Noise reduction method and device and range hood

Abstract

The invention provides a noise reduction method and device and a range hood. The method comprises the following steps: acquiring a current working mode of the range hood; at least one mode parameter corresponding to the current working mode is obtained, the at least one mode parameter is determined based on a noise reduction effect parameter and at least one influence factor, and the influence factor influences control over sound in a preset frequency range in the current working mode; at least one target control parameter is determined based on the at least one mode parameter and a current noise reduction algorithm, the at least one target control parameter is used for being adopted by a noise reduction module of the range hood, and the noise reduction module is used for reducing noise generated by the range hood. The invention provides a noise reduction scheme with higher adaptability, and the noise reduction effect can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of kitchen utensils, and in particular, to a noise reduction method, device and range hood. Background Art

[0002] A range hood is an electrical appliance used in the kitchen to suck out the oil fumes generated during cooking. In related technologies, the range hood generates sounds during operation, and these sounds are often regarded as noises by people. Usually, sound-absorbing materials are relied on for noise reduction. However, the noise reduction solution relying on sound-absorbing materials is not very effective. Summary of the Invention

[0003] In order to solve at least one of the above-mentioned technical problems, the present application provides a noise reduction method, device and range hood:

[0004] According to a first aspect of the present application, a noise reduction method is provided, including:

[0005] Obtaining the current working mode of the range hood;

[0006] Obtaining at least one mode parameter corresponding to the current working mode, the at least one mode parameter being determined based on a noise reduction effect parameter and at least one influencing factor, the influencing factor affecting the control of sounds in a preset frequency range in the current working mode;

[0007] Determining at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the at least one target control parameter being used by the noise reduction module of the range hood, and the noise reduction module being used to reduce the noise generated by the range hood.

[0008] According to a second aspect of the present application, a noise reduction device is provided, including:

[0009] A working mode acquisition module: configured to obtain the current working mode of the range hood;

[0010] A mode parameter acquisition module: configured to obtain at least one mode parameter corresponding to the current working mode, the at least one mode parameter being determined based on a noise reduction effect parameter and at least one influencing factor, the influencing factor affecting the control of sounds in a preset frequency range in the current working mode;

[0011] A control parameter obtaining module: configured to determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the at least one target control parameter being used by the noise reduction module of the range hood, and the noise reduction module being used to reduce the noise generated by the range hood

[0012] According to a third aspect of the present application, there is provided an oil fume extractor including the noise reduction device as described in the second aspect.

[0013] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, rather than limiting the present application.

[0014] Implementing the present application has the following beneficial effects:

[0015] The present application provides a more adaptable noise reduction solution, which is beneficial to improving the noise reduction effect. The present application obtains the current working mode of the oil fume extractor; then, obtains at least one mode parameter corresponding to the current working mode; furthermore, determines at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm. When considering the current working mode and introducing the corresponding mode parameter, a path for determining the mode parameter with the influencing factor and the noise reduction effect parameter is provided. The target control parameter is determined based on the mode parameter and the current noise reduction algorithm, and the determined target control parameter is adopted by the noise reduction module of the oil fume extractor. By establishing the association between the mode parameter, the noise reduction algorithm, and the control parameter, the present application makes the determination of the control parameter more in line with the working mode. At the same time, the control parameter as the result of data processing is also more convenient to be adopted by the noise reduction module to improve the timeliness of noise reduction.

[0016] According to the following detailed description of the exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present application will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above-mentioned objects, technical solutions, and beneficial effects of the present invention can be clearly obtained through the following detailed description of the specific embodiments that can implement the present invention, in combination with the description of the accompanying drawings.

[0018] The same reference numerals and symbols in the drawings and the specification represent the same or equivalent elements.

[0019] Figure 1 is a schematic flowchart of a noise reduction method provided by the present application;

[0020] Figure 2 is a schematic flowchart of updating algorithm parameters provided by the present application;

[0021] Figure 3 is a schematic flowchart of updating the step size provided by the present application;

[0022] Figure 4 is a block diagram of a fire control device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] Various exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0026] The special term "exemplary" here means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" here does not have to be construed as superior to or better than other embodiments.

[0027] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of multiple or any combination of at least two of multiple. For example, including at least one of A, B, and C can represent any one or more elements selected from the set composed of A, B, and C.

[0028] In addition, in order to better illustrate the present application, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present application can be implemented without some specific details. In some instances, methods, means, elements and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present application.

[0029] Figure 1The flowchart of a noise reduction method according to an embodiment of the present application is shown. As Figure 1 shown, the method includes:

[0030] S101: Obtain the current working mode of the range hood;

[0031] In the embodiment of the present application, the execution subject of the noise reduction method provided in the embodiment of the present application can be the range hood itself or the server side belonging to the same oil fume system as the range hood. The working mode of the range hood is designed to adapt to specific oil fume extraction requirements, and the specific oil fume extraction requirements can reflect the objective requirements of a specific cooking environment and / or the subjective requirements of the cooking operation object. The range hood provides multiple preset working modes, and the current working mode is any one of the multiple preset working modes.

[0032] The acquisition of the current working mode can use the received noise reduction instruction as the data source. The noise reduction instruction can be generated by the user's trigger. Exemplarily, 1) The oil fume system further includes a terminal, and the terminal can be directly or indirectly connected to the range hood or the server side through wired or wireless communication means. The terminal provides a user interaction interface, and the noise reduction instruction can be generated by the user by triggering the relevant controls provided by the user interaction interface. Correspondingly, the terminal sends the noise reduction instruction to the range hood or the server side. 2) The range hood provides a user interaction interface, and the noise reduction instruction can be generated by the user by triggering the relevant controls provided by the user interaction interface. Correspondingly, the range hood realizes the acquisition of the noise reduction instruction. 3) The range hood can be directly or indirectly connected to the server side through wired or wireless communication means. The range hood provides a user interaction interface, and the noise reduction instruction can be generated by the user by triggering the relevant controls provided by the user interaction interface. Correspondingly, the range hood sends the noise reduction instruction to the server side. Correspondingly, the current working mode can be selected by the user and carried by the noise reduction instruction. The current working mode can be obtained based on the noise reduction instruction.

[0033] The acquisition of the current working mode can be the response result of the received noise reduction instruction. After receiving the noise reduction instruction, the current working mode can be determined based on the current cooking environment and / or the current user selection. It should be understood that a preset working mode can be bound to a preset cooking environment and / or a preset user preference. For the current user selection, it can indicate the current user preference obtained through guiding information or the historical user preference. Then, the current working mode can be determined from multiple preset working modes according to the first type of matching relationship and / or the second type of matching relationship. The first type of matching relationship is determined based on the current cooking environment and the preset cooking environment. The second type of matching relationship is determined based on the current user preference (or historical user preference) and the preset user preference. The preset cooking environment can be the environment when processing ingredients in a stewing form, the environment when processing ingredients in a frying form, etc. The preset user preference can indicate the noise intensity that the user can tolerate. The noise intensity can be measured in decibels (dB). The preset user preference can also indicate the energy consumption of the noise reduction module that the user accepts. Because when the noise reduction module reduces the noise generated by the range hood, it is also accompanied by the consumption of electric energy.

[0034] S102: Obtain at least one mode parameter corresponding to the current working mode, where the at least one mode parameter is determined based on a noise reduction effect parameter and at least one influencing factor, and the influencing factor affects the control of sounds in a preset frequency range in the current working mode;

[0035] In the embodiments of the present application, taking the current working mode as an anchor point, at least one mode parameter corresponding thereto is introduced. When determining the mode parameter, first, at least one influencing factor is determined based on the current working mode, and then at least one mode parameter is determined based on the noise reduction effect parameter and at least one influencing factor. It can be understood that when the range hood is working, sounds will be generated, and these sounds are often regarded as noise by people. The noise reduction object in the embodiments of the present application is the sound within a preset frequency range, and the preset frequency range can be from 20 Hz to 250 Hz. It can also be considered that the noise reduction object in the embodiments of the present application is low-frequency noise. The influencing factor can be contributed by the noise reduction object itself. Considering that the current working mode is adapted to specific oil fume extraction requirements, the influencing factor can indicate information about the cooking environment and / or information about user preferences. The noise reduction effect parameter can be used as a noise reduction target. When the noise reduction effect parameter and the influencing factor are known and clear, the mode parameter can be obtained through constraints. Generally, the noise reduction effect parameter corresponds to the preset working mode, and the noise reduction effect parameter can be a parameter preset for the preset working mode and capable of reflecting the ideal noise reduction effect. If there is a bound preset user preference for the preset working mode, the noise reduction effect parameter can also be set with reference to the preset user preference. The noise reduction effect parameter can indicate the expected sound intensity after noise reduction, such as reducing the intensity of the current sound to 5 decibels; it can also indicate the expected reduction amplitude of the intensity of the current sound, such as reducing the intensity of the current sound by 30 decibels. Even if the influencing factors indicating the information about the cooking environment and / or information about user preferences all follow the information provided by the preset cooking environment and / or preset user preferences, due to the addition of the influencing factor contributed by the noise reduction object itself, the mode parameter will also be affected by the constraint relationship.

[0036] In one embodiment, the obtaining of at least one mode parameter corresponding to the current working mode may include the following three cases:

[0037] 1) When the current working mode is a first type of mode, determining the intensity of the current sound within the preset frequency range as the first influencing factor, determining the ideal energy consumption of the noise reduction module as the second influencing factor, and obtaining the at least one mode parameter based on the first influencing factor, the second influencing factor, and the first noise reduction effect parameter;

[0038] 2) When the current working mode is a second type of mode, determining the intensity of the current sound within the preset frequency range as the third influencing factor, and obtaining the at least one mode parameter based on the third influencing factor and the second noise reduction effect parameter;

[0039] 3) When the current working mode is the third type of mode, determine that the intensity of the current sound within the preset frequency range is the fourth influencing factor, determine the cooking environment information as the fifth influencing factor, determine the preference information of the cooking operation object as the sixth influencing factor, and obtain the at least one mode parameter based on the fourth influencing factor, the fifth influencing factor, the sixth influencing factor, and the third noise reduction effect parameter.

[0040] Examples of the selection of influencing factors and the determination of mode parameters in different working modes are provided here. By more finely determining the intermediate parameter of the mode parameter, it is beneficial to improve the accuracy of the determined target control parameter, and thus beneficial to the adaptability of the noise reduction solution provided by the embodiments of the present application in the dimension of the working mode. In the above 1)-3), the influencing factor contributed by the noise reduction object itself is reflected by the intensity of the current sound within the preset frequency range. The second influencing factor (the ideal energy consumption of the noise reduction module) in the above 1) is an influencing factor indicating information of user preference. The fifth influencing factor (cooking environment information) in the above 3) is an influencing factor indicating information of the cooking environment category. At this time, the cooking environment information may not follow the information provided by the preset cooking environment, but adopt the information provided by the actual cooking environment. The sixth influencing factor (the preference information of the cooking operation object) in the above 3) is an influencing factor indicating information of user preference.

[0041] Exemplarily, the first type of mode may be the standard mode M1, and the constraint relationship between the first influencing factor, the second influencing factor, the first noise reduction effect parameter, and the at least one mode parameter may be reflected by the following formula:

[0042] E M1 =α.N level -β.P

[0043] Wherein, E M1 is the first noise reduction effect parameter, α and β are mode parameters, N level is the first influencing factor, and P is the second influencing factor. In practical applications, α can take 0.5 and β can take 0.1.

[0044] The second type of mode may be the silent (or strong noise reduction) mode M2, and the constraint relationship between the third influencing factor, the second noise reduction effect parameter, and the at least one mode parameter may be reflected by the following formula:

[0045] E M2 =k.N level -b

[0046] Wherein, E M2 is the second noise reduction effect parameter, k and b are mode parameters, N levelIt is the third influencing factor. The second noise reduction parameters adopted by the mute mode and the strong noise reduction mode can be different. Generally, k in the mute mode is less than k in the strong noise reduction mode. In practical applications, in the mute mode, k can be set to 0.8 and b can be set to 0.2. In the strong noise reduction mode, k can be set to 1.2 and b can be set to 0.3.

[0047] The third type of mode can be the environmental perception mode M3. The constraint relationships among the fourth influencing factor, the fifth influencing factor, the sixth influencing factor, the third noise reduction effect parameter, and at least one mode parameter can be reflected by the following formula:

[0048] E M3 = g(N level , E env , U preference )

[0049] Among them, E M3 is the third noise reduction effect parameter, g is the comprehensive noise reduction effect function, and other parameters in g can be used as mode parameters. N level is the fourth influencing factor, E env is the fifth influencing factor, and U preference is the sixth influencing factor.

[0050] S103: Determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm. The at least one target control parameter is used to be adopted by the noise reduction module of the range hood, and the noise reduction module is used to reduce the noise generated by the range hood.

[0051] In the embodiments of the present application, at least one target control parameter is determined based on at least one mode parameter and the current noise reduction algorithm. It can be understood that the current noise reduction algorithm involves multiple parameter items, and the multiple parameter items include a first type of parameter item that is fixedly enabled and a second type of parameter item that is flexibly enabled. The target control parameter corresponds to the first type of parameter item. When determining at least one mode parameter, the second type of parameter item corresponding to the mode parameter is enabled. Based on the constraint relationship provided by the current noise reduction algorithm, when the mode parameter is known and clear, a clear target control parameter can be obtained.

[0052] The determined target control parameter is adopted by the noise reduction module of the range hood. The noise reduction module is used to reduce the noise generated by the range hood. The noise reduction means of the noise reduction module can be to generate a new sound for canceling the current sound, and the target control parameter serves for the generation of the new sound. The noise reduction module can include components such as a microphone, a speaker, a signal processor, a controller, and a filter. In practical applications, the noise reduction module can include a speaker arranged in the air duct of the range hood, and the parameter item corresponding to the target control parameter can be the parameter item of the speaker.

[0053] The embodiments of the present application will be described in detail below.

[0054] As a possible implementation, as Figure 2 shown, the current noise reduction algorithm includes at least one adjustable parameter. After determining at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the method further includes:

[0055] S201: After the noise reduction module performs noise reduction control using the at least one target control parameter, determine the actual noise reduction effect information;

[0056] S202: Update the at least one adjustable parameter based on the actual noise reduction effect information to update the current noise reduction algorithm.

[0057] The actual noise reduction effect information can be determined based on actual noise reduction effect parameters. Combining the previous description of the noise reduction effect parameters, the actual noise reduction effect parameters can indicate the actual sound intensity after noise reduction, or can also indicate the amplitude of the reduction of the current sound intensity. The parameter items corresponding to the adjustable parameters can be the above-mentioned first type of parameter items or the above-mentioned second type of parameter items. Using the actual noise reduction effect information as feedback to adjust the algorithm parameters, and then updating the current noise reduction algorithm. In this way, through the update of the noise reduction algorithm, support can be provided for determining more accurate control parameters, and thus more adaptable noise reduction can be achieved. It can be understood that the foregoing steps S102, S103, and here S201, S202 together constitute a noise reduction step that is iteratively executed. The iteration stop condition can indicate a preset number of iterations or can also indicate a received noise reduction stop instruction.

[0058] Exemplarily, the update of the algorithm parameters can refer to the following formula:

[0059]

[0060] Among them, θ(t) represents the parameter at the current moment, and θ(t + 1) represents the parameter at the next moment. η represents the learning rate, which can control the step size of parameter update. represents the gradient of the loss function J(θ) with respect to the parameter θ, which can represent the change rate of the noise reduction effect under the current parameters.

[0061] If E lf represents the actual noise reduction effect parameter, the actual noise reduction effect information is represented by which is the partial derivative of the actual noise reduction effect parameter with respect to the parameter θ. Correspondingly, the update of the algorithm parameters can refer to the following formula:

[0062]

[0063] Among them, γ represents the feedback control coefficient.

[0064] Further, the current noise reduction algorithm includes at least one adjustable parameter, and the method may further include the following steps: updating the at least one adjustable parameter according to the monitored environmental data to update the current noise reduction algorithm, where the environmental data is obtained by monitoring at least one of the following environmental items: light intensity item, temperature item, humidity item, smoke concentration item.

[0065] Introduce environmental data to adjust algorithm parameters, and then update the current noise reduction algorithm. In this way, the information involved in the parameter adjustment basis is richer and more comprehensive, which is more conducive to improving the environmental adaptability of algorithm parameter adjustment, such as adapting to extreme environments such as high temperature, high humidity, and low temperature. Of course, the environmental data can be involved in the adjustment of algorithm parameters together with the above actual noise reduction effect information.

[0066] Exemplarily, the update of algorithm parameters can refer to the following formula:

[0067] θ env = f(E);

[0068] E = {S light , S temp , S humidity , S smoke ...}

[0069]

[0070] Where, θ env represents the updated adjustable parameter, f represents the environmental adaptation function. E represents the environmental data, S light represents the sub-environmental data of the light intensity item, S temp represents the sub-environmental data of the temperature item, S humidity represents the sub-environmental data of the humidity item, S smoke represents the sub-environmental data of the smoke concentration item. S fused represents the sensor data fusion result, w i represents the sensor weight, s i represents the sensor data. When the sensor is an ambient light sensor, S fused represents the sub-environmental data of the light intensity item; when the sensor is a temperature sensor, S fused represents the sub-environmental data of the temperature item; when the sensor is a humidity sensor, S fused represents the sub-environmental data of the humidity item; when the sensor is a smoke sensor, S fused represents the sub-environmental data of the smoke concentration item.

[0071] In addition, the method may further include the following steps: determining whether the noise reduction module is abnormal by using a preset anomaly detection algorithm based on the environmental data and at least one module parameter of the noise reduction module. Considering that the sensors for monitoring environmental data may malfunction, and the noise reduction module may also malfunction. To reduce the impact of malfunctions on noise reduction, a preset anomaly detection algorithm can be used to determine whether the sensors are abnormal and whether the noise reduction module is abnormal, which can improve the noise reduction stability of the entire range hood or the fume system. Here, the module parameters may come from the above-mentioned at least one target control parameter, or may be parameters other than the target control parameters of the noise reduction module.

[0072] In practical applications, to cope with extreme environments such as high temperature, high humidity, and low temperature, a fault tolerance mechanism is introduced to ensure the noise reduction stability and reliability of the entire range hood or the fume system. The implementation of the fault tolerance mechanism is supported by redundant design and fault detection. Redundant design is adopted for key components of the sensors and the noise reduction module to ensure that the failure of a single component does not affect the overall function. The implementation of fault detection can refer to the following formula:

[0073] F detect =h(s i ,θ)

[0074] where F detect represents the fault detection function, and h represents the fault detection algorithm, corresponding to the above-mentioned preset anomaly detection algorithm.

[0075] In this way, when a key component fails, it can be timely identified through fault detection, and the standby component can be timely switched for use with the help of redundant design.

[0076] As a possible implementation manner, as Figure 3 shown, the current noise reduction algorithm includes at least one adjustable parameter. After determining at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the method further includes:

[0077] S301: After the noise reduction module performs noise reduction control using the at least one target control parameter, determining the actual noise reduction effect information;

[0078] S302: Updating the current step size corresponding to the adjustable parameter based on the actual noise reduction effect information.

[0079] The step size controls the distance of movement during parameter update. For the actual noise reduction effect, reference can be made to the relevant records in the foregoing steps S201 - S202, which will not be elaborated here. Using the actual noise reduction effect information as feedback to update the current step size is conducive to making the movement distance during the update of adjustable parameters more adaptable. For example, the current step size is step size 1, and step size 1 is updated to obtain step size 2. When it is necessary to update the adjustable parameters, the latest step size, that is, step size 2, is used to constrain the movement distance during the update.

[0080] Exemplarily, the update of the step size can refer to the following formula:

[0081]

[0082] where s new and s old represent the updated step size and the current step size respectively, and γ represents the feedback control coefficient. If E lf represents the actual noise reduction effect parameter, the actual noise reduction effect information is represented by which is the partial derivative of the actual noise reduction effect parameter with respect to the step size s.

[0083] For the current step size as the initial step size, the following provides two ways to obtain the current step size:

[0084] 1) The first way to obtain the current step size: First, obtain the mode reference step size and the mode additional step size corresponding to the current working mode, where the mode additional step size is determined based on a preset adjustment coefficient and the intensity of the current sound in the preset frequency range; then, obtain the current step size based on the mode reference step size and the mode additional step size. Considering the current working mode in the composition of the step size makes the reference movement distance during the update provided as the initial step size more accurate, and the step size update based on this is also more effective.

[0085] Exemplarily, the first type of mode can be the standard mode M1, and the current step size can be reflected by the following formula:

[0086] s M1 = s base1 + β 1 .N level

[0087] where s base1 is the mode reference step size, β 1 .N level is the mode additional step size, β 1 is the first adjustment coefficient, and N level is the intensity of the current sound in the preset frequency range. In practical applications, s base1 can be taken as 0.05, and β 1 can be taken as 0.01.

[0088] The second type of mode can be the mute (or strong noise reduction) mode M2, and the current step size can be expressed by the following formula:

[0089] s M2 = s base2 + β 2 .N level

[0090] where s base2 is the mode reference step size, β 2 .N level is the mode additional step size, β 2 is the second adjustment coefficient, and N level is the intensity of the current sound in the preset frequency range. In practical applications, in the mute mode, s base2 can be taken as 0.03, and β 2 can be taken as 0.005. In the strong noise reduction mode, s base2 can be taken as 0.07, and β 2 can be taken as 0.02.

[0091] The third type of mode can be the environmental perception mode M3, and the current step size can be expressed by the following formula:

[0092] s M3 = s base3 + β 31 .S temp + β 32 .S humidity

[0093] where s base3 is the mode reference step size, β 31 .S temp + β 32 .S humidity is the mode additional step size, β 31 , β 32 are both the third adjustment coefficients. S temp represents the sub-environment data of the temperature term, and S humidity represents the sub-environment data of the humidity term.

[0094] 2) The second method for obtaining the current step size: First, obtain the preset reference step size; then, obtain the environmental additional step size corresponding to the current cooking environment; furthermore, obtain the current step size based on the preset reference step size and the environmental additional step size. The composition of the step size also takes into account the current cooking environment, making the reference moving distance during update provided as the initial step size more accurate, and the step size update based on this is also more effective. Combining the foregoing example, s base3 can also be used as the preset reference step size here, and β 31 .S temp+β 32 .S humidity It can also be used as the environmental additional step length here.

[0095] Of course, starting from the intensity of the current sound within the preset frequency range, it is also possible to use s base +β.N level to represent the current step length. When the intensity is high (even extremely noisy), a larger step length is used to quickly respond to noise changes; when the intensity is low (even extremely quiet), a smaller step length is used to support overall stability.

[0096] It should be noted that the update of the step length can comprehensively consider the current working mode, the intensity of the current sound within the preset frequency range, and the actual noise reduction effect information brought by noise reduction at the previous moment.

[0097] The noise reduction solution provided by the embodiments of the present application significantly improves the control effect on low-frequency noise and can meet the noise reduction requirements of users in different environments. For multi-mode adaptive noise reduction, precise control of low-frequency noise is achieved, optimizing the user experience. For example, in the strong noise reduction mode, the low-frequency noise can be reduced to a level where the user can hardly feel it, thus improving the comfort during the cooking process. For the dynamic optimization of the step length, the noise reduction stability of the entire range hood or the oil fume system can be effectively improved. For example, in the strong noise reduction mode, it can quickly respond to noise changes and avoid affecting the operation stability of the noise reduction module due to an overly large step length; in the silent mode, a smaller step length is adopted to maintain the stable operation of the noise reduction module.

[0098] In addition, in a high-temperature environment, more adaptable noise reduction can be achieved through automatic mode selection, algorithm parameter update, and step length update, and it can also prevent the noise reduction module from overheating and failing. In a high-humidity environment, the adaptive noise reduction can automatically adjust the filter parameters to ensure that the noise reduction effect is not affected. In an environment with large light changes, more adaptable noise reduction can be achieved through automatic mode selection, algorithm parameter update, and step length update, which can ensure that users can experience the best noise reduction effect under different light conditions.

[0099] As can be seen from the technical solutions provided by the embodiments of the present application above, the embodiments of the present application provide a more adaptable noise reduction solution, which is beneficial to improving the noise reduction effect. The embodiments of the present application obtain the current working mode of the range hood; then, obtain at least one mode parameter corresponding to the current working mode; furthermore, determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm. When considering the current working mode and introducing the corresponding mode parameter, a path for determining the mode parameter with the influence factor and the noise reduction effect parameter is provided. The target control parameter is determined based on the mode parameter and the current noise reduction algorithm, and the determined target control parameter is adopted by the noise reduction module of the range hood. By establishing the association between the mode parameter, the noise reduction algorithm and the control parameter, the embodiments of the present application make the determination of the control parameter more compatible with the working mode. At the same time, the control parameter as the data processing result is also more convenient to be adopted by the noise reduction module to improve the timeliness of noise reduction.

[0100] The embodiments of the present application also provide a noise reduction device, as Figure 4 shown. The noise reduction device 40 includes:

[0101] A working mode acquisition module 401: configured to acquire the current working mode of the range hood;

[0102] A mode parameter acquisition module 402: configured to acquire at least one mode parameter corresponding to the current working mode, where the at least one mode parameter is determined based on a noise reduction effect parameter and at least one influence factor, and the influence factor affects the control of sounds in a preset frequency range in the current working mode;

[0103] A control parameter acquisition module 403: configured to determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, where the at least one target control parameter is used to be adopted by the noise reduction module of the range hood, and the noise reduction module is used to reduce the noise generated by the range hood.

[0104] In one embodiment, obtaining at least one mode parameter corresponding to the current working mode includes: when the current working mode is a first type of mode, determining the intensity of the current sound within the preset frequency range as a first influencing factor, determining the ideal energy consumption of the noise reduction module as a second influencing factor, and obtaining the at least one mode parameter based on the first influencing factor, the second influencing factor, and a first noise reduction effect parameter; when the current working mode is a second type of mode, determining the intensity of the current sound within the preset frequency range as a third influencing factor, and obtaining the at least one mode parameter based on the third influencing factor and a second noise reduction effect parameter; when the current working mode is a third type of mode, determining the intensity of the current sound within the preset frequency range as a fourth influencing factor, determining cooking environment information as a fifth influencing factor, determining the preference information of the cooking operation object as a sixth influencing factor, and obtaining the at least one mode parameter based on the fourth influencing factor, the fifth influencing factor, the sixth influencing factor, and a third noise reduction effect parameter.

[0105] In one embodiment, the current noise reduction algorithm includes at least one adjustable parameter, and the device further includes a first algorithm update module;

[0106] The first algorithm update module: is configured to determine actual noise reduction effect information after the noise reduction module performs noise reduction control using the at least one target control parameter; update the at least one adjustable parameter based on the actual noise reduction effect information to update the current noise reduction algorithm.

[0107] In one embodiment, the current noise reduction algorithm includes at least one adjustable parameter, and the device further includes a second algorithm update module;

[0108] The second algorithm update module: is configured to update the at least one adjustable parameter according to the monitored environmental data to update the current noise reduction algorithm, where the environmental data is obtained by monitoring at least one of the following environmental items: light intensity item, temperature item, humidity item, smoke concentration item.

[0109] In one embodiment, the device further includes an anomaly detection module;

[0110] The anomaly detection module: is configured to determine whether the noise reduction module is abnormal based on the environmental data and at least one module parameter of the noise reduction module by using a preset anomaly detection algorithm.

[0111] In one embodiment, the current noise reduction algorithm includes at least one adjustable parameter, and the device further includes a step size update module;

[0112] The step size update module: After the noise reduction module performs noise reduction control using the at least one target control parameter, it determines the actual noise reduction effect information; and updates the current step size corresponding to the adjustable parameter based on the actual noise reduction effect information.

[0113] In one embodiment, the current step size is obtained through the following steps: obtaining a mode reference step size and a mode additional step size corresponding to the current working mode, where the mode additional step size is determined based on a preset adjustment coefficient and the intensity of the current sound in the preset frequency range; and obtaining the current step size based on the mode reference step size and the mode additional step size.

[0114] In one embodiment, the current step size is obtained through the following steps: obtaining a preset reference step size; obtaining an environment additional step size corresponding to the current cooking environment; and obtaining the current step size based on the preset reference step size and the environment additional step size.

[0115] It should be noted that the device in the device embodiment and the method embodiment are based on the same inventive concept.

[0116] The embodiment of the present application also provides an oil fume machine, and the oil fume machine includes the above noise reduction device.

[0117] It should be noted that the device in the device embodiment and the method embodiment are based on the same inventive concept.

[0118] The embodiment of the present application also provides an oil fume system, including an oil fume machine and a server side. A communication relationship is established between the server side and the oil fume machine. The server side is used to obtain the current working mode of the oil fume machine, obtain at least one mode parameter corresponding to the current working mode, determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, and send the at least one target control parameter to the oil fume machine so that the noise reduction module of the oil fume machine uses the at least one target control parameter to reduce the noise generated by the oil fume machine.

[0119] The oil fume machine and the server side can be directly or indirectly connected through wired or wireless communication means. The server side can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server can include a network communication unit, a processor, and a memory, etc.

[0120] It should be noted that the system in the system embodiment and the method embodiment are based on the same inventive concept.

[0121] The embodiment of the present application also provides a computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the above method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0122] The embodiment of the present application also provides a computer program product, which includes at least one instruction or at least one program segment, and the at least one instruction or at least one program segment is loaded and executed by a processor to implement the above method.

[0123] The above embodiments only represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A noise reduction method, characterized in that: include: Get the current working mode of the range hood; Acquire at least one mode parameter corresponding to the current working mode, where the at least one mode parameter is determined based on a noise reduction effect parameter and at least one influencing factor, where the influencing factor affects control of sounds within a preset frequency range in the current working mode; At least one target control parameter is determined based on the at least one mode parameter and the current noise reduction algorithm, and the at least one target control parameter is used to be adopted by the noise reduction module of the range hood, and the noise reduction module is used to reduce the noise generated by the range hood.

2. The method according to claim 1, characterized in that The obtaining of at least one mode parameter corresponding to the current working mode includes: When the current working mode is the first type of mode, the intensity of the current sound in the preset frequency range is determined as a first influencing factor, the ideal energy consumption of the noise reduction module is determined as a second influencing factor, and the at least one mode parameter is obtained based on the first influencing factor, the second influencing factor and the first noise reduction effect parameter; In the case where the current working mode is the second type mode, determining the intensity of the current sound in the preset frequency range as a third impact factor, and obtaining the at least one mode parameter based on the third impact factor and the second noise reduction effect parameter; When the current working mode is the third type mode, the intensity of the current sound in the preset frequency range is determined as the fourth influencing factor, the cooking environment information is determined as the fifth influencing factor, the preference information of the cooking operation object is determined as the sixth influencing factor, and the at least one mode parameter is obtained based on the fourth influencing factor, the fifth influencing factor, the sixth influencing factor and the third noise reduction effect parameter.

3. The method according to claim 1, characterized in that The current noise reduction algorithm includes at least one adjustable parameter. After determining at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the method further includes: After the noise reduction module uses the at least one target control parameter to perform noise reduction control, determining actual noise reduction effect information; The at least one adjustable parameter is updated based on the actual noise reduction effect information to update the current noise reduction algorithm.

4. The method according to claim 1 or 3, characterized in that: The current noise reduction algorithm includes at least one adjustable parameter, and the method further includes: The at least one adjustable parameter is updated according to the monitored environmental data to update the current noise reduction algorithm, wherein the environmental data is obtained by monitoring at least one of the following environmental items: light intensity item, temperature item, humidity item, and smoke concentration item.

5. The method according to claim 4, characterized in that The method further comprises: A preset abnormality detection algorithm is used to determine whether the noise reduction module is abnormal based on the environmental data and at least one module parameter of the noise reduction module.

6. The method according to claim 1, characterized in that The current noise reduction algorithm includes at least one adjustable parameter. After determining at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, the method further includes: After the noise reduction module uses the at least one target control parameter to perform noise reduction control, determining actual noise reduction effect information; The current step size corresponding to the adjustable parameter is updated based on the actual noise reduction effect information.

7. The method according to claim 6, characterized in that The current step length is obtained by the following steps: Acquire a mode reference step and a mode additional step corresponding to the current working mode, wherein the mode additional step is determined based on a preset adjustment coefficient and the intensity of the current sound within the preset frequency range; The current step length is obtained based on the mode reference step length and the mode additional step length.

8. The method according to claim 6, characterized in that The current step length is obtained by the following steps: Get the preset benchmark step length; Get the additional step length of the environment corresponding to the current cooking environment; The current step length is obtained based on the preset reference step length and the environmental additional step length.

9. A noise reduction device, characterized in that: include: Working mode acquisition module: used to obtain the current working mode of the range hood; A mode parameter acquisition module: used to acquire at least one mode parameter corresponding to the current working mode, wherein the at least one mode parameter is determined based on a noise reduction effect parameter and at least one influencing factor, wherein the influencing factor affects the control of the sound within a preset frequency range in the current working mode; Control parameter acquisition module: used to determine at least one target control parameter based on the at least one mode parameter and the current noise reduction algorithm, and the at least one target control parameter is used to be adopted by the noise reduction module of the range hood, and the noise reduction module is used to reduce the noise generated by the range hood.

10. A range hood, characterized in that: Comprising the noise reduction device as claimed in claim 9.

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