CN111897230A - Sleep quality monitoring method and device, electrical equipment, storage medium and processor - Google Patents
Sleep quality monitoring method and device, electrical equipment, storage medium and processor Download PDFInfo
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Abstract
The invention discloses a sleep quality monitoring method, a sleep quality monitoring device, electrical equipment, a storage medium and a processor, wherein the method comprises the following steps: monitoring current sleep parameters of a user when the user is sleeping; determining the current sleep state of the user according to the current sleep parameters; and determining a relationship between the current sleep state and a set sleep state; the set sleep state comprises: a first sleep state or a second sleep state, wherein the depth of sleep in the first sleep state is greater than the depth of sleep in the second sleep state; and if the current sleep state is the second sleep state, performing auxiliary deepening intervention on the sleep-in depth of the current sleep state. The scheme of the invention can solve the problem that the user cannot be helped to improve the sleep quality, and achieves the effect of helping the user to improve the sleep quality.
Description
Technical Field
The invention belongs to the technical field of intelligent home furnishing, and particularly relates to a sleep quality monitoring method, a sleep quality monitoring device, electric equipment, a storage medium and a processor, in particular to an air conditioner intelligent adjusting method, a device, electric equipment, a storage medium and a processor for improving sleep based on respiratory sound or snore monitoring.
Background
With the development of the times and the acceleration of the life rhythm of people, modern people use the sleeping time for other things, and people who are rushing to work get worse and worse sleep quality. The current sleep situations of little sense, easy waking and love making sleepiness are that people are in great trouble, the problems of late sleep, irregular sleep, little sleep and the like generally exist, and the sleep quality directly influences the physical and mental health, the mental state and the like of people.
Good sleep is the basis of mental health, deep sleep enables cerebral cortical cells of a person to be in a full rest state, and is very important for stabilizing emotion, balancing mental state and restoring energy. Meanwhile, the good sleep can also enable a plurality of antibodies to be generated in the human body and enhance the disease resistance. Therefore, how to help users improve sleep quality is a problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The present invention aims to solve the above-mentioned drawbacks, and provide a sleep quality monitoring method, apparatus, electrical device, storage medium, and processor, so as to solve the problem that the user cannot be helped to improve the sleep quality, and achieve the effect of helping the user improve the sleep quality.
The invention provides a sleep quality monitoring method, which comprises the following steps: monitoring current sleep parameters of a user when the user is sleeping; determining the current sleep state of the user according to the current sleep parameters; and determining a relationship between the current sleep state and a set sleep state; the set sleep state comprises: a first sleep state or a second sleep state, wherein the depth of sleep in the first sleep state is greater than the depth of sleep in the second sleep state; and if the current sleep state is the second sleep state, performing auxiliary deepening intervention on the sleep-in depth of the current sleep state.
Optionally, the current sleep parameters include: respiratory, snoring, and/or sleeping movements; monitoring current sleep parameters of the user, including: a microphone module is adopted to collect the breath sound and/or snore of the user; and/or a millimeter wave module is adopted to collect the sleeping actions of the user; the microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
Optionally, wherein the determining the current sleep state of the user comprises: according to the corresponding relation between the set sleep parameters and the set sleep state, determining the set sleep state corresponding to the set sleep parameters which are the same as the current sleep state in the corresponding relation as the current sleep state of the user under the current sleep parameters; and/or performing auxiliary deepening intervention on the falling-asleep depth of the current sleep state, wherein the auxiliary deepening intervention comprises the following steps: according to the corresponding relation between the set sleep state and the set sleep music, determining the set sleep music corresponding to the set sleep state which is the same as the current sleep state in the corresponding relation as the current sleep music matched by the user in the current sleep state; and playing the current sleep music so as to utilize the current sleep music to perform auxiliary deepening intervention on the sleep-in depth of the current sleep state.
Optionally, the method further comprises: under the condition that the current sleep state is determined, awakening an acquisition mechanism of the current environment parameters of the sleep environment of the user to acquire the current environment parameters of the sleep environment of the user; determining the current comfortable sleeping environment parameters of the user according to the current sleeping state; and adjusting the current environment parameters in the sleeping environment of the user according to the current comfortable sleeping environment parameters.
Optionally, the determining the current comfortable sleep environment parameter of the user comprises: determining a set comfortable sleep environment parameter corresponding to a set sleep state which is the same as the current sleep state in the corresponding relation as the current comfortable sleep environment parameter matched by the user in the current sleep state according to the corresponding relation between the set sleep state and the set comfortable sleep environment parameter; wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter include: temperature and/or humidity.
In another aspect, the present invention provides a sleep quality monitoring apparatus, including: the monitoring unit is used for monitoring the current sleep parameters of the user under the condition that the user sleeps; the determining unit is used for determining the current sleep state of the user according to the current sleep parameters; the determining unit is further used for determining the relationship between the current sleep state and the set sleep state; the set sleep state comprises: a first sleep state or a second sleep state, wherein the depth of sleep in the first sleep state is greater than the depth of sleep in the second sleep state; and the control unit is used for performing auxiliary deepening intervention on the sleep-in depth of the current sleep state if the current sleep state is the second sleep state.
Optionally, the current sleep parameters include: respiratory, snoring, and/or sleeping movements; the monitoring unit monitors the current sleep parameters of the user, including: a microphone module is adopted to collect the breath sound and/or snore of the user; and/or a millimeter wave module is adopted to collect the sleeping actions of the user; the microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
Optionally, wherein the determining unit determines the current sleep state of the user, including: according to the corresponding relation between the set sleep parameters and the set sleep state, determining the set sleep state corresponding to the set sleep parameters which are the same as the current sleep state in the corresponding relation as the current sleep state of the user under the current sleep parameters; and/or the control unit performs auxiliary deepening intervention on the falling-asleep depth of the current sleep state, and the method comprises the following steps: according to the corresponding relation between the set sleep state and the set sleep music, determining the set sleep music corresponding to the set sleep state which is the same as the current sleep state in the corresponding relation as the current sleep music matched by the user in the current sleep state; and playing the current sleep music so as to utilize the current sleep music to perform auxiliary deepening intervention on the sleep-in depth of the current sleep state.
Optionally, the method further comprises: the monitoring unit is further configured to wake up an acquisition mechanism for current environment parameters of the sleep environment of the user to acquire the current environment parameters of the sleep environment of the user when the current sleep state is determined; the determining unit is further configured to determine a current comfortable sleep environment parameter of the user according to the current sleep state; the control unit is further configured to adjust the current environmental parameter in the sleep environment of the user according to the current comfortable sleep environmental parameter.
Optionally, the determining unit determines a current comfort sleep environment parameter of the user, including: determining a set comfortable sleep environment parameter corresponding to a set sleep state which is the same as the current sleep state in the corresponding relation as the current comfortable sleep environment parameter matched by the user in the current sleep state according to the corresponding relation between the set sleep state and the set comfortable sleep environment parameter; wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter include: temperature and/or humidity.
In accordance with another aspect of the present invention, there is provided an electrical apparatus, including: the sleep quality monitoring device is described above.
In accordance with the foregoing method, a further aspect of the present invention provides a storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus where the storage medium is located is controlled to execute the foregoing sleep quality monitoring method.
In accordance with the foregoing method, a further aspect of the present invention provides a processor for executing a program, wherein the program executes the sleep quality monitoring method described above.
Therefore, according to the scheme of the invention, the sleeping condition of the user is analyzed by monitoring the sleeping parameters of the user, such as the sleeping respiratory sound, the snoring sound, the sleeping action and the like, and the sleeping environment of the user is adjusted according to the analysis result, so that the sleeping environment of the user is improved to help the user to improve the sleeping quality, the problem that the user cannot be helped to improve the sleeping quality is solved, and the effect of helping the user to improve the sleeping quality is achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
Fig. 1 is a flowchart illustrating a sleep quality monitoring method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an embodiment of performing auxiliary deepening intervention on the falling-asleep depth of the current sleep state in the method of the present invention;
FIG. 3 is a flowchart illustrating an embodiment of improving a sleep environment according to a current sleep state in the method of the present invention;
fig. 4 is a schematic structural diagram of a sleep quality monitoring apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart diagram illustrating an embodiment of an air conditioner to intelligently adjust a comfortable sleep environment;
fig. 6 is a flow diagram of another embodiment of an air conditioner to intelligently adjust a comfortable sleep environment.
The reference numbers in the embodiments of the present invention are as follows, in combination with the accompanying drawings:
102-a monitoring unit; 104-a determination unit; 106-control unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to an embodiment of the present invention, a sleep quality monitoring method is provided, as shown in fig. 1, which is a schematic flow chart of an embodiment of the method of the present invention. The sleep quality monitoring method is mainly applied to electrical equipment such as an intelligent home and the like, such as an air conditioner, and the sleep quality monitoring method of the air conditioner can comprise the following steps: step S110 to step S140.
At step S110, current sleep parameters of the user are monitored in case the user is sleeping, i.e. in case the user is sleeping. For example: when a user is in a sleeping state, the sleeping parameters of the user, such as breathing sound, snore, turning-over action and the like, are collected.
Optionally, the current sleep parameter may include: respiratory sounds, snoring sounds, and/or sleeping actions. Wherein, the sleeping action is the action condition of the user during sleeping.
Optionally, the monitoring the current sleep parameters of the user in step S110 may include: a microphone module is adopted to collect the breath sound and/or snore of the user; and/or a millimeter wave module is adopted to collect the sleeping actions of the user.
The microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
For example: the air conditioner is provided with a microphone, a far-field speech recognition technology is used, and the microphone monitors the respiratory sound and the snore of a user through the far-field speech recognition technology, such as collecting the respiratory sound and the snore by the microphone; and monitoring the sleeping action of the user by combining the millimeter wave technology, such as monitoring the sleeping action by using millimeter waves.
For example: the user turns on the air conditioner to sleep. The air conditioner microphone and the millimeter wave sensor monitor the sleep state of the user. In the sleep state, the method may include: sound collection is carried out by utilizing a microphone, and the respiratory sound, snore and the like of the user are obtained; and (5) utilizing the millimeter wave sensor to monitor the action to obtain the turning-over action of the user and the like.
From this, gather the current sleep parameter when the user sleeps through adopting microphone module, millimeter wave module etc. can conveniently and accurately gather the current sleep parameter when the user sleeps on the one hand, thereby on the other hand can not disturb the user and can avoid influencing user's sleep quality.
At step S120, a current sleep state of the user is determined according to the current sleep parameter.
Optionally, the determining the current sleep state of the user according to the current sleep parameter in step S120 may include: and determining the set sleep state corresponding to the set sleep parameter which is the same as the current sleep state in the corresponding relation as the current sleep state of the user according to the corresponding relation between the set sleep parameter and the set sleep state.
For example: and identifying the sleeping condition of the user by utilizing the corresponding relation between the sleeping parameters and the sleeping state according to the acquired sleeping parameters, such as a sleeping state neural network model obtained by pre-training. The determination of the current sleep state and the like may be performed at the local end or at the server end.
For example: the air conditioner is configured with an intelligent monitoring module (a microphone module, a millimeter wave module, etc.), a speaker module, an intelligent networking module, an AI module, etc. The AI algorithm model can be a neural network model, a sleep state neural network model trained by setting a large amount of sample data, weight values of breathing sound, snore and turning-over action are set for training, and a better weight parameter is obtained through training, namely the optimal neural network model. The air-conditioning microphone monitors and identifies sleeping respiratory sound and snore of a user in real time by using a far-field speech recognition technology, the millimeter wave module of the air-conditioning monitors and identifies data of turning-over actions of the user in real time by using a millimeter wave technology, and the neural network sets a certain time period to identify the sleeping state of the user according to the monitoring data.
Therefore, the current sleep state of the user can be determined quickly and accurately according to the current sleep parameters of the user by determining the current sleep state of the user according to the corresponding relation between the set sleep parameters and the set sleep state.
And, at step S130, determining a relationship between the current sleep state and the set sleep states, i.e., determining to which one of the set sleep states the current sleep state belongs. The set sleep state may include: a first sleep state (e.g., deep sleep state) or a second sleep state (e.g., light sleep state), the depth of sleep in the first sleep state being greater than the depth of sleep in the second sleep state.
At step S140, if the current sleep state is the first sleep state (e.g., deep sleep state), there is no need to match sleep music; and if the current sleep state is the second sleep state (such as a light sleep state), performing auxiliary deepening intervention on the sleep-in depth of the current sleep state to improve the sleep quality of the user in the current sleep state.
For example: when the user is asleep, the sleep music is matched to guide the user to enter deep sleep by playing the sleep music. The sleep-assisting and healing music is properly played according to the sleep condition at the moment, so that the user enters a deep sleep state.
Therefore, when the current sleep parameter of the user is monitored under the condition that the user sleeps, and the sleep depth of the current sleep state is determined to belong to the shallow sleep state according to the current sleep parameter, the sleep depth of the current sleep state is assisted and deepened to intervene, so that the sleep quality of the user in the current sleep state is improved, and the sleep quality of the user is improved.
Optionally, with reference to a flowchart of an embodiment of performing auxiliary deepening intervention on the sleep-in depth of the current sleep state in the method of the present invention shown in fig. 2, a specific process of performing auxiliary deepening intervention on the sleep-in depth of the current sleep state in step S140 is further described, which may include: step S210 and step S230.
Step S210, according to a corresponding relationship between a set sleep state and set sleep music, determining the set sleep music corresponding to the set sleep state that is the same as the current sleep state in the corresponding relationship as the current sleep music matched by the user in the current sleep state.
Step S230, playing the current sleep music to perform assisted deepening intervention on the sleep-in depth of the current sleep state by using the current sleep music.
For example: after the sleep state neural network model identifies the sleep state of the user, the comfort level neural network model obtains music most suitable for assisting the current sleep state according to the sleep state matching. If the user is in light sleep, the corresponding audio is matched, and if the user is in deep sleep, the audio is matched and does not need to be played.
Therefore, the sleep quality of the user can be improved and improved by matching the current sleep music for the user when the user falls asleep and playing the current sleep music to assist and deepen the sleep depth of the current sleep state.
In an alternative embodiment, the method may further include: a process of improving a sleep environment according to a current sleep state.
Referring to the flowchart of fig. 3, a specific process of improving a sleep environment according to a current sleep state is further described, where the specific process includes: step S310 to step S330.
Step S310, in a case that the current sleep state is determined, waking up an acquisition mechanism for the current environment parameters of the sleep environment of the user to acquire the current environment parameters of the sleep environment of the user. For example: when the sleep condition of the user is identified, the air conditioner is awakened to acquire the current environment state information so as to obtain the current environment state of the environment where the user is located.
Step S320, determining the current comfortable sleep environment parameter of the user according to the current sleep state.
Optionally, the determining the current comfortable sleep environment parameter of the user according to the current sleep state in step S320 may include: and determining the set comfortable sleep environment parameters corresponding to the set sleep state which is the same as the current sleep state in the corresponding relation as the current comfortable sleep environment parameters matched by the user in the current sleep state according to the corresponding relation between the set sleep state and the set comfortable sleep environment parameters.
Wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter may include: temperature and/or humidity.
For example: awakening the air conditioner intelligent module to detect the current environment temperature, humidity and the like when the sleep state of the user is identified at each time, calculating a comfortable sleep environment suitable for the user according to the sleep condition of the user and the current environment by using the other environment comfort level neural network matching model at the moment, wherein the comfortable sleep environment comprises the required temperature, humidity and the like, and the air conditioner acquires the required temperature and humidity demand instruction and intelligently adjusts the temperature, the humidity and the like so as to meet the comfortable environment.
Therefore, the current comfortable sleep environment parameters of the user are determined according to the current sleep state of the user, and the current comfortable sleep environment parameters are adjusted to the current comfortable sleep environment parameters, so that the comfort of the user is improved, and the sleep quality is further improved.
Step S330, adjusting the current environmental parameter in the sleep environment of the user according to the current comfortable sleep environmental parameter, for example, controlling an environmental parameter adjusting device (e.g., an air conditioner) in the sleep environment of the user to operate according to the current comfortable sleep environmental parameter, so as to adjust the current environmental parameter to the comfortable environmental parameter in the current sleep state, thereby improving the current environmental parameter of the sleep environment of the user, and facilitating to maintain or even improve the sleep quality of the user. For example: according to the sleeping condition of the user and the current environment state of the environment where the user is located, the comfortable environment state is determined by utilizing the corresponding relation between the environment state and the sleeping condition, such as a comfort neural network model, and the current environment state is improved.
For example: the sleep quality is improved by utilizing sensor modules such as microphones and millimeter waves carried by electrical equipment such as air conditioners and the like, collecting sound absorption and snore through the microphones, monitoring the sleep action through the millimeter waves, and intelligently analyzing the sleep condition through an AI depth algorithm model and assisting the air conditioner to make corresponding sleep comfort environment adjustment. If the breathing sound, the snore, the turning-over action and the like of the user are analyzed and accurately calculated through the AI algorithm model, a comfortable sleeping environment can be identified and matched, and the air conditioner intelligently adjusts the temperature and the air speed.
For example: the air conditioner is provided with an intelligent monitoring module (such as a microphone module, a millimeter wave module and the like) and a loudspeaker module. The air conditioner is started when a user sleeps, the set temperature and wind speed modes are always in a fixed state within one night, at the moment, monitoring states of an air conditioner microphone, a millimeter wave sensor and the like are started, respiratory sound, snore sound, turning-over actions of sleeping and the like of the user during sleeping are monitored in real time, the air conditioner uploads collected data to a cloud server to process an AI deep learning algorithm model to identify the sleeping condition of the user and accurately calculate a sleeping environment suitable for the user to be comfortable, at the moment, an instruction for intelligently controlling the air conditioner is issued at the cloud end, the operation action of the air conditioner is intelligently adjusted, and the sleeping quality of the user is improved.
Through a large number of tests, the technical scheme of the embodiment is adopted, electrical equipment such as an air conditioner monitors the sleeping respiratory sound, the snoring sound and the sleeping actions of a user through the microphone, the millimeter wave and other sensors, the sleeping condition of the user is analyzed and identified, and the air conditioner intelligently helps the user to improve the sleeping quality.
According to the embodiment of the invention, a sleep quality monitoring device corresponding to the sleep quality monitoring method is also provided. Referring to fig. 4, a schematic diagram of an embodiment of the apparatus of the present invention is shown. This sleep quality monitoring device mainly uses on electric equipment such as intelligence house etc. air conditioner, and the sleep quality monitoring device of air conditioner can include: a monitoring unit 102, a determination unit 104 and a control unit 106.
In an alternative example, the monitoring unit 102 may be configured to monitor the current sleep parameters of the user while the user is sleeping, i.e. while the user is sleeping. For example: when a user is in a sleeping state, the sleeping parameters of the user, such as breathing sound, snore, turning-over action and the like, are collected. The specific functions and processes of the monitoring unit 102 are shown in step S110.
Optionally, the current sleep parameter may include: respiratory sounds, snoring sounds, and/or sleeping actions. Wherein, the sleeping action is the action condition of the user during sleeping.
Optionally, the monitoring unit 102 monitors the current sleep parameters of the user, which may include: the monitoring unit 102 may be further configured to employ a microphone module to collect the breathing sound and/or the snoring sound of the user; and/or, the monitoring unit 102 may be further configured to employ a millimeter wave module to collect sleep movements of the user.
The microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
For example: the air conditioner is provided with a microphone, and the microphone monitors the breathing sound and the snore of a user by using a far-field speech recognition technology, such as collecting the breathing sound and the snore by using the microphone. And monitoring the sleeping action of the user by combining the millimeter wave technology, such as monitoring the sleeping action by using millimeter waves.
For example: the user turns on the air conditioner to sleep. The air conditioner microphone and the millimeter wave sensor monitor the sleep state of the user. In the sleep state, the method may include: sound collection is carried out by utilizing a microphone, and the respiratory sound, snore and the like of the user are obtained; and (5) utilizing the millimeter wave sensor to monitor the action to obtain the turning-over action of the user and the like.
From this, gather the current sleep parameter when the user sleeps through adopting microphone module, millimeter wave module etc. can conveniently and accurately gather the current sleep parameter when the user sleeps on the one hand, thereby on the other hand can not disturb the user and can avoid influencing user's sleep quality.
In an optional example, the determining unit 104 may be configured to determine the current sleep state of the user according to the current sleep parameter. The specific function and processing of the determination unit 104 are referred to in step S120.
Optionally, the determining unit 104 determines the current sleep state of the user according to the current sleep parameter, which may include: the determining unit 104 may be further configured to determine, according to a corresponding relationship between a set sleep parameter and a set sleep state, a set sleep state corresponding to the set sleep parameter in the corresponding relationship, which is the same as the current sleep state, as the current sleep state of the user under the current sleep parameter.
For example: and identifying the sleeping condition of the user by utilizing the corresponding relation between the sleeping parameters and the sleeping state according to the acquired sleeping parameters, such as a sleeping state neural network model obtained by pre-training. The determination of the current sleep state and the like may be performed at the local end or at the server end.
For example: the air conditioner is configured with an intelligent monitoring module (a microphone module, a millimeter wave module, etc.), a speaker module, an intelligent networking module, an AI module, etc. The AI algorithm model can be a neural network model, a sleep state neural network model trained by setting a large amount of sample data, weight values of breathing sound, snore and turning-over action are set for training, and a better weight parameter is obtained through training, namely the optimal neural network model. The air-conditioning microphone monitors and identifies sleeping respiratory sound and snore of a user in real time by using a far-field speech recognition technology, the millimeter wave module of the air-conditioning monitors and identifies data of turning-over actions of the user in real time by using a millimeter wave technology, and the neural network sets a certain time period to identify the sleeping state of the user according to the monitoring data.
Therefore, the current sleep state of the user can be determined quickly and accurately according to the current sleep parameters of the user by determining the current sleep state of the user according to the corresponding relation between the set sleep parameters and the set sleep state.
In an optional example, the determining unit 104 may be further configured to determine a relationship between the current sleep state and the set sleep states, that is, to determine which sleep state of the set sleep states the current sleep state belongs to. The set sleep state may include: a first sleep state (e.g., deep sleep state) or a second sleep state (e.g., light sleep state), the depth of sleep in the first sleep state being greater than the depth of sleep in the second sleep state. The specific function and processing of the determination unit 104 are also referred to in step S130.
In an optional example, the control unit 106 may be configured to not match a sleep music if the current sleep state is the first sleep state (e.g., deep sleep state); and if the current sleep state is the second sleep state (such as a light sleep state), performing auxiliary deepening intervention on the sleep-in depth of the current sleep state to improve the sleep quality of the user in the current sleep state. The specific function and processing of the control unit 106 are shown in step S140.
For example: when the user is asleep, the sleep music is matched to guide the user to enter deep sleep by playing the sleep music. The sleep-assisting and healing music is properly played according to the sleep condition at the moment, so that the user enters a deep sleep state.
Therefore, when the current sleep parameter of the user is monitored under the condition that the user sleeps, and the sleep depth of the current sleep state is determined to belong to the shallow sleep state according to the current sleep parameter, the sleep depth of the current sleep state is assisted and deepened to intervene, so that the sleep quality of the user in the current sleep state is improved, and the sleep quality of the user is improved.
Optionally, the performing, by the control unit 106, an auxiliary deepening intervention on the falling-asleep depth of the current sleep state may include:
the control unit 106 may be further specifically configured to determine, according to a correspondence between a set sleep state and set sleep music, the set sleep music corresponding to the set sleep state that is the same as the current sleep state in the correspondence as the current sleep music matched by the user in the current sleep state. The specific functions and processes of the control unit 106 are also referred to in step S210.
The control unit 106 may be further specifically configured to play the current sleep music, so as to perform an auxiliary deepening intervention on the sleep-in depth of the current sleep state by using the current sleep music. The specific function and processing of the control unit 106 are also referred to in step S220.
For example: after the sleep state neural network model identifies the sleep state of the user, the comfort level neural network model obtains music most suitable for assisting the current sleep state according to the sleep state matching. If the user is in light sleep, the corresponding audio is matched, and if the user is in deep sleep, the audio is matched and does not need to be played.
Therefore, the sleep quality of the user can be improved and improved by matching the current sleep music for the user when the user falls asleep and playing the current sleep music to assist and deepen the sleep depth of the current sleep state.
In an alternative embodiment, the method may further include: the process of improving the sleep environment according to the current sleep state may specifically be as follows:
the monitoring unit 102 may be further configured to wake up an obtaining mechanism of the current environment parameter of the sleep environment of the user to obtain the current environment parameter of the sleep environment of the user when the current sleep state is determined. For example: when the sleep condition of the user is identified, the air conditioner is awakened to acquire the current environment state information so as to obtain the current environment state of the environment where the user is located. The specific functions and processes of the monitoring unit 102 are also shown in step S310.
The determining unit 104 may be further configured to determine a current comfortable sleep environment parameter of the user according to the current sleep state. The specific function and processing of the determination unit 104 are also referred to in step S320.
Optionally, the determining unit 104 determines the current comfortable sleep environment parameter of the user according to the current sleep state and the current environment parameter, which may include: the determining unit 104 may be further configured to determine, according to a corresponding relationship between a set sleep state and a set comfortable sleep environment parameter, a set comfortable sleep environment parameter corresponding to a set sleep state that is the same as the current sleep state in the corresponding relationship, as the current comfortable sleep environment parameter matched by the user in the current sleep state.
Wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter may include: temperature and/or humidity.
For example: awakening the air conditioner intelligent module to detect the current environment temperature, humidity and the like when the sleep state of the user is identified at each time, calculating a comfortable sleep environment suitable for the user according to the sleep condition of the user and the current environment by using the other environment comfort level neural network matching model at the moment, wherein the comfortable sleep environment comprises the required temperature, humidity and the like, and the air conditioner acquires the required temperature and humidity demand instruction and intelligently adjusts the temperature, the humidity and the like so as to meet the comfortable environment.
Therefore, the current comfortable sleep environment parameters of the user are determined according to the current sleep state of the user, and the current comfortable sleep environment parameters are adjusted to the current comfortable sleep environment parameters, so that the comfort of the user is improved, and the sleep quality is further improved.
The control unit 106 may be further configured to adjust the current environmental parameter in the sleep environment of the user according to the current comfortable sleep environmental parameter, for example, control an environmental parameter adjusting device (e.g., an air conditioner) in the sleep environment of the user to operate according to the current comfortable sleep environmental parameter, so as to adjust the current environmental parameter to the comfortable environmental parameter in the current sleep state, thereby improving the current environmental parameter of the sleep environment of the user, and facilitating to maintain or even improve the sleep quality of the user. For example: according to the sleeping condition of the user and the current environment state of the environment where the user is located, the comfortable environment state is determined by utilizing the corresponding relation between the environment state and the sleeping condition, such as a comfort neural network model, and the current environment state is improved. The specific function and processing of the control unit 106 are also referred to in step S330.
For example: the sleep quality is improved by utilizing sensor modules such as microphones and millimeter waves carried by electrical equipment such as air conditioners and the like, collecting sound absorption and snore through the microphones, monitoring the sleep action through the millimeter waves, and intelligently analyzing the sleep condition through an AI depth algorithm model and assisting the air conditioner to make corresponding sleep comfort environment adjustment. If the breathing sound, the snore, the turning-over action and the like of the user are analyzed and accurately calculated through the AI algorithm model, a comfortable sleeping environment can be identified and matched, and the air conditioner intelligently adjusts the temperature and the air speed.
For example: the air conditioner is provided with an intelligent monitoring module (such as a microphone module, a millimeter wave module and the like) and a loudspeaker module. The air conditioner is started when a user sleeps, the set temperature and wind speed modes are always in a fixed state within one night, at the moment, monitoring states of an air conditioner microphone, a millimeter wave sensor and the like are started, respiratory sound, snore sound, turning-over actions of sleeping and the like of the user during sleeping are monitored in real time, the air conditioner uploads collected data to a cloud server to process an AI deep learning algorithm model to identify the sleeping condition of the user and accurately calculate a sleeping environment suitable for the user to be comfortable, at the moment, an instruction for intelligently controlling the air conditioner is issued at the cloud end, the operation action of the air conditioner is intelligently adjusted, and the sleeping quality of the user is improved.
Since the processes and functions implemented by the apparatus of this embodiment substantially correspond to the embodiments, principles and examples of the method shown in fig. 1 to 3, the description of this embodiment is not detailed, and reference may be made to the related descriptions in the foregoing embodiments, which are not repeated herein.
Through a large number of tests, the technical scheme of the invention is adopted, the sensor of the electrical equipment such as the air conditioner monitors the data of the breathing sound, the snore, the sleeping action and the like of the user during sleeping, the algorithm model analyzes and identifies the sleeping condition of the user, the air conditioner action is intelligently adjusted, a comfortable sleeping environment is provided for the user, and the sleeping quality of the user is improved.
According to the embodiment of the invention, the electric equipment corresponding to the sleep quality monitoring device is also provided. The electric device may include: the sleep quality monitoring device is described above.
In some aspects, to monitor the sleep state of the user, the sleep state of the user may be monitored using the smart wearable device. The intelligent wearable device is worn by the user in the sleeping process, so that the sleeping state of the user is monitored by the intelligent wearable device. Therefore, by means of monitoring the sleep state of the user through the intelligent wearable device, although the sleep state of the user is monitored, the user needs to wear the intelligent wearable device all the time in the sleep process, discomfort of the user in the sleep state is inevitably caused, and the sleep effect of the user is easily poor. In addition, the intelligent wearable device is worn in the sleeping process, only the sleeping state is monitored, and the sleeping condition of the user is not improved.
In an optional embodiment, the scheme of the invention provides an intelligent sleep-assisting air conditioner, wherein the air conditioner monitors the sleeping respiratory sound, the snoring sound and the sleeping actions of a user through sensors such as a microphone, millimeter waves and the like, analyzes and identifies the sleeping condition of a person, and the air conditioner intelligently assists the user in improving the sleeping quality. That is to say, the air conditioner sensor monitors the data such as respiratory sound, snore, sleeping action and the like when the user sleeps, the algorithm model analyzes and identifies the sleeping condition of the user, the air conditioner action is intelligently adjusted, a comfortable sleeping environment is provided for the user, and the sleeping quality of the user is improved. Therefore, the sleep quality of the user can be improved in an auxiliary mode through intelligent air supply of the air conditioner and temperature adjustment under the condition that the sleep quality of the user is poor, and the user can be helped to sleep better and more fragrant.
In an optional example, sensor modules such as a microphone and a millimeter wave can be arranged in the air, the sensor modules monitor breathing sound, snore, actions and the like of a user during sleeping, and the AI deep learning algorithm model analyzes data collected by the breathing sound, the snore, the actions and the like and accurately calculates a comfortable sleeping environment, so that the sleeping of the user is improved, and the sleeping quality is improved.
The air conditioner is provided with a microphone, a far-field speech recognition technology is used, and the microphone monitors the respiratory sound and the snore of a user through the far-field speech recognition technology, such as collecting the respiratory sound and the snore by the microphone; the sleeping actions of the user are monitored by combining the millimeter wave technology, for example, the sleeping actions are monitored by using millimeter waves, and a comfortable sleeping environment is calculated by an algorithm model, so that the air conditioner can be intelligently adjusted according to sleeping states such as respiratory sound, snore and the like. For example: and adjusting the comfortable sleeping environment in real time according to the calculated comfortable sleeping environment, and intelligently playing the sleep-aid and healing-up music to assist the user to enter deep sleep. Therefore, sensor modules such as microphones and millimeter waves carried by electrical equipment such as air conditioners and the like are utilized, sound absorption and snore are collected through the microphones, sleeping actions are monitored through the millimeter waves, the AI deep algorithm model intelligently analyzes sleeping conditions and assists the air conditioners to make corresponding sleeping comfort level environment adjustment, and sleeping quality is improved.
For example: far-field speech recognition techniques, as opposed to near-field speech, typically operate at a distance of typically between 1 and 10 meters. Far-field speech recognition can better avoid the phenomenon that a user is mentally collided to be close to equipment and possibly has radiation, can monitor audio information sent by the user at a long distance, can monitor the audio information in a limited moving range and has wide applicability.
For example: the sleeping space environment is relatively quiet and stable, and when the sleeping movement of a person changes, the frequency of the millimeter waves is interfered to change, so that the sleeping movement of the person is detected.
In an alternative embodiment, a specific implementation process of the scheme of the present invention may be exemplarily described with reference to the examples shown in fig. 5 and fig. 6.
The air conditioner is provided with an intelligent monitoring module (such as a microphone module, a millimeter wave module and the like) and a loudspeaker module. The air conditioner uploads the collected data to a cloud server to process and identify the sleeping condition of the user through an AI deep learning algorithm model and accurately calculate a sleeping environment suitable for the user, and the cloud sends an air conditioner intelligent control instruction to intelligently adjust the operation action of the air conditioner so as to improve the sleeping quality of the user; and the sleep-assisting and healing music can be properly played according to the sleep condition at the moment, so that the user enters a deep sleep state.
FIG. 5 is a flowchart illustrating an embodiment of an air conditioner to intelligently adjust a comfortable sleep environment. As shown in fig. 5, the process of intelligently adjusting a comfortable sleep environment by an air conditioner may include:
and step 11, the user turns on the air conditioner to sleep.
And step 12, monitoring the sleep state of the user by the air conditioner microphone and the millimeter wave sensor. In the sleep state, the method may include: sound collection is carried out by utilizing a microphone, and the respiratory sound, snore and the like of the user are obtained; and (5) utilizing the millimeter wave sensor to monitor the action to obtain the turning-over action of the user and the like.
Step 13, analyzing and accurately calculating the breathing sound, snore, turning-over action and the like of the user through an AI algorithm model, identifying and matching a comfortable sleeping environment, and intelligently adjusting the temperature and the wind speed of the air conditioner; and the sleep-assisting and healing music can be properly played according to the sleep condition at the moment, so that the user enters a deep sleep state.
Fig. 6 is a flow diagram of another embodiment of an air conditioner to intelligently adjust a comfortable sleep environment. As shown in fig. 6, the process of intelligently adjusting a comfortable sleep environment by an air conditioner may include:
and step 21, when the user is in a sleeping state, collecting the breathing sound, the snore, the turning-over action and other sleep parameters of the user.
And step 22, identifying the sleep condition of the user by utilizing the corresponding relation between the sleep parameters and the sleep state according to the acquired sleep parameters, for example, by utilizing a sleep state neural network model obtained by training in advance.
And step 23, when the sleep condition of the user is identified, awakening the air conditioner to acquire the current environment state information so as to obtain the current environment state of the environment where the user is located.
And step 24, determining a comfortable environment state by utilizing the corresponding relation between the environment state and the sleep state, such as a comfort neural network model, according to the sleep state of the user and the current environment state of the environment where the user is located, and improving the current environment state. When the user is asleep, the sleep music is matched to guide the user to enter deep sleep by playing the sleep music.
The AI algorithm model can be a neural network model, a sleep state neural network model trained by setting a large amount of sample data, weight values of breathing sound, snore and turning-over action are set for training, and a better weight parameter, namely an optimal neural network model, is obtained through training. The air-conditioning microphone monitors and identifies sleeping respiratory sound and snore of a user in real time by using a far-field speech identification technology, a millimeter wave module of the air-conditioning monitors and identifies data of turning-over actions of the user in real time by using a millimeter wave technology, and a neural network sets a certain time period to identify the sleeping state of the user according to the monitoring data; awakening the air conditioner intelligent module to detect the current environment temperature, humidity and the like when the sleep state of the user is identified at each time, calculating a comfortable sleep environment suitable for the user according to the sleep condition of the user and the current environment by using the other environment comfort level neural network matching model at the moment, wherein the comfortable sleep environment comprises the required temperature, humidity and the like, and the air conditioner acquires the required temperature and humidity demand instruction and intelligently adjusts the temperature, the humidity and the like so as to meet the comfortable environment.
For example: setting a sleep state neural network model trained by a large amount of sample data, namely the model is an optimal model trained in advance, firstly designing a neural network algorithm program, and training the designed neural network algorithm model by a large amount of samples to continuously optimize weight parameters in the model until the optimal neural network algorithm model is obtained.
For example: and setting weight values of breathing sound, snore and turning-over action for training, wherein the training for setting weight parameter values refers to the training of the model by randomly initializing initial values after a neural network algorithm model is designed.
For example: the training to obtain the better weight parameter may include: in the designed neural network algorithm model, the initial value is initialized randomly, the training of the model is started, and the weight parameters are adjusted continuously and iteratively until better weight parameters are obtained.
For example: identifying the sleep state of the user according to the monitoring data may include: the neural network model judges and identifies the sleep state of the user according to data information such as respiratory sound, snore, actions and the like, the cloud model has a data corresponding matching table, if the acquired data can intelligently identify the sleep state information through the matching table, if the acquired data is not in the matching table, the algorithm model learns and identifies the sleep state, and the event is recorded in the matching table.
Preferably, after the sleep state neural network model identifies the sleep state of the user, the comfort neural network model matches the sleep state to obtain music most suitable for assisting the current sleep state. If the user is in light sleep, the corresponding audio is matched, and if the user is in deep sleep, the audio is matched and does not need to be played.
For example: the matching of the corresponding audio in the case of a shallow sleep may include: and matching the shallow sleep information if the shallow sleep corresponds to the audio stream of the shallow sleep through the sleep audio relation table corresponding to the sleep state.
For example: the air conditioner is configured with an intelligent monitoring module (a microphone module, a millimeter wave module, etc.), a speaker module, an intelligent networking module, an AI module, etc. In summer, a user starts an air conditioner to sleep, the air conditioner sets a refrigeration mode and a medium wind, the set temperature and wind speed mode are always in a fixed state within one night, the user just feels that the air conditioner adjusts the temperature to be just proper when sleeping, the body temperature of the user is reduced after falling asleep, the environment temperature is reduced along with the time of night, the user can feel that the environment temperature is high or low after sleeping (if the user feels that the environment temperature is too low and does not reach an ideal comfortable sleeping environment, so that the sleeping quality of the user is influenced), the physiological characteristics can show corresponding reactions, the breathing sound, the snore sound and the sleeping action of the user also have corresponding reaction changes, the air conditioner collects the data of the user such as the breathing sound, the snore sound, the sleeping action and the like, the air conditioner uploads the collected data to a cloud server, and a depth algorithm model on the server processes and identifies the sleeping condition of the user (at the moment, the user feels that the, The user is easy to catch a cold and catch a cold), and accurately calculate the comfortable sleeping environment (adjusting the temperature, the wind speed and the like of the air conditioner) suitable for the user, the cloud sends an instruction of intelligent control of the air conditioner, the operation of the air conditioner is intelligently adjusted, the comfortable and warm sleeping environment is adjusted, and the sleeping quality of the user is improved. And the sleep-assisting and healing music can be properly played according to the sleep condition at the moment, so that the user enters a deep sleep state.
For example: identifying a sleep condition of the user (when the user experiences an environment that is too cold, the user is prone to catch a cold, a cold) may include: the neural network model judges and identifies the sleeping condition of the user according to the data information of the breathing sound, the snore, the action and the like.
For example: accurately calculating a sleeping environment (adjusting air conditioner temperature, wind speed, etc.) suitable for user comfort, may include: the sensing conditions, heat or cold and the like of the body to the environment during sleeping can be reflected by data such as breathing sound, snore, action and the like, the state feedback performance of the body to the environment is identified, and the comfortable sleeping environment suitable for a user is accurately calculated.
Since the processes and functions implemented by the electrical apparatus of this embodiment substantially correspond to the embodiments, principles, and examples of the apparatus shown in fig. 4, the descriptions of this embodiment are not detailed herein, and refer to the related descriptions in the foregoing embodiments, which are not described herein again.
Through a large number of tests, the technical scheme of the invention is adopted, and under the condition of poor sleeping quality of the user, the intelligent air supply and temperature adjustment of electrical equipment such as an air conditioner are realized, so that the sleeping quality of the user is improved in an auxiliary manner, and the user is helped to sleep better and more fragrant.
According to the embodiment of the invention, a storage medium corresponding to the sleep quality monitoring method is also provided, and the storage medium comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the sleep quality monitoring method.
Since the processing and functions implemented by the storage medium of this embodiment substantially correspond to the embodiments, principles, and examples of the methods shown in fig. 1 to fig. 3, details are not described in the description of this embodiment, and reference may be made to the related descriptions in the foregoing embodiments, which are not described herein again.
Through a large number of tests, the technical scheme of the invention is adopted, sensor modules such as microphones, millimeter waves and the like are arranged on electrical equipment such as an air conditioner, the sensor modules monitor breathing sound, snore, actions and the like of a user during sleeping, and the AI deep learning algorithm model analyzes data collected by the breathing sound, the snore, the actions and the like and accurately calculates a comfortable sleeping environment, so that the sleeping of the user is improved, and the sleeping quality is improved.
According to an embodiment of the present invention, there is also provided a processor corresponding to the sleep quality monitoring method, the processor being configured to run a program, wherein the program is run to execute the sleep quality monitoring method described above.
Since the processing and functions implemented by the processor of this embodiment substantially correspond to the embodiments, principles, and examples of the methods shown in fig. 1 to fig. 3, details are not described in the description of this embodiment, and reference may be made to the related descriptions in the foregoing embodiments, which are not described herein again.
Through a large number of tests, the technical scheme of the invention is adopted, a microphone is arranged on electrical equipment such as an air conditioner, a far-field speech recognition technology is used, and the microphone monitors the respiratory sound and the snore of a user through the far-field speech recognition technology; the sleeping action of the user is monitored by combining the millimeter wave technology, and a comfortable sleeping environment is calculated through an algorithm model, so that the air conditioner can be intelligently adjusted according to the sleeping states of breathing sound, snore and the like.
In summary, it is readily understood by those skilled in the art that the advantageous modes described above can be freely combined and superimposed without conflict.
The above description is only an example of the present invention, and is not intended to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made in the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (13)
1. A sleep quality monitoring method, comprising:
monitoring current sleep parameters of a user when the user is sleeping;
determining the current sleep state of the user according to the current sleep parameters; and the number of the first and second groups,
determining a relationship between the current sleep state and a set sleep state; the set sleep state comprises: a first sleep state or a second sleep state, wherein the depth of sleep in the first sleep state is greater than the depth of sleep in the second sleep state;
and if the current sleep state is the second sleep state, performing auxiliary deepening intervention on the sleep-in depth of the current sleep state.
2. The sleep quality monitoring method according to claim 1, wherein the current sleep parameters include: respiratory, snoring, and/or sleeping movements;
monitoring current sleep parameters of the user, including:
a microphone module is adopted to collect the breath sound and/or snore of the user; and/or the presence of a gas in the gas,
a millimeter wave module is adopted to collect the sleeping actions of the user;
the microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
3. The sleep quality monitoring method according to claim 1, wherein,
the determining the current sleep state of the user comprises:
according to the corresponding relation between the set sleep parameters and the set sleep state, determining the set sleep state corresponding to the set sleep parameters which are the same as the current sleep state in the corresponding relation as the current sleep state of the user under the current sleep parameters;
and/or the presence of a gas in the gas,
and performing auxiliary deepening intervention on the falling-asleep depth of the current sleep state, wherein the auxiliary deepening intervention comprises the following steps:
according to the corresponding relation between the set sleep state and the set sleep music, determining the set sleep music corresponding to the set sleep state which is the same as the current sleep state in the corresponding relation as the current sleep music matched by the user in the current sleep state;
and playing the current sleep music so as to utilize the current sleep music to perform auxiliary deepening intervention on the sleep-in depth of the current sleep state.
4. The sleep quality monitoring method according to any one of claims 1 to 3, further comprising:
under the condition that the current sleep state is determined, awakening an acquisition mechanism of the current environment parameters of the sleep environment of the user to acquire the current environment parameters of the sleep environment of the user;
determining the current comfortable sleeping environment parameters of the user according to the current sleeping state;
and adjusting the current environment parameters in the sleeping environment of the user according to the current comfortable sleeping environment parameters.
5. The sleep quality monitoring method as claimed in claim 4, wherein the determining of the current comfortable sleep environment parameters of the user comprises:
determining a set comfortable sleep environment parameter corresponding to a set sleep state which is the same as the current sleep state in the corresponding relation as the current comfortable sleep environment parameter matched by the user in the current sleep state according to the corresponding relation between the set sleep state and the set comfortable sleep environment parameter;
wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter include: temperature and/or humidity.
6. A sleep quality monitoring device, comprising:
the monitoring unit is used for monitoring the current sleep parameters of the user under the condition that the user sleeps;
the determining unit is used for determining the current sleep state of the user according to the current sleep parameters; and the number of the first and second groups,
the determining unit is further configured to determine a relationship between the current sleep state and a set sleep state; the set sleep state comprises: a first sleep state or a second sleep state, wherein the depth of sleep in the first sleep state is greater than the depth of sleep in the second sleep state;
and the control unit is used for performing auxiliary deepening intervention on the sleep-in depth of the current sleep state if the current sleep state is the second sleep state.
7. The sleep quality monitoring apparatus according to claim 6, wherein the current sleep parameters include: respiratory, snoring, and/or sleeping movements;
the monitoring unit monitors the current sleep parameters of the user, including:
a microphone module is adopted to collect the breath sound and/or snore of the user; and/or the presence of a gas in the gas,
a millimeter wave module is adopted to collect the sleeping actions of the user;
the microphone module and/or the millimeter wave module are/is arranged in the sleeping environment of the user and/or on electrical equipment in the sleeping environment of the user.
8. The sleep quality monitoring apparatus according to claim 6, wherein,
the determining unit determines a current sleep state of the user, including:
according to the corresponding relation between the set sleep parameters and the set sleep state, determining the set sleep state corresponding to the set sleep parameters which are the same as the current sleep state in the corresponding relation as the current sleep state of the user under the current sleep parameters;
and/or the presence of a gas in the gas,
the control unit carries out auxiliary deepening intervention on the falling-asleep depth of the current sleep state, and the method comprises the following steps:
according to the corresponding relation between the set sleep state and the set sleep music, determining the set sleep music corresponding to the set sleep state which is the same as the current sleep state in the corresponding relation as the current sleep music matched by the user in the current sleep state;
and playing the current sleep music so as to utilize the current sleep music to perform auxiliary deepening intervention on the sleep-in depth of the current sleep state.
9. The sleep quality monitoring apparatus according to any one of claims 6 to 8, further comprising:
the monitoring unit is further configured to wake up an acquisition mechanism for current environment parameters of the sleep environment of the user to acquire the current environment parameters of the sleep environment of the user when the current sleep state is determined;
the determining unit is further configured to determine a current comfortable sleep environment parameter of the user according to the current sleep state;
the control unit is further configured to adjust the current environmental parameter in the sleep environment of the user according to the current comfortable sleep environmental parameter.
10. The sleep quality monitoring apparatus of claim 9, wherein the determining unit determines the current comfortable sleep environment parameter of the user, comprising:
determining a set comfortable sleep environment parameter corresponding to a set sleep state which is the same as the current sleep state in the corresponding relation as the current comfortable sleep environment parameter matched by the user in the current sleep state according to the corresponding relation between the set sleep state and the set comfortable sleep environment parameter;
wherein the current environmental parameter, the setting environmental parameter, and an environmental parameter of the current comfortable sleep environmental parameter include: temperature and/or humidity.
11. An electrical device, comprising: sleep quality monitoring apparatus according to any one of claims 6 to 10.
12. A storage medium, characterized in that the storage medium includes a stored program, wherein, when the program is executed, a device in which the storage medium is located is controlled to execute the sleep quality monitoring method according to any one of claims 1 to 5.
13. A processor, characterized in that the processor is configured to run a program, wherein the program is configured to execute the sleep quality monitoring method according to any one of claims 1 to 5 when running.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112462997A (en) * | 2020-12-14 | 2021-03-09 | 北京小米移动软件有限公司 | Content presentation method and device, electronic equipment and storage medium |
| CN112503741A (en) * | 2020-12-03 | 2021-03-16 | 珠海格力电器股份有限公司 | Equipment control method, device, equipment and storage medium |
| CN112524776A (en) * | 2020-11-17 | 2021-03-19 | 青岛海尔空调器有限总公司 | Indoor air conditioning method and system based on snore and readable storage medium |
| CN113091271A (en) * | 2021-04-06 | 2021-07-09 | 上海绿联软件股份有限公司 | Control method and device for repairing sleep air conditioner and electronic equipment |
| CN113288078A (en) * | 2021-03-29 | 2021-08-24 | 江西商思伏沌科技有限公司 | Sleep quality monitoring method, device and system |
| CN114777309A (en) * | 2022-03-16 | 2022-07-22 | 青岛海尔空调器有限总公司 | Air conditioner control method and device, electronic equipment and air conditioner |
| WO2022234773A1 (en) * | 2021-05-06 | 2022-11-10 | 重一 中津川 | Shelter |
| CN116459432A (en) * | 2023-05-29 | 2023-07-21 | 秦皇岛市惠斯安普医学系统股份有限公司 | Sleep quality monitoring and intervention management system and method |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108052012A (en) * | 2017-12-18 | 2018-05-18 | 张馨予 | sleep environment intelligent regulating system and method |
| CN109032223A (en) * | 2018-08-17 | 2018-12-18 | 三星电子（中国）研发中心 | A kind of sleep environment regulating device, system and method |
| CN109489212A (en) * | 2018-11-21 | 2019-03-19 | 珠海格力电器股份有限公司 | A kind of air conditioner intelligent sleep control method, adjustment system and equipment |
| CN109506342A (en) * | 2018-11-30 | 2019-03-22 | 珠海格力电器股份有限公司 | Control method, device and the air-conditioning of air-conditioning |
| CN110013235A (en) * | 2019-03-29 | 2019-07-16 | 张恒运 | A kind of smart home sleeping apparatus and system |
| CN209270576U (en) * | 2017-10-19 | 2019-08-20 | 深圳和而泰数据资源与云技术有限公司 | A kind of intelligent sleep system |
| CN111063375A (en) * | 2019-12-19 | 2020-04-24 | 珠海格力电器股份有限公司 | Music playing control system, method, equipment and medium |
| CN111197845A (en) * | 2018-11-19 | 2020-05-26 | Tcl集团股份有限公司 | Deep learning-based control method and system for air conditioner operation mode |
-
2020
- 2020-07-09 CN CN202010657066.3A patent/CN111897230A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN209270576U (en) * | 2017-10-19 | 2019-08-20 | 深圳和而泰数据资源与云技术有限公司 | A kind of intelligent sleep system |
| CN108052012A (en) * | 2017-12-18 | 2018-05-18 | 张馨予 | sleep environment intelligent regulating system and method |
| CN109032223A (en) * | 2018-08-17 | 2018-12-18 | 三星电子（中国）研发中心 | A kind of sleep environment regulating device, system and method |
| CN111197845A (en) * | 2018-11-19 | 2020-05-26 | Tcl集团股份有限公司 | Deep learning-based control method and system for air conditioner operation mode |
| CN109489212A (en) * | 2018-11-21 | 2019-03-19 | 珠海格力电器股份有限公司 | A kind of air conditioner intelligent sleep control method, adjustment system and equipment |
| CN109506342A (en) * | 2018-11-30 | 2019-03-22 | 珠海格力电器股份有限公司 | Control method, device and the air-conditioning of air-conditioning |
| CN110013235A (en) * | 2019-03-29 | 2019-07-16 | 张恒运 | A kind of smart home sleeping apparatus and system |
| CN111063375A (en) * | 2019-12-19 | 2020-04-24 | 珠海格力电器股份有限公司 | Music playing control system, method, equipment and medium |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112524776A (en) * | 2020-11-17 | 2021-03-19 | 青岛海尔空调器有限总公司 | Indoor air conditioning method and system based on snore and readable storage medium |
| CN112524776B (en) * | 2020-11-17 | 2022-04-19 | 青岛海尔空调器有限总公司 | Indoor air conditioning method and system based on snore and readable storage medium |
| CN112503741A (en) * | 2020-12-03 | 2021-03-16 | 珠海格力电器股份有限公司 | Equipment control method, device, equipment and storage medium |
| CN112462997A (en) * | 2020-12-14 | 2021-03-09 | 北京小米移动软件有限公司 | Content presentation method and device, electronic equipment and storage medium |
| CN113288078A (en) * | 2021-03-29 | 2021-08-24 | 江西商思伏沌科技有限公司 | Sleep quality monitoring method, device and system |
| CN113288078B (en) * | 2021-03-29 | 2024-05-17 | 江西商思伏沌科技有限公司 | Sleep quality monitoring method, device and system |
| CN113091271A (en) * | 2021-04-06 | 2021-07-09 | 上海绿联软件股份有限公司 | Control method and device for repairing sleep air conditioner and electronic equipment |
| WO2022234773A1 (en) * | 2021-05-06 | 2022-11-10 | 重一 中津川 | Shelter |
| CN114777309A (en) * | 2022-03-16 | 2022-07-22 | 青岛海尔空调器有限总公司 | Air conditioner control method and device, electronic equipment and air conditioner |
| CN116459432A (en) * | 2023-05-29 | 2023-07-21 | 秦皇岛市惠斯安普医学系统股份有限公司 | Sleep quality monitoring and intervention management system and method |
| CN116459432B (en) * | 2023-05-29 | 2023-09-08 | 秦皇岛市惠斯安普医学系统股份有限公司 | Sleep quality monitoring and intervention management system and method |
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