Range hood control method and device, electronic equipment and smart home appliance

Abstract

The present disclosure relates to a range hood control method and device, electronic equipment and smart home appliance. The range hood control method comprises the following steps: obtaining oil fume concentration fusion information by performing fusion processing based on particulate matter concentration information, odor concentration information, cooking environment temperature field information and oil fume concentration prediction information; obtaining a target fan frequency according to the oil fume concentration fusion information and fan operation parameter information; and generating fan operation control parameters according to the target fan frequency, wherein the fan operation control parameters are used to control the operation of the range hood fan. Thus, the oil fume concentration in the cooking environment can be perceived and analyzed comprehensively and multi-levelly from different dimensions and angles, the interference of the cooking environment on the detection of the oil fume concentration is reduced, the accuracy of the detection of the oil fume concentration is improved, the instantaneous fluctuation or delay of the real-time sensor data is compensated by the oil fume concentration prediction information, and thus the stability and accuracy of the fusion oil fume concentration are improved by using the trend of the cooking environment.

Description

Technical Field

[0001] This disclosure relates to the field of smart appliances, and more particularly to a range hood control method, device, electronic equipment, and smart home appliance. Background Technology

[0002] As people's quality of life improves, more and more traditional lifestyles are gradually changing, and the use of home appliances is becoming increasingly intelligent. While bringing more convenience to users, the functions of various home appliances are also becoming more diversified. However, the control method of range hoods is relatively simple, especially the adjustment method during cooking is relatively fixed, the convenience of fan adjustment is low, and the user experience is relatively poor. Summary of the Invention

[0003] In order to solve the aforementioned technical problems, this disclosure provides a control method, device, electronic equipment, and smart home appliance for range hoods.

[0004] According to a first aspect of this disclosure, a range hood control method is provided, the method comprising: Acquire information on particulate matter concentration, odor concentration, cooking environment temperature field, predicted oil fume concentration, and fan operating parameters; Based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information, fusion processing is performed to obtain oil fume concentration fusion information; Based on the fused information of oil fume concentration and the information of fan operating parameters, the target fan frequency is obtained, and the target fan frequency represents the AC frequency used to drive the range hood fan. Fan operation control parameters are generated based on the target fan frequency, and these parameters are used to control the operation of the range hood fan.

[0005] Optionally, the step of fusing the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the predicted oil fume concentration to obtain fused oil fume concentration information includes: The oil fume concentration fusion information is obtained by filtering the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information.

[0006] Optionally, the fume concentration fusion information includes a fume concentration fusion value, the odor concentration information includes an odor concentration value, the particulate matter concentration information includes a particulate matter concentration value, the cooking environment temperature field information includes cooking environment temperature field data, and the fume concentration prediction information includes a fume concentration prediction value. The fume concentration fusion information is calculated according to the following formula: C=K﹒ (ω1﹒C1+ω2﹒C2+ω3﹒f(Tf ))+(1-K)﹒ C d Where C is the combined concentration value of oil fume, C1 is the odor concentration value, C2 is the particulate matter concentration value, and f(T) is the concentration value of oil fume. f (T) represents the ambient temperature parameter for cooking. f For cooking environment temperature field data, f(T) f According to T f C was obtained. d ω1, ω2, and ω3 are the predicted values ​​of oil fume concentration, K is the filter adjustment coefficient, and ω1, ω2, and ω3 are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively.

[0007] Optionally, the particulate matter concentration information includes particulate matter concentration values, and the acquisition of particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and real-time current information includes: Acquire temperature, humidity, and particulate matter concentration sensor data; Based on the humidity information, the particulate matter concentration sensing data is subjected to humidity correction processing to obtain corrected particulate matter concentration data; and / or Based on the temperature information, the particulate matter concentration correction data is subjected to temperature drift processing to obtain the particulate matter concentration value.

[0008] Optionally, obtaining the target fan frequency based on the fused information of oil fume concentration and the fan operating parameters includes: Based on the fused information of oil fume concentration, the predicted fan frequency is obtained; Based on the fan operating parameters, the fused information of oil fume concentration, and the predicted fan frequency, the predicted fan operating information is obtained. If the predicted wind turbine operating information meets the preset operating conditions, the predicted wind turbine frequency is taken as the target wind turbine frequency.

[0009] Optionally, obtaining the target fan frequency based on the fused information of oil fume concentration and the fan operating parameters further includes: If the predicted wind turbine operating information meets the abnormal operating conditions, the predicted wind turbine frequency is down-processed to obtain the target wind turbine frequency.

[0010] The fan operating parameter information includes real-time input power; the predicted fan operating information includes the predicted input power of the fan at the next moment relative to the current moment; the oil fume concentration fusion information includes an oil fume concentration fusion value; and obtaining the predicted fan operating information based on at least one of the fan operating parameter information, the oil fume concentration fusion information, and the predicted fan frequency includes: Based on the real-time input power, the real-time oil fume concentration fusion value, and the predicted fan frequency, the predicted input power of the fan at the next moment relative to the current moment is obtained. Before using the predicted wind turbine frequency as the target wind turbine frequency when the predicted wind turbine operating information meets the preset operating conditions, the following steps are included: If the predicted input power is less than or equal to a preset power threshold, the predicted wind turbine operating information is determined to meet the preset operating conditions.

[0011] According to a second aspect of this disclosure, a range hood control device is provided, the device comprising: The acquisition module is used to acquire particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and fan operating parameter information. The fusion module is used to perform fusion processing based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information to obtain oil fume concentration fusion information. The frequency adjustment module is used to obtain the target fan frequency based on the oil fume concentration fusion information and the fan operating parameter information. The target fan frequency represents the AC frequency used to drive the range hood fan. The drive module is used to generate fan operation control parameters based on the target fan frequency, and the fan operation control parameters are used to control the operation of the range hood fan.

[0012] According to a third aspect of this disclosure, an electronic device is provided, characterized in that it includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the control method as described in any one of the first aspects by executing the instructions stored in the memory.

[0013] According to a fourth aspect of this disclosure, a smart home appliance is provided, characterized in that the smart home appliance employs the control method as described in any one of the first aspects, and the smart home appliance is a range hood or an integrated stove.

[0014] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided that stores at least one instruction or at least one program, said instruction or program being loaded and executed by a processor to implement the control method as described in any one aspect of the first aspect.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0016] Implementing this disclosure will have the following beneficial effects: This disclosure provides a range hood control method, device, electronic device, and smart home appliance. It obtains fume concentration fusion information by fusing particulate matter concentration information, odor concentration information, cooking environment temperature field information, and fume concentration prediction information. A target fan frequency is obtained based on the fume concentration fusion information and fan operating parameters. Fan operating control parameters are generated based on the target fan frequency to control the range hood fan operation. Therefore, it can comprehensively and multi-dimensionally perceive and analyze the fume concentration in the cooking environment from different dimensions and angles, reducing interference from the cooking environment on fume concentration detection and improving the accuracy of fume concentration detection. Furthermore, the fume concentration prediction information compensates for potential instantaneous fluctuations or delays in real-time sensor data, thereby leveraging the trends in the cooking environment to improve the stability and accuracy of the fused fume concentration.

[0017] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a range hood control method according to an embodiment of the present disclosure is shown. Figure 2 This diagram illustrates a process for determining particulate matter concentration values ​​according to embodiments of the present disclosure. Figure 3 A schematic flowchart illustrating the determination of a target wind turbine frequency according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram of the structure of a range hood control device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0020] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] 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 server 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 devices.

[0022] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0023] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0024] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more of a plurality of objects. For example, including at least one or more of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0025] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0026] Currently, range hood control technologies are mainly divided into the following three categories, all of which have significant limitations: (1) Mechanical control based on fixed gears: The range hood fan speed is switched by manually turning the knob or pressing the button. The fan frequency and speed are in a fixed mapping relationship. This control method cannot adapt to the real-time changes in the concentration of oil fumes during cooking, resulting in energy waste when the oil fume concentration is low and insufficient smoke exhaust when the oil fume concentration is high.

[0027] (2) PID control based on single sensor detection: Data is collected by a single particulate matter concentration sensor (such as a laser sensor) and the speed of the range hood fan is adjusted by combining the PID algorithm; however, since particulate matter sensors are easily affected by water vapor and other interference, it is easy to misjudge the concentration of oil fumes. For example, water vapor generated by the steamer may be mistaken for oil fumes.

[0028] (3) Adaptive adjustment based on fuzzy control: The fan speed is roughly adjusted by using a fuzzy logic rule base (such as "high concentration → high speed"); however, fuzzy rules need to be set manually and are difficult to cover all cooking scenarios.

[0029] In view of this, the present disclosure provides a range hood control method. The range hood control method of the present invention can be applied to range hoods, integrated stoves, smart home appliances, or smart terminals, such as the control unit of a range hood or integrated stove, or a smart terminal communicatively connected to a range hood or integrated stove. The integrated stove includes a stove unit and a fume extraction device, which can be an induction cooker, an electric cooker, or a gas cooker. The range hood is configured as a fume extraction device within the integrated stove, used to remove air from the cooking environment, especially air containing fumes and / or odors. The smart terminal is used to control the range hood or integrated stove. The smart terminal can be a smart appliance control device, a PC, a mobile phone, or a smart wearable device. The smart appliance control device is used to control at least one home appliance. For example, the smart terminal sends fan operation control parameters to the range hood or integrated stove, so that the range hood or integrated stove controls the operation of the range hood fan according to the fan operation control parameters. This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server products, the steps can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiments or accompanying drawings. Figure 1 This diagram illustrates a flow chart of a range hood control method according to an embodiment of the present disclosure, as shown below. Figure 1 As shown, the above method includes: Step S101: Obtain particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and fan operating parameter information.

[0030] Specifically, the particulate matter concentration information is used to indicate the concentration of suspended particulate matter in the air, including at least one particulate matter concentration value. This particulate matter concentration information is generated by detecting suspended particulate matter in the air using a particulate matter concentration sensor. For example, the particulate matter concentration sensor detects suspended particulate matter in the air based on the principle of laser scattering, and the particulate matter concentration sensor is used to measure PM2.5 and / or PM10 concentrations.

[0031] Odor concentration information is used to indicate the concentration of odor gases in the air, including at least one odor concentration value. Specifically, kitchen odors or odors during cooking originate from volatile organic compounds (VOCs), especially VOCs in cooking fumes, such as aldehydes and ketones. Odor concentration information can be generated by detecting a preset gas (e.g., VOCs) using an odor molecule sensor, thereby enabling the detection of cooking fume concentration. This odor concentration information is related to the cooking fume concentration.

[0032] Cooking environment temperature field information is used to indicate the temperature distribution state of the cooking environment. This information is generated by detecting the temperature of the cooking environment using a thermal imaging device, such as an infrared thermal imaging device. For example, an infrared thermal imaging device is used to collect and image the temperature in a preset cooking space to obtain the cooking environment temperature field information. This information includes at least one set of cooking environment temperature field data, which indicates the temperature distribution within the cooking environment. For instance, the infrared thermal imaging device acquires the temperature field distribution of a stovetop through non-contact temperature measurement.

[0033] The fan operating parameter information is used to indicate the operating status of the range hood fan. This information includes at least one of the fan's drive current status, fan efficiency, and rotational speed. The drive current status includes at least one of the frequency and peak voltage of the AC current input to the fan, which drives the fan's rotation. The fan efficiency indicates the efficiency with which electrical energy supplied to the fan is converted into mechanical energy. For example, the fan efficiency can be the ratio of the fan's output power to its input power, where the output power is mechanical power and the input power is electrical power input to the fan.

[0034] In a specific example, a multimodal sensing module is provided on the range hood or integrated stove. The multimodal sensing module is used to detect the state of cooking fumes and generate multimodal sensing information, which includes particulate matter concentration information, odor concentration information, and cooking environment temperature field information. The multimodal sensing module includes an odor molecule sensor, a particulate matter concentration sensor, and an infrared thermal imaging device. Preferably, the multimodal cooking fume concentration sensing module includes an integrated odor molecule sensor, a particulate matter concentration sensor, and an infrared thermal imaging unit.

[0035] The predicted oil fume concentration information is used to indicate the predicted concentration level of oil fumes in the cooking environment. The predicted oil fume concentration information is obtained by predicting oil fume concentration based on historical multimodal sensing information. The historical multimodal sensing information includes at least one of historical particulate matter concentration information, historical odor concentration information, and historical cooking environment temperature field information. The historical multimodal sensing information is the multimodal sensing information within a preset time period before the current time. The historical particulate matter concentration information is the particulate matter concentration information within the preset time period before the current time. The historical odor concentration information is the odor concentration information within the preset time period before the current time. The historical cooking environment temperature field information is the cooking environment temperature field information within the preset time period before the current time.

[0036] For example, before acquiring particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and fan operating parameter information, the following steps are included: Obtain a concentration prediction relationship or concentration prediction model between at least one of particulate matter concentration information, odor concentration information, and cooking environment temperature field information and oil fume concentration prediction information, as well as at least one of the historical particulate matter concentration information, the historical odor concentration information, and the historical cooking environment temperature field information; Based on at least one of the historical particulate matter concentration information, the historical odor concentration information, and the historical cooking environment temperature field information, the concentration prediction process is performed through the concentration prediction relationship or the concentration prediction model to obtain the oil fume concentration prediction information.

[0037] Step S102: Based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information, perform fusion processing to obtain oil fume concentration fusion information.

[0038] Specifically, the fused oil fume concentration information is used to indicate the oil fume concentration level, especially the actual oil fume concentration in a cooking environment (e.g., a kitchen). Particulate matter concentration information measures the concentration of suspended particulate matter in the air; odor concentration information measures oil fume concentration from the perspective of odor gas concentration in the air; cooking environment temperature field information measures the impact of cooking environment temperature on oil fumes; and oil fume concentration prediction information utilizes the trends of the cooking environment to compensate for potential instantaneous fluctuations or delays in real-time sensor data, improving the stability and accuracy of the fused oil fume concentration. Therefore, compared to using only particulate matter concentration information to measure the oil fume concentration in the cooking environment, sensing the actual oil fume concentration in the cooking environment through the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information allows for comprehensive and multi-layered perception and analysis of the oil fume concentration from different dimensions and angles, reducing interference from the cooking environment on oil fume concentration detection and improving the accuracy of oil fume concentration detection.

[0039] In an optional implementation, the step of fusing the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the predicted oil fume concentration to obtain fused oil fume concentration information includes: The oil fume concentration fusion information is obtained by filtering the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information.

[0040] Specifically, the filtering process includes Kalman filtering, improved Kalman filtering, or particle filtering, and the filtering process is performed by constructing a Kalman filter or particle filter.

[0041] In an optional implementation, the fume concentration fusion information includes a fume concentration fusion value, the odor concentration information includes an odor concentration value, the particulate matter concentration information includes a particulate matter concentration value, the cooking environment temperature field information includes cooking environment temperature field data, the fume concentration prediction information includes a fume concentration prediction value, and the fume concentration fusion information is calculated according to the following formula: C=K﹒ (ω1﹒C1+ω2﹒C2+ω3﹒f(T f ))+(1-K)﹒ C d Where C is the combined concentration value of oil fume; C1 represents the odor concentration value; C2 represents the particulate matter concentration value; f(T f ) is the temperature field transformation function of the cooking environment, T f For cooking environment temperature field data, f(T) f According to Tf get; C d This is the predicted value for oil fume concentration; K is the filter adjustment coefficient, and ω1, ω2, and ω3 are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively.

[0042] Optionally, the filter adjustment coefficient K can be set to a value of 0.3-0.95, preferably 0.5-0.9, and especially 0.6-0.8, such as 0.55, 0.65, 0.7, 0.85 or 0.9. Specifically, the filter adjustment coefficient K can be adjusted according to the cooking scenario. For cooking scenarios, range hoods or integrated stoves where the stability and timeliness of real-time multi-sensor data are relatively high, the filter adjustment coefficient K can be set to a higher value, such as K being set to a value of 0.5-0.95. For example, K can be 0.7, 0.8 or 0.9 to ensure the response speed to rapidly changing cooking scenarios such as stir-frying. The weight (1-K) corresponding to the stability of the historical predicted oil fume concentration is relatively low, such as 0.3, 0.2 or 0.1. This avoids relying solely on real-time data due to instantaneous interference and avoids over-reliance on historical data leading to response lag.

[0043] Cooking environment temperature field transformation function f(T) f () is the temperature field data of the cooking environment T f The data is then converted to obtain quantitative parameters. Specifically, the temperature field data T of the cooking environment collected by the infrared thermal imaging device is used to... f Using a preset mapping rule f(Z), it is converted into a quantized value f(T) of the same dimension as odor concentration and particulate matter concentration. f Z is the input variable of the preset mapping rule. In this embodiment, the input variable is the cooking environment temperature field data T. f .

[0044] For example, when the stove temperature is below the smoke point of grease, almost no fumes are produced, and the converted quantified value f(T) f When the stove temperature is greater than or equal to the smoke point of grease, the temperature field in the central area of ​​the stove rises sharply and forms a significant high-temperature zone. The converted quantized value f(T) f )big.

[0045] In one optional implementation, the particulate matter concentration sensing data obtained by the particulate matter concentration sensor from detecting suspended particulate matter in the air is subjected to at least one of humidity correction processing and temperature drift processing. The humidity correction processing is used to correct the detection deviation caused by ambient humidity to the particulate matter concentration detection, and the temperature drift processing is used to correct the detection deviation caused by ambient temperature to the particulate matter concentration detection.

[0046] Optionally, the particulate matter concentration information includes particulate matter concentration values. Figure 2A schematic flowchart illustrating the process of determining particulate matter concentration values ​​according to embodiments of the present disclosure is shown, such as... Figure 2 As shown, the acquisition of particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and real-time current information includes: Step S201: Acquire temperature information, humidity information, and particulate matter concentration sensing data.

[0047] Specifically, the temperature information is used to indicate the temperature of the environment where the particulate matter concentration sensor is located, the humidity information is used to indicate the humidity of the environment where the particulate matter concentration sensor is located, and the particulate matter concentration sensing data is the sensing data generated by the particulate matter concentration sensor to detect suspended particulate matter in the air.

[0048] Step S202, based on the humidity information, process the particulate matter concentration sensing data C p Humidity correction was performed to obtain particulate matter concentration correction data C. c .

[0049] Specifically, the humidity information includes a humidity value H, which can be absolute humidity or relative humidity, and particulate matter concentration correction data C. c Calculate using the following formula:

[0050] Wherein, H0 is the reference humidity value, which adopts the same absolute humidity or relative humidity as the humidity value H; δ is the humidity correction factor.

[0051] Step S203: Perform temperature drift processing on the particulate matter concentration correction data based on the temperature information to obtain the particulate matter concentration value.

[0052] Specifically, based on the temperature information, corresponding concentration correction information is obtained, and the particulate matter concentration correction data is subjected to temperature drift processing based on the concentration correction information to obtain the particulate matter concentration value.

[0053] For example, a mapping relationship between temperature information and concentration correction information is preset. Based on the temperature information and the mapping relationship, the concentration correction information corresponding to the temperature information is obtained. Optionally, temperature information and corresponding concentration correction information are stored in a temperature information-concentration correction information table. The concentration correction information is obtained by looking up the temperature information and the temperature information-concentration correction information table, or by a combination of table lookup and interpolation. For example, if the temperature information indicates a temperature of 50°C, the corresponding concentration correction information is determined according to the concentration correction information table. Based on this concentration correction information, temperature drift processing is performed on the particulate matter concentration correction data to obtain the particulate matter concentration value.

[0054] In an optional example, only humidity correction or temperature drift correction can be applied to the particulate matter concentration sensing data. The particulate matter concentration correction data C obtained in step S202 will then be processed accordingly. c As a particulate matter concentration value, or in step S203, temperature drift processing is performed on the particulate matter concentration sensing data based on the temperature information to obtain the particulate matter concentration value.

[0055] Step S103: Based on the fused information of oil fume concentration and the information of fan operating parameters, the target fan frequency is obtained. The target fan frequency represents the AC frequency used to drive the range hood fan.

[0056] Specifically, the rotational speed of the range hood fan corresponds to the frequency of the AC power supplied to the range hood fan. The rotational speed of the range hood fan is controlled by adjusting the AC power frequency. Based on the fused information of the oil fume concentration and the fan operating parameters, a target fan frequency is determined. This target fan frequency ensures that the range hood fan has an appropriate rotational speed, enabling the fan to effectively extract the oil fumes.

[0057] Figure 3 This diagram illustrates a flowchart of determining a target wind turbine frequency according to an embodiment of the present disclosure, such as... Figure 3 As shown, obtaining the target fan frequency based on the fused information of oil fume concentration and the fan operating parameters includes: Step S301: Based on the oil fume concentration fusion information, the predicted fan frequency is obtained.

[0058] Specifically, based on at least one of the fume concentration state corresponding to the fume concentration fusion information and the fume concentration change trend determined based on the fume concentration fusion information, the predicted fan frequency is obtained, thereby obtaining the fan frequency adapted to the fume concentration.

[0059] For example, the predicted wind turbine frequency is obtained according to the following formula:

[0060] Where f is the predicted wind turbine frequency, and f0 is the wind turbine base frequency; ΔC is the difference between the combined concentration value C of oil fume and the baseline concentration value; The rate of change of the oil fume concentration fusion value C over time; The fan frequency corresponding to the combined value C of oil fume concentration is the inverse function of the preset functional relationship between the fan frequency and the combined value C of oil fume concentration; α, β, and γ are the adjustment constants.

[0061] Specifically, the preset functional relationship between the fan frequency and the fusion value C of the oil fume concentration is preset. Optionally, the preset functional relationship can be optimized and updated by machine learning based on historical data through a cloud platform or back-end server, and the updated preset functional relationship can be sent to the range hood, integrated stove or smart terminal so that the range hood, integrated stove or smart terminal can control the operation of the fan according to the updated preset functional relationship.

[0062] Step S302: Based on the fan operating parameter information, the oil fume concentration fusion information, and the predicted fan frequency, the predicted fan operating information is obtained.

[0063] Specifically, this predicted fan operation information is used to indicate the predicted operating status of the range hood fan at the next time relative to the current time.

[0064] Optionally, the predicted fan operating information can be obtained by performing prediction processing through a neural network, such as an LSTM neural network, based on at least one of the fan operating parameter information, oil fume concentration fusion information, and predicted fan frequency.

[0065] Optional, the wind turbine operating parameter information includes real-time input power P. t The predicted wind turbine operating information includes the predicted input power P of the wind turbine at the next time t+1 relative to the current time. t+1 Predicting input power P t+1 The following formula is used to obtain:

[0066] Where L(*) represents the prediction processing relationship, and C t is the current or real-time fused value of oil fume concentration, and f is the predicted fan frequency.

[0067] In an alternative example, the wind turbine frequency f predicted by the above formula can be replaced with the wind turbine frequency at the current moment or in real time.

[0068] Optionally, the predicted wind turbine operating information also includes the predicted rotational speed W of the wind turbine at the next time t+1 relative to the current time. t+1 For example, the predicted rotational speed W t+1 Determined based on the predicted wind turbine frequency f.

[0069] Step S303: If the predicted wind turbine operating information meets the preset operating conditions, the predicted wind turbine frequency is taken as the target wind turbine frequency.

[0070] Specifically, the predicted wind turbine operating information includes the predicted input power P. t+1 and predicted rotational speed W t+1 At least one of them. If the predicted input power Pt+1 Less than or equal to the preset power threshold, or if the predicted speed W t+1 Less than or equal to a preset speed threshold, or if the predicted input power P t+1 Less than or equal to the preset power threshold, and the predicted speed W t+1 If the speed is less than or equal to the preset speed threshold, the predicted fan operating information is determined to meet the preset operating conditions.

[0071] Step S104: Generate fan operation control parameters based on the target fan frequency. The fan operation control parameters are used to control the operation of the range hood fan.

[0072] Specifically, the fan operation control parameters include the target fan frequency. The range hood driver provides an alternating current with the target fan frequency to the fan according to the fan operation control parameters. This alternating current is used to drive the fan to rotate.

[0073] In an optional implementation, obtaining the target fan frequency based on the fused oil fume concentration information and the fan operating parameter information further includes: If the predicted wind turbine operating information meets the abnormal operating conditions, the predicted wind turbine frequency is down-processed to obtain the target wind turbine frequency.

[0074] Optionally, if the predicted input power Pt+1 is greater than the preset power threshold, or the predicted speed Wt+1 is greater than the preset speed threshold, then the predicted fan operating information is determined to meet the abnormal operating conditions.

[0075] The frequency reduction process is used to reduce the predicted wind turbine frequency so that the predicted wind turbine operating information meets the preset operating conditions.

[0076] Optionally, if the predicted wind turbine operating information meets the abnormal operating conditions, an alarm message is generated. The alarm message is used to remind the user that the wind turbine is operating abnormally. The alarm message includes at least one of voice alarm message and display alarm message.

[0077] In one embodiment, the range hood, integrated cooktop, or smart appliance includes a multimodal sensing module and an intelligent control module. The multimodal sensing module includes an odor molecule sensor, a particulate matter concentration sensor, and an infrared thermal imaging device. In an alternative embodiment, the intelligent control module is located on an intelligent control device communicatively connected to the range hood, integrated cooktop, or smart appliance, and this intelligent control device is located on a smart terminal.

[0078] The odor molecule sensor has a sampling period of 100ms and an accuracy of ±0.1ppm.

[0079] The particulate matter concentration sensor is used to measure PM2.5 / PM10, with a measurement range of 0~1000 μg / m³.3 ; The infrared thermal imaging device has a resolution of 80×60 and a frame rate of 9Hz, and is used for temperature field modeling.

[0080] The intelligent control module utilizes the NVIDIA Jetson Nano edge computing platform and runs a multi-threaded algorithm. The module includes a data fusion thread, a dynamic adjustment thread, and a load prediction thread. The data fusion thread performs filtering, such as Kalman filtering, with an update cycle of 50-5000ms, for example, 100ms, 200ms, 500ms, 1000ms, or 2000ms; the test configuration update cycle is 200ms. The dynamic adjustment thread calculates the predicted wind turbine frequency with a calculation cycle of 100-2000ms, for example, 200ms, 500ms, or 1000ms; the test configuration calculation cycle is 500ms. The load prediction thread calculates the predicted input power; for the neural network used, such as an LSTM neural network, the corresponding input window length is 10 historical data points.

[0081] The motor is a permanent magnet synchronous motor with a rated power of 500W. It can perform stepless speed regulation from 0 to 120Hz. The motor driver integrates a space vector modulation algorithm, and the harmonic distortion rate is <3%.

[0082] In a laboratory simulation scenario, for the stir-fry mode, the initial oil fume concentration was set to 30 mg / m³. 3 The rate of change in oil fume concentration was +5 mg / m³ 3 / s. The fume extraction system's response frequency increased from 60Hz to 95Hz, achieving a smoke extraction efficiency of 98%, and the fan efficiency optimization effect was achieved through... Adjusting to 85Hz reduces energy consumption by 12%.

[0083] For stewing mode, the oil fume concentration remains stable at 15mg / m³. 3 The system automatically reduces the frequency to 45Hz, reducing fan energy consumption by 40%. The humidity compensation effect is that when the humidity increases from 60% to 80%, the error of particulate matter concentration after correction is reduced from ±15% to ±3%.

[0084] For fault tolerance testing, the effect of simulating odor sensor failure is as follows: the control device switches to particulate matter + temperature field control, and the smoke extraction efficiency remains at 90%. The effect of simulating fan overload is as follows: after exceeding the limit, the system frequency is reduced to 30Hz and an alarm is triggered.

[0085] For long-term operation testing, the system operated continuously for 30 days in a restaurant kitchen, with an average energy consumption of 0.15 kWh / h (compared to 0.23 kWh / h for traditional range hoods), a smoke extraction efficiency of ≥95% (compared to ≤85% for traditional range hoods), and 0 downtimes due to malfunctions (compared to an average of 2 downtimes per month for traditional range hoods under the same conditions).

[0086] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0087] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0088] Figure 4 A block diagram of a range hood control device according to an embodiment of the present disclosure is shown; as follows: Figure 4 As shown, the above-mentioned device includes: The acquisition module is used to acquire particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and fan operating parameter information. The fusion module is used to perform fusion processing based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information to obtain oil fume concentration fusion information. The frequency adjustment module is used to obtain the target fan frequency based on the oil fume concentration fusion information and the fan operating parameter information. The target fan frequency represents the AC frequency used to drive the range hood fan. The drive control module is used to generate fan operation control parameters based on the target fan frequency, and the fan operation control parameters are used to control the operation of the range hood fan.

[0089] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to execute the control method described in the above embodiments. The specific implementation can be referred to the description in the above embodiments, and for the sake of brevity, it will not be repeated here.

[0090] This disclosure also proposes a computer-readable storage medium storing at least one instruction or at least one program segment, which, when loaded and executed by a processor, implements the aforementioned method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0091] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured for the control method described above.

[0092] Electronic devices can be provided as terminals, servers, or other forms of devices.

[0093] Electronic devices can be mobile phones, computers, messaging devices, tablets, personal digital assistants, and other terminals.

[0094] Electronic devices may include one or more of the following components: processing components, memory, power supply components, input / output (I / O) interfaces, and communication components.

[0095] Processing components typically control the overall operation of an electronic device, such as operations associated with data communication and recording. A processing component may include one or more processors to execute instructions to complete all or part of the steps described above. Furthermore, a processing component may include one or more modules to facilitate interaction between the processing component and other components.

[0096] Memory is configured to store various types of data to support the operation of electronic devices. Examples of this data include instructions for any application or method used to operate on an electronic device. Memory can be implemented using any type of volatile or non-volatile storage device or a combination thereof.

[0097] Power supply components provide power to various components of electronic devices. Power supply components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic devices.

[0098] I / O interfaces provide interfaces between processing components and peripheral interface modules, such as keyboards, click wheels, buttons, etc.

[0099] The communication component is configured to facilitate wired or wireless communication between electronic devices and other devices. The electronic device can access a wireless network based on a communication standard. In one exemplary embodiment, the communication component includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID), infrared data association (IRA) technology, ultra-wideband (UWB) technology, Bluetooth technology, and other technologies.

[0100] In an exemplary embodiment, the electronic device may be implemented by one or more application-specific integrated circuits, digital signal processors, digital signal processing devices, programmable logic devices, field-programmable gate arrays, controllers, microcontrollers, microprocessors, or other electronic components to perform the control method described above.

[0101] This disclosure also proposes a smart home appliance, which adopts the control method described in the above technical solution, and the smart home appliance is a range hood or an integrated stove.

[0102] This disclosure also proposes a smart home appliance, which includes a range hood control device as described in the above technical solution, wherein the smart home appliance is a range hood or an integrated stove.

[0103] Optionally, the smart home appliance includes a range hood fan and a fan drive unit, wherein the fan drive unit is used to drive the range hood fan.

[0104] Specifically, the fan drive unit is used to receive the fan operation control parameters and drive the range hood fan according to the fan operation control parameters.

[0105] The variable frequency drive module is also used for stepless speed regulation and soft start of the fan, and dynamically adjusts the fan speed according to the fan operation control parameters.

[0106] The wind turbine drive unit includes a power factor correction unit, which is used to adjust the phase synchronization of the input current and voltage supplied to the wind turbine. Optionally, the power factor correction unit is configured as a power factor correction circuit. The power factor correction circuit can adjust the phase synchronization of the input current and voltage in real time, thereby reducing current waveform distortion, reducing the proportion of reactive power, and enabling more of the electrical energy input from the external power source to be converted into active power to drive the wind turbine, thereby improving the energy utilization rate from the power input end of the wind turbine.

[0107] Optionally, the smart home appliance includes the multimodal sensing module, which is used to detect the state of cooking fumes and generate multimodal sensing information. The multimodal sensing module includes the odor molecule sensor, the particulate matter concentration sensor, and the infrared thermal imaging device. Preferably, the multimodal cooking fume concentration sensing module includes an integrated odor molecule sensor, particulate matter concentration sensor, and infrared thermal imaging unit.

[0108] Optionally, the smart home appliance includes a human-computer interaction unit, which includes at least one of a touch screen and a voice control interface. Users can interact with the smart home appliance through at least one of the touch screen and the voice control interface, thereby realizing local monitoring of the smart home appliance.

[0109] The touch screen is used to receive user operation information and display preset information.

[0110] The voice control interface is at least used to receive voice information so that users can operate the smart home appliances by voice.

[0111] Optionally, the smart home appliance includes a communication unit for communicating with a cloud data platform or an external communication device, thereby enabling remote monitoring of the smart home appliance.

[0112] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for controlling a range hood, characterized in that, The method includes: Acquire information on particulate matter concentration, odor concentration, cooking environment temperature field, predicted oil fume concentration, and fan operating parameters; Based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information, fusion processing is performed to obtain oil fume concentration fusion information; Based on the fused information of oil fume concentration and the information of fan operating parameters, the target fan frequency is obtained, and the target fan frequency represents the AC frequency used to drive the range hood fan. Fan operation control parameters are generated based on the target fan frequency, and these parameters are used to control the operation of the range hood fan.

2. The method according to claim 1, characterized in that, The process of fusing the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the predicted oil fume concentration information to obtain fused oil fume concentration information includes: The oil fume concentration fusion information is obtained by filtering the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information.

3. The method according to claim 1 or 2, characterized in that, The fused information on oil fume concentration includes a fused value; the information on odor concentration includes an odor concentration value; the information on particulate matter concentration includes a particulate matter concentration value; the information on cooking environment temperature field includes cooking environment temperature field data; the information on oil fume concentration prediction includes a predicted value; and the fused information on oil fume concentration is calculated according to the following formula: C=K﹒(ω1﹒C1+ω2﹒C2+ω3﹒f(T f ))+(1-K)﹒C d Where C is the combined concentration value of oil fume, C1 is the odor concentration value, C2 is the particulate matter concentration value, and f(T) is the concentration value of oil fume. f (T) represents the ambient temperature parameter for cooking. f For cooking environment temperature field data, f(T) f According to T f C was obtained. d ω1, ω2, and ω3 are the predicted values ​​of oil fume concentration, K is the filter adjustment coefficient, and ω1, ω2, and ω3 are the first weight coefficient, the second weight coefficient, and the third weight coefficient, respectively.

4. The method according to claim 1, characterized in that, The particulate matter concentration information includes particulate matter concentration values. The acquisition of particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and real-time current information includes: Acquire temperature, humidity, and particulate matter concentration sensor data; Based on the humidity information, the particulate matter concentration sensing data is subjected to humidity correction processing to obtain corrected particulate matter concentration data; and / or Based on the temperature information, the particulate matter concentration correction data is subjected to temperature drift processing to obtain the particulate matter concentration value.

5. The method according to claim 1, characterized in that, The step of obtaining the target fan frequency based on the fused information of oil fume concentration and the fan operating parameters includes: Based on the fused information of oil fume concentration, the predicted fan frequency is obtained; Based on the fan operating parameters, the fused information of oil fume concentration, and the predicted fan frequency, the predicted fan operating information is obtained. If the predicted wind turbine operating information meets the preset operating conditions, the predicted wind turbine frequency is taken as the target wind turbine frequency.

6. The method according to claim 5, characterized in that, The step of obtaining the target fan frequency based on the fused information of oil fume concentration and the fan operating parameters further includes: If the predicted wind turbine operating information meets the abnormal operating conditions, the predicted wind turbine frequency is down-processed to obtain the target wind turbine frequency.

7. The method according to claim 5 or 6, characterized in that, The fan operating parameter information includes real-time input power; the predicted fan operating information includes the predicted input power of the fan at the next moment relative to the current moment; the oil fume concentration fusion information includes an oil fume concentration fusion value; and obtaining the predicted fan operating information based on at least one of the fan operating parameter information, the oil fume concentration fusion information, and the predicted fan frequency includes: Based on the real-time input power, the real-time oil fume concentration fusion value, and the predicted fan frequency, the predicted input power of the fan at the next moment relative to the current moment is obtained. Before using the predicted wind turbine frequency as the target wind turbine frequency when the predicted wind turbine operating information meets the preset operating conditions, the following steps are included: If the predicted input power is less than or equal to a preset power threshold, the predicted wind turbine operating information is determined to meet the preset operating conditions.

8. A range hood control device, characterized in that, The device includes: The acquisition module is used to acquire particulate matter concentration information, odor concentration information, cooking environment temperature field information, oil fume concentration prediction information, and fan operating parameter information. The fusion module is used to perform fusion processing based on the particulate matter concentration information, the odor concentration information, the cooking environment temperature field information, and the oil fume concentration prediction information to obtain oil fume concentration fusion information. The frequency adjustment module is used to obtain the target fan frequency based on the oil fume concentration fusion information and the fan operating parameter information. The target fan frequency represents the AC frequency used to drive the range hood fan. The drive control module is used to generate fan operation control parameters based on the target fan frequency, and the fan operation control parameters are used to control the operation of the range hood fan.

9. An electronic device, characterized in that, The system includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor implements the control method as described in any one of claims 1-7 by executing the instructions stored in the memory.

10. A smart home appliance, characterized in that, The smart home appliance adopts the control method as described in any one of claims 1-7, and the smart home appliance is a range hood or an integrated stove.

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