Predictive sleep aids, devices, electronic equipment, and vehicles

Abstract

This application provides a predictive sleep aid method, device, electronic device, and vehicle. The predictive sleep aid method is applied to a vehicle, which includes a speaker. The method includes: if the vehicle enters a rest mode, playing sleep aid music through the speaker and acquiring audio and image features of the vehicle's environment; based on a sound prediction model, predicting the ambient noise of the vehicle's environment over a future period based on the audio and image features; adjusting the sleep aid music according to the ambient noise, and playing the adjusted sleep aid music through the speaker. This application combines the audio and image features of the vehicle's environment to predict potential ambient noise in advance, and then adjusts the sleep aid music according to the predicted ambient noise. This allows for better integration into the in-vehicle environment, achieving a more accurate and effective noise reduction and sleep aid effect. It also flexibly adapts to various noise environments, improving the practicality and adaptability of the sleep aid music.

Description

Technical Field

[0001] This application relates to the field of sleep aid technology, and more particularly to a predictive sleep aid method, device, electronic device, and vehicle. Background Technology

[0002] With the development of technology, automobiles are becoming increasingly intelligent, and people's demands for cars are constantly rising, especially the requirements for user comfort. This is mainly because people are spending more time in their cars. For example, people spend a lot of time resting and taking naps in their cars. However, vehicles are usually parked in noisy public places, making it difficult for users to fall asleep quickly. Therefore, it is necessary to improve the intelligence level of cars to achieve a sleep-inducing effect in rest mode.

[0003] Under current technological conditions, playing preset white noise music is one of the effective ways to improve the sleep-inducing effect of car rest mode. However, white noise resources are mostly limited to fixed audio files, such as simulated flowing water sounds, birdsong, and other natural sounds. As a result, existing sleep-inducing methods are not very effective when dealing with complex and varied real-world noise environments. Summary of the Invention

[0004] In view of this, this application provides a predictive sleep aid method, device, electronic device, and vehicle to solve the technical problem that existing sleep aid methods are not effective in dealing with complex and ever-changing real-world noise environments.

[0005] This application provides a predictive sleep aid method applied to a vehicle, the vehicle including a speaker. The predictive sleep aid method includes: if the vehicle enters a rest mode, playing sleep aid music through the speaker and acquiring audio and image features of the environment in which the vehicle is located; based on a sound prediction model, predicting the environmental noise of the environment in which the vehicle is located in the future according to the audio and image features; adjusting the sleep aid music according to the environmental noise, and playing the adjusted sleep aid music through the speaker.

[0006] The predictive sleep aid method provided in this application involves playing sleep-aid music through the speaker when the vehicle enters rest mode, and acquiring the audio and image features of the vehicle's environment. Based on a sound prediction model, the method predicts the ambient noise of the vehicle's environment over a future period based on the audio and image features. The method then adjusts the sleep-aid music according to the ambient noise and plays the adjusted music through the speaker. In this way, by combining the audio and image features of the vehicle's environment, this application predicts the possible ambient noise in the vehicle's environment in advance, and then adjusts the sleep-aid music according to the predicted ambient noise. This allows the music to better integrate into the in-vehicle environment, creating a quieter and more comfortable resting space for the user, achieving a more precise and effective noise reduction and sleep aid effect. Furthermore, it can flexibly adapt to various noise environments, improving the practicality and adaptability of the sleep-aid music.

[0007] In some embodiments of this application, adjusting the sleep-aid music according to the ambient noise includes: performing noise reduction processing on the ambient noise based on an intelligent noise reduction model to generate a noise-reduced frequency corresponding to the ambient noise; and adjusting the sleep-aid music according to the noise-reduced frequency.

[0008] In some embodiments of this application, the step of performing noise reduction processing on the environmental noise based on an intelligent noise reduction model to generate a noise-reduced frequency corresponding to the environmental noise includes: extracting feature parameters of the environmental noise; adjusting the noise reduction parameters of the intelligent noise reduction model according to the feature parameters to obtain adjusted noise reduction parameters; and performing noise reduction processing on the environmental noise based on the intelligent noise reduction model and the adjusted noise reduction parameters to generate the noise-reduced frequency.

[0009] In some embodiments of this application, adjusting the noise reduction parameters according to the feature parameters to obtain the adjusted noise reduction parameters includes: classifying the environmental noise according to the feature parameters to obtain the category of the environmental noise; determining a noise reduction strategy according to the category of the environmental noise; and adjusting the noise reduction parameters of the intelligent noise reduction model according to the noise reduction strategy.

[0010] In some embodiments of this application, the predictive sleep aid method further includes: obtaining user feedback information; and updating the noise reduction parameters of the intelligent noise reduction model based on the feedback information.

[0011] In some embodiments of this application, the vehicle further includes an audio sensor and an image sensor. The step of acquiring the audio and image features of the environment in which the vehicle is located includes: acquiring audio data of the environment in which the vehicle is located in real time through the audio sensor; acquiring image data outside the vehicle in real time through the image sensor; extracting features from the audio data to obtain the audio features; identifying moving objects in the image data and predicting the trajectory of the moving objects to obtain the image features.

[0012] In some embodiments of this application, the predictive sleep aid method further includes: obtaining corresponding music materials from a preset database based on the user's preference information; and adjusting the sleep aid music according to the music materials.

[0013] Secondly, this application also provides a predictive sleep aid device applied to a vehicle, the vehicle including a speaker, the predictive sleep aid device comprising: an acquisition module, configured to play sleep aid music through the speaker when the vehicle enters a rest mode, and acquire audio and image features of the environment in which the vehicle is located; a prediction module, configured to predict the environmental noise of the environment in which the vehicle is located within a future period based on a sound prediction model, according to the audio features and the image features; and an adjustment module, configured to adjust the sleep aid music according to the environmental noise, and play the adjusted sleep aid music through the speaker.

[0014] Thirdly, embodiments of this application also provide an electronic device, the electronic device comprising: a memory storing computer-readable instructions; and a processor executing the computer-readable instructions stored in the memory to implement the predictive sleep aid method as described in the above embodiments.

[0015] Fourthly, embodiments of this application also provide a vehicle, the vehicle including the electronic equipment described in the above embodiments.

[0016] Understandably, the predictive sleep aid device of the second aspect, the electronic device of the third aspect, and the vehicle of the fourth aspect all correspond to the predictive sleep aid method of the first aspect. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding predictive sleep aid methods provided above, and will not be repeated here. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the hardware structure of a vehicle provided in one embodiment of this application.

[0018] Figure 2 This is a flowchart illustrating a predictive sleep aid method provided in an embodiment of this application.

[0019] Figure 3This is a detailed flowchart of step S10 in a predictive sleep aid method provided in an embodiment of this application.

[0020] Figure 4 This is a detailed flowchart of step S12 in a predictive sleep aid method provided in an embodiment of this application.

[0021] Figure 5 This is a schematic diagram of the functional modules of a predictive sleep aid device provided in an embodiment of this application.

[0022] Component Symbol Explanation

[0023] Vehicle 1

[0024] Electronic devices 10

[0025] Memory 11

[0026] Processor 12

[0027] Speaker 20

[0028] Audio sensor 30

[0029] Image sensor 40

[0030] Predictive sleep aid device 100

[0031] Get Module 110

[0032] Prediction Module 120

[0033] Adjustment module 130

[0034] The following detailed description, in conjunction with the accompanying drawings, will further illustrate this application. Detailed Implementation

[0035] To better understand the above-mentioned objectives, features, and advantages of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0036] The following description sets forth many specific details to provide a full understanding of this application. The described embodiments are only some, not all, of the embodiments of this application.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0038] It should be further noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0039] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0040] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0041] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the hardware structure of vehicle 1 provided in an embodiment of this application.

[0042] Specifically, the vehicle 1 provided in this application embodiment includes an electronic device 10. The electronic device 10 may be an on-board device of the vehicle 1.

[0043] The predictive sleep aid method provided in this application embodiment is applied to one or more electronic devices 10. The electronic device 10 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0044] In other embodiments, the electronic device 10 can also be any electronic product that is communicatively connected to the vehicle 1, such as a personal computer, tablet computer, etc. The electronic device 10 can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0045] In some embodiments of this application, the network where the electronic device 10 is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0046] In some embodiments of this application, vehicle 1 further includes a speaker 20, an audio sensor 30, and an image sensor 40. In these embodiments, multiple audio sensors 30 can be installed at different locations on vehicle 1 to collect audio data of the environment in which vehicle 1 is located. For example, the audio sensor 30 may include, but is not limited to, a microphone. The audio sensor 30 has good anti-interference capabilities and a wide frequency response range to ensure accurate collection of various audio data in the environment in which vehicle 1 is located, such as traffic noise, pedestrian sounds, and ambient sounds. In these embodiments, multiple image sensors 40 can be installed at different locations on vehicle 1 to collect image data of the environment surrounding vehicle 1, such as the type of surrounding vehicles, traffic flow, and road type. For example, the image sensor 40 may include, but is not limited to, an infrared camera. The image sensor 40 has high resolution and good low-light performance to clearly capture image data of the environment in which vehicle 1 is located under different lighting conditions.

[0047] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a predictive sleep aid method provided in an embodiment of this application.

[0048] Specifically, predictive sleep aids include the following steps. Depending on different needs, the order of some steps in the flowchart can be changed, and some steps can be omitted.

[0049] Step S10: If the vehicle enters rest mode, play soothing music through the speakers and acquire the audio and image features of the vehicle's environment.

[0050] In some embodiments of this application, the rest mode refers to a special state that vehicle 1 enters to provide passengers with a comfortable and quiet resting environment. When vehicle 1 enters rest mode, vehicle 1 must be stationary and in parking gear (e.g., P gear) to ensure that vehicle 1 will not move unexpectedly during rest mode, thereby ensuring user safety. Specifically, the user can trigger rest mode through the central control screen of vehicle 1 or a voice assistant.

[0051] In some embodiments of this application, the sleep-aid music can be preset music. Preset music includes, but is not limited to: (1) natural sounds, such as the sound of waves, rain, wind, and streams, which can simulate the natural environment and help people relax and reduce stress. (2) soft music, such as light music, piano music, and violin music, which have gentle melodies and steady rhythms and help lower heart rate and promote sleep. (3) white noise, such as the sound of air conditioners and fans, which can mask distracting background noise and help people fall asleep more easily. (4) meditation music, such as combining natural sounds and soft music, adding meditation guidance words or meditation music elements, which can help guide passengers into a meditative state and relax their mind and body. (5) personalized music, such as preset popular relaxing music or music of a specific style, such as jazz or classical music, according to the user's personal preferences.

[0052] In some embodiments of this application, sleep-aid music may be stored in a preset database.

[0053] Specifically, the steps for obtaining the audio and image features of the environment in which vehicle 1 is located include: acquiring audio data of the environment in which vehicle 1 is located in real time through audio sensor 30; acquiring image data outside vehicle 1 in real time through image sensor 40; extracting features from the audio data to obtain audio features; identifying moving objects in the image data and predicting the motion trajectory of the moving objects to obtain image features.

[0054] It should be noted that the method for obtaining the audio and image features of the environment in which vehicle 1 is located will be discussed later. Figure 3 The steps shown are described in detail, and will not be repeated here to avoid repetition.

[0055] Step S11: Based on the sound prediction model, predict the ambient noise of the vehicle's environment in the future based on audio features and image features.

[0056] In some embodiments of this application, the method for constructing a sound prediction model includes: selecting a suitable sound prediction model based on specific application scenarios and requirements. Common models include deep learning-based neural network models (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks, etc.) and statistical prediction models (e.g., time series analysis models). Audio features and image features are fused through feature concatenation, feature mapping, and feature interaction, so that the sound prediction model can simultaneously utilize information from both audio and image features for prediction. The selected sound prediction model is trained using historical audio and image features as a training set. By adjusting model parameters and optimizing the algorithm, the selected sound prediction model can accurately predict environmental noise over a future period by combining audio and image features.

[0057] Specifically, audio and image features are input into a trained sound prediction model, which calculates the ambient noise over a future period (e.g., 5 minutes, 10 minutes, etc.) based on the input data. The output of the sound prediction model includes information such as the type, intensity, and timing of the ambient noise that may occur in the future. For example, if the image sensor 40 detects a moving object (e.g., a motor vehicle) approaching near vehicle 1, it retrieves relevant sound information about the moving object from a pre-set sound feature database based on the object's type (e.g., motor vehicle, pedestrian, animal, etc.), speed, and distance. This information, combined with the sound features from the sound data collected by the audio sensor 30, can then predict possible sounds such as horn sounds and engine sounds.

[0058] In some embodiments of this application, the predictive sleep aid method further includes: continuously collecting new audio features and image features, and using them to update and optimize the sound prediction model, so that the sound prediction model can adapt to constantly changing environmental conditions and improve the predictive performance of the sound prediction model.

[0059] Step S12: Adjust the sleep-aid music according to the ambient noise and play the adjusted sleep-aid music through a speaker.

[0060] Specifically, the steps for adjusting sleep-aid music based on ambient noise include: processing ambient noise using an intelligent noise reduction model to generate a noise-reduced frequency corresponding to the ambient noise; and adjusting the sleep-aid music based on the noise-reduced frequency.

[0061] The steps for generating reduced noise frequencies based on an intelligent noise reduction model include: extracting feature parameters of the environmental noise; adjusting the noise reduction parameters of the intelligent noise reduction model according to the feature parameters to obtain the adjusted noise reduction parameters; and performing noise reduction processing on the environmental noise based on the intelligent noise reduction model and the adjusted noise reduction parameters to generate reduced noise frequencies.

[0062] It should be noted that the details on how to perform noise reduction processing on environmental noise based on the intelligent noise reduction model and generate corresponding denoised noise frequencies will be discussed later. Figure 4 The steps shown are described in detail, and will not be repeated here to avoid repetition.

[0063] The steps for adjusting sleep-aid music based on noise-canceling audio frequencies include: analyzing the noise-canceling audio frequency to obtain its volume level, frequency distribution, dynamic range, and other characteristics; adjusting the volume of the sleep-aid music based on the noise-canceling audio frequency's volume level. For example, if the noise-canceling audio frequency is relatively noisy, the volume of the sleep-aid music can be appropriately increased to mask any remaining noise; if the noise-canceling audio frequency is already very quiet, the volume of the music can be appropriately decreased to avoid disturbing sleep. Adjusting the rhythm and timbre of the sleep-aid music based on the frequency distribution and dynamic range of the noise-canceling audio frequency. For example, if the noise-canceling audio frequency has a large amount of low-frequency components, sleep-aid music with more low-frequency components can be selected to enhance immersion; if the noise-canceling effect is particularly good, with almost no noise, softer, more dreamy sleep-aid music can be selected to create a dreamy sleep atmosphere. The adjusted sleep-aid music is then mixed with the noise-canceling audio frequency to ensure that the two achieve a harmonious unity in terms of volume, timbre, and rhythm, thereby creating a comfortable and sleep-inducing audio environment that allows users to easily fall asleep in a tranquil atmosphere.

[0064] The predictive sleep aid method provided in this application involves playing sleep-aid music through speaker 20 when vehicle 1 enters rest mode, and acquiring the audio and image features of the environment in which vehicle 1 is located. Based on a sound prediction model, the environmental noise of the environment in which vehicle 1 is located in the future is predicted according to the audio and image features. The sleep-aid music is adjusted according to the environmental noise and played back through speaker 20. Based on this, this application combines the audio and image features of the environment in which vehicle 1 is located to predict the environmental noise that may occur in the environment in which vehicle 1 is located in advance, and then adjusts the sleep-aid music according to the predicted environmental noise. This allows the music to blend better into the in-vehicle environment, creating a quieter and more comfortable resting space for the user, achieving a more accurate and effective noise reduction and sleep aid effect. It can also flexibly adapt to various noise environments, improving the practicality and adaptability of the sleep-aid music.

[0065] In some embodiments of this application, the predictive sleep aid method further includes: obtaining corresponding music materials from a preset database based on the user's preference information; and adjusting the sleep aid music according to the music materials.

[0066] Specifically, user preferences for music styles are collected through online questionnaires or applications, such as whether they prefer gentle piano music, natural white noise, relaxing jazz, or other types of music. If a user has previously used similar sleep-aid music services, their music preferences can be inferred by analyzing their historical behavioral data (such as playback history, likes / favorites, skipped tracks, etc.). A pre-stored database contains various types and styles of music materials, such as instrumental music, natural sounds, and guided vocals, to meet the diverse needs of different users. Music materials are categorized and tagged, for example, with terms like "gentle," "soothing," "natural," and "meditation," to facilitate quick retrieval and matching of user preferences. The database ensures high-quality music materials, free of background noise and copyright issues, and capable of consistently providing a comfortable listening experience. Based on the user's provided preferences, the database can be searched and matched to find the music materials that best suit the user's needs. Alternatively, recommendation algorithms (such as collaborative filtering and content-based recommendation) can be used, combined with the user's historical behavior and preference information, to provide a personalized music recommendation list, allowing users to preview the recommended music materials and select or adjust them according to their preferences. The volume and rhythm of the music are adjusted according to the user's selection to ensure that the sleep-aiding music is neither too harsh nor too bland, creating a comfortable sleep atmosphere.

[0067] In some embodiments of this application, the predictive sleep aid method further includes adjusting the sleep aid music according to the user's sleep needs. Specifically, based on the user's sleep needs, natural sounds (such as rain sounds, ocean waves, birdsong, etc.) or guided voices (such as meditation instructions, relaxation breathing techniques, etc.) can be added to the sleep aid music to enhance its sleep-aiding effect. Users can also adjust certain parameters of the music (such as volume, playback order, loop mode, etc.) according to their own sleep needs to meet their unique sleep requirements.

[0068] In some embodiments of this application, the predictive sleep aid method further includes: periodically collecting user feedback on sleep-aid music, such as satisfaction levels and suggestions for improvement. The collected feedback is analyzed to understand the user's actual needs and changes in preferences. Based on the analysis results of user feedback, the preset database, recommendation algorithm, and sleep-aid music adjustment strategy are continuously optimized to improve user experience and satisfaction, thereby achieving the best sleep aid effect.

[0069] Please refer to Figure 3 , Figure 3 This is a detailed flowchart of step S10 in a predictive sleep aid method provided in an embodiment of this application.

[0070] This embodiment is a detailed description of the foregoing embodiment, further illustrating how to obtain the audio and image features of the environment in which vehicle 1 is located. Specifically, it includes the following steps:

[0071] Step S20: Audio data of the vehicle's environment is collected in real time using an audio sensor.

[0072] Specifically, multiple audio sensors 30 can be installed at different locations in vehicle 1 to collect audio data of the environment in which vehicle 1 is located. For example, the audio sensors 30 may include, but are not limited to, microphones. The audio sensors 30 have good anti-interference capabilities and a wide frequency response range to ensure accurate collection of various audio data in the environment in which vehicle 1 is located, such as traffic noise, pedestrian sounds, and natural ambient sounds.

[0073] Step S21: Real-time image data of the exterior of vehicle 1 is acquired using an image sensor.

[0074] Specifically, multiple image sensors 40 can be installed at different locations on vehicle 1 to collect image data of the environment surrounding vehicle 1, such as the types of surrounding vehicles 1, traffic flow, road type, etc. For example, the image sensors 40 include, but are not limited to, infrared cameras. The image sensors 40 have high resolution and good low-light performance so that they can clearly capture image data of the environment in which vehicle 1 is located under different lighting conditions.

[0075] Step S22: Extract features from the audio data to obtain audio features.

[0076] In some embodiments of this application, the audio data may be preprocessed before feature extraction to remove noise, enhance signal quality, or adjust the signal format. Preprocessing steps may include: using filters or algorithms to remove background noise and improve the signal-to-noise ratio; segmenting continuous audio data into shorter frames (typically 20-30 milliseconds); and applying a window function to each frame after segmentation to reduce discontinuities between frames.

[0077] Specifically, Fourier transform can be used to convert audio data from the time domain to the frequency domain, thereby extracting frequency domain features such as amplitude, phase, frequency components, and Mel-frequency cepstral coefficients. Furthermore, deep learning models can be used to automatically learn specific features of audio data, such as volume, timbre, and rhythm.

[0078] In some embodiments of this application, after feature extraction, the extracted audio features are combined into a feature vector, which serves as input for subsequent analysis, recognition, or classification tasks. The dimension and specific content of the feature vector depend on the requirements of the specific application and the number and type of features selected.

[0079] Step S23: Identify moving objects in the image data and predict the motion trajectory of the moving objects to obtain image features.

[0080] In some embodiments of this application, before identifying moving objects in image data, the acquired image data is preprocessed, such as by filtering, denoising, and enhancing contrast, in order to improve the quality of the image data and increase the accuracy of subsequent processing.

[0081] Specifically, background subtraction algorithms (such as Gaussian mixture models and Bayesian models) are used to separate the background from the moving object to identify the moving object. After detecting the moving object, machine learning or deep learning algorithms (such as support vector machines, random forests, and convolutional neural networks) are used to classify and identify the moving object. Features are extracted from the detected moving object, including geometric features (such as size, shape, and position) and motion features (such as velocity, acceleration, and direction), which serve as input parameters for subsequent trajectory prediction. A dynamic model and statistical behavior patterns are learned from historical motion trajectory data. Based on the dynamic model, the motion trajectory of the moving object is predicted, thereby obtaining image features. Optimization algorithms (such as particle swarm optimization) are used to optimize the predicted motion trajectory to improve the accuracy and robustness of motion prediction.

[0082] Please refer to Figure 4 , Figure 4 This is a detailed flowchart illustrating step S12 of a predictive sleep aid method provided in an embodiment of this application.

[0083] This embodiment is a detailed description of the foregoing embodiments, further illustrating how to perform noise reduction processing on environmental noise based on an intelligent noise reduction model to generate noise-reduced frequencies corresponding to the environmental noise. Specifically, it includes the following steps:

[0084] Step S30: Extract the characteristic parameters of environmental noise.

[0085] Specifically, the characteristic parameters include, but are not limited to, the type of environmental noise (e.g., traffic noise, human voice, machine noise, etc.), volume, and time (e.g., the time period, duration, and change pattern of the environmental noise).

[0086] Step S31: Adjust the noise reduction parameters of the intelligent noise reduction model according to the feature parameters to obtain the adjusted noise reduction parameters.

[0087] Specifically, the steps of adjusting the noise reduction parameters based on the feature parameters to obtain the adjusted noise reduction parameters include: classifying environmental noise according to the feature parameters to obtain the category of environmental noise; determining the noise reduction strategy according to the category of environmental noise; and adjusting the noise reduction parameters of the intelligent noise reduction model according to the noise reduction strategy.

[0088] Specifically, noise reduction strategies can be pre-set. For example, more efficient filtering algorithms can be used for high-frequency environmental noise, while more flexible noise reduction logic can be used for intermittent environmental noise to avoid accidentally eliminating important sounds. Based on the noise reduction strategy, specific parameters in the intelligent noise reduction model can be adjusted, such as the filter cutoff frequency, gain control, and time window length, to optimally suppress noise while preserving necessary environmental sounds.

[0089] In one embodiment of this application, the predictive sleep aid method further includes: obtaining user feedback information; and updating the noise reduction parameters of the intelligent noise reduction model based on the feedback information to optimize the noise reduction effect of the intelligent noise reduction model and improve the overall user experience and sleep aid effect.

[0090] Step S32: Based on the intelligent noise reduction model, the ambient noise is processed according to the adjusted noise reduction parameters to generate a noise-reduced frequency.

[0091] Specifically, by using an intelligent noise reduction model and adjusted noise reduction parameters, environmental noise is processed in real time or near real time to generate a noise-reduced frequency. The noise-reduced frequency effectively reduces noise interference while retaining necessary environmental sounds, so as to more effectively cancel or reduce external noise interference and significantly improve the sleep aid effect in rest mode, thereby helping users fall asleep better or maintain a high-quality sleep state.

[0092] In the above embodiments, by extracting the characteristic parameters of ambient noise and adjusting the noise reduction parameters of the intelligent noise reduction model accordingly, noise components in the audio can be significantly reduced or eliminated. This process makes the noise-reduced audio clearer and purer, thereby improving the overall quality of the noise-reduced audio.

[0093] Please see Figure 5 , Figure 5 This is a schematic diagram of the functional modules of the predictive sleep aid device 100 provided in the embodiments of this application.

[0094] In this embodiment, based on the same concept as the predictive sleep aid method in the above embodiments, this application also provides a predictive sleep aid device 100, which can be used to perform the above-described predictive sleep aid method. For ease of explanation, the schematic diagram of the composition of the predictive sleep aid device 100 embodiment only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the predictive sleep aid device 100, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0095] Specifically, the predictive sleep aid device 100 includes an acquisition module 110, a prediction module 120, and an adjustment module 130. The acquisition module 110 is used to play sleep aid music through the speaker 20 when the vehicle 1 enters rest mode, and to acquire the audio and image features of the environment in which the vehicle 1 is located. The prediction module 120 is used to predict the ambient noise of the environment in which the vehicle 1 is located within a future period based on a sound prediction model and the audio and image features. The adjustment module 130 is used to adjust the sleep aid music according to the ambient noise and play the adjusted sleep aid music through the speaker 20.

[0096] The predictive sleep aid device 100 provided in this application embodiment plays sleep aid music through speaker 20 when vehicle 1 enters rest mode, and acquires the audio and image features of the environment in which vehicle 1 is located; based on a sound prediction model, it predicts the environmental noise of the environment in which vehicle 1 is located in the future based on the audio and image features; it adjusts the sleep aid music according to the environmental noise, and plays the adjusted sleep aid music through speaker 20. Based on this, this application combines the audio and image features of the environment in which vehicle 1 is located to predict the environmental noise that may occur in the environment in which vehicle 1 is located in advance, and then adjusts the sleep aid music according to the predicted environmental noise. This allows it to better integrate into the in-vehicle environment, creating a quieter and more comfortable resting space for users, achieving a more accurate and effective noise reduction and sleep aid effect, and can also flexibly adapt to various different noise environments, improving the practicality and adaptability of the sleep aid music.

[0097] Combination Figure 1 As shown, in some embodiments of this application, the electronic device 10 includes, but is not limited to, a memory 11 storing computer-readable instructions; and a processor 12 executing the computer-readable instructions stored in the memory 11 to implement the predictive sleep aid method of the above embodiments.

[0098] The predictive sleep aid method provided in this application embodiment is applied to one or more electronic devices 10. The electronic device 10 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0099] In some embodiments of this application, the electronic device 10 can be communicatively connected to devices such as desktop computers, laptops, PDAs, and cloud servers. The electronic device 10 can interact with users via keyboards, mice, remote controls, touchpads, or voice control devices.

[0100] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 10 and does not constitute a limitation on the electronic device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device 10 may also include input / output devices, network access devices, buses, etc.

[0101] Processor 12 acquires the operating system and various installed applications of electronic device 10. Processor 12 acquires these applications to implement the steps described in the various predictive sleep aid method embodiments above, for example... Figure 2 , Figure 3 , Figure 4 The steps are shown.

[0102] For example, computer-readable instructions may be divided into one or more modules / units, one or more of which are stored in memory 11 and retrieved by processor 12 to complete this application. One or more modules / units may be a series of computer instruction segments capable of performing a specific function, which describe the process of retrieving computer-readable instructions in electronic device 10.

[0103] In some embodiments of this application, the network where the electronic device 10 is located includes, but is not limited to: the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0104] In some embodiments of this application, memory 11 is used to store program code and various data, such as a predictive sleep aid device installed in electronic device 10, and to enable high-speed, automatic access to programs or data during the operation of electronic device 10. Memory 11 may include read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0105] In some embodiments of this application, the memory 11 may also be an external memory and / or an internal memory of the electronic device 10. Furthermore, the memory 11 may be a physical memory, such as a memory module, a TF card (Trans-flash Card), etc.

[0106] In some embodiments of this application, the processor 12 may be a central processing unit (CPU), or other general-purpose processors, 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-purpose processor may be a microprocessor or any conventional processor. The processor 12 is the computational core and control center of the electronic device 10, connecting various parts of the electronic device 10 through various interfaces and lines, and calling data stored in the memory 11 to execute various functions of the electronic device 10 and process data, such as performing predictive sleep aid functions.

[0107] In some embodiments of this application, the processor 12 is used to obtain the operating system of the electronic device 10 and various installed applications. For example, the processor 12 obtains a predictive sleep aid program to implement the predictive sleep aid method described in the above embodiments, for example... Figure 2 , Figure 3 , Figure 4 The steps are shown.

[0108] In some embodiments of this application, the electronic device 10 may further include a power supply (not shown) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 12 through a power management device, thereby enabling functions such as charging, discharging, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 10 may also include Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0109] In some embodiments of this application, if the modules / submodules integrated in the electronic device 10 are implemented as software functional submodules and sold or used as independent components, they can be stored in a storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a storage medium, and when acquired by the processor 12, they can implement the above-mentioned... Figure 2 , Figure 3 , Figure 4 The steps of the various method embodiments shown.

[0110] In some embodiments of this application, computer-readable instructions may include computer-readable instruction code, which may be in the form of source code, object code, accessible file, or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer-readable instruction code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0111] The memory 11 in the electronic device 10 stores multiple instructions to implement a predictive sleep aid method, and the processor 12 can acquire multiple instructions to implement the predictive sleep aid method of the above embodiment.

[0112] Specifically, the processor 12's implementation method for the above instructions can be found in [reference needed]. Figure 2 , Figure 3 , Figure 4 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0114] The modules described as separate components may or may not be physically separate. The components shown as modules 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 embodiment according to actual needs.

[0115] In the various embodiments of this application, the functional modules can be integrated into one processing submodule, or each submodule can exist physically separately, or two or more submodules can be integrated into one submodule. The integrated submodules described above can be implemented in hardware or in a combination of hardware and software functional modules.

[0116] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0117] Furthermore, it is clear that the word "comprising" does not exclude other submodules or steps, and the singular does not exclude the plural. Multiple submodules or devices described in this application may also be implemented by a single submodule or device through software or hardware.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A predictive sleep aid method, characterized in that, Applied to a vehicle, the vehicle including a speaker, the predictive sleep aid method includes: If the vehicle enters rest mode, sleep-aid music is played through the speaker, and the audio and image features of the vehicle's environment are obtained. Obtaining the image features of the vehicle's environment includes: identifying moving objects in the image data and predicting the movement trajectory of the moving objects to obtain the image features. Based on the sound prediction model, the ambient noise of the vehicle's environment in the future is predicted according to the audio features and the image features. Adjusting the sleep-aid music according to the ambient noise and playing the adjusted sleep-aid music through the speaker, wherein adjusting the sleep-aid music according to the ambient noise includes: performing noise reduction processing on the ambient noise based on an intelligent noise reduction model to generate a noise-reduced frequency corresponding to the ambient noise; and adjusting the sleep-aid music according to the noise-reduced frequency.

2. The predictive sleep aid method as described in claim 1, characterized in that, The step of performing noise reduction processing on the environmental noise based on the intelligent noise reduction model to generate the corresponding noise-reduced frequency frequency of the environmental noise includes: Extract the characteristic parameters of the environmental noise; The noise reduction parameters of the intelligent noise reduction model are adjusted according to the feature parameters to obtain the adjusted noise reduction parameters; Based on the intelligent noise reduction model, the environmental noise is processed according to the adjusted noise reduction parameters to generate the noise-reduced frequency.

3. The predictive sleep aid method as described in claim 2, characterized in that, The step of adjusting the noise reduction parameters based on the feature parameters to obtain the adjusted noise reduction parameters includes: The environmental noise is classified according to the characteristic parameters to obtain the category of the environmental noise; A noise reduction strategy is determined based on the category of the environmental noise, and the noise reduction parameters of the intelligent noise reduction model are adjusted based on the noise reduction strategy.

4. The predictive sleep aid method as described in claim 2, characterized in that, The predictive sleep aid method also includes: Obtain user feedback information; Based on the feedback information, the noise reduction parameters of the intelligent noise reduction model are updated.

5. The predictive sleep aid method as described in claim 1, characterized in that, The vehicle also includes an audio sensor and an image sensor, and the acquisition of audio features of the vehicle's environment includes: The audio sensor collects audio data of the vehicle's environment in real time. The image sensor acquires image data of the exterior of the vehicle in real time. The audio data is subjected to feature extraction to obtain the audio features.

6. The predictive sleep aid method as described in claim 1, characterized in that, The predictive sleep aid method also includes: Based on the user's preference information, retrieve the corresponding music material from a pre-set database; The sleep-aid music is adjusted according to the music material.

7. A predictive sleep aid device, characterized in that, Applied to a vehicle, the vehicle including a speaker, the predictive sleep aid device includes: The acquisition module is used to play sleep-aid music through the speaker when the vehicle enters rest mode, and to acquire the audio and image features of the environment in which the vehicle is located. Acquiring the image features of the environment in which the vehicle is located includes: identifying moving objects in the image data and predicting the motion trajectory of the moving objects to obtain the image features. The prediction module is used to predict the ambient noise of the vehicle's environment in the future period based on the sound prediction model, according to the audio features and the image features. An adjustment module is used to adjust the sleep-aid music according to the ambient noise and play the adjusted sleep-aid music through the speaker. The adjustment of the sleep-aid music according to the ambient noise includes: performing noise reduction processing on the ambient noise based on an intelligent noise reduction model to generate a noise-reduced frequency corresponding to the ambient noise; and adjusting the sleep-aid music according to the noise-reduced frequency.

8. An electronic device, characterized in that, The electronic device includes: a memory storing computer-readable instructions; and a processor executing the computer-readable instructions stored in the memory to implement the predictive sleep aid method as described in any one of claims 1 to 6.

9. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 8.

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