CN112806967B - Teenager sleep quality monitoring method and device, computer equipment and storage medium - Google Patents
Teenager sleep quality monitoring method and device, computer equipment and storage medium Download PDFInfo
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- 230000003860 sleep quality Effects 0.000 title claims abstract description 105
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Abstract
The application relates to the technical field of sleep monitoring, in particular to a teenager sleep quality monitoring method, a device, computer equipment and a storage medium, wherein the teenager sleep quality monitoring method comprises the following steps: acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprise sleep time data and sleep action data; inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data; calculating deep sleep proportion data according to the deep sleep time data and the sleep time data; and acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and transmitting the sleep quality data to a third party communication platform. The application has the effect of monitoring the sleep quality of teenagers.
Description
Technical Field
The present application relates to the technical field of sleep monitoring, and in particular, to a method and apparatus for monitoring sleep quality of teenagers, a computer device, and a storage medium.
Background
At present, the sleep quality is one of important parameters for keeping the health of a human body, and is a link which is easy to be ignored in the ordinary working and learning processes of people.
In the growth process of teenagers, besides the mental and academic industries, the growth time is also the golden time in the body, and after normal study, reasonable diet collocation, exercise and rest are all necessary. Wherein, sufficient and high-quality sleep is an important link in the growth process of teenagers
With respect to the related art described above, the inventors considered that it was necessary to monitor the sleep quality of teenagers for their healthy growth.
Disclosure of Invention
In order to monitor the sleep quality of teenagers, the application provides a sleep quality monitoring method, a sleep quality monitoring device, computer equipment and a storage medium for teenagers.
The first object of the present application is achieved by the following technical solutions:
a teenager sleep quality monitoring method, the teenager sleep quality monitoring method comprising:
acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprise sleep time data and sleep action data;
inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data;
calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
and acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and transmitting the sleep quality data to a third party communication platform.
By adopting the technical scheme, whether the sleeping time of the target to be detected is enough or not can be judged by detecting the sleeping time data, meanwhile, by acquiring the sleeping action data, whether the target to be detected is in a sleeping state or not can be judged, and by acquiring the deep sleeping time data, the deep sleeping proportion data is obtained, so that whether the sleeping quality of the target to be detected is enough or not can be judged by combining the sleeping time data, after the sleeping quality data to be detected is obtained, the sleeping quality data is fed back to a third party plausible platform, the analysis of the sleeping quality condition of the target to be detected or the sleeping quality condition of the target group to be detected can be facilitated, and further, the strategy for improving the sleeping quality of the teenagers is facilitated to be improved.
The present application may be further configured in a preferred example to: the step of obtaining the sleep action data of the target to be detected comprises the following steps:
acquiring the respiratory frequency data and the target turning frequency data of the target to be detected;
and taking the respiratory frequency data and the target turning frequency data as the sleep action data.
By adopting the technical scheme, because the respiratory rate and the turn-over frequency of the human body are lower than those of the human body when the human body is in deep sleep or when the human body is in shallow sleep, whether the human body enters into deep sleep or not can be judged according to the sleep action data by acquiring the respiratory rate and the turn-over frequency of the target to be detected.
The present application may be further configured in a preferred example to: before the step of inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data, the teenager sleep quality monitoring method further comprises the following steps:
acquiring historical deep sleep detection data, and acquiring deep sleep behavior data from each piece of historical deep sleep detection data;
acquiring respiratory behavior data and turnover behavior data from each deep sleep behavior data;
and training the breathing behavior data and the turning behavior data of each deep sleep behavior data by adopting machine learning to obtain the deep sleep detection model.
By adopting the technical scheme, the breathing behavior data and the turning behavior data are obtained from the historical deep sleep detection data, so that the deep sleep detection model which can judge whether to enter deep sleep according to the breathing frequency and the turning frequency of the target to be detected is obtained, and the training set for training the deep sleep detection model is enriched in practical use because the data attribute of the breathing behavior data and the turning behavior data is consistent with the breathing frequency data and the target turning frequency data.
The present application may be further configured in a preferred example to: the step of inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data specifically comprises the following steps:
inputting the respiratory frequency data and the target turn-over frequency data in the sleep action data into the deep sleep detection model for matching;
and if the respiratory frequency data of the target to be detected is matched with the respiratory behavior data and the target turning frequency data of the target to be detected is matched with the turning behavior data, starting timing to obtain the deep sleep time data.
By adopting the technical scheme, whether the target to be detected enters deep sleep or not can be judged by inputting the sleep action data into the deep sleep detection model, so that the deep sleep time data can be obtained.
The present application may be further configured in a preferred example to: the step of obtaining sleep quality data according to the sleep time data and the deep sleep proportion data specifically comprises the following steps:
acquiring preset sleep time reference data and preset deep sleep proportion reference data;
and comparing the sleep time data and the deep sleep proportion data with the sleep time reference data and the deep sleep proportion reference data respectively, and acquiring the sleep quality data according to a comparison result.
By adopting the technical scheme, the sleep time reference data and the deep sleep proportion reference data can be compared, so that the sleep quality data can be obtained through comparison results.
The second object of the present application is achieved by the following technical solutions:
a teenager sleep quality monitoring device, the teenager sleep quality monitoring device comprising:
the data detection module is used for acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data;
the model calculation module is used for inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data;
the proportion calculation module is used for calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
and the data sending module is used for obtaining sleep quality data according to the sleep time data and the deep sleep proportion data and sending the sleep quality data to a third party communication platform.
By adopting the technical scheme, whether the sleeping time of the target to be detected is enough or not can be judged by detecting the sleeping time data, meanwhile, by acquiring the sleeping action data, whether the target to be detected is in a sleeping state or not can be judged, and by acquiring the deep sleeping time data, the deep sleeping proportion data is obtained, so that whether the sleeping quality of the target to be detected is enough or not can be judged by combining the sleeping time data, after the sleeping quality data to be detected is obtained, the sleeping quality data is fed back to a third party plausible platform, the analysis of the sleeping quality condition of the target to be detected or the sleeping quality condition of the target group to be detected can be facilitated, and further, the strategy for improving the sleeping quality of the teenagers is facilitated to be improved.
The third object of the present application is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the teenager sleep quality monitoring method described above when the computer program is executed.
The fourth object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the teenager sleep quality monitoring method described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. by detecting the sleep time data, whether the sleep time of the target to be detected is sufficient or not can be judged, meanwhile, by acquiring the sleep action data, whether the target to be detected is in a sleeping state or not can be judged, by acquiring the deep sleep time data, the deep sleep proportion data is obtained, so that whether the sleep time is sufficient or not can be combined, the sleep quality of the target to be detected is judged, after the sleep quality data to be detected is obtained, the sleep quality data is fed back to a third party flat-message platform, and the analysis of the sleep quality condition of the target to be detected or the group of the target to be detected can be facilitated, so that the strategy for specifying and improving the teenager sleep quality is facilitated;
2. the respiratory behavior data and the turnover behavior data are obtained from the historical deep sleep detection data, so that a deep sleep detection model which can judge whether to enter deep sleep according to the respiratory frequency and the turnover frequency of a target to be detected is obtained, and the training set for training the deep sleep detection model is enriched in practical use because the data attribute of the respiratory behavior data and the turnover behavior data is consistent with the respiratory frequency data and the target turnover frequency data;
3. by setting the sleep time reference data and the deep sleep proportion reference data, the sleep time data and the deep sleep time proportion data can be compared, and therefore the sleep quality data can be obtained through comparison results.
Drawings
FIG. 1 is a flow chart of a teenager sleep quality monitoring method according to an embodiment of the application;
FIG. 2 is a flowchart showing the implementation of step S10 in a teenager sleep quality monitoring method according to an embodiment of the application;
FIG. 3 is a flow chart of another implementation of the teenager sleep quality monitoring method in an embodiment of the application;
FIG. 4 is a flowchart showing the implementation of step S20 in the teenager sleep quality monitoring method according to an embodiment of the present application;
FIG. 5 is a flowchart showing the implementation of step S40 in the teenager sleep quality monitoring method according to an embodiment of the present application;
FIG. 6 is a schematic block diagram of a teenager sleep quality monitoring system in accordance with an embodiment of the application;
fig. 7 is a schematic diagram of an apparatus in an embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, the application discloses a teenager sleep quality monitoring method, which specifically comprises the following steps:
s10: and acquiring sleep condition data of the target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data.
In this embodiment, the target to be detected refers to teenagers who need to monitor sleep quality. Sleep condition data refers to data of behavior of sleeping of a target to be detected. The sleep time data refers to data of a time length of sleep of a target to be detected. The sleep motion data refers to data of a motion of an object to be detected while sleeping.
Specifically, in the application, the device for detecting the object to be detected is contained in the pillow, so that the detection of sleep quality is performed without additional opening or using other devices.
When the sleep time data is acquired, the total length between the time when the target to be detected starts to sleep and the time when the target to be detected is judged to finish sleeping is acquired as the sleep time data. The mode of acquiring the sleeping time of the target to be detected can be that a pressure sensor is embedded in the pillow, when the head of the target to be detected applies pressure to the pillow, the sleeping time of the target to be detected can be judged, and the sleeping time is synchronously acquired; when the pressure sensor detects that the head of the object to be detected leaves the pillow, the current time can be marked, after the preset time, for example, half an hour, the pressure applied by the head of the user to the pillow is not detected again, the object to be detected is judged to finish sleeping, and the marked time is taken as the sleeping finishing time.
Further, when the sleep motion data is acquired, the pressure sensors embedded in the pillow can be used for acquiring, namely, the pressure sensors are uniformly arranged in the pillow, and when the target to be detected starts sleeping, the position of the head of the target to be detected on the pillow is determined by acquiring the pressure value detected by each pressure sensor, so that the change of the position of the head of the target to be detected on the pillow is used as the sleep motion data.
S20: and inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data.
In the present embodiment, the deep sleep detection model refers to a model for detecting the length of time for which the user enters deep sleep while sleeping. The deep sleep time data refers to the total length of time the user enters deep sleep each time he sleeps.
Specifically, the deep sleep detection model can be obtained by acquiring characterization data of a human body, such as data of turning over frequency, breathing frequency and the like, in advance when the human body enters deep sleep, and training the characterization data through an artificial intelligence or machine learning means.
Further, when the sleep quality of the target to be detected is detected, the sleep quality of the target to be detected can be judged according to the proportion of the target to be detected in deep sleep every time the target to be detected sleeps, namely, the detected sleep action data can be input into the deep sleep detection model, so that whether the target to be detected enters deep sleep or not is judged through the deep sleep detection model, when the target to be detected enters deep sleep, timing is started, and when the target to be detected ends the deep sleep, timing is stopped, and further, the sum of the times of entering deep sleep in the sleep time data is obtained as the deep sleep time data when the target to be detected ends the sleep.
S30: and calculating the deep sleep proportion data according to the deep sleep time data and the sleep time data.
In this embodiment, the deep sleep proportion data refers to the proportion of the sum of times to enter deep sleep to the total time of sleep each time the target to be detected sleeps.
Specifically, the deep sleep ratio data is obtained by dividing the deep sleep time data by the sleep time data.
S40: and acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and transmitting the sleep quality data to a third party communication platform.
In the present embodiment, sleep quality data refers to data of the quality of each sleep of an object to be detected.
Specifically, according to the sleep time data, determining the time when the target to be detected starts to sleep and the total sleeping duration, wherein the time is used for determining whether the target to be detected is in a night state and whether the sleeping time is sufficient; meanwhile, by combining the deep sleep proportion data, the sleep quality data is comprehensively judged. When judging the sleep quality data, the time ratio of the deep sleep can be counted when the sleep is performed with high quality, the ratio between the deep sleep proportion data and the time ratio is taken as a basic score, for example, the ratio is 85%, the corresponding basic score can be 85 minutes, meanwhile, since teenagers stay up to night usually due to academic or greedy play, judging whether the sleep time data reach 8 hours, if the sleep time data do not reach 8 hours, setting a coefficient according to the difference value of 8 hours of the sleep time distance, wherein the larger the difference value is, the smaller the coefficient is; further, the product of the coefficient and the base score is used as sleep quality data.
Further, after the sleep quality data is obtained, the sleep quality data is sent to a third party communication platform, where the third party communication platform may be a parent belonging to the target to be detected, or a teacher of the target to be detected.
In this embodiment, by detecting sleep time data, it may be determined whether the sleep time of the target to be detected is sufficient every night, and by acquiring sleep action data, it may be determined whether to enter a deep sleep state from the sleeping behavior of the target to be detected, and by acquiring deep sleep time data, deep sleep proportion data may be obtained, so that whether the sleep quality of the target to be detected is sufficient can be determined in combination with the sleep time, and after the sleep quality data to be detected is obtained, the sleep quality data is fed back to the third party platform, so that analysis of the sleep quality of the target to be detected or the target group to be detected may be facilitated, and further, strategy for specifying and improving the teenager sleep quality may be facilitated.
In one embodiment, as shown in fig. 2, in step S10, acquiring sleep motion data of an object to be detected includes:
s11: and acquiring respiratory frequency data and target turning frequency data of a target to be detected.
In this embodiment, the respiratory rate data refers to the respiratory rate of the target to be detected while sleeping. The target turning frequency data refers to the turning frequency of a target to be detected when sleeping.
Specifically, the respiratory frequency of the human body is slower than that of the person when the person wakes up when the person sleeps, and the frequency of turning over is lower than that of the person in a light sleep or insomnia state, so that the respiratory frequency data and the target turning-over frequency data of the target to be detected are acquired in real time after the target to be detected is judged to start sleeping.
The method for acquiring the respiratory rate data of the target to be detected can be to judge the respiratory rate data of the target to be detected by utilizing the pressure sensor embedded in the pillow due to the fact that the action of the human body changes when the human body breathes, namely when the human body exhales and exhales, and the pressure of the neck on the pillow changes regularly when the human body sleeps. The method of obtaining the target turning frequency data may be a method in step S10, that is, uniformly arranging pressure sensors in the pillow, detecting a change in the position of the target head to be detected by each pressure sensor, and determining whether to make a turning action, that is, if the position of the target head to be detected changes, indicating that the target to be detected makes a turning action.
Further, counting the times of turning actions of the target to be detected in unit time, and further obtaining target turning frequency data.
S12: and taking the breathing frequency data and the target turning frequency data as sleep action data.
Specifically, the detected respiratory frequency data and the target turn-over frequency data form sleep action data of a target to be detected.
In one embodiment, as shown in fig. 3, before step S20, the teenager sleep quality monitoring method further includes:
s201: historical deep sleep detection data are acquired, and deep sleep behavior data are acquired from each historical deep sleep detection data.
In the present embodiment, the historical deep sleep detection data refers to detection data that detects deep sleep of a person over a period of time. The deep sleep behavior data is data of actions such as breathing, heart rate and turning over when the human body goes into deep sleep.
Specifically, the deep sleep behavior data such as the respiratory rate and the turn-over frequency of the human body in a deep sleep state during sleep can be obtained from each historical deep sleep data by using a database storing the historical deep sleep detection data of clinical diagnosis. When the historical deep sleep detection data is recorded, a corresponding instrument can be adopted to detect the sleeping condition of the personnel, so that the historical deep sleep detection data is obtained; or when the steps S10-S40 are executed to monitor the sleep quality of the target to be detected, the monitored data corresponding to the sleep quality data sent to the third party communication platform is used as historical deep sleep detection data and stored in the database, that is, it can be understood that in the process of continuously monitoring the sleep quality of the target to be detected by using the deep sleep detection model, the monitored data is used as a training set of the deep sleep detection model, so that the deep sleep detection model is continuously perfected in the actual use process.
S202: respiratory behavior data and turn-over behavior data are obtained from each deep sleep behavior data.
In the present embodiment, the breathing behavior data refers to data of the breathing frequency when each person is in deep sleep. The turn-over behavior data refers to data of frequency of turning over when each person is in deep sleep.
Specifically, from each deep sleep behavior data, the breathing behavior data and the turning behavior data are obtained according to the corresponding data types.
S203: and training the breathing behavior data and the turning behavior data of each deep sleep behavior data by adopting machine learning to obtain a deep sleep detection model.
Specifically, the breathing behavior data and the turning behavior data of each deep sleep behavior data are used as training sets, and the data in the training sets are trained in an artificial intelligent machine learning mode, so that the deep sleep detection model is obtained.
In one embodiment, as shown in fig. 4, in step S20, the sleep action data is input into a preset deep sleep detection model to obtain deep sleep time data, which specifically includes:
s21: and inputting the breathing frequency data and the target turning frequency data in the sleep action data into a deep sleep detection model for matching.
Specifically, after sleep motion data of a target to be detected is acquired, respiratory frequency data and target turn-over frequency data in the sleep motion data are input into the deep sleep detection model for matching.
S22: if the breathing frequency data of the target to be detected is matched with the breathing behavior data, and the target turning frequency data of the target to be detected is matched with the turning behavior data, starting timing, and obtaining deep sleep time data.
Specifically, when the deep sleep detection model is trained, the breathing behavior data and the turning behavior data are used as training sets for training, so that the breathing frequency data corresponding to the data attribute of the breathing behavior data and the turning frequency data corresponding to the data attribute of the turning behavior data are input into the deep sleep detection model for matching, and whether the target to be detected enters deep sleep can be judged according to whether the breathing frequency data and the target turning frequency data are matched with the situation in deep sleep.
Further, when the respiratory frequency data of the target to be detected is matched with the respiratory behavior data, and the target turning frequency data of the target to be detected is matched with the turning behavior data, the target to be detected is indicated to enter deep sleep, namely, the deep sleep time data is recorded.
In one embodiment, as shown in fig. 5, in step S40, sleep quality data is obtained according to sleep time data and deep sleep proportion data, which specifically includes:
s41: and acquiring preset sleep time reference data and preset deep sleep proportion reference data.
In this embodiment, the sleep time reference data refers to the total length of sleep time per night for teenager health. The deep sleep proportion reference data refers to the proportion of the time of deep sleep of teenager health to the total sleep time.
Specifically, the preset sleep time reference data and the preset deep sleep ratio reference data, for example, sleep time reference data of 8 hours and deep sleep ratio reference data of 25%, can be obtained by integrating the opinions of a plurality of medical staff involved in studies of teenager health.
S42: and respectively comparing the sleep time data and the deep sleep proportion data with the sleep time reference data and the deep sleep proportion reference data, and acquiring sleep quality data according to the comparison result.
Specifically, after comparing the sleep time data with the sleep time reference data, comparing the deep sleep proportion data with the deep sleep proportion reference data, setting the sleep time reference data to a threshold interval related to sleep quality, for example, the sleep time is 7-8 hours, the sleep is considered to be high-quality, the sleep time is 5-6 hours, the sleep is considered to be medium-quality, the sleep time is less than 5 hours, and the sleep is considered to be low-quality.
Further, the depth sleep proportion reference data is set to be a threshold value interval related to sleep quality, and when the comparison result of the sleep time data is high-quality sleep, the depth sleep proportion reference data is compared with the depth sleep time proportion data, for example, the proportion is greater than 24%, the high-quality sleep is determined, the proportion is 20% -24%, the medium-quality sleep is determined, the proportion is less than 20% hours, and the low-quality sleep is determined.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In one embodiment, there is provided a teenager sleep quality monitoring apparatus, which corresponds to the teenager sleep quality monitoring method in the above embodiment one by one. As shown in fig. 6, the teenager sleep quality monitoring device comprises a data detection module, a model calculation module, a proportion calculation module and a data transmission module. The functional modules are described in detail as follows:
the data detection module is used for acquiring sleep condition data of the target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data;
the model calculation module is used for inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data;
the proportion calculation module is used for calculating the deep sleep proportion data according to the deep sleep time data and the sleep time data;
and the data sending module is used for obtaining sleep quality data according to the sleep time data and the deep sleep proportion data and sending the sleep quality data to the third-party communication platform.
Optionally, the data detection module includes:
the target behavior acquisition submodule is used for acquiring respiratory frequency data and target turning frequency data of a target to be detected;
the data detection sub-module is used for taking the breathing frequency data and the target turning frequency data as sleep action data.
Optionally, the teenager sleep quality monitoring device further includes:
the historical data acquisition module is used for acquiring historical deep sleep detection data and acquiring deep sleep behavior data from each piece of historical deep sleep detection data;
the data splitting module is used for acquiring breathing behavior data and turning behavior data from each deep sleep behavior data;
the model training module is used for training the breathing behavior data and the turning behavior data of each deep sleep behavior data by adopting machine learning to obtain a deep sleep detection model.
Optionally, the model calculation module includes:
the data input sub-module is used for inputting the breathing frequency data and the target turn-over frequency data in the sleep action data into the deep sleep detection model for matching;
and the timing sub-module is used for starting timing and obtaining deep sleep time data if the breathing frequency data of the target to be detected is matched with the breathing behavior data and the target turning frequency data of the target to be detected is matched with the turning behavior data.
Optionally, the data sending module includes:
the reference value acquisition sub-module is used for acquiring preset sleep time reference data and preset deep sleep proportion reference data;
the comparison sub-module is used for respectively comparing the sleep time data and the deep sleep proportion data with the sleep time reference data and the deep sleep proportion reference data and obtaining sleep quality data according to the comparison result.
The specific limitation of the teenager sleep quality monitoring device may be referred to the limitation of the teenager sleep quality monitoring method hereinabove, and will not be described herein. The above-mentioned individual modules in the teenager sleep quality monitoring device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing historical deep sleep detection data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a teenager sleep quality monitoring method.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data;
the sleep action data are input into a preset deep sleep detection model to obtain deep sleep time data;
calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
and acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and transmitting the sleep quality data to a third party communication platform.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data;
the sleep action data are input into a preset deep sleep detection model to obtain deep sleep time data;
calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
and acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and transmitting the sleep quality data to a third party communication platform.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (7)
1. The teenager sleep quality monitoring method is characterized by comprising the following steps of:
acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprises sleep time data and sleep action data, the sleep action data comprises breathing frequency data and target turning frequency data, specifically, the breathing frequency data of the target to be detected is judged through pressure changes detected by pressure sensors, the pressure sensors are uniformly arranged in a pillow, the change of the position of the head of the target to be detected is detected through each pressure sensor, and whether turning action is made is judged to obtain the target turning frequency data;
the sleep action data are input into a preset deep sleep detection model to obtain deep sleep time data, wherein the deep sleep detection model is obtained by training breathing behavior data and turning behavior data of historical deep sleep detection data in advance;
calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
acquiring sleep quality data according to the sleep time data and the deep sleep proportion data, and sending the sleep quality data to a third party communication platform, so as to be beneficial to analyzing the sleep quality condition of a target to be detected and improve the strategy for improving the sleep quality of teenagers;
specifically, the step of obtaining sleep quality data according to the sleep time data and the deep sleep proportion data specifically includes:
acquiring preset sleep time reference data and preset deep sleep proportion reference data;
and comparing the sleep time data and the deep sleep proportion data with the sleep time reference data and the deep sleep proportion reference data respectively, and acquiring the sleep quality data according to a comparison result, wherein the sleep quality data is obtained by multiplying a basic score and a sleep quality coefficient, the basic score is used for counting the time ratio of deep sleep when high-quality sleep is performed, and the ratio between the deep sleep proportion data and the time ratio is used as the basic score.
2. The teenager sleep quality monitoring method of claim 1, wherein the step of inputting the sleep motion data into a preset deep sleep detection model to obtain deep sleep time data specifically comprises the steps of:
inputting the respiratory frequency data and the target turn-over frequency data in the sleep action data into the deep sleep detection model for matching;
and if the respiratory frequency data of the target to be detected is matched with the respiratory behavior data and the target turning frequency data of the target to be detected is matched with the turning behavior data, starting timing to obtain the deep sleep time data.
3. A teenager sleep quality monitoring device, characterized in that the teenager sleep quality monitoring device comprises:
the data detection module is used for acquiring sleep condition data of a target to be detected, wherein the sleep condition data comprise sleep time data and sleep action data, the sleep action data comprise breathing frequency data and target turning frequency data, specifically, the breathing frequency data of the target to be detected are judged through pressure changes detected by the pressure sensors, the pressure sensors are uniformly arranged in the pillow, the change of the position of the head of the target to be detected is detected through each pressure sensor, and whether turning action is performed is judged to obtain the target turning frequency data;
the model calculation module is used for inputting the sleep action data into a preset deep sleep detection model to obtain deep sleep time data, wherein the deep sleep detection model is obtained by training breathing behavior data and turning behavior data of historical deep sleep detection data in advance;
the proportion calculation module is used for calculating deep sleep proportion data according to the deep sleep time data and the sleep time data;
the data sending module is used for obtaining sleep quality data according to the sleep time data and the deep sleep proportion data, sending the sleep quality data to a third party communication platform, and helping to analyze the sleep quality condition of a target to be detected so as to promote a strategy for improving the sleep quality of teenagers;
specifically, the step of obtaining sleep quality data according to the sleep time data and the deep sleep proportion data specifically includes:
the reference value acquisition sub-module is used for acquiring preset sleep time reference data and preset deep sleep proportion reference data;
the comparison sub-module is used for respectively comparing the sleep time data and the deep sleep proportion data with the sleep time reference data and the deep sleep proportion reference data, and obtaining the sleep quality data according to a comparison result, wherein the sleep quality data is obtained by multiplying a basic score and a sleep quality coefficient, the basic score is used for counting the time ratio of deep sleep when the user sleeps with high quality, and the ratio between the deep sleep proportion data and the time ratio is used as the basic score.
4. A teenager sleep quality monitoring device as claimed in claim 3, wherein the data detection module comprises:
the target behavior acquisition submodule is used for acquiring the breathing frequency data and the target turning frequency data of the target to be detected;
the data detection sub-module is used for taking the respiratory frequency data and the target turn-over frequency data as the sleep action data.
5. The teenager sleep quality monitoring device of claim 4, wherein the teenager sleep quality monitoring device further comprises:
the historical data acquisition module is used for acquiring historical deep sleep detection data and acquiring deep sleep behavior data from each piece of historical deep sleep detection data;
the data splitting module is used for acquiring breathing behavior data and turning behavior data from each deep sleep behavior data;
the model training module is used for training the breathing behavior data and the turning behavior data of each deep sleep behavior data by adopting machine learning to obtain the deep sleep detection model.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the teenager sleep quality monitoring method of any of claims 1 to 2 when the computer program is executed.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the teenager sleep quality monitoring method of any of claims 1 to 2.
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