Range hood and working condition identification method and noise reduction method thereof

Abstract

The invention provides a range hood and a working condition identification method and a noise reduction method.The working condition identification method of the range hood comprises the steps that audio information of the range hood in a plurality of continuous time periods is obtained; performing working condition identification on the plurality of segments of audio information to obtain a plurality of types of working condition classification results; counting the quantity information of each type of working condition classification result, and obtaining the maximum quantity information; and determining working condition information of the range hood based on the maximum quantity information. Audio information collected in real time is classified, the actual working condition information corresponding to the range hood is determined according to the classification result, and the control parameters of the active noise reduction controller are adjusted according to the actual working condition information, so that a good noise reduction effect is achieved.

Description

Technical Field

[0001] This disclosure relates to the field of smart home appliance design, and in particular to a range hood and its operating condition identification method and noise reduction method. Background Technology

[0002] As people's living standards improve and technologies such as the internet, big data, artificial intelligence, and voice interaction become more widespread, traditional lifestyles are gradually changing, and the use of home appliances is increasingly moving towards intelligentization. While bringing more convenience to users, the functions of various home appliances are also becoming more diversified.

[0003] Currently, the application of active noise reduction technology in range hoods is highly dependent on external environmental conditions, such as end resistance, duct size, and kitchen layout. These factors can more or less interfere with the noise generation or propagation path of the range hood. Furthermore, the controller for active noise reduction is strongly correlated with these factors; if external environmental factors change, a fixed controller cannot achieve optimal noise reduction performance. Summary of the Invention

[0004] The technical problem to be solved by this disclosure is to overcome the defect in the prior art that the noise reduction controller cannot achieve the optimal noise reduction effect due to changes in environmental factors, and to provide a range hood and its operating condition identification method and noise reduction method.

[0005] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0006] According to a first aspect of this disclosure, a method for identifying the operating condition of a range hood is provided, the method comprising:

[0007] Acquire audio information of the range hood over several consecutive time periods;

[0008] The operating conditions of several audio segments are identified to obtain several types of operating condition classification results;

[0009] The number of results for each type of working condition classification is counted, and the maximum number of results is obtained.

[0010] Based on the maximum quantity information, the operating condition information of the range hood is determined.

[0011] Optionally, the step of determining the operating condition information of the range hood based on the maximum quantity information includes:

[0012] In response to the existence of a maximum quantity of information greater than or equal to a first threshold, and / or, the operating condition classification result corresponding to the maximum quantity of information appears consecutively in adjacent time periods, then the operating condition classification result corresponding to the maximum quantity of information is the current operating condition information of the range hood.

[0013] If there is no maximum quantity of information greater than the first threshold, and the working condition classification result corresponding to the maximum quantity of information does not appear consecutively in adjacent time periods, then the step of obtaining the audio information of the range hood in several consecutive time periods is re-executed.

[0014] Optionally, the range hood's duct is equipped with several reference microphones, and the step of acquiring audio information of the range hood over several consecutive time periods includes:

[0015] Acquire several segments of audio information from different reference microphones within the same time period;

[0016] The step of performing work condition identification on several audio segments to obtain several work condition classification results includes:

[0017] Set corresponding weight information for different reference microphones located inside the flue;

[0018] Acquire the audio information corresponding to different reference microphones within the same time period;

[0019] The audio information is subjected to working condition identification to obtain a first classification result corresponding to the audio information;

[0020] Based on the weight information and the first classification result information, the working condition classification result is obtained.

[0021] Optionally, the step of setting corresponding weight information for different reference microphones disposed inside the flue includes:

[0022] Accuracy information is calibrated for different reference microphones installed inside the flue using a preset calibration method;

[0023] The weight information corresponding to different reference microphones is calculated based on the accuracy information.

[0024] Optionally, the step of performing condition identification on several segments of the audio information to obtain several results includes:

[0025] Based on a preset algorithm, the different audio information is converted into frequency spectrum information;

[0026] The frequency spectrum information is input into a preset classification model to obtain the operating condition classification results corresponding to different frequency spectrum information.

[0027] According to a second aspect of this disclosure, a method for reducing noise in a range hood is provided, the method comprising:

[0028] The operating condition information of the range hood during the target time period is obtained based on the operating condition identification method of the range hood as described in the first aspect of this disclosure;

[0029] Configure the corresponding noise reduction parameters for the noise reduction controller based on the aforementioned operating condition information;

[0030] The noise reduction process is performed using the configured noise reduction controller.

[0031] According to a third aspect of this disclosure, a range hood operating condition identification system is provided, the range hood operating condition identification system comprising:

[0032] An audio acquisition module is used to acquire audio information of the range hood over several consecutive time periods.

[0033] The working condition classification module is used to identify the working conditions of several audio segments to obtain several types of working condition classification results.

[0034] The quantity confirmation module is used to count the quantity information of each type of working condition classification result and obtain the maximum quantity information;

[0035] The operating condition confirmation module is used to determine the operating condition information of the range hood based on the maximum quantity information.

[0036] Optionally, the operating condition confirmation module is further configured to respond to the existence of a maximum quantity of information greater than or equal to a first threshold, and / or the operating condition classification result corresponding to the maximum quantity of information appears consecutively in adjacent time periods, then the operating condition classification result corresponding to the maximum quantity of information is the current operating condition information of the range hood;

[0037] The operating condition confirmation module is further configured to respond to the fact that there is no maximum quantity information greater than the first threshold, and the operating condition classification result corresponding to the maximum quantity information does not appear consecutively in adjacent time periods, then re-execute the step of obtaining the audio information of the range hood in several consecutive time periods.

[0038] Optionally, the range hood is provided with a number of reference microphones inside the flue, and the audio acquisition module is also used to acquire a number of audio information segments from different reference microphones within the same time period;

[0039] The working condition classification module is also used to set corresponding weight information for different reference microphones set inside the flue.

[0040] Acquire the audio information corresponding to different reference microphones within the same time period;

[0041] The audio information is subjected to working condition identification to obtain a first classification result corresponding to the audio information;

[0042] Based on the weight information and the first classification result information, the working condition classification result is obtained.

[0043] Optionally, the working condition classification module is also used to calibrate the accuracy information of different reference microphones installed inside the flue in a preset calibration method;

[0044] The weight information corresponding to different reference microphones is calculated based on the accuracy information.

[0045] Optionally, the working condition classification module is further configured to convert different audio information into frequency spectrum information based on a preset algorithm;

[0046] The frequency spectrum information is input into a preset classification model to obtain the operating condition classification results corresponding to different frequency spectrum information.

[0047] According to a fourth aspect of this disclosure, a noise reduction system for a range hood is provided, the range hood noise reduction system comprising:

[0048] The operating condition information acquisition module is used to acquire the operating condition information of the range hood within a target time period based on the operating condition identification system of the range hood as described in the third aspect of this disclosure.

[0049] The parameter configuration module is used to configure the corresponding noise reduction parameters for the noise reduction controller according to the operating condition information.

[0050] The noise reduction processing module is used to control the noise reduction process performed by the configured noise reduction controller.

[0051] According to a fifth aspect of this disclosure, a range hood is provided, the range hood including the operating condition identification system of the range hood described in the third aspect of this disclosure, and / or the noise reduction system of the range hood described in the fourth aspect of this disclosure.

[0052] According to a sixth aspect of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the operating condition identification method for a range hood as described in the first aspect of this disclosure, and / or the noise reduction method for a range hood as described in the second aspect of this disclosure.

[0053] According to a seventh aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the operating condition identification method for a range hood as described in the first aspect of this disclosure, and / or the noise reduction method for a range hood as described in the second aspect of this disclosure.

[0054] According to the eighth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the operating condition identification method for a range hood as described in the first aspect of this disclosure, and / or the noise reduction method for a range hood as described in the second aspect of this disclosure.

[0055] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0056] The positive and progressive effects of this disclosure are as follows:

[0057] The operating condition identification method for range hoods provided in this disclosure improves the accuracy of operating condition identification by jointly judging the operating condition results corresponding to multiple audio data without changing the structure of the range hood. The noise reduction method for range hoods provided in this disclosure determines the actual operating condition information of the range hood based on the classification results, and adjusts the control parameters of the active noise reduction controller according to the actual operating condition information to achieve a better noise reduction effect. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating the operating condition identification method for a range hood provided in Embodiment 1 of this disclosure;

[0059] Figure 2 This is a schematic diagram of the process for obtaining the working condition classification result by identifying the working condition in Embodiment 1 of this disclosure;

[0060] Figure 3 This is a flowchart illustrating the noise reduction method for a range hood provided in Embodiment 2 of this disclosure;

[0061] Figure 4 This is a schematic diagram of the operating condition identification system for a range hood provided in Embodiment 3 of this disclosure;

[0062] Figure 5 This is a schematic diagram of the noise reduction system of the range hood provided in Embodiment 4 of this disclosure;

[0063] Figure 6 This is a schematic diagram of the structure of the electronic device provided in Embodiment 6 of this disclosure. Detailed Implementation

[0064] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0065] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0066] Currently, noise reduction for range hoods typically uses solutions such as fxLMS (Filtered-x Least Mean Square, an adaptive filtering algorithm widely used in active noise cancellation systems). The biggest drawbacks are twofold: First, this technology requires significantly more microphones, necessitating the addition of error microphones at the range hood's air outlet (or other audio evaluation areas) to assess the current controller's noise reduction effectiveness and feed it back to the system. Second, it requires extensive and complex calculations to adjust controller parameters in real-time, placing far higher demands on hardware computing power than fixed-controller solutions, thus increasing both cost and development complexity.

[0067] Alternatively, audio information can be classified using machine learning. For example, microphone signals can be fed into a 2D CNN (two-dimensional convolutional neural network), the network outputs the identified scene types, and then the noise reduction controller is determined based on the scene type. The disadvantage is that this classification method requires the scene to be as accurate as possible, and the model's accuracy may need to be above 95%. This will inevitably result in a relatively large model structure, which is not convenient for embedded deployment.

[0068] In view of this, this disclosure provides a range hood and its operating condition identification method and noise reduction method. The method collects and classifies audio information in real time, determines the actual operating condition information of the range hood based on the classification results, and adjusts the control parameters of the active noise reduction controller according to the actual operating condition information to achieve a better noise reduction effect.

[0069] Example 1

[0070] like Figure 1 As shown, this embodiment provides a method for identifying the operating condition of a range hood. The identification method includes:

[0071] S11: Obtain audio information of the range hood over several consecutive time periods;

[0072] S12: Perform working condition identification on several audio segments to obtain several types of working condition classification results;

[0073] S13: Calculate the quantity information of each type of working condition classification result and obtain the maximum quantity information;

[0074] S14: Determine the operating status information of the range hood based on the maximum quantity information.

[0075] The operating condition identification method for range hoods provided in this disclosure improves the accuracy of operating condition identification by jointly judging the operating condition results corresponding to multiple audio data without changing the structure of the range hood.

[0076] Specifically, step S14 includes:

[0077] In response to the existence of a maximum quantity of information greater than or equal to a first threshold, and / or the working condition classification result corresponding to the maximum quantity of information appears consecutively in adjacent time periods, the working condition classification result corresponding to the maximum quantity of information is the current working condition information of the range hood.

[0078] If there is no maximum number of information greater than the first threshold, and the working condition classification result corresponding to the maximum number of information does not appear consecutively in adjacent time periods, then the step of obtaining the audio information of the range hood in several consecutive time periods is re-executed.

[0079] In one specific implementation, it is set to perform k judgments, each judgment requiring 1 second of audio data. When collecting data, the audio data corresponding to n seconds can be collected at once, or the audio data for n seconds can be collected in n separate times. When performing k judgments, it is only necessary to ensure that the audio data corresponding to k seconds selected within the judgment time is continuous data.

[0080] Input the corresponding k audio data into the preset model to obtain k judgment results (i.e., working condition classification results). If the same scene is identified more than or equal to k / 2 times among the k results, then the final classification result is that scene. If the scene is classified less than or equal to k / 2 times, then the scene with the most frequent classification result is extracted, and it is checked whether the result has appeared consecutively in the k judgments. If it has appeared, then the final classification result is that scene. If it has not appeared, then the number of judgments needs to be increased to meet one of the above two judgment conditions until the final classification result is determined.

[0081] The specific judgment conditions in this embodiment are only illustrative examples, and corresponding judgment conditions can be set according to specific working conditions.

[0082] If 6 judgments are made and the judgment result is AAABBC, that is, there are three classification results, namely ABC. The corresponding number of working condition classification results is 3, 2, and 1. At this time, the maximum number of information is 3, and its corresponding working condition classification result is A. The number is greater than or equal to half of the total number of judgment results. Therefore, the current working condition information of the range hood is A.

[0083] If the judgment result is AAABBB, there are two maximum classification results, and both of them appear consecutively. In this case, the number of judgments needs to be increased, such as making a 7th judgment, until the maximum number of information is found. The subsequent judgment results are illustrated in the example above and will not be repeated here.

[0084] In this embodiment, the accuracy of the working condition results corresponding to multiple audio data is improved by jointly judging the working condition results through specific judgment conditions.

[0085] The process of acquiring audio information from the range hood over several consecutive time periods, including the installation of several reference microphones inside the duct, includes:

[0086] Acquire several audio segments from different reference microphones within the same time period;

[0087] like Figure 2 As shown, the steps for performing work condition identification on several audio segments to obtain several work condition classification results include:

[0088] S21: Set corresponding weight information for different reference microphones installed inside the flue;

[0089] S22: Obtain audio information corresponding to different reference microphones within the same time period;

[0090] S23: Perform operating condition identification on the audio information to obtain the first classification result of the corresponding audio information;

[0091] S24: Obtain the working condition classification result based on the weight information and the first classification result information.

[0092] In one specific implementation, when there are multiple reference microphones, it is necessary to process the audio data of multiple different reference microphones. For example, if there are N microphones, N classification results will be output accordingly.

[0093] If the preset number of classification and recognition attempts is k, then the current operating status of the range hood will be determined through k recognition attempts. K recognition attempts mean that at least k microphone audio data points of a preset audio length need to be collected. For specific data collection and recognition details, please refer to the example above; further explanation is not provided here.

[0094] The above k*N (N is the number of microphones) audio data are input into a preset classification model (such as a neural network) to obtain k*N classification results. At this point, it is necessary to process the k judgment results corresponding to different reference microphones.

[0095] Taking the determination result of N reference microphones in the first of k trials as an example:

[0096] The judgment results corresponding to the N reference microphones are a11, a12, a13...a1N, and the final classification judgment result is:

[0097] classification1=a11*weight1+a12*weight2+a13*weight……+a1N*weightN

[0098] Where weightN is the weight of different reference microphones when making classification decisions, and the constraint is weight1+weight2+……+weightN=1.

[0099] Based on the above calculation method, the environmental determination result of the first N reference microphones is obtained as classification1, and so on, the microphone environmental determination result of k times is calculated.

[0100] By setting a joint judgment strategy for multiple reference microphones and assigning corresponding weights to different reference microphones, the accuracy of judging the working condition results is further improved.

[0101] The step of setting corresponding weight information for different reference microphones installed inside the flue includes:

[0102] Accuracy information is calibrated for different reference microphones installed inside the flue using a preset calibration method;

[0103] The weight information corresponding to different reference microphones is calculated based on the accuracy information.

[0104] For example, the classification accuracy can be determined based on the classification accuracy of a reference microphone during normal training. That is, once the classification model and parameters are determined, the accuracy is verified using data from N microphones: the accuracy of microphone 1 is accuracy1, the accuracy of microphone 2 is accuracy2, ..., the accuracy of microphone N is accuracyN. Then, the weights can be calculated based on these accuracies.

[0105] Weight of microphone i

[0106] In special cases, if the accuracy of a reference microphone is less than 50%, the weight of that microphone can be set to 0 directly, because if the accuracy is too low, it will affect the final result.

[0107] By calibrating the accuracy of the reference microphone and setting corresponding weights, the accuracy of the judgment of working conditions can be further improved.

[0108] In this embodiment, the steps of performing condition identification on several audio segments to obtain several results include: converting different audio segments into frequency spectrum information based on a preset algorithm;

[0109] Input the frequency spectrum information into the preset classification model to obtain the working condition classification results corresponding to different frequency spectrum information.

[0110] In one specific implementation, audio data from the range hood during operation is collected. The collected audio data is then truncated to a predetermined length according to preset requirements. The truncated audio data undergoes time-frequency conversion processing (e.g., using Fast Fourier Transform) to obtain corresponding frequency domain data. Based on this, the frequency domain data is converted into a Mel spectrogram (where the horizontal axis represents time and the vertical axis represents frequency; classification is then performed based on the corresponding frequency information, such as setting corresponding operating conditions for different frequency ranges). The Mel spectrogram is then input into a preset classification model (e.g., a neural network model) to obtain the classification result for the corresponding audio data.

[0111] By utilizing a pre-defined classification model, the operating condition classification results corresponding to audio information can be quickly obtained, thereby improving the efficiency of operating condition classification.

[0112] Example 2

[0113] like Figure 3 As shown in this embodiment, a noise reduction method for a range hood includes:

[0114] S31: A method for identifying the operating conditions of a range hood is used to obtain the operating condition information of the range hood during a target time period;

[0115] S32: Configure the corresponding noise reduction parameters for the noise reduction controller based on the operating condition information;

[0116] S33: The noise reduction process is performed using the configured noise reduction controller.

[0117] The noise reduction method for range hoods provided in this disclosure determines the actual operating condition information of the range hood based on the classification results, and adjusts the control parameters of the active noise reduction controller according to the actual operating condition information to achieve a better noise reduction effect.

[0118] Example 3

[0119] Corresponding to the aforementioned embodiments of the operating condition identification method for range hoods, this disclosure also provides embodiments of the operating condition identification system for range hoods.

[0120] like Figure 4 As shown, this embodiment provides a range hood operating condition identification system, which includes:

[0121] The audio acquisition module 101 is used to acquire audio information of the range hood over several consecutive time periods.

[0122] The working condition classification module 102 is used to identify the working conditions of several audio segments in order to obtain several types of working condition classification results.

[0123] The quantity confirmation module 103 is used to count the quantity information of each type of working condition classification result and obtain the maximum quantity information;

[0124] The operating condition confirmation module 104 is used to determine the operating condition information of the range hood based on the maximum quantity information.

[0125] In this embodiment, the working condition confirmation module is also used to respond to the existence of a maximum quantity of information greater than or equal to a first threshold, and / or the working condition classification result corresponding to the maximum quantity of information appears consecutively in adjacent time periods, then the working condition classification result corresponding to the maximum quantity of information is the current working condition information of the range hood.

[0126] The operating condition confirmation module 104 is also used to respond to the fact that there is no maximum quantity information greater than the first threshold, and the operating condition classification result corresponding to the maximum quantity information does not appear consecutively in adjacent time periods, then re-execute the step of obtaining the audio information of the range hood in several consecutive time periods.

[0127] In this embodiment, the range hood is equipped with several reference microphones inside the flue, and the audio acquisition module 101 is also used to acquire several audio segments from different reference microphones within the same time period.

[0128] The working condition classification module 102 is also used to set corresponding weight information for different reference microphones set inside the flue;

[0129] Acquire audio information corresponding to different reference microphones within the same time period;

[0130] The audio information is subjected to working condition identification in order to obtain the first classification result of the corresponding audio information;

[0131] Based on the weight information and the first classification result information, the working condition classification result is obtained.

[0132] In this embodiment, the working condition classification module 102 is also used to calibrate the accuracy information of different reference microphones set inside the flue in a preset calibration method;

[0133] The weight information corresponding to different reference microphones is calculated based on the accuracy information.

[0134] In this embodiment, the working condition classification module 102 is also used to convert different audio information into frequency spectrum information based on a preset algorithm;

[0135] Input the frequency spectrum information into the preset classification model to obtain the working condition classification results corresponding to different frequency spectrum information.

[0136] The operating condition recognition system for range hoods provided in this disclosure improves the accuracy of operating condition recognition by jointly judging the operating condition results corresponding to multiple audio data without changing the structure of the range hood.

[0137] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0138] Example 4

[0139] Corresponding to the aforementioned embodiments of noise reduction methods for range hoods, this disclosure also provides embodiments of noise reduction systems for range hoods.

[0140] like Figure 5 As shown, this embodiment provides a noise reduction system for a range hood, which includes:

[0141] The operating condition information acquisition module 201 is used to acquire the operating condition information of the range hood within a target time period based on the operating condition identification system of the range hood as described in the third aspect of this disclosure.

[0142] The parameter configuration module 202 is used to configure the corresponding noise reduction parameters for the noise reduction controller according to the operating condition information.

[0143] The noise reduction processing module 203 is used to control the noise reduction processing performed by the configured noise reduction controller.

[0144] The noise reduction system for range hoods provided in this disclosure determines the actual operating conditions of the range hood based on the classification results, and adjusts the control parameters of the active noise reduction controller according to the actual operating conditions to achieve a better noise reduction effect.

[0145] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0146] Example 5

[0147] This embodiment provides a range hood, which includes the working condition recognition system in embodiment 3 and / or the noise reduction system in embodiment 4.

[0148] The range hood provided in this embodiment can identify the operating conditions by jointly judging the operating conditions corresponding to multiple audio data without changing the structure of the range hood, thereby improving the accuracy of the operating condition recognition. At the same time, it can also determine the actual operating condition information of the range hood based on the classification results, and adjust the control parameters of the active noise reduction controller according to the actual operating condition information to achieve a better noise reduction effect and improve the overall product performance of the range hood.

[0149] In this embodiment, the range hood can be controlled by a voice module, which is equipped with a controller, a voice receiving module, and a voice parsing module. The voice receiving module receives user commands, and the voice parsing module parses the commands. Based on the parsed commands, the controller controls the smart home appliance to perform corresponding operations, thereby realizing intelligent control of the smart home appliance and improving the user experience.

[0150] The range hood in this embodiment can also adopt other intelligent interactive functions, such as gesture interaction and fingerprint recognition. The specific settings or adjustments can be made according to actual needs to further improve the intelligence level of smart home appliances and bring a better user experience.

[0151] Example 6

[0152] like Figure 6 As shown, Figure 6 This is a schematic diagram of the corresponding electronic device provided in Embodiment 6 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the operating condition identification method for the range hood provided in Embodiment 1 above, and / or the noise reduction method for the range hood provided in Embodiment 2. Figure 6 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0153] like Figure 6As shown, the electronic device 30 can be represented in the form of a general computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0154] Bus 33 includes a data bus, an address bus, and a control bus.

[0155] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0156] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0157] The processor 31 performs various functional applications and data processing, such as the methods described in the above embodiments of this disclosure, by running computer programs stored in the memory 32.

[0158] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 6 As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0159] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0160] Example 7

[0161] This disclosure also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the range hood operating condition identification method provided in Embodiment 1 above, and / or the range hood noise reduction method provided in Embodiment 2.

[0162] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0163] Example 8

[0164] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the range hood operating condition identification method provided in Embodiment 1 above, and / or the range hood noise reduction method provided in Embodiment 2.

[0165] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0166] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method for identifying the operating condition of a range hood, characterized in that, The identification method includes: Acquire audio information of the range hood over several consecutive time periods; The operating conditions of several audio segments are identified to obtain several types of operating condition classification results; The number of results for each type of working condition classification is counted, and the maximum number of results is obtained. Based on the maximum quantity information, the operating condition information of the range hood is determined.

2. The method for identifying the operating condition of a range hood according to claim 1, characterized in that, The step of determining the operating condition information of the range hood based on the maximum quantity information includes: In response to the existence of a maximum quantity of information greater than or equal to a first threshold, and / or, the operating condition classification result corresponding to the maximum quantity of information appears consecutively in adjacent time periods, then the operating condition classification result corresponding to the maximum quantity of information is the current operating condition information of the range hood. If there is no maximum quantity of information greater than the first threshold, and the working condition classification result corresponding to the maximum quantity of information does not appear consecutively in adjacent time periods, then the step of obtaining the audio information of the range hood in several consecutive time periods is re-executed.

3. The method for identifying the operating condition of a range hood according to claim 1, characterized in that, The range hood has several reference microphones installed inside its duct. The step of acquiring audio information of the range hood over several consecutive time periods includes: Acquire several segments of audio information from different reference microphones within the same time period; The step of performing work condition identification on several audio segments to obtain several work condition classification results includes: Set corresponding weight information for different reference microphones located inside the flue; Acquire the audio information corresponding to different reference microphones within the same time period; The audio information is subjected to working condition identification to obtain a first classification result corresponding to the audio information; Based on the weight information and the first classification result information, the working condition classification result is obtained.

4. The method for identifying the operating condition of a range hood according to claim 3, characterized in that, The step of setting corresponding weight information for different reference microphones installed inside the flue includes: Accuracy information is calibrated for different reference microphones installed inside the flue using a preset calibration method; The weight information corresponding to different reference microphones is calculated based on the accuracy information.

5. The method for identifying the operating condition of a range hood according to any one of claims 1-4, characterized in that, The step of performing condition identification on several audio segments to obtain several results includes: Based on a preset algorithm, the different audio information is converted into frequency spectrum information; The frequency spectrum information is input into a preset classification model to obtain the operating condition classification results corresponding to different frequency spectrum information.

6. A method for reducing noise in a range hood, characterized in that, The noise reduction method includes: The operating condition identification method for a range hood as described in any one of claims 1-5 is used to obtain the operating condition information of the range hood during a target time period; Configure the corresponding noise reduction parameters for the noise reduction controller based on the aforementioned operating condition information; The noise reduction process is performed using the configured noise reduction controller.

7. A working condition identification system for a range hood, characterized in that, The range hood operating condition identification system includes: An audio acquisition module is used to acquire audio information of the range hood over several consecutive time periods. The working condition classification module is used to identify the working conditions of several audio segments to obtain several types of working condition classification results. The quantity confirmation module is used to count the quantity information of each type of working condition classification result and obtain the maximum quantity information; The operating condition confirmation module is used to determine the operating condition information of the range hood based on the maximum quantity information.

8. A noise reduction system for a range hood, characterized in that, The range hood noise reduction system includes: The operating condition information acquisition module is used to acquire the operating condition information of the range hood within a target time period based on the operating condition identification system of the range hood as described in 7. The parameter configuration module is used to configure the corresponding noise reduction parameters for the noise reduction controller according to the operating condition information. The noise reduction processing module is used to control the noise reduction process performed by the configured noise reduction controller.

9. A range hood, characterized in that, The range hood includes the operating condition identification system of the range hood as described in claim 7, and / or the noise reduction system of the range hood as described in claim 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the operating condition identification method for the range hood according to any one of claims 1-5, and / or the noise reduction method for the range hood according to claim 6.

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