Air conditioner control method, control device and air conditioner

Abstract

The application discloses an air conditioner control method, a control device and an air conditioner. The control method comprises the following steps: obtaining an original sound signal, and separating a target noise signal generated by a fan of the air conditioner from the original sound signal; and controlling a running speed of the fan of the air conditioner based on the target noise signal. The technical scheme provided by the application can improve the control accuracy for the noise of the air conditioner.

Description

Technical Field

[0001] This application belongs to the field of air conditioning control technology, and in particular relates to an air conditioning equipment control method, control device and air conditioning equipment. Background Technology

[0002] Wind noise control is a crucial aspect of improving the performance of air conditioning equipment. Currently, wind noise control in air conditioning equipment typically involves physically collecting sound signals using noise sensors and adjusting the equipment's noise level based on the signal strength. However, the sound signals directly collected by noise sensors cannot fully and accurately reflect the wind noise situation of the air conditioning unit, leading to inaccurate wind noise control in practice. Therefore, improving the accuracy of noise control for air conditioning equipment is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The embodiments of this application provide an air conditioning equipment control method, control device, and air conditioning equipment, which can at least improve the accuracy of noise control for air conditioning equipment to a certain extent.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to a first aspect of the present application, an air conditioning equipment control method is provided, the method comprising: acquiring an original sound signal and separating a target noise signal generated by the fan of the air conditioning equipment from the original sound signal; and controlling the operating speed of the fan of the air conditioning equipment based on the target noise signal.

[0006] In some embodiments of this application, based on the foregoing scheme, separating the target noise signal generated by the fan of the air conditioning equipment from the original sound signal includes: identifying non-target noise signals in the original sound signal; filtering the non-target noise signals in the original sound signal to obtain the target noise signal.

[0007] In some embodiments of this application, based on the foregoing scheme, filtering the non-target noise signals in the original sound signal to obtain the target noise signal includes: filtering the non-target noise signals in the original sound signal through a pre-built first machine learning model or digital filtering model to obtain the target noise signal.

[0008] In some embodiments of this application, based on the foregoing scheme, controlling the fan speed of the air conditioning equipment based on the target noise signal includes: determining the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and controlling the fan speed of the air conditioning equipment based on the noise intensity.

[0009] This application embodiment can effectively distinguish interference signals in the original sound signal by separating the target noise signal generated by the fan of the air conditioning equipment from the original sound signal, thereby preventing the mis-collection and mis-identification of non-noise signals as noise signals. Based on the target noise signal separated from the original sound signal, the operating speed of the fan of the air conditioning equipment can be controlled, which can improve the accuracy of noise control of the air conditioning equipment in actual process.

[0010] According to a second aspect of the present application, an air conditioning equipment control method is provided, the method comprising: acquiring a target noise signal generated by a fan of the air conditioning equipment; determining a noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and controlling the operating speed of the fan of the air conditioning equipment based on the noise intensity.

[0011] In some embodiments of this application, based on the foregoing scheme, determining the noise intensity corresponding to the target noise signal by using a pre-trained second machine learning model includes: determining the spectral information of the target noise signal; and determining the noise intensity corresponding to the target noise signal by using a pre-trained second machine learning model based on the spectral information.

[0012] In some embodiments of this application, based on the foregoing scheme, before determining the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model, the method further includes: acquiring the spectral information of the fan of the air conditioning equipment under various airflow conditions; and training the second machine learning model based on the spectral information of the fan under various airflow conditions.

[0013] In some embodiments of this application, based on the foregoing scheme, controlling the fan speed of the air conditioning equipment based on the noise intensity includes: obtaining a noise intensity tolerance value and calculating the noise difference between the noise intensity and the noise intensity tolerance value; and controlling the fan speed of the air conditioning equipment based on the noise difference.

[0014] In some embodiments of this application, based on the foregoing scheme, controlling the fan speed of the air conditioning equipment based on the noise difference includes: when the absolute value of the noise difference is greater than a predetermined threshold, adjusting the fan speed of the air conditioning equipment stepwise according to a predetermined adjustment granularity until the absolute value of the noise difference is less than or equal to the predetermined threshold.

[0015] In some embodiments of this application, based on the aforementioned scheme, controlling the fan speed of the air conditioning equipment based on the noise difference includes: determining the adjustment range for the fan speed by looking up a table based on the noise difference; and adjusting the fan speed of the air conditioning equipment according to the adjustment range.

[0016] This application embodiment determines the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model. On the one hand, this improves the accuracy and precision of noise intensity determination. On the other hand, since the second machine learning model is obtained through pre-training, there is no need to manually build a mathematical model. Therefore, it is not limited to a specific noise intensity recognition scenario, making the obtained second machine learning model highly portable and greatly saving on-site and technical manpower costs.

[0017] According to a third aspect of the embodiments of this application, an air conditioning equipment control device is provided, the device comprising: a first acquisition unit, configured to acquire an original sound signal and separate a target noise signal generated by the fan of the air conditioning equipment from the original sound signal; and a first control unit, configured to control the operating speed of the fan of the air conditioning equipment based on the target noise signal.

[0018] According to a fourth aspect of the present application, an air conditioning equipment control device is provided, the device comprising: a second acquisition unit for acquiring a target noise signal generated by a fan of the air conditioning equipment; a determination unit for determining a noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and a second control unit for controlling the operating speed of the fan of the air conditioning equipment based on the noise intensity.

[0019] According to a fifth aspect of the embodiments of this application, an air conditioning device is provided, the air conditioning device including one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to implement the method described in any embodiment of the first aspect above, or to implement the method described in any embodiment of the second aspect above.

[0020] The beneficial effects of the embodiments of the third to fifth aspects described above can be referred to the beneficial effects of the first and second aspects and the embodiments of the first and second aspects described above, and will not be repeated here.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0023] Figure 1 A schematic diagram of the air conditioning equipment in an embodiment of this application is shown;

[0024] Figure 2 A flowchart of the air conditioning equipment control method of the first aspect of the present application is shown;

[0025] Figure 3 The spectrum of a target noise signal in an embodiment of this application is shown;

[0026] Figure 4 A flowchart of the air conditioning equipment control method of the second aspect of the present application is shown;

[0027] Figure 5 A schematic diagram of a scenario illustrating the air conditioning equipment control method in an embodiment of this application is shown;

[0028] Figure 6 A block diagram of an air conditioning equipment control device according to a third aspect of an embodiment of this application is shown;

[0029] Figure 7 A block diagram of an air conditioning equipment control device according to a fourth aspect of an embodiment of this application is shown;

[0030] Figure 8 A schematic diagram of the structure of the air conditioning device according to the fifth aspect of this application is shown. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0033] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0035] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0036] Figure 1 A schematic diagram of the structure of an air conditioning device according to an embodiment of this application is shown. The air conditioning device 100 may include a memory 101, a processor 102, a sound collector 103, and a fan 104.

[0037] The memory 101 is used to store computer program instructions for executing the air conditioning equipment control method of this application, the sound collector 103 is used to collect sound signals around the air conditioning equipment, and the processor 102 is used to execute the computer program instructions for the air conditioning equipment control method of this application according to the sound signals collected by the sound collector 103, so as to control the operating speed of the fan 104 and thereby control the noise generated by the operation of the fan.

[0038] In this application, the fan of an air conditioning unit is generally installed in the indoor unit of the air conditioning unit. Its main function is to bring the cold or hot air generated in the compressor into the room in the form of air delivery, thereby regulating the indoor temperature. However, the operation of the fan will generate noise in the room, and the faster the fan operates, the greater the noise generated. Therefore, this application provides an air conditioning unit control method to accurately control the indoor noise generated by the air conditioning unit.

[0039] Based on the above description, in a first aspect of the embodiments of this application, the following will be combined with Figure 2 The air conditioning equipment control method provided in the embodiments of this application will be described in detail as follows:

[0040] See Figure 2 The flowchart illustrates an air conditioning equipment control method according to a first aspect of this application. This step can be executed by an air conditioning equipment control device, which can be located as follows: Figure 1 The processor 102 shown may also be the same processor 102. The control method may include steps 210 to 230:

[0041] Step 210: Acquire the original sound signal and separate the target noise signal generated by the fan of the air conditioning equipment from the original sound signal.

[0042] Step 230: Based on the target noise signal, control the fan speed of the air conditioning equipment.

[0043] In this application, raw sound signals can be collected using a sound acquisition device within the air conditioning unit, for example, Figure 1 The sound collector 103 shown is shown.

[0044] In this application, in addition to the sound signal generated by the operation of the fan in the air conditioning unit, other sound signals may also exist indoors, such as human voices, music playback, and sounds coming from outside. Therefore, the raw sound signal collected by the sound acquisition device may contain other sound signals besides those generated by the fan. Based on this, the target noise signal generated by the fan of the air conditioning unit can be separated from the raw sound signal first, and then the fan speed of the air conditioning unit can be controlled based on the target noise signal.

[0045] In such Figure 2 In one embodiment of step 210 shown, separating the target noise signal generated by the fan of the air conditioning equipment from the original sound signal can be achieved by performing the following steps 211 to 212:

[0046] Step 211: Identify non-target noise signals in the original sound signal.

[0047] Step 212: Filter the non-target noise signals in the original sound signal to obtain the target noise signal.

[0048] In this embodiment, before identifying non-target noise signals in the original sound signal, it is necessary to acquire and learn the sound characteristics of the target noise signal in advance, such as the frequency characteristics, amplitude characteristics, etc. of the target noise signal. Then, sound signals that conform to the sound characteristics of the target noise signal are identified as target noise signals, and sound signals that do not conform to the sound characteristics of the target noise signal are identified as non-target noise signals.

[0049] In this application, the target noise signal needs to be acquired before acquiring the acoustic characteristics of the target noise signal.

[0050] It should be noted that the acquisition of target noise signals must be carried out in an environment free from interference from other noise signals.

[0051] It should also be noted that during the process of collecting the target noise signal, the relative positions of the sound acquisition device and the air conditioning equipment are consistent with the relative positions of the sound acquisition device and the air conditioning equipment in actual application. For example, the sound acquisition device is installed in a specific location of the air conditioning equipment.

[0052] The advantage of the above approach is that, since the location and intensity information of the target noise signal are relatively stable, without fluctuations in intensity, and without acquisition errors caused by changes in the relative positions of the sound acquisition device and the air conditioning equipment, it is reasonable to collect target noise signals over a period of time to represent the actual noise output of the air conditioning equipment, thereby improving the accuracy of the sound characteristics of the acquired target noise signal. For example... Figure 3 The image shows a spectrum of a target noise signal in an embodiment of this application. The acoustic characteristics of the target noise signal can be seen from the spectrum.

[0053] In this application, after identifying non-target noise signals in the original sound signal, the non-target noise signals in the original sound signal can be filtered to obtain the target noise signal.

[0054] In one embodiment of step 212, the non-target noise signal in the original sound signal may be filtered by a pre-built first machine learning model to obtain the target noise signal.

[0055] Machine learning is the science of artificial intelligence. The main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms through experience learning. Specifically, it uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. It enables machines to have the functions of perception, reasoning, and decision-making.

[0056] In this embodiment, the first machine learning model can be trained with the original sound signal including the target noise signal and the non-target noise signal, so that it can distinguish the target noise signal and the non-target noise signal in the original sound signal and filter the non-target noise signal.

[0057] In this embodiment, the first machine learning model can be constructed based on any one of the following algorithms: decision tree algorithm, Naive Bayes algorithm, support vector machine algorithm, random forest algorithm, association rule algorithm, artificial neural network algorithm, and deep learning algorithm.

[0058] In another embodiment of step 212, the non-target noise signal in the original sound signal can be filtered by a pre-built digital filtering model to obtain the target noise signal.

[0059] A digital filtering model is an algorithm composed of digital multipliers, adders, and delay units. Its function is to process the digital code of the input discrete signal to change the signal spectrum. Among them, the method of filtering non-noise signals by digital filtering models is to use a digital computer to process the non-noise signals. The processing is to perform calculations according to a pre-programmed program.

[0060] In this embodiment, the digital filtering model can be constructed based on the Discrete Fourier Transform (DFT) algorithm or the Fast Fourier Transform (FFT) algorithm. The FFT algorithm is a fast algorithm for implementing the Discrete Fourier Transform, which uses the complex form of the Discrete Fourier Transform to calculate the real form of the Discrete Fourier Transform.

[0061] Continue to refer to Figure 2 In one embodiment of step 230, controlling the fan speed of the air conditioning equipment based on the target noise signal can be achieved by performing the following steps 231 to 232:

[0062] Step 231: Determine the noise intensity corresponding to the target noise signal using a pre-trained second machine learning model.

[0063] Step 232: Based on the noise intensity, control the fan speed of the air conditioning equipment.

[0064] In some embodiments of this application, by separating the target noise signal generated by the fan of the air conditioning equipment from the original sound signal, the interference signal in the original sound signal can be effectively distinguished, thereby preventing the problem of misacquisition and misidentification caused by treating non-noise signals as noise signals. Based on the target noise signal separated from the original sound signal, the fan speed of the air conditioning equipment can be controlled, which can improve the accuracy of noise control of the air conditioning equipment in actual process.

[0065] In a second aspect of the embodiments of this application, it will be combined with Figure 4 Another air conditioning equipment control method provided in the embodiments of this application will be described in detail:

[0066] See Figure 4 The flowchart illustrates an air conditioning equipment control method according to the second aspect of an embodiment of this application. This step can be executed by an air conditioning equipment control device, which can be located as follows: Figure 1 The processor 102 shown may also be the same processor 102. The control method may include steps 250 to 290:

[0067] Step 250: Obtain the target noise signal generated by the fan of the air conditioning equipment.

[0068] Step 270: Determine the noise intensity corresponding to the target noise signal using a pre-trained second machine learning model.

[0069] Step 290: Based on the noise intensity, control the operating speed of the fan in the air conditioning equipment.

[0070] In this application, the target noise signal generated by the fan of the air conditioning equipment can be obtained according to the technical solution proposed in the first aspect of the embodiment of this application, that is, firstly, the original sound signal is obtained by the sound acquisition device, and then the target noise signal generated by the fan of the air conditioning equipment is separated from the original sound signal.

[0071] In such Figure 4 In one embodiment of step 270 shown, determining the noise intensity corresponding to the target noise signal using a pre-trained second machine learning model can be achieved by performing the following steps 271 to 272:

[0072] Step 271: Determine the spectral information of the target noise signal.

[0073] Step 272: Based on the spectrum information, determine the noise intensity corresponding to the target noise signal using a pre-trained second machine learning model.

[0074] In this application, the spectrum is short for frequency spectral density, which is the distribution curve of frequency. Complex oscillations are decomposed into harmonic oscillations with different amplitudes and frequencies, and the graph of the amplitudes of these harmonic oscillations arranged by frequency is called the spectrum.

[0075] In this embodiment, before determining the noise intensity corresponding to the target noise signal using a pre-trained second machine learning model, steps 2721 to 2722 can also be performed as follows:

[0076] Step 2721: Obtain the spectrum information of the air conditioner's fan under various airflow conditions.

[0077] Step 2722: Train the second machine learning model based on the spectral information of the fan under various airflow conditions.

[0078] In this application, the air volume control logic of the fan in the air conditioning equipment can be a discrete control logic with gear information or a continuous control logic without gear information.

[0079] When the fan control logic in an air conditioning unit is discrete, continuous acquisition of the noise signal's spectrum information is not necessary. Instead, the spectrum information of the noise signal can be acquired for each fan speed setting (i.e., each fan speed condition). For example, if the fan in an air conditioning unit has three fan speed settings (A, B, and C), then the spectrum information of the noise signal for each of the three speed settings (A, B, and C) can be acquired.

[0080] When the air volume control logic of the fan in the air conditioning equipment is a continuous control logic, the noise accuracy information can be integrated and weighted to meet the acquisition requirements. That is, the noise information is integrated over a continuous period of time, and then the actual magnitude of the noise information in that continuous period of time is calculated based on the weighted value, as shown in the following formula (1):

[0081]

[0082] Where qi represents the unit weight; f(t) represents the time weight; and F(jw) represents the frequency domain point amplitude value.

[0083] For example, if the air volume of the fan in the air conditioning equipment is distributed in the range of 1 to 100, the noise information with an air volume distribution of 28 to 29 over a period of time can be integrated, and then the actual noise information over that continuous period of time can be calculated based on the weighted value.

[0084] In one embodiment of step 2722, the second machine learning model can be trained in an unsupervised manner based on the spectral information of the fan under various airflow conditions. That is, the spectral information of the noise signal under each airflow condition is input into the second machine learning model, the second machine learning model learns the spectral information of the noise signal under that airflow condition, and then outputs a noise intensity.

[0085] In another embodiment of step 2722, the second machine learning model can be trained in a supervised manner based on the spectral information of the fan under various airflow conditions. When training the second machine learning model in a supervised manner, it is also necessary to obtain the actual noise intensity corresponding to the spectral information of the noise signal under each airflow condition beforehand. Then, based on the spectral information of the fan under each airflow condition and the actual noise intensity under each airflow condition, the second machine learning model is trained so that the second machine learning model can determine the noise intensity corresponding to the target noise signal according to the spectral information of the target noise signal.

[0086] Therefore, the second machine learning model trained in the above manner can determine the noise intensity corresponding to the target noise signal. Its advantages are twofold: firstly, determining the noise intensity corresponding to the target noise signal using the trained second machine learning model improves the accuracy and precision of noise intensity determination; secondly, the trained second machine learning model eliminates the need for manually building mathematical models, thus not being limited to a specific noise intensity recognition scenario, resulting in strong portability and significantly saving on-site and technical manpower costs.

[0087] Continue to refer to Figure 4 In one embodiment of step 290, controlling the fan speed of the air conditioning equipment based on the noise intensity can be achieved by performing the following steps 291 to 292:

[0088] Step 291: Obtain the noise intensity tolerance value and calculate the noise difference between the noise intensity and the noise intensity tolerance value.

[0089] Step 292: Based on the noise difference, control the fan speed of the air conditioning equipment.

[0090] In this application, the noise intensity tolerance value refers to the maximum noise intensity allowed to be generated indoors by the fan in the air conditioning equipment. It can be set by the user according to actual needs; for example, the user can set it via a remote control or a mobile app.

[0091] In this embodiment, controlling the fan speed of the air conditioning equipment based on the noise difference can include at least two of the following situations:

[0092] In one case, when the noise difference is greater than 0, the operating speed of the fan in the air conditioning equipment can be reduced to decrease the noise intensity generated by the fan in the air conditioning equipment.

[0093] In another scenario, when the noise difference is less than 0, the fan speed of the air conditioning unit can be increased to increase the output air volume of the air conditioning unit, provided that the noise intensity does not exceed the noise intensity tolerance value.

[0094] Furthermore, in one embodiment of step 292 above, controlling the fan speed of the air conditioning equipment based on the noise difference can be performed as follows:

[0095] When the absolute value of the noise difference is greater than a predetermined threshold, the fan speed of the air conditioning equipment is adjusted stepwise according to a predetermined adjustment granularity until the absolute value of the noise difference is less than or equal to the predetermined threshold.

[0096] In this embodiment, the incremental difference ΔQ can be calculated from the determined noise intensity T0. By dividing the incremental difference ΔQ according to the adjustment granularity U, the step adjustment rhythm is further subdivided to finally meet the target noise requirements, as shown in the following formula (2):

[0097]

[0098] Where T0 represents noise intensity; ΔQ represents incremental difference; U represents adjustment granularity; and T represents noise intensity tolerance.

[0099] Specifically, for example, the predetermined threshold is set to 1 dB, the noise difference is 10 dB, and the predetermined adjustment granularity is 2 fan speed units. In this case, the absolute value of the noise difference is 10 dB, which is greater than the predetermined threshold of 1 dB. Therefore, the air conditioner's fan can be controlled to decrease by 2 fan speed units first, and then a new noise difference can be calculated. If the absolute value of the new noise difference is still greater than the predetermined threshold, the air conditioner's fan can be controlled to decrease by 2 fan speed units again. This process is repeated to control the air conditioner's fan speed step by step according to the adjustment granularity of 2 fan speed units, until the absolute value of the noise difference is less than or equal to 1 dB.

[0100] In this embodiment, the fan speed of the air conditioning equipment is adjusted stepwise according to a predetermined adjustment granularity. This has the advantage of preventing the fan speed from approaching a stop due to excessive adjustment or the fan speed from being too fast, which could damage the fan and generate excessive noise, thereby improving the noise control accuracy.

[0101] In another embodiment of step 292 above, controlling the fan speed of the air conditioning equipment based on the noise difference can also be achieved by performing the following steps 2921 to 2922:

[0102] Step 2921: Based on the noise difference, determine the adjustment range for the fan operating speed by looking up a table.

[0103] Step 2922: Adjust the fan speed of the air conditioning equipment according to the adjustment range.

[0104] In this embodiment, the correspondence between the noise difference and the fan speed adjustment range can be determined in advance through experiments. For example, if the noise difference is 10dB, the fan speed adjustment range is 8 fan speed units; if the noise difference is 5dB, the fan speed adjustment range is 3 fan speed units.

[0105] In this embodiment, by adjusting the fan speed of the air conditioning equipment according to the adjustment range, the advantage is that the fan speed of the air conditioning equipment can be quickly adjusted, thereby improving the control efficiency of the fan noise of the air conditioning equipment.

[0106] To enable those skilled in the art to better understand the embodiments in this application, the following will be combined with Figure 5 Please provide an explanation.

[0107] See Figure 5 The diagram shows a scenario of the air conditioning equipment control method in an embodiment of this application.

[0108] like Figure 5 As shown, firstly, the fan 501 generates a noise signal 502 during operation. The voice recognition system 503 acquires the noise signal 502, filters it, and determines the noise intensity corresponding to the noise signal 502. The processor 505 determines control data based on the noise intensity and the noise intensity tolerance value set by the user through a mobile APP, and feeds the control data back to the fan 501. Under the control of the control data, the fan 501 adjusts its own fan speed and generates a new noise signal 502. Subsequently, the fan speed of the air conditioning equipment is controlled based on the new noise signal 502.

[0109] In some embodiments of this application, the technical solutions provide that the noise intensity corresponding to the target noise signal is determined by a pre-trained second machine learning model. On the one hand, this improves the accuracy and precision of determining the noise intensity. On the other hand, since the second machine learning model is obtained through pre-training, there is no need to manually build a mathematical model. Therefore, it is not limited to a specific noise intensity recognition scenario, making the obtained second machine learning model highly portable and greatly saving on-site and technical manpower costs.

[0110] The following describes an embodiment of the apparatus described in this application, which can be used to execute the air conditioning equipment control method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the air conditioning equipment control method described above.

[0111] In a third aspect of the embodiments of this application, it will be combined with Figure 6 The following describes an air conditioning equipment control device provided in an embodiment of this application:

[0112] See Figure 6 The diagram shows a block diagram of an air conditioning equipment control device according to a third aspect of an embodiment of this application.

[0113] like Figure 6 As shown, the air conditioning equipment control device 600 according to an embodiment of this application includes: a first acquisition unit 601 and a first control unit 602.

[0114] The first acquisition unit 601 is used to acquire the original sound signal and separate the target noise signal generated by the fan of the air conditioning equipment from the original sound signal; the first control unit 602 is used to control the operating speed of the fan of the air conditioning equipment based on the target noise signal.

[0115] In some embodiments of this application, based on the foregoing scheme, the first acquisition unit 601 is configured to: identify non-target noise signals in the original sound signal; filter the non-target noise signals in the original sound signal to obtain the target noise signal.

[0116] In some embodiments of this application, based on the foregoing scheme, the first acquisition unit 601 is further configured to: filter the non-target noise signal in the original sound signal through a pre-built first machine learning model or digital filtering model to obtain the target noise signal.

[0117] In some embodiments of this application, based on the foregoing scheme, the first control unit 602 is configured to: determine the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and control the fan operating speed of the air conditioning equipment based on the noise intensity.

[0118] In a fourth aspect of the embodiments of this application, it will be combined with Figure 7 Another air conditioning equipment control device provided in the embodiments of this application will be described:

[0119] See Figure 7 The diagram shows a block diagram of an air conditioning equipment control device according to a fourth aspect of an embodiment of this application.

[0120] like Figure 7 As shown, the air conditioning equipment control device 700 according to an embodiment of this application includes: a second acquisition unit 701, a determination unit 702, and a second control unit 703.

[0121] The second acquisition unit 701 is used to acquire the target noise signal generated by the fan of the air conditioning equipment; the determination unit 702 is used to determine the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and the second control unit 703 is used to control the operating speed of the fan of the air conditioning equipment based on the noise intensity.

[0122] In some embodiments of this application, based on the foregoing scheme, the determining unit 702 is configured to: determine the spectral information of the target noise signal; and determine the noise intensity corresponding to the target noise signal by means of a pre-trained second machine learning model based on the spectral information.

[0123] In some embodiments of this application, based on the foregoing scheme, the device further includes: a third acquisition unit, configured to acquire the spectral information of the fan of the air conditioning equipment under various airflow states before determining the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; and a training unit, configured to train the second machine learning model based on the spectral information of the fan under various airflow states.

[0124] In some embodiments of this application, based on the foregoing scheme, the second control unit 703 is configured to: acquire a noise intensity tolerance value and calculate the noise difference between the noise intensity and the noise intensity tolerance value; and control the fan operating speed of the air conditioning equipment based on the noise difference.

[0125] In some embodiments of this application, based on the foregoing scheme, the second control unit 703 is configured to: when the absolute value of the noise difference is greater than a predetermined threshold, adjust the fan speed of the air conditioning equipment stepwise according to a predetermined adjustment granularity until the absolute value of the noise difference is less than or equal to the predetermined threshold.

[0126] In some embodiments of this application, based on the aforementioned scheme, the second control unit 703 is configured to: determine the adjustment range for the fan operating speed by looking up a table based on the noise difference; and adjust the fan operating speed of the air conditioning equipment according to the adjustment range.

[0127] Based on the same inventive concept, the fifth aspect of this application also provides an air conditioning device, see reference. Figure 1 As shown, it includes a sound collector and a fan, for reference. Figure 8 As shown, it also includes a memory 804, a processor 802, and a computer program stored in the memory 804 and executable on the processor 802. When the processor 802 executes the computer program, it implements the air conditioning equipment control method described in the first or second aspect above.

[0128] Among them, Figure 8 In this document, a bus architecture (represented by bus 800) is used. Bus 800 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 802 and memory represented by memory 804. Bus 800 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 805 provides an interface between bus 800 and receiver 801 and transmitter 803. Receiver 801 and transmitter 803 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 802 is responsible for managing bus 800 and general processing, while memory 804 can be used to store data used by processor 802 during operation.

[0129] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0132] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0133] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for controlling an air conditioning device, characterized in that, The method includes: The original sound signal is acquired, and non-target noise signals in the original sound signal are identified; the non-target noise signals in the original sound signal are filtered by a pre-built first machine learning model or digital filtering model to obtain the target noise signal; The spectral information of the target noise signal is determined; based on the spectral information, the noise intensity corresponding to the target noise signal is determined by a pre-trained second machine learning model; the second machine learning model is trained based on the spectral information of the air conditioner's fan under various airflow conditions. Obtain the noise intensity tolerance value and calculate the noise difference between the noise intensity and the noise intensity tolerance value; Based on the noise difference, the operating speed of the air conditioning fan is controlled. The step of controlling the fan speed of the air conditioning equipment based on the noise difference includes: When the absolute value of the noise difference is greater than a predetermined threshold, the fan speed of the air conditioning equipment is adjusted stepwise according to a predetermined adjustment granularity until the absolute value of the noise difference is less than or equal to the predetermined threshold.

2. The method according to claim 1, characterized in that, The step of controlling the fan speed of the air conditioning equipment based on the noise difference includes: Based on the noise difference, the adjustment range for the fan operating speed is determined by looking up a table; Adjust the fan speed of the air conditioning equipment according to the adjustment range.

3. An air conditioning equipment control device, characterized in that, The device includes: The first acquisition unit is used to acquire the original sound signal and identify non-target noise signals in the original sound signal; and to filter the non-target noise signals in the original sound signal through a pre-built first machine learning model or digital filtering model to obtain the target noise signal. A first control unit is configured to determine the spectral information of the target noise signal; based on the spectral information, determine the noise intensity corresponding to the target noise signal through a pre-trained second machine learning model; the second machine learning model is trained based on the spectral information of the air conditioner's fan under various airflow conditions; obtain a noise intensity tolerance value, and calculate the noise difference between the noise intensity and the noise intensity tolerance value; based on the noise difference, control the fan operating speed of the air conditioner. The step of controlling the fan speed of the air conditioning equipment based on the noise difference includes: when the absolute value of the noise difference is greater than a predetermined threshold, adjusting the fan speed of the air conditioning equipment stepwise according to a predetermined adjustment granularity until the absolute value of the noise difference is less than or equal to the predetermined threshold.

4. An air conditioning device, characterized in that, It includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to implement the method as described in any one of claims 1-2.

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