Air conditioner sound scene optimization method based on large model, air conditioner and storage medium

Abstract

The invention provides an air conditioner sound scene optimization method based on a large model, an air conditioner and a storage medium. The method comprises the steps of obtaining audio data in a current environment; analyzing the audio data by using a preset artificial intelligence large model, and identifying all noise data in the audio data; extracting feature data of all the noise data, and matching corresponding music data according to the feature data; performing audio fusion on the music data and the noise data to obtain an output audio; and playing the output audio. By means of the air conditioner sound scene optimization method based on the large model, noise can be converted into music in real time, the irritability of a user is reduced, meanwhile, natural perception of environment sound can be reserved, and auditory vacuum of traditional noise reduction is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioners. Specifically, it relates to an air conditioner soundscape optimization method based on a large model, and also relates to an air conditioner applying the air conditioner soundscape optimization method based on the large model, and further relates to a computer-readable storage medium applying the air conditioner soundscape optimization method based on the large model. Background Art

[0002] Currently, the treatment of environmental noise mainly relies on the following two types of technologies, but both have significant limitations. For example, physical sound insulation: blocking the propagation of noise through sound-absorbing materials or sound-insulating structures (such as noise-canceling earplugs), but it cannot adapt to open environments (such as offices, public places), and completely eliminating environmental sounds may cause users to lose the sense of security in the real scene. Active noise reduction: suppressing noise using the principle of phase cancellation, but it is only effective for regular low-frequency noise, has a poor effect on sudden high-frequency noise, and cannot provide alternative auditory content.

[0003] In an existing air conditioner, the air conditioning system automatically and real-time monitors the noise type during the operation of the air conditioner. When continuous abnormal noise appears, it is judged that the sound analysis device analyzes parameters such as the frequency and volume of the noise (such as dripping sound), and transmits them to the cloud. After calculation and analysis, the cloud transmits the corresponding type of covering sound (such as rain sound), which is emitted by the sound-emitting device of the indoor unit of the air conditioner to help users sleep better. However, in this solution, covering sound is used to cover the noise. As Figure 1 shown, covering sound A covers noise B, that is, covering the low volume with a high volume, so that users cannot hear the noise and can only hear the covering sound, which is likely to have the problem that the covering sound is too loud and affects the user experience.

[0004] Therefore, a more optimized soundscape optimization method needs to be considered. Summary of the Invention

[0005] The first object of the present invention is to provide an air conditioner soundscape optimization method based on a large model that can convert noise into music in real time, reduce user irritation, and at the same time retain the natural perception of environmental sounds, avoiding the "auditory vacuum" of traditional noise reduction.

[0006] The second object of the present invention is to provide an air conditioner that can convert noise into music in real time, reduce user irritation, and at the same time retain the natural perception of environmental sounds, avoiding the "auditory vacuum" of traditional noise reduction.

[0007] The third object of the present invention is to provide a computer-readable storage medium that can convert noise into music in real time, reduce user irritation, and at the same time retain the natural perception of environmental sounds, avoiding the "auditory vacuum" of traditional noise reduction.

[0008] To achieve the above first objective, the air conditioner soundscape optimization method based on a large model provided by the present invention includes: obtaining audio data in the current environment; using a preset artificial intelligence large model to analyze the audio data and identify all noise data in the audio data; extracting feature data of all the noise data and matching corresponding music data according to the feature data; performing audio fusion on the music data and the noise data to obtain output audio; and playing the output audio.

[0009] As can be seen from the above solution, the air conditioner soundscape optimization method based on a large model of the present invention analyzes the audio data in the current environment by using a preset artificial intelligence large model, extracts the features of the noise data to match the music data, can achieve personalized music selection, and enables the generated music to better adapt to different types of noise. At the same time, the music data and the noise data are fused, rather than simply masking or eliminating the noise, but allowing the music and the noise to naturally fuse. While reducing the impact of the noise, a certain amount of environmental sound is retained, making the overall auditory experience more natural, optimizing the soundscape effect to the greatest extent, and improving the user experience.

[0010] In a further solution, the step of performing audio fusion on the music data and the noise data to obtain output audio includes: using the music data as the main audio track data, using the noise data as the secondary audio track data, and fusing the main audio track data and the secondary audio track data.

[0011] As can be seen, using the music data as the main audio track data can ensure that the user mainly hears the music, giving full play to the leading role of the music in improving the auditory experience and relieving irritable emotions, and enabling the user to obtain a more pleasant auditory enjoyment. Fusing the noise data as the secondary audio track data, rather than simply eliminating the noise, can utilize the existence of the noise to a certain extent. Through the fusion process with the music, it no longer appears abrupt and harsh, achieving a reasonable transformation of the noise. This fusion method with clear primary and secondary helps to more specifically process the noise, making the fusion effect more natural and optimized, better reducing the high-frequency harshness, and improving the overall soundscape quality.

[0012] In a further solution, the step of performing audio fusion on the music data and the noise data to obtain output audio includes: removing the preset audio in the music data, replacing the preset audio with the noise data, and obtaining the output audio.

[0013] As can be seen, removing the preset audio in the music data and replacing it with the noise data can specifically process the audio content, avoiding the interference of useless audio on the overall audio effect, and thus more accurately optimizing the audio. At the same time, according to the characteristics of the noise and the needs of the user, appropriate music data is selected for fusion, thereby significantly reducing the high-frequency harshness, reducing the irritability of the user, and improving the user's auditory experience.

[0014] In a further solution, the steps of performing audio fusion on music data and noise data to obtain output audio include: adjusting the playback parameters of the output audio according to the rhythm and frequency of the noise data.

[0015] Thus, adjusting the playback parameters of the output audio according to the rhythm and frequency of the noise data enables the output audio to better fuse with the noise, effectively masking or weakening the adverse auditory sensations brought by the noise, further enhancing the effect of audio fusion, optimizing the overall soundscape quality, and significantly reducing the interference of the noise to the user.

[0016] In a further solution, the steps of matching corresponding music data according to feature data include: obtaining scene requirement information and matching corresponding music data according to the feature data and the scene requirement information.

[0017] Thus, matching music data by combining the feature data of the noise and the scene requirement information can more accurately find music suitable for the current environment. For example, according to the characteristics of the noise such as frequency and rhythm, and different scene requirements such as resting in the bedroom or moving in the living room, music with a suitable rhythm and style can be matched to optimize the soundscape effect to the greatest extent and enhance the user's auditory experience.

[0018] In a further solution, the steps of obtaining scene requirement information include: obtaining at least one of the user's music requirement instruction, the current operating function of the air conditioner, the current time, and the current location.

[0019] It can be seen that by integrating various information such as user instructions, air conditioner functions, time, and location, the system can intelligently adjust music data in real time according to different scenario requirements, providing a more practical soundscape optimization solution and demonstrating stronger adaptability and intelligence. Obtaining the user's music demand instructions can directly understand the user's specific preferences for music, such as the preferred music types and styles, making the matched music more in line with the user's personal taste, improving the user's satisfaction with the air conditioner soundscape optimization, and creating a truly personalized auditory experience for the user. The current operating functions of the air conditioner, such as the wind speed and the cooling / heating mode, can better handle the noise generated by the air conditioner operation. For example, when the air conditioner has a high wind speed and high noise, more precisely matching music that can effectively cover up the noise ensures good soundscape optimization effects in different air conditioner operating states. Considering the current time can make the matched music fit the atmosphere of different time periods. For example, in the early morning, choosing lively and soothing music helps users wake up; at night, playing gentle and sleep-aiding music creates a quiet and comfortable sleep environment, allowing the music to blend more naturally into the daily life rhythm and further enhancing the user experience. Adjusting the music data according to the current location can make the music coordinate with the surrounding environment. For example, in the bedroom, playing more private and quiet music; in public areas such as the living room, choosing music more suitable for socializing and relaxation, enhancing the user's auditory comfort in different locations and making the soundscape optimization more targeted and reasonable.

[0020] In a further solution, after playing the output audio, it further includes: obtaining feedback information on the output audio, using a preset artificial intelligence large model to learn and optimize the music generation strategy based on the feedback information, and performing audio fusion on the music data and noise data based on the learned and optimized music generation strategy.

[0021] It can be seen that by obtaining the feedback information on the output audio, the artificial intelligence large model can understand the user's satisfaction with the current music generation strategy and the existing problems. Based on these feedbacks, the model can specifically learn and optimize the music generation strategy, making the subsequently generated music more in line with the user's needs and expectations and continuously improving the quality and effect of music generation. With the continuous optimization of the music generation strategy, the output audio heard by the user will be more in line with their personal preferences and actual needs.

[0022] In a further solution, after obtaining the audio data in the current environment, it further includes: performing filtering and noise reduction operations on the audio data.

[0023] It can be seen that through filtering and denoising, unnecessary interference signals and noise components in the audio data can be effectively removed, making the subsequent analyzed and processed audio data purer. This helps to improve the recognition and analysis accuracy of the effective information in the audio data by the large artificial intelligence model, thereby more accurately identifying the noise data and providing a high-quality data basis for subsequent soundscape optimization.

[0024] To achieve the second object of the present invention, the present invention provides an air conditioner including a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned air conditioner soundscape optimization method based on the large model are implemented.

[0025] To achieve the third object of the present invention, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a controller, the steps of the above-mentioned air conditioner soundscape optimization method based on the large model are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of submerged sound covering noise in the prior art.

[0027] Figure 2 It is a system block diagram of an air conditioner applying the air conditioner soundscape optimization method based on the large model of the present invention.

[0028] Figure 3 It is a flowchart of an embodiment of the air conditioner soundscape optimization method based on the large model of the present invention.

[0029] Figure 4 It is a schematic diagram of audio fusion of music data and noise data in an embodiment of the air conditioner soundscape optimization method based on the large model of the present invention.

[0030] The present invention will be further described below in conjunction with the drawings and embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The air conditioner soundscape optimization method based on the large model of the present invention is a computer program applied to an air conditioning system, used to control the air conditioner for soundscape optimization.

[0032] Preferably, as Figure 2 shown, the air conditioning system includes a main control module 1, an audio recognition module 2, a power amplifier module 3, and a cloud processing center 4. The audio recognition module 2 and the power amplifier module 3 are both electrically connected to the main control module 1. The audio recognition module 2 is used to obtain the audio data of the current environment, and the power amplifier module 3 is used to play audio. The cloud processing center 4 interacts with the main control module 1. The main control module 1 can analyze and process the collected audio data, or send the audio data to the cloud processing center 4 for processing. Preferably, the main control module 1 is used to analyze and process the collected audio data.

[0033] Example of an air conditioner soundscape optimization method based on a large model:

[0034] As Figure 1 shown, in this embodiment, when the air conditioner soundscape optimization method based on the large model is working, it first executes step S1 to obtain the audio data in the current environment. When the air conditioner is in operation and soundscape optimization is required, the soundscape optimization mode can be entered through a remote control terminal or a control panel, etc. After entering the soundscape optimization mode, in order to process the noise in the current environment, it is necessary to obtain the audio data in the current environment. The microphone array in the audio recognition module 2 can be used to continuously capture the audio data in the surrounding environment.

[0035] In this embodiment, after obtaining the audio data in the current environment, it further includes: performing filtering and denoising operations on the audio data. Through filtering and denoising, unnecessary interference signals and noise components in the audio data can be effectively removed, making the subsequent analyzed and processed audio data cleaner. This helps to improve the recognition and analysis accuracy of the effective information in the audio data by the artificial intelligence (AI) large model, so as to more accurately identify the noise data, improve the signal-to-noise ratio, and ensure the accuracy of subsequent analysis. Performing filtering and denoising operations on the audio data is a well-known technology for those skilled in the art and will not be elaborated here.

[0036] After obtaining the audio data in the current environment, execute step S2, and use a preset artificial intelligence large model to analyze the audio data to identify all the noise data in the audio data. In order to facilitate the processing of the noise data in the current environment, the voice recognition algorithm in the pre-artificial intelligence large model is used to identify all the noise data, and different types of noises such as human voices, bird calls, insect chirps, and car horns are identified, as well as information such as the spectrum, wave peaks, and wave valleys of the noise.

[0037] After identifying all the noise data, execute step S3, extract the characteristic data of all the noise data, and match the corresponding music data according to the characteristic data. Extract the key features from the classified noises. The characteristic data includes information such as the rhythm pattern and frequency distribution of the noise. Based on the extracted characteristic data, use the preset artificial intelligence large model to automatically match the harmonious music data according to the characteristic data of the noise. For example, match the music data with the sound of at least one type of noise in the noise data.

[0038] In this embodiment, the step of matching corresponding music data according to the feature data includes: obtaining scene requirement information, and matching corresponding music data according to the feature data and the scene requirement information. Matching music data by combining the feature data of noise and the scene requirement information can more accurately find music suitable for the current environment. For example, according to the characteristics of noise such as frequency and rhythm, and different scene requirements such as resting in the bedroom or moving in the living room, music with appropriate rhythm and style can be matched to optimize the soundscape effect to the greatest extent and enhance the user's auditory experience.

[0039] In this embodiment, the step of obtaining scene requirement information includes: obtaining at least one of the user's music requirement instruction, the current operating function of the air conditioner, the current time, and the current location. By integrating various information such as user instructions, air conditioner functions, time, and location, the system can intelligently adjust the music data in real time according to different scene requirements, provide a more practical soundscape optimization plan, and demonstrate stronger adaptability and intelligence level. Obtaining the user's music requirement instruction can directly understand the user's specific preferences for music, such as the preferred music types and styles, making the matched music more in line with the user's personal taste, enhancing the user's satisfaction with the air conditioner soundscape optimization, and creating a truly personalized auditory experience for the user. The current operating function of the air conditioner, such as the wind speed and the cooling / heating mode, can better handle the noise generated by the air conditioner operation. For example, when the air conditioner has a high wind speed and large noise, more precisely matching music that can effectively cover up the noise can ensure good soundscape optimization effects in different air conditioner operating states. Considering the current time can make the matched music fit the atmosphere of different time periods. For example, in the early morning, choose lively and soothing music to help users wake up; at night, play gentle and sleep-aiding music to create a quiet and comfortable sleeping environment, allowing the music to blend more naturally into the daily life rhythm and further enhancing the user experience. Adjusting the music data according to the current location can make the music coordinated with the surrounding environment. For example, in the bedroom, play more private and quiet music; in public areas such as the living room, choose more suitable music for socializing and relaxation to enhance the user's auditory comfort in different locations and make the soundscape optimization more targeted and reasonable.

[0040] After matching the corresponding music data according to the feature data, step S4 is executed to perform audio fusion on the music data and the noise data to obtain the output audio. In order to reduce the impact of noise and at the same time retain a certain amount of ambient sound to make the overall auditory feeling more natural and optimize the soundscape effect, the music data and the noise data are fused instead of simply covering up or eliminating the noise, which can improve the user experience.

[0041] In one embodiment, the steps of performing audio fusion on music data and noise data to obtain output audio include: using the music data as the main audio track data and the noise data as the secondary audio track data, and fusing the main audio track data and the secondary audio track data. Using the music data as the main audio track data can ensure that the user mainly hears the music, giving full play to the leading role of the music in improving the auditory experience and relieving irritability, so that the user can obtain a more pleasant auditory enjoyment. Fusing the noise data as the secondary audio track data, rather than simply eliminating the noise, can make use of the existence of the noise to a certain extent. Through the fusion process with the music, the noise no longer seems obtrusive and harsh, achieving a reasonable transformation of the noise. This way of clearly distinguishing the primary and secondary helps to process the noise more specifically, making the fusion effect more natural and optimized, better reducing the high-frequency harshness and improving the overall soundscape quality. Since the noise is not completely eliminated but fused as the secondary audio track data, the natural perception of the ambient sound can be retained to a certain extent, avoiding the "auditory vacuum" effect that may be caused by traditional noise reduction methods, allowing the user to still have a certain perception of the surrounding environment, and increasing the safety and comfort of use.

[0042] In another embodiment, the steps of performing audio fusion on music data and noise data to obtain output audio include: removing the preset audio in the music data and replacing the preset audio with the noise data to obtain the output audio. When removing the preset audio in the music data and replacing it with the noise data, the noise in reality is intercepted and added to the music. For example Figure 4 in, the noise D is intercepted and added to the music C; or a certain section (with background sound similar to noise) of the music file is removed or not played, so that the noise in reality can be heard audibly, and then the music can be heard. Removing the preset audio in the music data and using the noise data for replacement can specifically process the audio content, avoiding the interference of useless audio on the overall audio effect, and thus optimizing the audio more precisely. At the same time, according to the characteristics of the noise and the user's needs, appropriate music data is selected for fusion, thereby significantly reducing the high-frequency harshness, reducing the user's irritability, and improving the user's auditory experience.

[0043] In this embodiment, the steps of performing audio fusion on music data and noise data to obtain the output audio include: adjusting the playback parameters of the output audio according to the rhythm and frequency of the noise data. By intelligently adjusting playback parameters such as the volume, pitch, and rhythm of the music according to the rhythm and frequency of the noise data, the output audio can be better fused with the noise, effectively masking or weakening the adverse auditory sensations brought by the noise, further enhancing the effect of audio fusion, optimizing the overall soundscape quality, and significantly reducing the interference of the noise to the user. Moreover, since the rhythm and frequency of the noise change in real time with factors such as the environment and the operating state of the air conditioner, the dynamic adjustment of the playback parameters of the output audio allows the user to obtain a comfortable auditory experience in different scenarios. For example, when the noise changes due to the change in the air conditioner wind speed, the output audio can respond in real time and always maintain a good soundscape effect. By reasonably adjusting the playback parameters, a better balance between the output audio and the noise is achieved, avoiding auditory fatigue caused by unreasonable audio rhythm and frequency. When the user stays in such a comfortable soundscape environment for a long time, irritable emotions can be reduced, and a more pleasant mood can be enjoyed when using the air conditioner.

[0044] After obtaining the output audio, step S5 is executed to play the output audio. When playing the audio, it can be played through controlling the power amplifier module 3.

[0045] After playing the output audio, step S6 is executed to obtain feedback information on the output audio, and use a preset artificial intelligence large model to learn and optimize the music generation strategy based on the feedback information. Based on the learned and optimized music generation strategy, the music data and the noise data are subjected to audio fusion. After playing the output audio, the user can rate the generated music or put forward preference suggestions to optimize and improve the suggestions based on the originally fused and generated music. By obtaining the feedback information on the output audio, the artificial intelligence large model can understand the user's satisfaction with the current music generation strategy and the existing problems. Based on these feedbacks, the model can specifically learn and optimize the music generation strategy, making the music generated by the music generation strategy more in line with the user's needs and expectations in the future, and continuously improving the quality and effect of music generation. As the music generation strategy is continuously optimized, the output audio heard by the user will be more in line with their personal preferences and actual needs.

[0046] As can be seen from the above, the air conditioner soundscape optimization method based on a large model of the present invention analyzes the audio data in the current environment by using a preset artificial intelligence large model, extracts the characteristics of the noise data to match the music data, and can realize personalized music selection, so that the generated music can be better adapted to different types of noise. At the same time, the music data and the noise data are fused, rather than simply masking or eliminating the noise, but making the music and the noise naturally fuse. While reducing the impact of the noise, a certain amount of environmental sound is retained, making the overall auditory experience more natural, optimizing the soundscape effect to the greatest extent, and improving the user experience.

[0047] Air conditioner embodiment:

[0048] The air conditioner of this embodiment includes a controller, and when the controller executes a computer program, it implements the steps in the above embodiment of the air-conditioning soundscape optimization method based on a large model.

[0049] For example, the computer program can be divided into one or more modules, and one or more modules are stored in the memory and executed by the controller to complete the present invention. One or more modules can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the air conditioner.

[0050] The air conditioner may include, but is not limited to, a controller and a memory. Those skilled in the art can understand that the air conditioner may include more or fewer components, or combine certain components, or different components. For example, the air conditioner may also include input / output devices, network access devices, buses, etc.

[0051] For example, the controller can be a Central Processing Unit (CPU), or it can also be other general controllers, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general controller can be a microcontroller or the controller can also be any conventional controller, etc. The controller is the control center of the air conditioner and connects various parts of the entire air conditioner using various interfaces and lines.

[0052] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, the controller can implement various functions of the air conditioner. For example, the memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function (such as the voice reception function, the function of converting voice into text, etc.); the data storage area can store the data created according to the use of the mobile phone (such as audio data, text data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, memory, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0053] Embodiment of computer-readable storage medium:

[0054] If the modules integrated in the air conditioner in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the embodiments of the above-mentioned method for optimizing the air conditioner soundscape based on the large model, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the controller, it can implement the steps of the embodiments of the above-mentioned method for optimizing the air conditioner soundscape based on the large model. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0055] It should be noted that the above are only the preferred embodiments of the present invention, but the design concept of the invention is not limited thereto. Any non-substantive modifications made to the present invention using this concept also fall within the protection scope of the present invention.

Claims

1. An air conditioner soundscape optimization method based on a large model, comprising: Obtain audio data in the current environment; It is characterized in that: Use a preset artificial intelligence large model to analyze the audio data and identify all noise data in the audio data; Extract the characteristic data of all the noise data, and match the corresponding music data according to the characteristic data; Perform audio fusion on the music data and the noise data to obtain output audio; Play the output audio.

2. The air conditioner soundscape optimization method based on a large model according to claim 1, wherein: The step of performing audio fusion on the music data and the noise data to obtain output audio includes: Use the music data as the main audio track data, use the noise data as the secondary audio track data, and fuse the main audio track data and the secondary audio track data.

3. The air conditioner soundscape optimization method based on a large model according to claim 1, wherein: The step of performing audio fusion on the music data and the noise data to obtain output audio includes: Eliminate the preset audio in the music data, and replace the preset audio with the noise data to obtain the output audio.

4. The air conditioner soundscape optimization method based on a large model according to claim 1, wherein: The step of performing audio fusion on the music data and the noise data to obtain output audio includes: Adjust the playback parameters of the output audio according to the rhythm and frequency of the noise data.

5. The air conditioner soundscape optimization method based on a large model according to any one of claims 1 to 4, wherein: The step of matching the corresponding music data according to the characteristic data includes: Obtain scene requirement information, and match the corresponding music data according to the characteristic data and the scene requirement information.

6. The air conditioner soundscape optimization method based on a large model according to claim 5, wherein: The step of obtaining scene requirement information includes: Obtain at least one of the user's music requirement instruction, the current operating function of the air conditioner, the current time, and the current location.

7. The air conditioner soundscape optimization method based on a large model according to any one of claims 1 to 4, wherein: After playing the output audio, it further includes: Obtain feedback information on the output audio, use the preset artificial intelligence large model to learn and optimize the music generation strategy for the feedback information, and perform audio fusion on the music data and the noise data based on the learned and optimized music generation strategy.

8. The air conditioner soundscape optimization method based on a large model according to any one of claims 1 to 4, wherein: After obtaining the audio data in the current environment, it further includes: Perform filtering and noise reduction operations on the audio data.

9. An air conditioner, comprising a processor and a memory, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the steps of the air conditioner soundscape optimization method based on a large model according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the controller, the steps of the air conditioner soundscape optimization method based on a large model according to any one of claims 1 to 8 are implemented.

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