Control methods, devices, pillows, and media for smart pillows

Abstract

The present application relates to the field of smart pillow control technology, and discloses a control method, device, pillow and medium for a smart pillow, wherein the method includes: obtaining an evaluation start signal; obtaining stage sleep state data and stage sleep reaction data according to a specified human body identifier in the evaluation start signal; determining a first control scheme according to the signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data; when the current time is greater than the wake-up start time in the first control scheme, controlling the smart pillow to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state, and the wake-up service is performed through the dynamically determined first control scheme, thereby improving the wake-up effect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent pillow control, and in particular to a control method, device, pillow and medium of an intelligent pillow. Background Art

[0002] One third of a person's life is spent in sleep. Sleep is a necessity of life and the human body cannot live without it. Due to the activities in life and work, it is difficult to wait for the human body to wake up naturally, so it is necessary to actively wake the human body to make it enter the awake state from the sleeping state. Different people have different needs for awakening. At present, customized alarm clocks are used to wake up the human body, resulting in average awakening effects. Summary of the invention

[0003] Based on this, it is necessary to address the technical problem that the existing technology uses a customized alarm to wake up the human body, resulting in a general wake-up effect, and propose a control method, device, pillow and medium for a smart pillow.

[0004] In a first aspect, a control method for a smart pillow is provided, the method comprising:

[0005] Get the evaluation start signal;

[0006] Acquiring stage sleep state data and stage sleep response data according to the designated human body identifier in the evaluation start signal;

[0007] determining a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data;

[0008] When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme.

[0009] In a second aspect, a control device for a smart pillow is provided, the device comprising:

[0010] A signal acquisition module, used to acquire an evaluation start signal;

[0011] A data acquisition module, used for acquiring stage sleep state data and stage sleep response data according to a specified human body identifier in the evaluation start signal;

[0012] a scheme determination module, configured to determine a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data;

[0013] The wake-up control module is used to control the smart pillow to play normal wake-up music according to the first control scheme when the current time is greater than the wake-up start time in the first control scheme.

[0014] In a third aspect, a smart pillow is provided, which includes an audio unit, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is communicatively connected to the audio unit, and the processor implements the steps of the control method of the above-mentioned smart pillow when executing the computer program.

[0015] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method of the above-mentioned smart pillow are implemented.

[0016] The control method, device, pillow and medium of the smart pillow of the present application, wherein the method obtains the evaluation start signal, obtains the stage sleep state data and the stage sleep reaction data according to the specified human body identifier in the evaluation start signal, determines the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data, and controls the smart pillow to play normal wake-up music according to the first control scheme when the current time is greater than the wake-up start time in the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state, and the wake-up service is performed through the dynamically determined first control scheme, thereby improving the wake-up effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] in:

[0019] Figure 1 A diagram showing an application environment of a control method for a smart pillow in one embodiment;

[0020] Figure 2 is a flow chart of a control method of a smart pillow in one embodiment;

[0021] Figure 3 is a structural block diagram of a control device of a smart pillow in one embodiment;

[0022] Figure 4 4 is a structural block diagram of a smart pillow in one embodiment. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The control method of the smart pillow provided by the embodiment of the present invention can be applied in Figure 1 In the application environment, the application environment includes: server 2, client 3, smart pillow 1, client 3 communicates with server 2 through the network, smart pillow 1 communicates with server 2 through the network, and smart pillow 1 communicates with client 3 through the network.

[0025] In one embodiment, the client 3 is configured to implement a control method for a smart pillow, the method comprising: obtaining an evaluation start signal; obtaining stage sleep state data and stage sleep reaction data according to a specified human body identifier in the evaluation start signal; determining a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data; when the current time is greater than the wake-up start time in the first control scheme, controlling the smart pillow to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state, and the wake-up service is performed through the dynamically determined first control scheme, thereby improving the wake-up effect.

[0026] In one embodiment, the smart pillow 1 is configured to implement a control method for the smart pillow, the method comprising: obtaining an evaluation start signal; obtaining stage sleep state data and stage sleep reaction data based on a specified human body identifier in the evaluation start signal; determining a first control scheme based on a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data; when the current time is greater than the wake-up start time in the first control scheme, controlling the smart pillow to play normal wake-up music according to the first control scheme.

[0027] It can be understood that the various collected data required by the control method of the smart pillow can be collected by the smart pillow 1, or by other smart home devices, or partly by the smart pillow 1 and partly by other smart home devices.

[0028] It can be understood that the server 2 is used for training various models, and the trained models are sent to the client 3 and / or the smart pillow 1, and the trained models are applied to the control method of the smart pillow of the present application.

[0029] The smart pillow includes an audio unit 12, a memory 13, a processor 11, and a computer program stored in the memory 13 and executable on the processor 11. The processor 11 is communicatively connected with the audio unit 12. When the processor 11 executes the computer program, the steps of the control method of the smart pillow of the present application are implemented.

[0030] The audio unit 319 includes one or more players, and the players may be speakers.

[0031] The client 3 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server 2 may be implemented by an independent server or a server cluster consisting of multiple servers.

[0032] The present invention is described in detail below through specific embodiments.

[0033] See also Figure 2 As shown, Figure 2 A flow chart of a control method of a smart pillow provided by an embodiment of the present invention, the method comprising the following steps:

[0034] S1: Get the evaluation start signal;

[0035] Specifically, an evaluation start signal sent by a third-party application (eg, an application in a smart gateway) may be obtained, or an evaluation start signal actively triggered by a program file implementing the present application according to preset conditions may be obtained.

[0036] The evaluation start signal is a signal for evaluating a control scheme for wake-up.

[0037] S2: acquiring stage sleep state data and stage sleep response data according to the designated human body identifier in the evaluation start signal;

[0038] Specifically, the stage sleep state data corresponding to the designated human body identification and the stage sleep response data corresponding to the designated human body identification may be acquired from the preset storage space.

[0039] The stage sleep state data is the description data of the sleep state data generated after the sleep start signal. The stage sleep state data describes the corresponding relationship between the sleep state data, the start time, and the end time.

[0040] The stage sleep response data is the sleep response data after the sleep start signal. The sleep response data describes the behavioral data during sleep. The sleep response data includes but is not limited to: body movement data and breathing data. Body movement data is the description data of the number of tossing and turning, awake time, and number of times leaving the bed. Breathing data includes heart rate, respiratory rate, and snoring statistics.

[0041] The designated human body identifier is the human body identifier carried by the sleep initiation signal, and the human body identifier may be data that uniquely identifies a human body, such as a human body name or a human body ID.

[0042] S3: determining a first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data;

[0043] Specifically, the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data may be input into a pre-trained wake-up music prediction model for classification prediction to obtain a wake-up vector.

[0044] The wake-up music prediction model is a multi-classification model. The model structure and model training method of the wake-up music prediction model can be selected from the existing technology and will not be described in detail here.

[0045] The value of each vector element in the wake-up vector is a probability value. Each vector element in the wake-up vector corresponds to a classification category, and the classification category expresses a policy description value.

[0046] The maximum probability value is found from the wake-up vector, and the strategy description value corresponding to the probability value is used as the value of each parameter in the first control scheme.

[0047] S4: When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme.

[0048] Specifically, when the current time is greater than the wake-up start time in the first control scheme, it is the normal wake-up stage. According to the first control scheme, the audio unit of the smart pillow is controlled to play normal wake-up music to achieve wake-up.

[0049] It is to be understood that, in this application, the current time refers to the real time.

[0050] Normal wake-up music is music that enters the wake-up service as expected by the user.

[0051] It can be understood that the dynamically determined first control scheme is more in line with the human body's music preference and sleep state. The music played based on the first control scheme enables the human body to wake up and get up with a pleasant mood and a wake-up time that is consistent with the sleep state, thereby achieving a better wake-up effect.

[0052] This embodiment determines the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data. When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state. The wake-up service is performed through the dynamically determined first control scheme, which improves the wake-up effect.

[0053] In one embodiment, before the step of obtaining the evaluation start signal, the method further includes:

[0054] S51: Obtaining a sleep start signal;

[0055] Specifically, a sleep start signal input by a user through a client or a button in a smart pillow may be obtained, a sleep start signal sent by a third-party application (e.g., an application in a smart gateway) may be obtained, or a sleep start signal actively triggered by a program file implementing the present application according to preset conditions may be obtained. For example, when the pressure sensor of the smart pillow detects pressure, it actively triggers a sleep start signal.

[0056] The sleep start signal is a signal to start sleeping.

[0057] S52: In response to the sleep start signal, subtract the preset advance time from the start time of the preset wake-up time range corresponding to the designated human body identifier to obtain an evaluation start time, and generate the evaluation start signal when the current time is greater than the evaluation start time;

[0058] Specifically, the preset wake-up time range is a preset time range. The preset wake-up time range includes a start time and an end time. That is, the wake-up service is performed on the human body corresponding to the specified human body identifier within the preset wake-up time range.

[0059] The start time of the preset wake-up time range corresponding to the designated human body identifier is subtracted from the preset advance time, and the subtracted data is used as the evaluation start time, thereby providing a basis for determining the first control scheme before entering the preset wake-up time range corresponding to the designated human body identifier. When the current time is greater than the evaluation start time, it means entering the normal wake-up service, and the evaluation start signal is generated.

[0060] S53: In response to the sleep start signal, obtain a sleep abnormality awakening signal corresponding to the designated human body identifier, and in response to the sleep abnormality awakening signal, control the smart pillow to play sleep abnormality awakening music according to a preset second control scheme.

[0061] Specifically, in response to the sleep start signal, if a sleep abnormality awakening signal corresponding to the designated human body identifier is obtained, the smart pillow is controlled to play sleep abnormality awakening music according to a preset second control scheme.

[0062] The preset second control scheme is a preset wake-up scheme, which includes one or more of the wake-up time adjustment sub-scheme, music wake-up sub-scheme, massage wake-up sub-scheme, neck temperature adjustment wake-up sub-scheme and brain temperature adjustment wake-up sub-scheme.

[0063] Optionally, the abnormal sleep wake-up signal is a signal generated when the human body corresponding to the designated human body identifier has apnea. The abnormal sleep wake-up signal can be generated by the program file implementing the present application according to the received respiratory data (for example, the smart pillow detects the respiratory data, and the program file implementing the present application compares the respiratory data with the preset apnea rule. If the respiratory data is within the apnea rule, it is determined that the human body has apnea, and if the respiratory data is outside the apnea rule, it is determined that the human body's breathing is normal), or it can be sent by a third-party application.

[0064] Sleep abnormality awakening music is music that can quickly awaken the human body in real time when sleep abnormality occurs.

[0065] After receiving the sleep start signal, this embodiment monitors the sleep abnormality wake-up signal corresponding to the sleep abnormality, and based on the sleep abnormality wake-up signal, controls the smart pillow to play the sleep abnormality wake-up music according to the preset second control scheme, thereby realizing effective abnormal wake-up service; and generates the evaluation start signal when the current time is greater than the evaluation start time, thereby providing a basis for realizing the wake-up service based on the dynamically determined first control scheme.

[0066] In one embodiment, the step of determining the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data comprises:

[0067] S31: Obtaining an initial music awakening solution corresponding to the designated human body identifier;

[0068] Specifically, the initial music awakening solution corresponding to the designated human body identifier may be obtained from a preset storage space, or the initial music awakening solution corresponding to the designated human body identifier may be obtained from a third-party application.

[0069] The initial music awakening solution includes a music segment identifier corresponding to the audio segment used for awakening, and the music segment identifier is data such as a segment ID that uniquely identifies a music segment.

[0070] S32: Determine the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep response data and the initial music wake-up scheme.

[0071] Specifically, the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep reaction data and the initial music awakening scheme can be input into a pre-trained awakening music prediction model for classification prediction to obtain a awakening vector.

[0072] This embodiment achieves dynamic fine-tuning of the initial music wake-up scheme by using the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep reaction data and the initial music wake-up scheme, so that the first control scheme is more in line with the requirements corresponding to the specified human body identifier, thereby improving the wake-up effect.

[0073] In one embodiment, before the step of obtaining the sleep start signal, the method further includes:

[0074] S61: Acquire user description data corresponding to the specified human body identifier;

[0075] Specifically, the user description data corresponding to the designated human body identifier is pre-set data.

[0076] The user description data includes: name, gender, age, sleep needs and health data.

[0077] Sleep need is a descriptive data of the total sleep duration range and deep sleep duration range.

[0078] Health data refers to the descriptive data of the health status of the five internal organs in traditional Chinese medicine.

[0079] It is understandable that music awakening is determined in combination with traditional Chinese medicine theory. For example, 1:00 p.m. to 23:00 belongs to the operation of the gallbladder meridian, so the awakening music related to the gallbladder meridian is played, and the awakening music related to the liver meridian is played from 1:00 to 3:00.

[0080] S62: Determine the initial music awakening scheme corresponding to the designated human body identifier according to the user description data and a preset standard music awakening scheme;

[0081] Specifically, the user description data and the preset standard music wake-up scheme are input into the pre-trained initial scheme prediction model for classification prediction to obtain a scheme vector.

[0082] The value of each vector element in the solution vector is a probability value. Each vector element in the solution vector corresponds to a classification category, which describes an operation identifier. By finding the first N largest probability values ​​corresponding to a sub-solution identifier in the solution vector, the operation identifier corresponding to the found probability value is used as the operation identifier corresponding to the specified human body identifier. N is a constant pre-set as needed.

[0083] The initial scenario prediction model is a multi-classification model. The model structure and model training method of the initial scenario prediction model can be selected from the existing technology and will not be described in detail here.

[0084] The preset standard music awakening scheme is a preset awakening scheme. The initial music awakening scheme is part or all of the data of the preset standard music awakening scheme.

[0085] S63: Acquire an update start signal;

[0086] The update start signal is a signal for updating the initial music wake-up solution.

[0087] Specifically, it can be an update start signal input by a user, an update start signal sent by a third-party application, or an update start signal actively triggered by a program file implementing the present application. For example, the update start signal is triggered regularly at 10 am every Tuesday.

[0088] S64: Responding to the update start signal, obtaining the historical wake-up scheme, historical sleep state data, historical sleep reaction data and historical wake-up effect description data of the designated human body identifier;

[0089] The historical wake-up scheme is the wake-up scheme used in history. The historical sleep state data is the sleep state data when the historical wake-up scheme is used. The historical sleep reaction data is the sleep reaction data when the historical wake-up scheme is used. The historical wake-up effect description data is the effect data of the historical wake-up service, where the historical wake-up effect description data includes but is not limited to: the actual wake-up duration.

[0090] S65: inputting the historical awakening scheme, the historical sleep state data, the historical sleep reaction data and the historical awakening effect description data into a pre-trained first model for classification prediction to obtain a first vector;

[0091] Specifically, the historical awakening scheme, the historical sleep state data, the historical sleep reaction data and the historical awakening effect description data are spliced ​​and input into a pre-trained first model for classification prediction, and the predicted vector is used as the first vector.

[0092] The first pre-trained model is a multi-classification model. The model structure and model training method of the first model can be selected from the prior art and will not be described in detail here.

[0093] S66: Determine an indicator evaluation value of each wake-up indicator according to the first vector, wherein the wake-up indicator includes: a wake-up duration indicator and a wake-up posture change indicator;

[0094] Specifically, the value of each vector element in the first vector is a probability value. Each vector element in the first vector corresponds to a classification category, which describes the correspondence between the indicator identifier and the indicator value. The indicator identifier can be data such as an indicator name, an indicator ID, etc. that uniquely identifies a wake-up indicator. By finding the maximum probability value corresponding to an indicator identifier in the first vector, the indicator value corresponding to the probability value is used as the indicator evaluation value of the wake-up indicator corresponding to the indicator identifier.

[0095] The wake-up duration indicator is an indicator that describes the wake-up duration. The wake-up posture change indicator is an indicator that describes the change in human posture when performing the wake-up service (starting to play the wake-up music).

[0096] S67: Combining the wake-up indicator and the indicator evaluation value corresponding to the wake-up indicator into a data pair;

[0097] Specifically, the indicator identifier of the same wake-up indicator and the indicator evaluation value form a data pair.

[0098] S68: splicing each of the data pairs, the standard music awakening scheme, and the initial music awakening scheme and inputting the spliced ​​data into a pre-trained second model for classification prediction to obtain a second vector;

[0099] Specifically, each of the data pairs, the standard music awakening scheme and the initial music awakening scheme are spliced ​​and input into a pre-trained second model for classification prediction, and the predicted vector is used as the second vector.

[0100] The second model is a multi-classification model. The model structure and model training method of the second model can be selected from the existing technology and will not be described in detail here.

[0101] It can be understood that all control parameters corresponding to the second model may be all control parameters in the standard music awakening scheme, or may be part of the control parameters in the standard music awakening scheme.

[0102] S69: Determine the updated initial music awakening scheme corresponding to the designated human body identifier according to the second vector.

[0103] Specifically, the value of each vector element in the second vector is a probability value. Each vector element in the second vector corresponds to a classification category, and the classification category describes the corresponding relationship between the control parameter and the parameter value. By finding the maximum probability value corresponding to the control parameter in the second vector, the parameter value corresponding to the probability value is used as the value corresponding to the control parameter in the initial music wake-up scheme corresponding to the specified human body identifier.

[0104] This embodiment first determines the indicator evaluation value of each wake-up indicator through the historical wake-up scheme, the historical sleep state data, the historical sleep reaction data and the historical wake-up effect description data, and then updates the initial music wake-up scheme corresponding to the designated human body identifier through each of the data pairs, the standard music wake-up scheme and the initial music wake-up scheme, thereby achieving effective learning of the initial music wake-up scheme and improving the accuracy of scheme learning.

[0105] In one embodiment, after the step of obtaining the sleep start signal, the method further includes:

[0106] S71: Responding to the sleep initiation signal, acquiring environmental data and an initial music relaxation program corresponding to a designated human body identifier in the sleep initiation signal;

[0107] The environmental data includes one or more of temperature, humidity, light intensity and sound intensity.

[0108] The initial music relaxation program is an initial music relaxation program pre-set for a human body corresponding to a specified human body identifier. The initial music relaxation program includes a music segment identifier corresponding to an audio segment used for relaxation.

[0109] Specifically, when the sleep start signal is received, environmental data can be obtained from a third-party application, or environmental data can be generated based on data detected by the smart pillow.

[0110] The initial music relaxation program is related to the basic information (such as name, gender, age), sleep needs and health description data of the human body corresponding to the specified human body identifier. Optionally, the health description data includes health status description data of the five internal organs of traditional Chinese medicine and disease description data.

[0111] It can be understood that each control parameter in the initial music relaxation program has a preset value.

[0112] S72: Determine the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep start signal, obtain a third control scheme, and control the smart pillow to play relaxation music according to the third control scheme;

[0113] In the process of controlling the smart pillow according to the third control scheme, an adjustment signal is obtained, and in response to the adjustment signal, current sleep state data and sleep reaction data to be analyzed are obtained, and the values ​​of the control parameters in the third control scheme are adjusted according to the current sleep state data and the sleep reaction data to be analyzed.

[0114] Specifically, the signal generation time of the environmental data and the sleep start signal is spliced, and the spliced ​​data is input into the pre-trained third model for classification prediction of the control parameters to obtain a target vector. The value of each vector element in the target vector is a probability value. Each vector element in the target vector corresponds to a classification category, which describes the correspondence between the music clip identifier, the control parameter and the parameter value. By finding the maximum probability value corresponding to a control parameter of a music clip identifier in the target vector, the parameter value corresponding to the probability value is used as the value corresponding to the control parameter in the third control scheme.

[0115] The third model is a multi-classification model. The model structure and model training method of the third model can be selected from the existing technology and will not be described in detail here.

[0116] It can be understood that all control parameters corresponding to the third model may be all control parameters in the initial music relaxation program, or may be part of the control parameters in the initial music relaxation program.

[0117] Among them, according to the third control scheme, the audio unit of the smart pillow is controlled to play relaxing music to relax the human body. Relaxation of the human body is conducive to falling asleep, thereby achieving the effect of helping sleep.

[0118] The adjustment signal is a signal for fine-tuning the third control scheme. In the process of controlling the smart pillow according to the third control scheme, the adjustment signal input by the user can be obtained, or it can be an adjustment signal actively triggered by the present application at a preset time interval, or it can be an adjustment signal actively triggered by a specific signal obtained by the program file of the present application. For example, the specific signal is that the light intensity changes within the first preset time length (which is a preset time length value) exceeds the preset threshold. At this time, the present application actively triggers the specific signal, and then actively triggers the adjustment signal according to the specific signal.

[0119] The current sleep state data is the current sleep state data. The value of the sleep state data is one of: falling asleep stage, light sleep stage, deep sleep stage and awake stage.

[0120] The sleep response data to be analyzed is the sleep response data within the preset time window. The end time of the preset time window is the signal generation time of the adjustment signal. The sleep response data describes the behavioral data during sleep. The sleep response data includes but is not limited to: body movement data and breathing data. Body movement data is the description data of the number of tossing over, awake time, and number of times leaving the bed. Breathing data includes heart rate, breathing rate, and snoring statistics.

[0121] The current sleep state data and the sleep response data to be analyzed are spliced ​​and input into a dynamic prediction model for classification prediction to obtain a vector to be analyzed. The value of each vector element in the vector to be analyzed is a probability value. Each vector element in the vector to be analyzed corresponds to a classification category, which describes the correspondence between the control parameter and the parameter value. By finding the maximum probability value corresponding to the control parameter in the vector to be analyzed, the parameter value corresponding to the probability value is used as the value corresponding to the control parameter in the third control scheme.

[0122] The dynamic prediction model is a multi-classification model. The model structure and model training method of the scenario prediction model can be selected from the existing technology and will not be described here.

[0123] All control parameters corresponding to the dynamic prediction model may be all control parameters in the third control scheme, or may be part of the control parameters in the third control scheme.

[0124] It can be understood that the control method of the smart pillow of the present application can be implemented by one or more of the client, server and smart pillow, that is, it can be implemented by the smart pillow or the client, or it can be implemented by the client and the smart pillow together, or it can be implemented by the client, server and smart pillow together.

[0125] It is understandable that the subtle adjustment to the third control scheme makes the third control scheme more in line with the human body's music preference and sleeping state. The music played based on the slightly adjusted third control scheme enables the human body to be more relaxed, and the human body in a relaxed state can enter deep sleep faster, thereby achieving a better sleep-aiding effect.

[0126] This embodiment determines the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep start signal, obtains the third control scheme, and controls the smart pillow to play relaxing music according to the third control scheme, thereby controlling the smart pillow to play relaxing music based on the third control scheme that conforms to the current environment, sleep time, and human body needs, so as to relax the human body and achieve the effect of helping sleep; dynamically adjusts the value of the control parameter of the third control scheme based on the current sleep state data and the sleep response data to be analyzed, and realizes subtle adjustments to the third control scheme, so that the third control scheme is more in line with the relaxation needs of the current stage, thereby further improving the sleep-aiding effect.

[0127] In one embodiment, the step of determining the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep initiation signal to obtain the third control scheme includes:

[0128] S7211: Inputting the initial music relaxation program, the environmental data, and the signal generation time of the sleep initiation signal into a pre-trained third model for classification prediction to obtain a third vector;

[0129] Specifically, the signal generation time of the initial music relaxation program, the environmental data and the sleep initiation signal is spliced ​​and input into a pre-trained third model for classification prediction, and the predicted vector is used as the third vector.

[0130] S7212: Determine, according to the third vector, the value of the control parameter of each relaxing audio clip in the initial music relaxation scheme to obtain a first sub-scheme, wherein the control parameters of the relaxing audio clip include: a play order and a play parameter, and the play parameter includes: one or more of a play rhythm, a music style, a play volume, and a play duration;

[0131] Specifically, in the third vector, the maximum probability value corresponding to a control parameter of a relaxation audio segment is found, and the parameter value corresponding to the probability value is used as the value corresponding to the control parameter in the first sub-scheme.

[0132] S7213: Sort the first sub-scheme in positive order according to the playback order to obtain the music relaxation sub-scheme in the third control scheme.

[0133] Specifically, the first sub-scheme is sorted in positive order according to the playback order, and the sorted data is used as the music relaxation sub-scheme in the third control scheme.

[0134] Optionally, the step of sorting the first sub-scheme in positive order according to the playback order to obtain the music relaxation sub-scheme in the third control scheme includes: sorting the first sub-scheme in positive order according to the playback order, splicing the sorted relaxation audio clips according to the first sub-scheme, and using the spliced ​​audio as the music relaxation sub-scheme in the third control scheme. That is to say, at this time, directly integrating the playback parameters into the spliced ​​audio is conducive to improving the fluency of the playback, thereby further improving the relaxation effect.

[0135] The initial music relaxation scheme, the environmental data and the signal generation time of the sleep start signal described in this embodiment determine the values ​​of control parameters of each relaxation audio clip in the initial music relaxation scheme. The control parameters of the relaxation audio clip include: playback order and playback parameters. The playback parameters include: playback rhythm, music style, playback volume and playback duration. One or more of these, thereby realizing personalized and dynamic generation of the music relaxation sub-scheme in the third control scheme, thereby improving the effect of music inducing sleep.

[0136] In one embodiment, the step of controlling the smart pillow to play relaxing music according to the third control scheme includes:

[0137] S7221: Get the audio segment playback start signal;

[0138] The audio segment play start signal is a signal to start playing the relaxation audio segment.

[0139] Specifically, the second preset time length (which is a preset time length value) before the relaxation audio segment is played actively triggers the audio segment play start signal.

[0140] S7222: Responding to the audio segment play start signal, obtaining the current sleeping posture, real-time sleeping data and music play preference data of the designated human body identifier;

[0141] Specifically, after the audio segment playing start signal is obtained, the current sleeping position (that is, the current sleeping position) of the designated human body identifier is obtained.

[0142] The music playing preference data is the pre-set description data of the music playing preference during sleep. The music playing preference data includes but is not limited to: music type, singer and song language.

[0143] The current sleeping position can be determined based on the pressure distribution data of the head in the smart pillow, or it can be determined through images taken by smart home devices (for example, infrared images).

[0144] The real-time sleep data is the sleep state data of the designated human body identification at the time when the signal of the start signal is generated in the audio segment, wherein the value of the sleep state data is one of: falling asleep, light sleep, deep sleep and awake stage.

[0145] S7223: Inputting the speaker data of the smart pillow, the target music segment identifier corresponding to the audio segment playback start signal, the current sleeping posture, the real-time sleep data and the music playback preference data into a pre-trained fourth model for classification prediction to obtain a fourth vector, wherein the speaker data includes a speaker identifier and speaker position data;

[0146] Specifically, the speaker data of the smart pillow, the target music segment identifier corresponding to the audio segment playback start signal, the current sleeping posture, the real-time sleep data and the music playback preference data are spliced ​​and input into the pre-trained fourth model for classification prediction, and the predicted data is used as the fourth vector.

[0147] S7224: Determine a target speaker identifier according to the fourth vector;

[0148] Specifically, the value of each vector element in the fourth vector is a probability value. Each vector element in the fourth vector corresponds to a classification category, and the classification category describes a speaker identification. By finding the largest probability value in the fourth vector, the speaker identification corresponding to the probability value is used as the target speaker identification.

[0149] S72215: According to the value of the playback parameter corresponding to the target music segment identifier, control the speaker corresponding to the target speaker identifier in the smart pillow to play the relaxing audio segment corresponding to the target music segment identifier.

[0150] Specifically, the speaker corresponding to the target speaker identifier in the smart pillow is controlled to play the relaxing audio segment corresponding to the target music segment identifier according to the value of the playback parameter corresponding to the target music segment identifier.

[0151] This embodiment determines the most suitable target speaker identifier based on the speaker data of the smart pillow, the target music clip identifier corresponding to the audio segment playback start signal, the current sleeping position, the real-time sleep data and the music playback preference data, and then controls the speaker corresponding to the target speaker identifier in the smart pillow according to the value of the playback parameter corresponding to the target music clip identifier to play the relaxing audio clip corresponding to the target music clip identifier, thereby allowing the speaker that best meets the user's needs to play relaxing music, which is conducive to further improving the effect of music relaxation.

[0152] See also Figure 3 As shown, in one embodiment, a control device for a smart pillow is provided, the device comprising:

[0153] The signal acquisition module 801 is used to acquire an evaluation start signal;

[0154] A data acquisition module 802 is used to acquire stage sleep state data and stage sleep response data according to the specified human body identifier in the evaluation start signal;

[0155] A scheme determination module 803, configured to determine a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data;

[0156] The wake-up control module 804 is used to control the smart pillow to play normal wake-up music according to the first control scheme when the current time is greater than the wake-up start time in the first control scheme.

[0157] This embodiment determines the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data. When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state. The wake-up service is performed through the dynamically determined first control scheme, which improves the wake-up effect.

[0158] In one embodiment, the device further comprises: an opening module, a first processing module, and a second processing module;

[0159] The start module is used to obtain a sleep start signal;

[0160] The first processing module is used to respond to the sleep start signal, subtract the preset advance time from the start time of the preset wake-up time range corresponding to the specified human body identifier to obtain the evaluation start time, and generate the evaluation start signal when the current time is greater than the evaluation start time;

[0161] The second processing module is used to respond to the sleep start signal, obtain the sleep abnormality awakening signal corresponding to the designated human body identifier, respond to the sleep abnormality awakening signal, and control the smart pillow to play sleep abnormality awakening music according to a preset second control scheme.

[0162] In one embodiment, the step of determining the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data in the scheme determination module 803 includes:

[0163] Obtaining an initial music wake-up solution corresponding to the specified human body identifier;

[0164] The first control scheme is determined according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep reaction data and the initial music wake-up scheme.

[0165] In one embodiment, the device further comprises: a scheme updating module, wherein the scheme updating module is configured to:

[0166] Obtaining user description data corresponding to the specified human body identifier;

[0167] Determine the initial music awakening scheme corresponding to the designated human body identifier according to the user description data and a preset standard music awakening scheme;

[0168] Get the update start signal;

[0169] In response to the update start signal, obtaining the historical wake-up scheme, historical sleep state data, historical sleep reaction data and historical wake-up effect description data of the designated human body identifier;

[0170] Inputting the historical awakening scheme, the historical sleep state data, the historical sleep reaction data and the historical awakening effect description data into a pre-trained first model for classification prediction to obtain a first vector;

[0171] Determine an indicator evaluation value of each wake-up indicator according to the first vector, wherein the wake-up indicator includes: a wake-up duration indicator and a wake-up posture change indicator;

[0172] The wake-up indicator and the indicator evaluation value corresponding to the wake-up indicator form a data pair;

[0173] splicing each of the data pairs, the standard music awakening scheme, and the initial music awakening scheme, and inputting the spliced ​​data into a pre-trained second model for classification prediction to obtain a second vector;

[0174] The updated initial music awakening scheme corresponding to the designated human body identifier is determined according to the second vector.

[0175] In one embodiment, the device further comprises: a relaxing music playing module, wherein the relaxing music playing module is used to:

[0176] In response to the sleep initiation signal, acquiring environmental data and an initial music relaxation program corresponding to a designated human body identifier in the sleep initiation signal;

[0177] Determine the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep start signal, obtain a third control scheme, and control the smart pillow to play relaxation music according to the third control scheme;

[0178] In the process of controlling the smart pillow according to the third control scheme, an adjustment signal is obtained, and in response to the adjustment signal, current sleep state data and sleep reaction data to be analyzed are obtained, and the values ​​of the control parameters in the third control scheme are adjusted according to the current sleep state data and the sleep reaction data to be analyzed.

[0179] In one embodiment, the step of determining the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep initiation signal in the relaxation music playing module to obtain the third control scheme includes:

[0180] Inputting the initial music relaxation program, the environmental data and the signal generation time of the sleep initiation signal into a pre-trained third model for classification prediction to obtain a third vector;

[0181] According to the third vector, determining the value of the control parameter of each relaxing audio clip in the initial music relaxation scheme to obtain a first sub-scheme, wherein the control parameters of the relaxing audio clip include: a play order and a play parameter, and the play parameter includes: one or more of a play rhythm, a music style, a play volume and a play duration;

[0182] The first sub-scheme is sorted in positive order according to the play order to obtain the music relaxation sub-scheme in the third control scheme.

[0183] In one embodiment, the step of controlling the smart pillow to play relaxing music according to the third control scheme in the relaxing music playing module includes:

[0184] Get the audio segment playback start signal;

[0185] In response to the audio segment playback start signal, obtaining the current sleeping posture, real-time sleeping data and music playback preference data of the designated human body identifier;

[0186] Inputting the speaker data of the smart pillow, the target music segment identifier corresponding to the audio segment playback start signal, the current sleeping posture, the real-time sleep data and the music playback preference data into a pre-trained fourth model for classification prediction to obtain a fourth vector, wherein the speaker data includes a speaker identifier and speaker position data;

[0187] determining a target speaker identifier according to the fourth vector;

[0188] According to the value of the playback parameter corresponding to the target music segment identifier, the speaker corresponding to the target speaker identifier in the smart pillow is controlled to play the relaxing audio segment corresponding to the target music segment identifier.

[0189] In one embodiment, a smart pillow is provided, the smart pillow comprising an audio unit 12, a memory 13, a processor 11, and a computer program stored in the memory 13 and executable on the processor 11, the processor 11 is in communication connection with the audio unit 12, and the processor 1011 implements a control method of the smart pillow when executing the computer program, the method comprising the following steps:

[0190] Get the evaluation start signal;

[0191] Acquiring stage sleep state data and stage sleep response data according to the designated human body identifier in the evaluation start signal;

[0192] determining a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data;

[0193] When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme.

[0194] This embodiment determines the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data. When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state. The wake-up service is performed through the dynamically determined first control scheme, which improves the wake-up effect.

[0195] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a control method for a smart pillow is implemented, wherein the method is used to control the smart pillow, and the method comprises the following steps:

[0196] Get the evaluation start signal;

[0197] Acquiring stage sleep state data and stage sleep response data according to the designated human body identifier in the evaluation start signal;

[0198] determining a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data;

[0199] When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme.

[0200] This embodiment determines the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep reaction data. When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme. The dynamically determined first control scheme is more in line with the human body's music preference and sleep state. The wake-up service is performed through the dynamically determined first control scheme, which improves the wake-up effect.

[0201] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0202] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0203] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0204] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A control method for a smart pillow, characterized in that: The method comprises: Get the evaluation start signal; Acquiring stage sleep state data and stage sleep response data according to the designated human body identifier in the evaluation start signal; determining a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data; When the current time is greater than the wake-up start time in the first control scheme, the smart pillow is controlled to play normal wake-up music according to the first control scheme; Before the step of obtaining the evaluation start signal, the method further includes: Get the sleep start signal; In response to the sleep start signal, subtract the preset advance time from the start time of the preset wake-up time range corresponding to the designated human body identifier to obtain the evaluation start time, and generate the evaluation start signal when the current time is greater than the evaluation start time; In response to the sleep start signal, obtaining a sleep abnormality awakening signal corresponding to the designated human body identifier, and in response to the sleep abnormality awakening signal, controlling the smart pillow to play sleep abnormality awakening music according to a preset second control scheme; The step of determining a first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data comprises: Obtaining an initial music wake-up solution corresponding to the specified human body identifier; determining the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep response data and the initial music wake-up scheme; Before the step of obtaining the sleep start signal, the method further includes: Obtaining user description data corresponding to the specified human body identifier; Determine the initial music awakening scheme corresponding to the designated human body identifier according to the user description data and a preset standard music awakening scheme; Get the update start signal; In response to the update start signal, obtaining the historical wake-up scheme, historical sleep state data, historical sleep reaction data and historical wake-up effect description data of the designated human body identifier; Inputting the historical awakening scheme, the historical sleep state data, the historical sleep reaction data and the historical awakening effect description data into a pre-trained first model for classification prediction to obtain a first vector; Determine an indicator evaluation value of each wake-up indicator according to the first vector, wherein the wake-up indicator includes: a wake-up duration indicator and a wake-up posture change indicator; The wake-up indicator and the indicator evaluation value corresponding to the wake-up indicator form a data pair; splicing each of the data pairs, the standard music awakening scheme, and the initial music awakening scheme, and inputting the spliced ​​data into a pre-trained second model for classification prediction to obtain a second vector; Determining the updated initial music awakening scheme corresponding to the designated human body identifier according to the second vector; After the step of obtaining the sleep start signal, the method further includes: In response to the sleep initiation signal, acquiring environmental data and an initial music relaxation program corresponding to a designated human body identifier in the sleep initiation signal; Determine the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep start signal, obtain a third control scheme, and control the smart pillow to play relaxation music according to the third control scheme; In the process of controlling the smart pillow according to the third control scheme, an adjustment signal is obtained, and in response to the adjustment signal, current sleep state data and sleep reaction data to be analyzed are obtained, and the values ​​of the control parameters in the third control scheme are adjusted according to the current sleep state data and the sleep reaction data to be analyzed.

2. The control method of the smart pillow according to claim 1, characterized in that: The step of determining the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep initiation signal to obtain the third control scheme comprises: Inputting the initial music relaxation program, the environmental data and the signal generation time of the sleep initiation signal into a pre-trained third model for classification prediction to obtain a third vector; According to the third vector, determining the value of the control parameter of each relaxing audio clip in the initial music relaxation scheme to obtain a first sub-scheme, wherein the control parameters of the relaxing audio clip include: a play order and a play parameter, and the play parameter includes: one or more of a play rhythm, a music style, a play volume and a play duration; The first sub-scheme is sorted in positive order according to the play order to obtain the music relaxation sub-scheme in the third control scheme.

3. The control method of the smart pillow according to claim 2, characterized in that: The step of controlling the smart pillow to play relaxing music according to the third control scheme comprises: Get the audio segment playback start signal; In response to the audio segment playback start signal, obtaining the current sleeping posture, real-time sleeping data and music playback preference data of the designated human body identifier; Inputting the speaker data of the smart pillow, the target music segment identifier corresponding to the audio segment playback start signal, the current sleeping posture, the real-time sleep data and the music playback preference data into a pre-trained fourth model for classification prediction to obtain a fourth vector, wherein the speaker data includes a speaker identifier and speaker position data; determining a target speaker identifier according to the fourth vector; According to the value of the playback parameter corresponding to the target music segment identifier, the speaker corresponding to the target speaker identifier in the smart pillow is controlled to play the relaxing audio segment corresponding to the target music segment identifier.

4. A control device for a smart pillow, characterized in that: The device comprises: A signal acquisition module, used to acquire an evaluation start signal; A data acquisition module, used for acquiring stage sleep state data and stage sleep response data according to a specified human body identifier in the evaluation start signal; a scheme determination module, configured to determine a first control scheme according to a signal generation time of a sleep start signal corresponding to the evaluation start signal, the stage sleep state data, and the stage sleep response data; A wake-up control module, used to control the smart pillow to play normal wake-up music according to the first control scheme when the current time is greater than the wake-up start time in the first control scheme; The device further comprises: an opening module, a first processing module and a second processing module; The start module is used to obtain a sleep start signal; The first processing module is used to respond to the sleep start signal, subtract the preset advance time from the start time of the preset wake-up time range corresponding to the specified human body identifier to obtain the evaluation start time, and generate the evaluation start signal when the current time is greater than the evaluation start time; The second processing module is used to respond to the sleep start signal, obtain the abnormal sleep awakening signal corresponding to the specified human body identifier, respond to the abnormal sleep awakening signal, and control the smart pillow to play abnormal sleep awakening music according to a preset second control scheme; The step of determining the first control scheme in the scheme determination module according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data and the stage sleep response data comprises: Obtaining an initial music wake-up solution corresponding to the specified human body identifier; determining the first control scheme according to the signal generation time of the sleep start signal corresponding to the evaluation start signal, the stage sleep state data, the stage sleep response data and the initial music wake-up scheme; The device further comprises: a scheme updating module, wherein the scheme updating module is used to: Obtaining user description data corresponding to the specified human body identifier; Determine the initial music awakening scheme corresponding to the designated human body identifier according to the user description data and a preset standard music awakening scheme; Get the update start signal; In response to the update start signal, obtaining the historical wake-up scheme, historical sleep state data, historical sleep reaction data and historical wake-up effect description data of the designated human body identifier; Inputting the historical awakening scheme, the historical sleep state data, the historical sleep reaction data and the historical awakening effect description data into a pre-trained first model for classification prediction to obtain a first vector; Determine an indicator evaluation value of each wake-up indicator according to the first vector, wherein the wake-up indicator includes: a wake-up duration indicator and a wake-up posture change indicator; The wake-up indicator and the indicator evaluation value corresponding to the wake-up indicator form a data pair; splicing each of the data pairs, the standard music awakening scheme, and the initial music awakening scheme, and inputting the spliced ​​data into a pre-trained second model for classification prediction to obtain a second vector; Determining the updated initial music awakening scheme corresponding to the designated human body identifier according to the second vector; The device further comprises: a relaxing music playing module, wherein the relaxing music playing module is used for: In response to the sleep initiation signal, acquiring environmental data and an initial music relaxation program corresponding to a designated human body identifier in the sleep initiation signal; Determine the value of the control parameter in the initial music relaxation scheme according to the environmental data and the signal generation time of the sleep start signal, obtain a third control scheme, and control the smart pillow to play relaxation music according to the third control scheme; In the process of controlling the smart pillow according to the third control scheme, an adjustment signal is obtained, and in response to the adjustment signal, current sleep state data and sleep reaction data to be analyzed are obtained, and the values ​​of the control parameters in the third control scheme are adjusted according to the current sleep state data and the sleep reaction data to be analyzed.

5. A smart pillow, characterized in that: The smart pillow includes an audio unit, a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is communicatively connected to the audio unit. When the processor executes the computer program, the steps of the control method of the smart pillow as described in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the control method of the smart pillow as described in any one of claims 1 to 3 are implemented.

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