided in any embodiment of this application.

[0218] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0219] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0220] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method of power regulation for a range hood, the method comprising: The method comprises the following steps: During use of the target range hood, obtaining an environmental parameter of a target user kitchen where the target range hood is located and a target working parameter of the target range hood; Determining a current fume suction efficiency of the target range hood based on the environmental parameter of the target user kitchen, the target working parameter, and a target regression model; Setting a current power of the target range hood corresponding to the current fume suction efficiency as a power threshold to obtain a second power, and determining a second fume suction efficiency matched with the second power based on the target regression model; In a case where the second fume suction efficiency is determined to be within a preset fume suction efficiency range, the second power is determined as a target power of the target range hood.

2. The power adjustment method of a range hood according to claim 1, characterized by, The environmental parameter of the target user kitchen comprises at least one of the following: temperature, humidity, pressure, and fume concentration; The target working parameter of the target range hood comprises at least one of the following: relative position with a cooking bench, power, and fume concentration at an outlet; Correspondingly, the obtaining of the environmental parameter of the target user kitchen where the target range hood is located and the target working parameter of the target range hood comprises the following steps: Respectively obtaining, by a temperature sensor, humidity sensor, pressure gauge, and particulate matter sensor separately arranged in the target user kitchen, the temperature, humidity, pressure, and fume concentration of the target user kitchen; or Determining the temperature, humidity, pressure, or fume concentration of the target user kitchen by other electrical equipment; Determining a vertical distance between the target range hood and the cooking bench, and / or a horizontal coverage range of the target range hood, and determining the relative position of the target range hood with the cooking bench based on the vertical distance and / or the horizontal coverage range; Determining a current working gear of the target range hood, and determining the power of the target range hood based on the current working gear and a user manual of the target range hood; Determining the fume concentration at the outlet of the target range hood by a particulate matter sensor arranged at the outlet of the target range hood.

3. The power adjustment method of a range hood according to claim 1, wherein The target regression model is obtained by the following steps: Obtaining a sample data set; the sample data set comprises environmental parameters of each reference user kitchen where each range hood is located and reference working parameters of each range hood during use of different range hoods; Dividing the sample data set into at least two sample data groups, determining one of the sample data groups as a first test set, and determining the remaining sample data groups as a first training set; Iteratively training an initial regression model based on the first training set, and verifying the first regression model obtained by training in a case where an iteration stop condition is met; Continuing to determine one of the sample data groups as a second test set, and determining the remaining sample data groups as a second training set; Iteratively training an initial regression model based on the first training set, and verifying the second regression model obtained by training in a case where an iteration stop condition is met, until all sample data groups are used as a test set; Determining a standard regression model based on the verified regression models. determine a target regression model based on the standard regression model, the environmental parameters of the target user kitchen, and the target working parameters of the target range hood.

4. The power adjustment method of a range hood according to claim 3, wherein The determination of the target regression model based on the standard regression model, the environmental parameters of the target user kitchen, and the target working parameters of the target range hood comprises: standardizing the environmental parameters and the target working parameters of each target user kitchen respectively; adjusting the parameter weights of the standard regression model based on the standardization results to obtain the target regression model.

5. The range hood power regulation method according to claim 1, wherein, The determination of the current smoke suction efficiency of the target range hood based on the environmental parameters of the target user kitchen, the target working parameters, and the target regression model comprises: inputting the environmental parameters of the target user kitchen and the target working parameters into the target regression model trained in advance to obtain the current smoke suction efficiency of the target range hood.

6. The range hood power regulation method according to claim 2, wherein, The determination of the second smoke suction efficiency matched with the second power based on the target regression model comprises: determining second environmental parameters of the target user kitchen and a second oil fume concentration at the outlet of the target range hood corresponding to the generation time of the second power; inputting the second environmental parameters, the second power, the second oil fume concentration, and the relative position between the target range hood and the cooking hob into the target regression model to obtain the second smoke suction efficiency.

7. The power adjustment method of a range hood according to claim 6, wherein After obtaining the second smoke suction efficiency, the method further comprises: determining whether the second smoke suction efficiency is within a preset smoke suction efficiency range; if yes, determining the second power as the target power of the target range hood; otherwise, continuing to determine whether the difference between the second smoke suction efficiency and the first smoke suction efficiency is greater than a preset parameter, and if yes, continuing to increase the second power by a set power threshold to obtain a third power, and determining the third power as the target power of the target range hood; wherein the first smoke suction efficiency is matched with the current power of the target range hood.

8. The power adjustment method of a range hood according to claim 7, wherein, The method further comprises: if it is determined that the difference between the second smoke suction efficiency and the first smoke suction efficiency is less than or equal to the preset parameter, reducing the current power of the target range hood corresponding to the current smoke suction efficiency by a set power threshold to obtain a fourth power; determining a fourth smoke suction efficiency matched with the fourth power based on the target regression model; until it is determined that the fourth smoke suction efficiency is within the preset smoke suction efficiency range.

9. The power adjustment method of a range hood according to claim 8, wherein, The preset parameter is a target value / the maximum power of the target range hood.

10. A power regulating device for a range hood, comprising: The method comprises: a parameter acquisition module configured to acquire environmental parameters of a target user kitchen where a target range hood is located and target working parameters of the target range hood during use of the target range hood; a first determination module configured to determine a current smoke suction efficiency of the target range hood based on the environmental parameters of the target user kitchen, the target working parameters, and a target regression model; a second determination module configured to increase a current power of the target range hood corresponding to the current smoke suction efficiency by a set power threshold to obtain a second power, and determine a second smoke suction efficiency matched with the second power based on the target regression model. The target power determination module is configured to determine the second power as the target power of the target range hood when it is determined that the second oil fume extraction efficiency is within a preset oil fume extraction efficiency range.

11. A range hood characterized by, The range hood comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the power adjustment method of the range hood according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the power adjustment method of the range hood according to any one of claims 1-9 when executed.

13. A computer program product comprising a computer program which, when executed by a processor, implements the power adjustment method of the range hood according to any one of claims 1-9.

Images