Predictive sleep aiding method and device, electronic equipment and vehicle

Abstract

The invention provides a predictive sleep aiding method and device, electronic equipment and a vehicle, the predictive sleep aiding method is applied to the vehicle, the vehicle comprises a loudspeaker, and the method comprises the steps that if the vehicle enters a rest mode, sleep aiding music is played through the loudspeaker, and audio features and image features of the environment where the vehicle is located are acquired; on the basis of a sound prediction model, according to the audio features and the image features, predicting environmental noise of the environment where the vehicle is located within a period of time in the future; and adjusting the sleep-aiding music according to the environmental noise, and playing the adjusted sleep-aiding music through a loudspeaker. According to the method, the audio features and the image features of the environment where the vehicle is located are combined, the environment noise possibly occurring in the environment where the vehicle is located is predicted in advance, then the sleep-aiding music is adjusted according to the predicted environment noise, the sleep-aiding music can be better fused into the environment in the vehicle, the more accurate and effective noise reduction and sleep-aiding effect is achieved, and the user experience is improved. And the sleep-aiding music can be flexibly adapted to various different noise environments, so that the practicability and adaptability of the sleep-aiding music are improved.

Description

Technical Field

[0001] The present application relates to the field of sleep aid technology, and in particular to a predictive sleep aid method, device, electronic equipment and vehicle. Background Art

[0002] With the development of science and technology, cars are becoming more and more intelligent, and people's demand for cars is also increasing, especially the demand for the comfort of car use is getting higher and higher. The main reason is that people spend more time in cars. For example, people spend more time resting and taking naps in cars. However, vehicles are usually parked in public places with noisy environments, making it difficult for users to fall asleep in a short period of time. Therefore, it is necessary to improve the intelligence level of cars to achieve a sleep-aiding effect in rest mode.

[0003] Under existing technical conditions, playing preset white noise light music has become one of the effective means to improve the sleep-aiding effect of the car rest mode. White noise resources are mostly limited to fixed audio files, such as simulated natural sounds such as flowing water and birdsong, which results in poor sleep-aiding effects of existing sleep-aiding methods when dealing with complex and changeable actual noise environments. Summary of the invention

[0004] In view of this, the present application provides a predictive sleep-aiding method, device, electronic device and vehicle to solve the technical problem that existing sleep-aiding methods have poor actual sleep-aiding effects when dealing with complex and changeable actual noise environments.

[0005] An embodiment of the present application provides a predictive sleep-aiding method, which is applied to a vehicle, wherein the vehicle includes a speaker, and the predictive sleep-aiding method includes: if the vehicle enters a rest mode, playing sleep-aiding music through the speaker, and acquiring audio features and image features of the environment in which the vehicle is located; based on a sound prediction model, predicting the ambient noise of the environment in which the vehicle is located within a period of time in the future according to the audio features and the image features; adjusting the sleep-aiding music according to the ambient noise, and playing the adjusted sleep-aiding music through the speaker.

[0006] The predictive sleep-aiding method provided in the embodiment of the present application is as follows: if the vehicle enters the rest mode, sleep-aiding music is played through the speaker, and the audio features and image features of the environment in which the vehicle is located are obtained; based on the sound prediction model, the ambient noise of the environment in which the vehicle is located in a period of time in the future is predicted according to the audio features and the image features; the sleep-aiding music is adjusted according to the ambient noise, and the adjusted sleep-aiding music is played through the speaker. Based on this, the present application combines the audio features and image features of the vehicle's environment to predict in advance the ambient noise that may occur in the vehicle's environment, and then adjusts the sleep-aiding music according to the predicted ambient noise, which can be better integrated 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-aiding effect, and can also flexibly adapt to a variety of different noise environments, improving the practicality and adaptability of sleep-aiding music.

[0007] In some embodiments of the present application, adjusting the sleep-aiding 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 reduction audio corresponding to the ambient noise; and adjusting the sleep-aiding music according to the noise reduction audio.

[0008] In some embodiments of the present application, the noise reduction processing of the environmental noise based on the intelligent noise reduction model to generate the noise reduction audio corresponding to the environmental noise includes: extracting characteristic parameters of the environmental noise; adjusting the noise reduction parameters of the intelligent noise reduction model according to the characteristic parameters to obtain adjusted noise reduction parameters; based on the intelligent noise reduction model, performing noise reduction processing on the environmental noise according to the adjusted noise reduction parameters to generate the noise reduction audio.

[0009] In some embodiments of the present application, adjusting the noise reduction parameters according to the characteristic parameters to obtain the adjusted noise reduction parameters includes: classifying the environmental noise according to the characteristic 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 the present application, the predictive sleep aid method further includes: obtaining feedback information from the user; and updating the noise reduction parameters of the intelligent noise reduction model according to the feedback information.

[0011] In some embodiments of the present application, the vehicle also includes an audio sensor and an image sensor, and acquiring audio features and image features of the environment in which the vehicle is located includes: collecting audio data of the environment in which the vehicle is located in real time through the audio sensor; collecting image data outside the vehicle in real time through the image sensor; performing feature extraction on the audio data to obtain the audio features; identifying moving objects in the image data, and predicting the motion trajectory of the moving objects to obtain the image features.

[0012] In some embodiments of the present application, the predictive sleep-aiding method further includes: acquiring corresponding music materials from a preset database according to the user's preference information; and adjusting the sleep-aiding music according to the music materials. In a second aspect, an embodiment of the present application further provides a predictive sleep-aid device, which is applied to a vehicle, wherein the vehicle includes a speaker, and the predictive sleep-aid device includes: an acquisition module, which is used to play sleep-aid music through the speaker when the vehicle enters a rest mode, and obtain audio features and image features of the environment in which the vehicle is located; a prediction module, which is used to predict the ambient noise of the environment in which the vehicle is located within a period of time in the future based on the audio features and the image features based on a sound prediction model; and an adjustment module, which is used to adjust the sleep-aid music according to the ambient noise, and play the adjusted sleep-aid music through the speaker.

[0013] In a third aspect, an embodiment of the present application further provides an 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-aiding method as described in the above embodiment.

[0014] In a fourth aspect, an embodiment of the present application further provides a vehicle, which includes the electronic device as described in the above embodiment.

[0015] It can be understood that the predictive sleep-aiding device of the second aspect, the electronic device of the third aspect, and the vehicle of the fourth aspect provided above all correspond to the predictive sleep-aiding method of the first aspect. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding predictive sleep-aiding method provided above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the hardware structure of a vehicle provided in one embodiment of the present application.

[0017] Figure 2 It is a flowchart of a predictive sleep-aiding method provided in one embodiment of the present application.

[0018] Figure 3It is a detailed flowchart of step S10 in the predictive sleep-aiding method provided in one embodiment of the present application.

[0019] Figure 4 It is a detailed flowchart of step S12 in the predictive sleep-aiding method provided in one embodiment of the present application.

[0020] Figure 5 Schematic diagram of the functional modules of a predictive sleep aid device provided in one embodiment of the present application.

[0021] Component Symbols Vehicle 1 Electronic devices 10 Memory 11 Processor 12 Speaker 20 Audio sensor 30 Image sensor 40 Predictive sleep aid device100 Acquisition module 110 Prediction module 120 Adjustment module 130 The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the implementation methods of the present application and the features in the implementation methods can be combined with each other without conflict.

[0023] In the following description, many specific details are set forth to facilitate a full understanding of the present application. The described implementations are only part of the implementations of the present application, rather than all the implementations.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0025] It should be further noted that, in this article, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0026] In this application, "at least one" means one or more, and "more" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0027] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0028] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the hardware structure of the vehicle 1 provided in one embodiment of the present application.

[0029] Specifically, the vehicle 1 provided in the embodiment of the present application includes an electronic device 10. The electronic device 10 may be an onboard device of the vehicle 1.

[0030] The predictive sleep-aiding method provided in the embodiment of the present application 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 a microprocessor, an application-specific integrated circuit (ASIC), a programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0031] In other embodiments, the electronic device 10 may also be any electronic product that is communicatively connected to the vehicle 1, such as a personal computer, a tablet computer, etc. The electronic device 10 may interact with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice control device.

[0032] In some embodiments of the present application, the network where the electronic device 10 is located includes but is not limited to: the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0033] In some embodiments of the present application, the vehicle 1 further includes a speaker 20, an audio sensor 30 and an image sensor 40. In the embodiments of the present application, a plurality of audio sensors 30 may be installed at different locations of the vehicle 1 to collect audio data of the environment in which the 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 capability and a wide frequency response range to ensure that various audio data in the environment in which the vehicle 1 is located can be accurately collected, such as traffic noise, pedestrian sounds, natural environment sounds, etc. In the embodiments of the present application, a plurality of image sensors 40 may be installed at different locations of the vehicle 1 to collect image data of the environment around the vehicle 1, such as the types of surrounding vehicles 1, traffic flow, road types, etc. For example, the image sensor 40 includes but is not limited to an infrared camera. The image sensor 40 has high resolution and good low-light performance, so that image data of the environment in which the vehicle 1 is located can be clearly captured under different lighting conditions.

[0034] Please refer to Figure 2 , Figure 2 It is a flowchart of a predictive sleep-aiding method provided in one embodiment of the present application.

[0035] Specifically, the predictive sleep aid method includes the following steps. According to different needs, the order of some steps in the flowchart can be changed, and some steps can be omitted.

[0036] Step S10: If the vehicle enters the rest mode, sleep-aiding music is played through the speaker, and audio features and image features of the vehicle's environment are obtained. In some embodiments of the present application, rest mode refers to a special state that the vehicle 1 enters in order to provide passengers with a comfortable and quiet rest environment. When the vehicle 1 enters the rest mode, the vehicle 1 must be stationary and the gear of the vehicle 1 is in the parking gear (such as P gear) to ensure that the vehicle 1 will not move accidentally during the rest mode, thereby ensuring the safety of the user. Specifically, the user can trigger the rest mode through the central control screen of the vehicle 1 or the voice assistant.

[0037] In some embodiments of the present application, the sleep-aiding music may be preset music. The preset music includes but is not limited to: (1) natural sounds, such as the sound of waves, rain, wind, streams, etc. These sounds can simulate the natural environment, help people relax and relieve stress. (2) soft music, such as light music, piano music, violin music, etc. These music have soft melodies and steady rhythms, which help lower heart rate and promote sleep. (3) white noise, such as the sound of air conditioners and fans, etc. White noise can cover distracting background noise and help people fall asleep more easily. (4) meditation music, for example, natural sounds and soft music can be combined, and meditation guiding words or meditation music elements can be added to help guide passengers into a meditative state and relax their body and mind. (5) personalized music, for example, some popular relaxation music or music of a specific style, such as jazz, classical music, etc., can be preset according to the user's personal preferences.

[0038] In some embodiments of the present application, sleep-aiding music may be stored in a preset database.

[0039] Specifically, the steps of obtaining the audio features and image features of the environment in which the vehicle 1 is located include: collecting audio data of the environment in which the vehicle 1 is located in real time through the audio sensor 30; collecting image data outside the vehicle 1 in real time through the 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.

[0040] It should be noted that how to obtain the audio features and image features of the environment in which the vehicle 1 is located will be described in detail later. Figure 3 The steps shown are described in detail and will not be repeated here to avoid repetition.

[0041] Step S11, based on the sound prediction model, according to the audio features and the image features, predict the environmental noise of the environment in which the vehicle is located within a period of time in the future.

[0042] In some embodiments of the present application, the method for constructing a sound prediction model includes: selecting a suitable sound prediction model according to specific application scenarios and requirements. Common models include neural network models based on deep learning (such as convolutional neural networks, recurrent neural networks, long short-term memory networks, etc.), prediction models based on statistics (such as time series analysis models), etc. The audio features and image features are fused by feature splicing, feature mapping, feature interaction, etc., so that the sound prediction model can use both sound features and image features for prediction. The selected sound prediction model is trained using historical audio features and historical image features as training sets. By adjusting the model parameters and optimizing the algorithm, the selected sound prediction model can accurately predict the environmental noise in the future by combining sound features and image features.

[0043] Specifically, the audio features and image features are input into the trained sound prediction model, and the sound prediction model calculates the environmental noise in the future (e.g., 5 minutes, 10 minutes, etc.) based on the input data. The output result of the sound prediction model includes information such as the type, intensity, and time of the environmental noise that may appear in the future. For example, if the image sensor 40 detects that a moving object (e.g., a motor vehicle) is approaching the vehicle 1, the relevant sound information of the moving object is obtained from the preset sound feature database based on the type of the moving object (e.g., a motor vehicle, a pedestrian, an animal, etc.), the speed, and the interval distance, and then combined with the sound features in the sound data collected by the audio sensor 30, the possible horn sound, engine sound, and other sounds can be predicted.

[0044] In some embodiments of the present application, the predictive sleep aid method also 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 adapts to changing environmental conditions and improves the prediction performance of the sound prediction model.

[0045] Step S12, adjusting the sleep-aiding music according to the ambient noise, and playing the adjusted sleep-aiding music through a speaker.

[0046] Specifically, the step of adjusting the sleep-aiding music according to the ambient noise includes: performing noise reduction processing on the ambient noise based on the intelligent noise reduction model to generate a noise reduction audio corresponding to the ambient noise; and adjusting the sleep-aiding music according to the noise reduction audio.

[0047] The steps of performing noise reduction processing on the environmental noise based on the intelligent noise reduction model and generating noise reduction audio corresponding to the environmental noise include: extracting characteristic parameters of the environmental noise; adjusting the noise reduction parameters of the intelligent noise reduction model according to the characteristic 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 noise reduction audio.

[0048] It should be noted that how to reduce the ambient noise based on the intelligent noise reduction model and generate the noise reduction audio corresponding to the ambient noise will be discussed later. Figure 4 The steps shown are described in detail and will not be repeated here to avoid repetition.

[0049] The step of adjusting the sleep-aiding music according to the noise reduction audio includes: analyzing the noise reduction audio to obtain the volume level, frequency distribution, dynamic range and other characteristics of the noise reduction audio. According to the volume level of the noise reduction audio, the volume of the sleep-aiding music is adjusted. For example, if the noise reduction audio is relatively noisy, the volume of the sleep-aiding music can be appropriately increased to cover up the remaining noise; if the noise reduction audio is already very quiet, the volume of the music can be appropriately reduced to avoid disturbing sleep. According to the frequency distribution and dynamic range of the noise reduction audio, the rhythm and timbre of the sleep-aiding music are adjusted. For example, if there are more low-frequency parts in the noise reduction audio, sleep-aiding music with more low-frequency components can be selected to enhance the sense of immersion; if the noise reduction effect is particularly good and there is almost no noise, softer and more dreamy sleep-aiding music can be selected to create a dreamy sleeping atmosphere. The adjusted sleep-aiding music is mixed with the noise reduction audio to ensure that the two achieve a harmonious and unified effect in terms of volume, timbre, rhythm, etc., thereby creating an audio environment that is both comfortable and conducive to sleep, allowing users to fall asleep easily in a quiet atmosphere.

[0050] The predictive sleep-aiding method provided in the embodiment of the present application is as follows: if vehicle 1 enters rest mode, sleep-aiding music is played through speaker 20, and audio features and image features of the environment in which vehicle 1 is located are obtained; based on the sound prediction model, the ambient noise of the environment in which vehicle 1 is located in a period of time in the future is predicted according to the audio features and image features; the sleep-aiding music is adjusted according to the ambient noise, and the adjusted sleep-aiding music is played through speaker 20. Based on this, the present application combines the audio features and image features of the environment in which vehicle 1 is located, predicts the ambient noise that may occur in the environment in which vehicle 1 is located in advance, and then adjusts the sleep-aiding music according to the predicted ambient noise, which can be better integrated 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-aiding effect, and can also flexibly adapt to a variety of different noise environments, improving the practicality and adaptability of sleep-aiding music.

[0051] In some embodiments of the present application, the predictive sleep-aiding method further includes: acquiring corresponding music materials from a preset database according to the user's preference information; and adjusting the sleep-aiding music according to the music materials.

[0052] Specifically, through online questionnaires or applications, collect users' preferences for music styles, such as soft piano music, natural white noise, relaxing jazz or other types of music. If the user has used similar sleep-aiding music services before, the user's music preferences can be inferred by analyzing the user's historical behavior data (such as play records, likes / collections, skipping tracks, etc.). Pre-store various types and styles of music materials in a preset database, such as pure music, natural sounds, human voice guidance, etc., to meet the preferences of different users. Classify and label the music materials, such as "gentle", "soothing", "natural", "meditation", etc., to quickly retrieve and match user preferences. Ensure that the music materials in the database are of high quality, without noise, no copyright issues, and can continuously provide a comfortable listening experience. According to the preference information provided by the user, search and match can be performed in the preset database to find the music materials that best meet the user's needs. It is also possible to use recommendation algorithms (such as collaborative filtering, content-based recommendation, etc.), combined with the user's historical behavior and preference information, to provide users with a personalized music recommendation list, and allow users to preview the recommended music materials and select or adjust them according to their preferences. According to the music material selected by the user, its volume and rhythm are adjusted to ensure that the sleep-aiding music is neither too harsh nor too bland, and can create a comfortable sleeping atmosphere.

[0053] In some embodiments of the present application, the predictive sleep-aiding method further includes: adjusting the sleep-aiding music according to the user's sleep-aiding needs. Specifically, according to the user's sleep-aiding needs, natural sounds (such as rain, waves, birdsong, etc.) or human voice guidance (such as meditation instructions, relaxation breathing methods, etc.) can be added to the sleep-aiding music to enhance the sleep-aiding effect of the sleep-aiding music. Users can also adjust certain parameters of the music (such as volume, playback order, loop mode, etc.) according to their own sleep-aiding needs to meet their unique sleep needs.

[0054] In some embodiments of the present application, the predictive sleep-aiding method further includes: regularly collecting user feedback on sleep-aiding music, such as satisfaction, improvement suggestions, etc. Analyze the collected feedback to understand the user's real needs and preference changes. Based on the analysis results of user feedback information, continuously optimize the preset database, recommendation algorithm, and sleep-aiding music adjustment strategy to improve user experience and satisfaction, so as to achieve the best sleep-aiding effect.

[0055] Please refer to Figure 3 , Figure 3 Detailed flowchart of step S10 in the predictive sleep aid method provided in one embodiment of the present application.

[0056] This embodiment is a specific description of the above embodiment, and further describes how to obtain the audio features and image features of the environment in which the vehicle 1 is located. Specifically, the following steps are included: Step S20: collecting audio data of the vehicle's environment in real time through an audio sensor.

[0057] Specifically, multiple audio sensors 30 may be installed at different locations of the vehicle 1 to collect audio data of the environment in which the 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 capability and a wide frequency response range to ensure that various audio data in the environment in which the vehicle 1 is located can be accurately collected, such as traffic noise, pedestrian sounds, natural environment sounds, etc.

[0058] Step S21 : collecting image data outside the vehicle 1 in real time through an image sensor.

[0059] Specifically, multiple image sensors 40 may be installed at different locations of the vehicle 1 to collect image data of the environment around the vehicle 1, such as the types of surrounding vehicles 1, traffic flow, road types, etc. For example, the image sensor 40 includes but is not limited to an infrared camera. The image sensor 40 has high resolution and good low-light performance, so that image data of the environment in which the vehicle 1 is located can be clearly captured under different lighting conditions.

[0060] Step S22, extracting features from the audio data to obtain audio features.

[0061] In some embodiments of the present application, the audio data may be preprocessed before feature extraction to remove noise, enhance signal quality, or adjust the signal format. The preprocessing step may include: using a filter or algorithm to remove background noise and improve the signal-to-noise ratio. Splitting continuous audio data into shorter frames (usually 20-30 milliseconds). In order to reduce the discontinuity between frames, a window function may also be applied to each frame after framing.

[0062] Specifically, the audio data can be converted from the time domain to the frequency domain through Fourier transform, and then the frequency domain features such as the amplitude, phase, frequency components, and Mel frequency cepstrum coefficients of the audio data can be extracted. It is also possible to automatically learn specific features of the audio data such as volume, timbre, and rhythm based on the deep learning model.

[0063] In some embodiments of the present application, after feature extraction, the various extracted audio features are combined into a feature vector, which is used 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.

[0064] Step S23, identifying the moving object in the image data, and predicting the motion trajectory of the moving object to obtain image features.

[0065] In some embodiments of the present application, before identifying moving objects in image data, the collected image data is preprocessed, such as filtering, denoising, contrast enhancement, etc., to improve the quality of the image data and improve the accuracy of subsequent processing.

[0066] Specifically, background subtraction algorithms (such as Gaussian mixture models, Bayesian models, etc.) are used to separate the background from the moving objects to identify the moving objects. After the moving objects are detected, machine learning or deep learning algorithms (such as support vector machines, random forests, convolutional neural networks, etc.) are used to classify and identify the moving objects. Feature extraction is performed on the detected moving objects, including geometric features (such as size, shape, position, etc.) and motion features (such as speed, acceleration, direction, etc.), which are used as input parameters for subsequent trajectory prediction. The dynamic model and statistical behavior patterns are learned from the historical motion trajectory data, and the motion trajectory of the moving object is predicted based on the dynamic model to obtain image features. The predicted motion trajectory is optimized using an optimization algorithm (such as a particle swarm optimization algorithm) to improve the accuracy and robustness of motion prediction.

[0067] Please refer to Figure 4 , Figure 4 It is a detailed flowchart of step S12 in the predictive sleep-aiding method provided in one embodiment of the present application.

[0068] This embodiment is a specific description of the above embodiment, and further describes how to perform noise reduction processing on environmental noise based on an intelligent noise reduction model to generate noise reduction audio corresponding to the environmental noise. Specifically, the following steps are included: Step S30: extracting characteristic parameters of environmental noise.

[0069] Specifically, the characteristic parameters include but are not limited to the type of environmental noise (such as traffic noise, human voice, machine noise, etc.), volume, time (such as the time period, duration, and change pattern of the environmental noise), etc.

[0070] Step S31, adjusting the noise reduction parameters of the intelligent noise reduction model according to the characteristic parameters to obtain adjusted noise reduction parameters.

[0071] Specifically, the steps of adjusting the noise reduction parameters according to the characteristic parameters to obtain the adjusted noise reduction parameters include: classifying the environmental noise according to the characteristic parameters to obtain the category of the environmental noise; determining the 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.

[0072] Specifically, the noise reduction strategy can be pre-set, for example, for high-frequency environmental noise, a more efficient filtering algorithm is used; for intermittent environmental noise, a more flexible noise reduction logic is used to avoid mistakenly eliminating important sounds. According to the noise reduction strategy, the specific parameters in the intelligent noise reduction model are adjusted, such as the filter cutoff frequency, gain control, time window length, etc., to optimally suppress noise while retaining the necessary environmental sounds.

[0073] In one embodiment of the present application, the predictive sleep-aiding method further includes: obtaining user feedback information; updating the noise reduction parameters of the intelligent noise reduction model according to the feedback information to optimize the noise reduction effect of the intelligent noise reduction model and improve the overall user experience and sleep-aiding effect.

[0074] Step S32: Based on the intelligent noise reduction model and the adjusted noise reduction parameters, the ambient noise is subjected to noise reduction processing to generate a noise reduction audio.

[0075] Specifically, the intelligent noise reduction model and adjusted noise reduction parameters are used to perform real-time or quasi-real-time noise reduction processing on the ambient noise to generate a noise-reduced audio. The noise reduction audio effectively reduces the interference of noise while retaining the necessary ambient sounds, so as to more effectively offset or reduce the interference of external noise, and significantly improve the sleep-aiding effect in the rest mode, thereby helping users fall asleep better or maintain high-quality sleep.

[0076] In the above embodiment, by extracting characteristic parameters of the ambient noise and adjusting the noise reduction parameters of the intelligent noise reduction model accordingly, the 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.

[0077] See also Figure 5 , Figure 5 Schematic diagram of the functional modules of the predictive sleep aid device 100 provided in an embodiment of the present application.

[0078] In this embodiment, based on the same concept as the predictive sleep-aiding method in the above embodiment, the present application also provides a predictive sleep-aiding device 100, which can be used to perform the above predictive sleep-aiding method. For ease of explanation, the schematic diagram of the composition of the embodiment of the predictive sleep-aiding device 100 only shows the parts related to the embodiment of the present application. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the predictive sleep-aiding device 100, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0079] 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 the rest mode, and to obtain the audio features and image features of the environment in which the vehicle 1 is located. The prediction module 120 is used to predict the environmental noise of the environment in which the vehicle 1 is located within a period of time in the future based on the sound prediction model, the audio features and the image features. The adjustment module 130 is used to adjust the sleep aid music according to the environmental noise, and play the adjusted sleep aid music through the speaker 20.

[0080] The predictive sleep-aiding device 100 provided in the embodiment of the present application, if the vehicle 1 enters the rest mode, plays sleep-aiding music through the speaker 20, and obtains the audio features and image features of the environment in which the vehicle 1 is located; based on the sound prediction model, predicts the environmental noise of the environment in which the vehicle 1 is located in a period of time in the future according to the audio features and image features; adjusts the sleep-aiding music according to the environmental noise, and plays the adjusted sleep-aiding music through the speaker 20. Based on this, the present application combines the audio features and image features of the environment in which the vehicle 1 is located, predicts the environmental noise that may occur in the environment in which the vehicle 1 is located in advance, and then adjusts the sleep-aiding music according to the predicted environmental noise, which can be better integrated 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-aiding effect, and can also flexibly adapt to a variety of different noise environments, improving the practicality and adaptability of sleep-aiding music.

[0081] Combination Figure 1 As shown, in some embodiments of the present 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 embodiment.

[0082] The predictive sleep-aiding method provided in the embodiment of the present application 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 a microprocessor, an application-specific integrated circuit (ASIC), a programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0083] In some embodiments of the present application, the electronic device 10 can be connected to a desktop computer, a notebook, a PDA, a cloud server, etc. The electronic device 10 can interact with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0084] Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 10 and does not constitute a limitation on the electronic device 10 . The electronic device 10 may include more or fewer components than shown in the diagram, or a combination of certain components, or different components. For example, the electronic device 10 may also include input and output devices, network access devices, buses, etc.

[0085] The processor 12 obtains the operating system of the electronic device 10 and various installed applications. The processor 12 obtains the application to implement the steps in the above-mentioned various predictive sleep aid method embodiments, for example Figure 2 , Figure 3 , Figure 4 Steps shown.

[0086] Exemplarily, the computer readable instructions may be divided into one or more modules / units, one or more modules / units are stored in the memory 11, and are obtained by the processor 12 to complete the present application. One or more modules / units may be a series of computer instruction segments capable of completing a specific function, and the instruction segment is used to describe the acquisition process of the computer readable instructions in the electronic device 10.

[0087] In some embodiments of the present application, the network where the electronic device 10 is located includes, but is not limited to: the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0088] In some embodiments of the present application, the memory 11 is used to store program codes and various data, such as a predictive sleep aid device installed in the electronic device 10, and to achieve high-speed and automatic access to programs or data during the operation of the electronic device 10. The memory 11 may include a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0089] In some embodiments of the present 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 memory in a physical form, such as a memory stick, a TF card (Trans-flash Card), and the like.

[0090] In some embodiments of the present application, the processor 12 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 12 is the computing core and control center of the electronic device 10, and uses various interfaces and lines to connect various parts of the entire electronic device 10, and calls the data stored in the memory 11 to execute various functions of the electronic device 10 and process data, such as executing the predictive sleep aid function.

[0091] In some embodiments of the present 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 the predictive sleep aid program to implement the predictive sleep aid method of the above embodiment, for example Figure 2 , Figure 3 , Figure 4 Steps shown.

[0092] In some embodiments of the present application, the electronic device 10 may also include a power supply (not shown) for supplying power to each component. Preferably, the power supply may be logically connected to the processor 12 through a power management device, so that the power management device can manage charging, discharging, and power consumption. The power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 10 may also include a Bluetooth module, a Wi-Fi module, etc., which will not be described in detail here.

[0093] In some embodiments of the present application, if the modules / submodules integrated in the electronic device 10 are implemented in the form of software functional submodules and sold or used as independent workpieces, they can be stored in a storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware to complete through computer-readable instructions. The computer-readable instructions can be stored in a storage medium. When the computer-readable instructions are obtained by the processor 12, the above-mentioned Figure 2 , Figure 3 , Figure 4 The steps of each method embodiment are shown.

[0094] In some embodiments of the present application, the computer-readable instructions may include computer-readable instruction codes, which may be in source code form, object code form, accessible files, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer-readable instruction code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0095] The memory 11 in the electronic device 10 stores a plurality of instructions to implement a predictive sleep-aiding method, and the processor 12 may obtain the plurality of instructions to implement the predictive sleep-aiding method of the above embodiment.

[0096] Specifically, the specific implementation method of the processor 12 for the above instructions can refer to Figure 2 , Figure 3 , Figure 4 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0098] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] Each functional module in each embodiment of the present application can be integrated into a processing submodule, or each submodule can exist physically separately, or two or more submodules can be integrated into one submodule. The above-mentioned integrated submodule can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0100] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present application is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0101] In addition, it is obvious that the word "comprising" does not exclude other submodules or steps, and the singular does not exclude the plural. The multiple submodules or devices stated in this application can also be implemented by one submodule or device through software or hardware.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A predictive sleep aid method, characterized in that: Applied to a vehicle, the vehicle includes a speaker, and the predictive sleep aid method includes: If the vehicle enters a rest mode, sleep-aiding music is played through the speaker, and audio features and image features of the environment in which the vehicle is located are obtained; Based on the sound prediction model, according to the audio features and the image features, predict the environmental noise of the environment in which the vehicle is located within a period of time in the future; The sleep-aiding music is adjusted according to the ambient noise, and the adjusted sleep-aiding music is played through the speaker.

2. The predictive sleep aid method according to claim 1, characterized in that: The adjusting the sleep-aiding music according to the ambient noise comprises: Performing noise reduction processing on the environmental noise based on an intelligent noise reduction model to generate a noise reduction audio corresponding to the environmental noise; The sleep-aiding music is adjusted according to the noise reduction audio.

3. The predictive sleep aid method according to claim 2, characterized in that: The performing noise reduction processing on the environmental noise based on the intelligent noise reduction model to generate the noise reduction audio corresponding to the environmental noise includes: Extracting characteristic parameters of the environmental noise; Adjusting the noise reduction parameters of the intelligent noise reduction model according to the characteristic parameters to obtain adjusted noise reduction parameters; Based on the intelligent noise reduction model, according to the adjusted noise reduction parameters, noise reduction processing is performed on the environmental noise to generate the noise reduction audio frequency.

4. The predictive sleep-aiding method according to claim 3, characterized in that: The step of adjusting the noise reduction parameter according to the characteristic parameter to obtain the adjusted noise reduction parameter comprises: Classifying the environmental noise according to the characteristic parameters to obtain the category of the environmental noise; A noise reduction strategy is determined according to the category of the environmental noise, and noise reduction parameters of the intelligent noise reduction model are adjusted according to the noise reduction strategy.

5. The predictive sleep aid method according to claim 3, characterized in that: The predictive sleep-aiding method further comprises: Obtain user feedback; According to the feedback information, the noise reduction parameters of the intelligent noise reduction model are updated.

6. The predictive sleep aid method according to claim 1, characterized in that: The vehicle further includes an audio sensor and an image sensor, and the acquiring of audio features and image features of the environment in which the vehicle is located includes: The audio sensor collects audio data of the environment in which the vehicle is located in real time; Collecting image data outside the vehicle in real time by using the image sensor; Performing feature extraction on the audio data to obtain the audio feature; The moving object in the image data is identified, and the motion trajectory of the moving object is predicted to obtain the image feature.

7. The predictive sleep aid method according to claim 1, characterized in that: The predictive sleep-aiding method further comprises: According to the user's preference information, the corresponding music material is obtained from the preset database; The sleep-aiding music is adjusted according to the music material.

8. A predictive sleep aid device, characterized in that: Applied to a vehicle, the vehicle includes a speaker, and the predictive sleep aid device includes: an acquisition module, configured to play sleep-aiding music through the speaker and acquire audio features and image features of the environment in which the vehicle is located if the vehicle enters a rest mode; A prediction module, configured to predict the ambient noise of the environment in which the vehicle is located within a period of time in the future based on the sound prediction model and the audio features and the image features; The adjustment module is used to adjust the sleep-aiding music according to the environmental noise and play the adjusted sleep-aiding music through the speaker.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing computer-readable instructions; and a processor executing the computer-readable instructions stored in the memory to implement the predictive sleep-aiding method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: The vehicle comprises the electronic device as claimed in claim 9.

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