CN111281341A - Sleep evaluation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a sleep evaluation method, a sleep evaluation device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring sleep state data; determining sleep index data according to the sleep state data; calculating sleep index data by using a sleep quality model to obtain sleep information, wherein the sleep information represents sleep quality information, and the sleep quality model comprises the following steps: sleep level, sleep duration, sleep regularity, and sleep breathing. In the implementation process, the sleep index data is determined according to the acquired sleep state data; calculating sleep index data by using a sleep quality model to obtain sleep information comprising sleep degree, sleep duration, sleep rule and sleep breathing; that is to say, the sleep evaluation method measures the sleep quality information of the user through four dimensions of the sleep degree, the sleep duration, the sleep rule and the sleep breathing, so that the accuracy of obtaining the sleep information representing the sleep quality of the user is improved.
Description
Technical Field
The present application relates to the technical field of computer communication and data processing, and in particular, to a sleep evaluation method and apparatus, an electronic device, and a storage medium.
Background
Currently, sleep-related products on the market include: hardware products that use sensors to monitor and count the quality of sleep of a user, and sleep software products that do statistics based on the user's sleep data, however these sleep-related products only give single-dimensional statistics, such as: the intelligent bracelet can monitor the heart rate data of the user through the bioelectrical impedance sensor or the optical heart rate sensor, and monitor the motion data and the sleep data of the user through the acceleration sensor and the electrodermal reaction sensor, so that the intelligent bracelet can count and obtain the sleep information representing the sleep quality of the user through the heart rate data, the motion data and the sleep data.
In specific practice, it is found that the dimensionality of the sleep information obtained through the heart rate data, the exercise data and the sleep data is single, that is, the obtained sleep information representing the quality of sleep of a user is not accurate enough.
Disclosure of Invention
An object of the embodiments of the present application is to provide a sleep evaluation method, apparatus, electronic device, and storage medium, which are used to solve the problem that obtaining sleep information representing the quality of sleep of a user is not accurate enough.
The embodiment of the application provides a sleep evaluation method, which comprises the following steps: acquiring sleep state data; determining sleep index data according to the sleep state data; calculating the sleep index data by using a sleep quality model to obtain sleep information, wherein the sleep information represents quality information of sleep, and the sleep quality model comprises: sleep level, sleep duration, sleep regularity, and sleep breathing. In the implementation process, the sleep index data is determined according to the acquired sleep state data; calculating sleep index data by using a sleep quality model to obtain sleep information comprising sleep degree, sleep duration, sleep rule and sleep breathing; that is to say, the sleep evaluation method measures the sleep quality information of the user through four dimensions of the sleep degree, the sleep duration, the sleep rule and the sleep breathing, so that the accuracy of obtaining the sleep information representing the sleep quality of the user is improved.
Optionally, in an embodiment of the present application, the sleep information includes: sleep quality score and/or sleep grade; after the obtaining sleep information, further comprising: and sending the sleep quality score and/or the sleep grade to the first terminal equipment. In the implementation process, the sleep quality score and/or the sleep grade are/is sent to the first terminal equipment; therefore, the user corresponding to the first terminal device can more easily know the sleep quality score and/or the sleep grade.
Optionally, in this embodiment of the present application, after obtaining the sleep information, the method further includes: and generating a control command according to the sleep information, and sending the control command to a sleep-assisting device, wherein the control command is used for enabling the sleep-assisting device to assist a user in sleeping. In the implementation process, a control command is generated according to the sleep information and sent to the sleep-assisting device, and the control command is used for enabling the sleep-assisting device to assist the user in sleeping; therefore, a user using the sleep-assisting device can enter a sleep state more quickly, and the sleep quality of the user is improved.
Optionally, in this embodiment of the present application, the acquiring sleep state data includes: receiving the sleep state data sent by a data acquisition device, wherein the sleep state data is acquired by using at least one sensor of the data acquisition device. In the implementation process, the sleep state data sent by the data acquisition equipment is received, and the sleep state data is acquired by using at least one sensor of the data acquisition equipment; thereby effectively increasing the speed of acquiring sleep state data.
Optionally, in this embodiment of the present application, the sleep state data includes: sleep type data; the acquiring sleep state data includes: and counting data of a preset sleep type questionnaire measuring table to obtain the sleep type data. In the implementation process, the sleep type data is obtained by counting the data of a preset sleep type questionnaire measuring table; that is, whether the user is staying up or not is determined by counting the data of the preset sleep type questionnaire measuring table, and the sleep type data is determined as the sleep state data, so that the rationality of obtaining the sleep information is effectively improved.
Optionally, in this embodiment of the present application, the sleep state data further includes: the sleep index data comprises the following data, namely the sleep time, the getting-up time, the sleep heart rate and the sleep breathing rate: the sleep time length of the first sleep degree, the sleep time length of the second sleep degree, the sleep time length of the third sleep degree, the sleep time length, the proportion parameter of a preset time range and the average sleep time length in preset days; the determining sleep index data according to the sleep state data includes: determining the duration of the first sleep degree, the duration of the second sleep degree, the duration of the third sleep degree, the length of the falling asleep, the proportional parameter of the preset time range and the average sleep duration in the preset days according to the sleep type data, the falling asleep moment, the getting-up moment, the sleep heart rate and the sleep respiration rate; wherein the first sleep level is greater than the second sleep level, and the second sleep level is greater than the third sleep level. In the implementation process, the proportion parameter of a preset time range and the average sleep time in preset days are determined according to the sleep type data, the time length of the first sleep degree, the time length of the second sleep degree, the time length of the third sleep degree, the time of falling asleep, the time of getting up, the time length of falling asleep, the sleep heart rate and the sleep respiration rate; therefore, the rationality of obtaining the sleep index data is effectively improved.
Optionally, in this embodiment of the present application, after obtaining the sleep information, the method further includes: and analyzing the sleep information by using the sleep quality model to obtain a sleep problem. In the implementation process, the sleep information is analyzed by using the sleep quality model to obtain the sleep problem; thereby speeding up the acquisition of sleep problems.
Optionally, in this embodiment of the present application, after obtaining the sleep question, the method further includes: determining a sleep improvement recommendation from the sleep information using the sleep quality model. In the implementation process, the sleep improvement suggestion is determined according to the sleep information by using the sleep quality model; thereby effectively increasing the speed of obtaining sleep improvement advice.
Optionally, in this embodiment of the present application, the determining a sleep improvement suggestion according to the sleep information by using the sleep quality model includes: obtaining a recommendation data repository comprising a plurality of sleep improvement recommendations having priority levels; and screening out a sleep improvement suggestion with the highest priority level from the suggestion data warehouse according to the sleep information by using the sleep quality model. In the implementation process, by obtaining a recommendation data repository, the recommendation data repository includes a plurality of sleep improvement recommendations with priority levels; screening a sleep improvement suggestion with the highest priority level from a suggestion data warehouse by using a sleep quality model according to the sleep information; therefore, the sleep improvement suggestions with different priorities are effectively improved for different users, namely, the individuation of the sleep improvement suggestions of different users is realized.
Optionally, in an embodiment of the present application, the method further includes: sending the sleep problem and/or the sleep improvement suggestion to a second terminal device. In the implementation process, the sleep problem and/or sleep improvement suggestion is sent to the second terminal equipment; therefore, a user using the second terminal device can more easily know the sleep problem and/or the sleep improvement suggestion.
An embodiment of the present application further provides a sleep evaluation device, including: the state data acquisition module is used for acquiring sleep state data; the index data determining module is used for determining sleep index data according to the sleep state data; a sleep information obtaining module, configured to calculate the sleep index data by using a sleep quality model to obtain sleep information, where the sleep information represents quality information of sleep, and the sleep quality model includes: sleep level, sleep duration, sleep regularity, and sleep breathing.
Optionally, in an embodiment of the present application, the sleep information includes: sleep quality score and/or sleep grade; further comprising: and the first information sending module is used for sending the sleep quality score and/or the sleep grade to the first terminal equipment.
Optionally, in an embodiment of the present application, the method further includes: and the command generation and transmission module is used for generating a control command according to the sleep information and transmitting the control command to the sleep-assisting equipment, wherein the control command is used for enabling the sleep-assisting equipment to assist the user in sleeping.
Optionally, in an embodiment of the present application, the status data obtaining module includes: the state data receiving module is used for receiving the sleep state data sent by the data acquisition equipment, and the sleep state data is acquired by using at least one sensor of the data acquisition equipment.
Optionally, in this embodiment of the present application, the sleep state data includes: sleep type data; the status data acquisition module comprises: and the type data acquisition module is used for counting the data of a preset sleep type questionnaire measuring table to acquire the sleep type data.
Optionally, in this embodiment of the present application, the sleep state data further includes: the sleep index data comprises the following data, namely the sleep time, the getting-up time, the sleep heart rate and the sleep breathing rate: the sleep time length of the first sleep degree, the sleep time length of the second sleep degree, the sleep time length of the third sleep degree, the sleep time length, the proportion parameter of a preset time range and the average sleep time length in preset days; the index data determination module includes: a data determining submodule, configured to determine, according to the sleep type data, the time of falling asleep, the time of getting up, the sleep heart rate, and the sleep breathing rate, a time length of the first sleep degree, a time length of the second sleep degree, a time length of the third sleep degree, the time length of falling asleep, a proportional parameter of the preset time range, and an average sleep time length within the preset number of days; wherein the first sleep level is greater than the second sleep level, and the second sleep level is greater than the third sleep level.
Optionally, in an embodiment of the present application, the method further includes: and the sleep problem obtaining module is used for analyzing the sleep information by using the sleep quality model to obtain a sleep problem.
Optionally, in an embodiment of the present application, the method further includes: an improvement suggestion determination module to determine a sleep improvement suggestion based on the sleep information using the sleep quality model.
Optionally, in an embodiment of the present application, the improvement suggestion determination module includes: a data repository obtaining module to obtain a suggested data repository, the suggested data repository including a plurality of sleep improvement suggestions having priority levels; and the improvement suggestion screening module is used for screening out the sleep improvement suggestion with the highest priority level from the suggestion data warehouse according to the sleep information by using the sleep quality model.
Optionally, in an embodiment of the present application, the method further includes: and the second information sending module is used for sending the sleep problem and/or the sleep improvement suggestion to second terminal equipment.
An embodiment of the present application further provides an electronic device, including: a processor and a memory storing machine-readable instructions executable by the processor, the machine-readable instructions when executed by the processor performing the method as described above.
The embodiment of the present application also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method as described above is executed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic diagram illustrating a sleep evaluation method provided by an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a sleep quality model provided by an embodiment of the present application;
fig. 3 is a schematic diagram of a sleep evaluation apparatus provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Before describing the sleep evaluation method provided by the embodiment of the present application, some concepts related to the embodiment of the present application are described below:
the Rapid Eye Movement (REM) is a stage of sleep in animals, also known as Rapid Eye Movement sleep. During this phase, the eyeball moves rapidly while the body muscles relax. Fast eye movement sleep is also known as fast wave sleep, parasympathetic sleep or desynchronized sleep because of the rapid eye movement phenomenon that accompanies this phase, and breathing and heartbeat become irregular, general muscle relaxation, especially dystonia in the muscle groups that maintain posture, electroencephalogram presents desynchronized fast waves, and the activity of the neurons of the brain is the same as when awake, presenting fast, low-voltage desynchronized brain waves.
DataBase (DB) is a collection for storing electronic data or electronic files, and may be regarded as an electronic file cabinet, in short, and a user may add, intercept, update, delete, etc. to the data in the file. A "database" is a collection of data that is stored together in a manner that can be shared by multiple users, has as little redundancy as possible, and is independent of the application. The database includes: a memory database, a relational database, and a non-relational database.
The Memory database refers to a data set searched based on a Random Access Memory (RAM), and is characterized by a fast read/write speed, and therefore, the Memory database is also called a cache database, and a common Memory database is, for example: memcached and Redis, etc.
A relational database refers to a database that organizes data using a relational model, and the relational database stores data in the form of rows and columns for easy understanding by users, and the series of rows and columns of the relational database is called a table, and a common relational database is, for example: mysql, PostgreSQL, Oracle, SQLSever, etc.
A non-relational database, also called nosql (not Only SQL), means that it is not Only Structured Query Language (SQL), but also mainly includes the following components according to the difference of the Structured method and the application scenario: three non-relational databases of columnar storage, document storage and key-value pair storage are oriented, and a common non-relational database comprises: a grakn knowledge map database, a Neo4j database, a Hadoop subsystem HBase, MongoDB, CouchDB and the like.
A server refers to a device that provides computing services over a network, such as: x86 server and non-x 86 server, non-x 86 server includes: mainframe, minicomputer, and UNIX server. Certainly, in a specific implementation process, the server may specifically select a mainframe or a minicomputer, where the mainframe refers to a dedicated processor that mainly supports a closed and dedicated device for providing Computing service of a UNIX operating system, and that uses Reduced Instruction Set Computing (RISC), single-length fixed-point instruction average execution speed (MIPS), and the like; a mainframe, also known as a mainframe, refers to a device that provides computing services using a dedicated set of processor instructions, an operating system, and application software.
It should be noted that the sleep evaluation method provided in the embodiments of the present application may be executed by an electronic device, where the electronic device refers to a device terminal having a function of executing a computer program or the server described above, and the device terminal includes, for example: a smart phone, a Personal Computer (PC), a tablet computer, a Personal Digital Assistant (PDA), a Mobile Internet Device (MID), a network switch or a network router, and the like. The sleep assessment method described above may also be performed by sleep-related device products such as: electronic pillow, electronic crib and provide equipment such as intelligent stereo set of supplementary sleep.
Before introducing the sleep evaluation method provided by the embodiment of the present application, application scenarios to which the sleep evaluation method is applicable are introduced, where the application scenarios include, but are not limited to: evaluating sleep quality by using the sleep evaluation method to obtain sleep information, wherein the sleep information can comprise: sleep quality score, sleep level, sleep problems and/or sleep improvement advice, and the like, and can also assist the user in sleeping according to the sleep information, and the like.
Please refer to fig. 1, which is a schematic diagram illustrating a sleep evaluation method provided in an embodiment of the present application; the sleep evaluation method may include the steps of:
step S110: the electronic device acquires sleep state data.
The sleep state data refers to sleep state data which is acquired by the electronic equipment and is about a user; the user refers to a user who needs to evaluate the sleep quality service, and the sleep state data may include: the duration of the first sleep level, the duration of the second sleep level, and the duration of the third sleep level; the first sleep degree is greater than the second sleep degree, the second sleep degree is greater than the third sleep degree, the deeper the sleep degree, the better the sleep quality, therefore, the duration of the first sleep degree can be understood as the deep sleep duration, the duration of the second sleep degree can be understood as the light sleep duration, and the duration of the third sleep degree can be understood as the waking duration. In the deep sleep period, the cerebral cortical cells of the human are in a sufficient rest state and are very important for stabilizing emotion, balancing mental state and recovering energy, so the deep sleep time is very important for determining the sleep quality; meanwhile, a plurality of antibodies can be generated in the human body, so that the disease resistance is enhanced; therefore, it is reasonable to use the deep sleep duration overnight to gauge the sleep quality.
There are many embodiments of the above described acquisition of sleep state data as follows:
first, the sleep state data sent by other devices may be received, and the step S110 of acquiring the sleep state data may include the following steps:
step S111: the electronic equipment receives the sleep state data sent by the data acquisition equipment.
Data acquisition equipment (data acquisition equipment), which refers to equipment that acquires sleep state data, selects and collects data from data sources for a particular need, and may include at least one sensor, such as: sound sensors, light sensors, pressure sensors, humidity sensors, temperature sensors, and the like.
The above embodiment of the electronic device receiving the sleep state data sent by the data acquisition device is, for example: the electronic equipment receives sleep state data sent by the data acquisition equipment through a Transmission Control Protocol (TCP); the sleep state data may be acquired using at least one sensor of the data acquisition device. The TCP protocol is also called network communication protocol, is the most basic protocol of Internet and the foundation of Internet, and consists of IP protocol in network layer and TCP protocol in transmission layer. The communication may be based on transmission control protocol/internet protocol (TCP/IP) or may be based on hypertext transfer protocol. In the implementation process, the sleep state data sent by the data acquisition equipment is received, and the sleep state data is acquired by using at least one sensor of the data acquisition equipment; thereby effectively increasing the speed of acquiring sleep state data.
In a second manner, the pre-stored data may be counted to obtain sleep state data, where the sleep state data may further include: sleep type data; the above-mentioned acquiring of the sleep state data, i.e., step S110, may include the steps of:
step S112: the electronic equipment counts the data of a preset sleep type questionnaire measuring table to obtain sleep type data.
The sleep type data refers to a personal mode of the physical and mental circadian rhythm of the user, and the sleep type data directly influences the judgment of whether to stay up all night; that is, not everyone spends the same day in exactly the same way, and each person has their own sleep pattern, which is a personal pattern that determines the circadian rhythm of the person. There are two cases of the sleep type data described above: a morning star person and a night star person; the early-onset star people are judged to be staying up when the early-onset star people go to sleep beyond 24 hours, the late-night star people are judged to be staying up when the late-onset star people go to sleep beyond 2 hours in the morning, and the staying up judgment is not carried out on the basis of 24 hours any more, so that the staying up judgment is more scientific and reasonable.
The embodiment of step S112 described above is, for example: designing questions about sleep types in a preset sleep type questionnaire measuring table, and counting answer data about the questions about the sleep types to obtain sleep type data. The preset sleep type questionnaire measuring meter refers to a preset sleep type questionnaire self-rating meter, and the measuring meter can be a sleep type measuring meter of Munich university, and of course, in a specific implementation process, measuring meters of other mechanisms can be adopted. In the implementation process, the sleep type data is obtained by counting the data of a preset sleep type questionnaire measuring table; that is, whether the user is staying up or not is determined by counting the data of the preset sleep type questionnaire measuring table, and the sleep type data is determined as the sleep state data, so that the rationality of obtaining the sleep information is effectively improved.
After step S110, step S120 is performed: the electronic device determines sleep index data according to the sleep state data.
The sleep index data refers to index data obtained by calculation according to the sleep state data of the user; specifically, the sleep metric data may include: presetting a proportion parameter of a time range and an average sleeping time within preset days; the predetermined time range and the predetermined number of days will be described in detail below. Of course, in a specific implementation process, the sleep index data herein may further include: a length of time to fall asleep, a length of time for an overall sleep session, a number of wakefulness times for the first 90 minutes, a number of wakefulness times for an overall sleep session, a length of wakefulness for an overall sleep session, a REM length, a length of time to get up, a respiratory quality, and a number of ideal sleeps for the last 7 days, etc.; the total sleep process duration may be equal to the sum of the deep sleep duration, the light sleep duration, and the REM duration, for example.
In a specific implementation process, the step S120 has various implementations, specifically for example: the sleep state data may further include: time to sleep, time to get up, sleep heart rate and sleep breathing rate; the above-mentioned step S120 may include the steps of:
step S121: the electronic equipment determines the duration of the first sleep degree, the duration of the second sleep degree, the duration of the third sleep degree, the sleeping duration, the proportional parameter of a preset time range and the average sleeping duration in preset days according to the sleep type data, the sleeping time, the getting-up time, the sleep heart rate and the sleep respiration rate.
The preset time range refers to a preset time range; the preset time range may be set as the first 90 minutes, and the ratio parameter of the preset time range may be a deep sleep ratio of the first 90 minutes, a light sleep ratio of the first 90 minutes, or a wakefulness ratio of the first 90 minutes.
The preset days refer to preset days; the preset number of days may be set to the last 7 days, etc., and the average sleep time period within the preset number of days may be the average sleep time period of the last 7 days.
The embodiment of step S121 described above includes, for example: using statistical data of the sleep type data, the time of falling asleep, the time of getting up, the sleep heart rate, the sleep breathing rate and the like of the first user to obtain the waking time, the light sleep time, the deep sleep time, the time of falling asleep, the deep sleep proportion of the first 90 minutes, the light sleep proportion of the first 90 minutes, the waking proportion of the first 90 minutes, the average sleep time of the last 7 days, the ideal sleep time of the last 7 days and the like of the first user; the specific deep sleep state, light sleep state and waking state can be specifically determined by the sleep heart rate and the sleep breathing rate, the deep sleep proportion of the first 90 minutes is the deep sleep duration of the first 90 minutes/90 × 100%, the waking proportion of the first 90 minutes is the waking duration of the first 90 minutes/90 × 100%, the average sleep duration of the last 7 days is the average sleep duration of near 7 nights, and the ideal sleep duration of the last 7 days is the number of days for which more than 7.5 hours of sleep are obtained every night in near 7 nights. Of course, in a specific implementation process, the sleep index data may further include: the overall deep sleep ratio is the overall deep sleep period/sleep period × 100%, and the overall wakefulness ratio is the overall wakefulness period/sleep period × 100%, and the like.
In the implementation process, the proportion parameter of a preset time range and the average sleep time in preset days are determined according to the sleep type data, the time length of the first sleep degree, the time length of the second sleep degree, the time length of the third sleep degree, the time of falling asleep, the time of getting up, the time length of falling asleep, the sleep heart rate and the sleep respiration rate; therefore, the rationality of obtaining the sleep index data is effectively improved.
After step S120, step S130 is performed: the electronic equipment calculates the sleep index data by using the sleep quality model to obtain the sleep information.
Please refer to fig. 2, which is a schematic diagram of a sleep quality model provided by an embodiment of the present application; the sleep quality model refers to an algorithm model for evaluating sleep quality, also called a sleep quality assessment model, and includes four dimensions, which are: sleep level, sleep duration, sleep regularity and sleep breathing; the sleep degree is also called as the sleep depth, the sleep depth accounts for 50% of the sleep quality score, the sleep duration accounts for 30% of the sleep quality score, the sleep rule accounts for 20% of the sleep quality score, the sleep breathing is also called as the breathing monitoring, and the breathing monitoring is a deduction item of the sleep quality score; specifically, the sleep quality score is calculated, for example: if the score of the sleep depth is 33, the score of the sleep duration is 22, the score of the sleep regularity is 11 and the score of the sleep breathing is 10, then the calculation method of the sleep quality score is 33+22+11-10 ═ 56, and then 56 here can be understood as the sleep quality score.
Certainly, in a specific implementation process, a sleep liability theory can also be utilized, and the sleep liability theory can be simply understood as that for general people, the sleep duration is supposed to be 7.5 hours, and 52.5 hours of sleep is obtained every week, which is most ideal; bringing the ideal sleep quantity in the near 7 days into a single-day sleep evaluation system; in addition, the sleep quality is evaluated according to the sleep index data such as the time for falling asleep and the sleep efficiency, so that the sleep quality can be more accurately evaluated. The sleep evaluation method adopts a sleep type measuring table of Munich university to determine whether the user stays up all night, so that the evaluation result of the sleep quality is more reasonable.
The sleep information refers to quality information representing sleep; sleep information here includes, but is not limited to: the sleep quality score is obtained in the manner shown in fig. 2, the value range of the sleep quality score is, for example, 0 to 100, the obtained sleep quality score may be 70, 80 or 90, and the specific score may be obtained according to specific situations; the sleep information herein may also include sleep levels; sleep levels can be divided into four levels, e.g., good, medium, and bad; the method of calculating the sleep level will be explained below.
The embodiment of step S130 described above is, for example: calculating the sleep index data by using a sleep quality model, wherein the obtained result can be a sleep quality score, and the sleep quality score refers to the degree of good sleep quality represented by a score; then, the sleep grade is determined according to the sleep quality score, and the specific obtaining method of the sleep grade comprises the following steps: if the sleep quality score is between 92 and 100, the sleep grade is 'excellent'; if the sleep quality score is between 85 and 91, the sleep grade is 'good'; if the sleep quality score is between 62 and 84, the sleep grade is 'medium'; if the sleep quality score is between 0 and 61, the sleep grade is "poor", and the boundary value in each of the above conditions includes the value itself.
In the implementation process, the sleep index data is determined according to the acquired sleep state data; calculating sleep index data by using a sleep quality model to obtain sleep information comprising sleep degree, sleep duration, sleep rule and sleep breathing; that is to say, the sleep evaluation method measures the sleep quality information of the user through four dimensions of the sleep degree, the sleep duration, the sleep rule and the sleep breathing, so that the accuracy of obtaining the sleep information representing the sleep quality of the user is improved.
Optionally, after obtaining the sleep information, the sleep information may also be sent to other devices, that is, after step S130, the following steps may also be included:
after step S130, step S140 is performed: the electronic device sends the sleep quality score and/or the sleep level to the first terminal device.
The first terminal device refers to a device for displaying a sleep quality score and/or a sleep level, where the specific type of the device should not be limited, and specifically, the first terminal device includes but is not limited to: smart bracelets, electronic watches, smart pillows, cell phones, and/or sleep instruments, and the like.
The embodiment of step S140 described above is, for example: the electronic device may send the sleep quality score and/or the sleep level to one first terminal device, or the electronic device may send the sleep quality score and/or the sleep level to a plurality of first terminal devices, and the specific sending mode of the electronic device to the first terminal device may be sending through a wireless network, or sending through a wired network, or sending through an internet mode in which the wired network and the wireless network are mixed. In the implementation process, the sleep quality score and/or the sleep grade are/is sent to the first terminal equipment; therefore, the user corresponding to the first terminal device can more easily know the sleep quality score and/or the sleep grade.
Optionally, after the sleep information is obtained, a control command for assisting sleep may be sent to other devices according to the sleep information; that is, after step S130, the following steps may be further included:
after step S130, step S150 is performed: the electronic equipment generates a control command according to the sleep information, and the electronic equipment sends the control command to the sleep-assisting equipment.
The control command refers to a command used by the electronic device to control other devices, and the control command is used for enabling the sleep-assisting device to assist the user in sleeping, and the commands are, for example: playing a reminding sound or picture for sleeping in time, playing music for helping sleep, releasing fragrance (such as lavender fragrance) for helping sleep or physically assisting sleep and the like; physical assisted sleep such as: the crib is gently shaken or the massage mattress is turned on to have a massage function, and the like.
Sleep-aid device refers to a terminal device for assisting a user to sleep better, and the sleep-aid device includes but is not limited to: smart bracelets, electronic watches, smart pillows, mobile phones, and/or sleep instruments, etc.; the functions of the sleep-aid device herein include, but are not limited to: playing music for helping sleep, releasing fragrance (such as lavender fragrance) for helping sleep, or physically assisting sleep.
The embodiment of step S150 described above is, for example: according to the sleep information obtained by statistics, the user can sleep in time at twelve points, but the user is monitored to be still in a waking state in real time, at the moment, the electronic device can send a control command to the sleep aid device, play reminding sounds or pictures for timely sleeping, play music for helping sleep or release fragrance (such as lavender fragrance) for helping sleep and the like. In the implementation process, a control command is generated according to the sleep information and sent to the sleep-assisting device, and the control command is used for enabling the sleep-assisting device to assist the user in sleeping; therefore, a user using the sleep-assisting device can enter a sleep state more quickly, and the sleep quality of the user is improved.
Optionally, after obtaining the sleep information, the sleep problem may also be determined according to the sleep analysis; that is, after step S130, the following steps may be further included:
after step S130, step S160 is performed: the electronic equipment analyzes the sleep information by using the sleep quality model to obtain the sleep problem.
The embodiment of step S160 described above is, for example: analyzing sleep information and looking up a problem interpretation table according to various data of sleep quality scores obtained by using a sleep quality model to obtain sleep problems; the problem interpretation table may be set as a following table (the table is only an exemplary table showing a part of contents, and the problem interpretation table may be set according to specific situations, and the contents of the table may not be limited).
In the implementation process, the sleep information is analyzed by using the sleep quality model to obtain the sleep problem; thereby speeding up the acquisition of sleep problems.
Optionally, after obtaining the sleep problem, an improvement suggestion may be provided according to the sleep problem, and then the sleep evaluation method may further include the steps of:
after step S160, step S170 is performed: the electronic device determines a sleep improvement recommendation from the sleep information using the sleep quality model.
The sleep improvement suggestion refers to an improvement suggestion provided for a sleep problem of a user, and specifically includes: the user's sleep problem is "too long sleep duration", then a sleep improvement suggestion can be provided that "long sleep helps recovery, but also takes care of moderation. Sleep too much, which easily causes the activity of brain cells to be reduced, memory to be damaged, and the like. In the implementation process, the sleep improvement suggestion is determined according to the sleep information by using the sleep quality model; thereby effectively increasing the speed of obtaining sleep improvement advice.
The above-mentioned determination of the sleep improvement advice according to the sleep information using the sleep quality model, i.e., the step S170, may include the steps of:
step S171: the electronic device obtains a suggested data repository.
A data warehouse, which refers to a theme-oriented, integrated, relatively stable data set reflecting historical changes, and can be used for supporting auxiliary sleep improvement suggestions; the specific implementation manner of the data warehouse is various, and specific examples thereof are as follows: the data warehouse is implemented using a file system, where a file system includes a distributed storage file system or the like, or a database, where a database includes: memory databases, relational databases, non-relational databases, and the like. The suggestion data warehouse is used for storing the sleep problem and the sleep improvement suggestion corresponding to the sleep problem and the corresponding association relationship between the sleep problem and the sleep improvement suggestion.
The embodiment of step S171 described above includes, for example: the recommendation data warehouse is established according to a plurality of sleep problems and sleep improvement recommendations corresponding to the sleep problems and corresponding association relations between the sleep problems and the sleep improvement recommendations, and the recommendation data warehouse can use a relational database or a non-relational database, and certainly can use both the relational database and the non-relational database, and the data warehouse is for example the following table contents.
It is to be understood that, as shown in the above table, a plurality of sleep improvement suggestions may correspond to one and the same sleep problem in the suggestion data warehouse, wherein the plurality of sleep improvement suggestions may have relative priorities, and therefore, a sleep improvement suggestion with the highest priority level may be selected from the plurality of sleep improvement suggestions, wherein the suggestion data warehouse includes a plurality of sleep improvement suggestions with priority levels.
Step S172: the electronic device screens out a sleep improvement suggestion with the highest priority level from the suggestion data base according to the sleep information by using the sleep quality model.
The embodiment of step S172 described above is, for example: if the user's sleep quality level is excellent, there are three sleep improvement suggestions: the priority level of the first improvement suggestion is P0, and the content of the first improvement suggestion is that 'efficient sleep can fully recover the body, reduce the stress and make you more optimistic'; the priority level of the second improvement suggestion is P1, and the content of the second improvement suggestion is 'good sleep can make the head awake in the daytime, improve the work and study efficiency, and you do very well'; the priority level of the third improvement suggestion is P2, and the content of the third improvement suggestion is 'sweat streaming down should be avoided after getting up, fatigue is caused by excessive exercise, and more preferably walking fast morning'; here, a smaller number in the priority levels indicates a higher priority level, and it may be determined that the first improvement suggestion has the highest priority level, and thus, the first improvement suggestion may be determined as a sleep improvement suggestion having the highest priority level. It will be appreciated that the three improvement suggestions herein may not be made based on the presence of sleep problems, but directly based on the maximum score terms given.
The embodiment of step S172 described above is, for example: if the sleep quality grade of the user is good, and the total sleep arousal proportion of the user is higher according to the depth analysis of the sleep degree, two sleep improvement suggestions can be provided: the priority level of the first improvement suggestion is P0, smoking, drinking strong tea or coffee and the like are not suitable before sleep, caffeine and other substances can stimulate central nerves, excite people and not easily fall asleep, and can cause frequent urination at night and influence sleep; the priority level of the second improvement suggestion is P1, and people do not drink too much water before sleeping, so that the conditions that the sleep continuity is influenced and the physical recovery is poor due to night rising are avoided. Here, a smaller number in the priority levels indicates a higher priority level, and it may be determined that the first improvement suggestion has the highest priority level, and thus, the first improvement suggestion may be determined as a sleep improvement suggestion having the highest priority level. It is understood that if the sleep quality level of the user is good, the above improvement suggestions are given according to the priorities, and as illustrated in the above example, the case where the higher sleep total wakefulness ratio is the discount, which may reflect that the user is a problem of the sleep level, and the above P0 and P1 suggestions are the improvement suggestions provided according to the problem.
In the implementation process, by obtaining a recommendation data repository, the recommendation data repository includes a plurality of sleep improvement recommendations with priority levels; screening a sleep improvement suggestion with the highest priority level from a suggestion data warehouse by using a sleep quality model according to the sleep information; therefore, the sleep improvement suggestions with different priorities are effectively improved for different users, namely, the individuation of the sleep improvement suggestions of different users is realized.
Optionally, after obtaining the sleep problem and/or sleep improvement suggestion, the sleep problem and/or sleep improvement suggestion may be further sent to other devices, and then the sleep evaluation method may further include the following steps:
after step S170, step S180 is performed: the electronic device sends a sleep problem and/or sleep improvement advice to the second terminal device.
The embodiment of step S180 described above is, for example: the electronic equipment sends a sleep problem and/or sleep improvement suggestion to the second terminal equipment in a browser request and server response mode of the client; the Browser request and Server response mode of the client refers to a Browser/Server (B/S) mode; of course, the communication mode may also send the sleep problem and/or the sleep improvement suggestion to the second terminal device in a Client/Server (C/S) mode.
In the implementation process, the sleep problem and/or sleep improvement suggestion is sent to the second terminal equipment; therefore, a user using the second terminal device can more easily know the sleep problem and/or the sleep improvement suggestion.
Please refer to fig. 3, which is a schematic diagram of a sleep evaluation apparatus according to an embodiment of the present application; the embodiment of the present application provides a sleep evaluation apparatus 300, including:
a status data obtaining module 310, configured to obtain sleep status data.
An index data determination module 320 configured to determine sleep index data according to the sleep state data.
A sleep information obtaining module 330, configured to calculate the sleep index data by using a sleep quality model, and obtain sleep information, where the sleep information represents quality information of sleep, and the sleep quality model includes: sleep level, sleep duration, sleep regularity, and sleep breathing.
Optionally, in this embodiment of the present application, the sleep information includes: sleep quality score and/or sleep grade; the apparatus sleep evaluation may further comprise:
and the first information sending module is used for sending the sleep quality score and/or the sleep grade to the first terminal equipment.
Optionally, in this embodiment of the present application, the method may further include:
and the command generating and sending module is used for generating a control command according to the sleep information and sending the control command to the sleep-assisting device, wherein the control command is used for enabling the sleep-assisting device to assist the user in sleeping.
Optionally, in this embodiment of the present application, the status data obtaining module includes:
the data acquisition equipment comprises a state data receiving module used for receiving sleep state data sent by the data acquisition equipment, wherein the sleep state data is acquired by at least one sensor of the data acquisition equipment.
Optionally, in this embodiment of the present application, the sleep state data includes: sleep type data; a status data acquisition module comprising:
and the type data acquisition module is used for counting the data of the preset sleep type questionnaire measuring table to acquire the sleep type data.
Optionally, in this embodiment of the present application, the sleep state data further includes: the length of time of first sleep degree, the length of time of second sleep degree, the length of time of third sleep degree, the time of falling asleep, the time of getting up, the length of falling asleep, sleep heart rate and sleep respiration rate, the sleep index data include: presetting a proportion parameter of a time range and an average sleeping time within preset days; an indicator data determination module comprising:
the data determination submodule is used for determining a proportion parameter of a preset time range and an average sleep time within preset days according to the sleep type data, the time length of the first sleep degree, the time length of the second sleep degree, the time length of the third sleep degree, the time of falling asleep, the time of getting up, the time length of falling asleep, the sleep heart rate and the sleep respiration rate; the first sleep degree is larger than the second sleep degree, and the second sleep degree is larger than the third sleep degree.
Optionally, in an embodiment of the present application, the apparatus further includes:
and the sleep problem obtaining module is used for analyzing the sleep information by using the sleep quality model to obtain the sleep problem.
Optionally, in this embodiment of the present application, the apparatus may further include:
an improvement suggestion determination module to determine a sleep improvement suggestion based on the sleep information using the sleep quality model.
Optionally, in an embodiment of the present application, the improvement suggestion determination module includes:
a data repository obtaining module to obtain a suggested data repository, the suggested data repository including a plurality of sleep improvement suggestions having priority levels.
And the improvement suggestion screening module is used for screening out the sleep improvement suggestion with the highest priority level from the suggestion data warehouse according to the sleep information by using the sleep quality model.
Optionally, in this embodiment of the present application, the sleep evaluation apparatus may further include:
and the second information sending module is used for sending the sleep problem and/or the sleep improvement suggestion to the second terminal equipment.
It should be understood that the apparatus corresponds to the above-mentioned embodiment of the sleep evaluation method, and can perform the steps related to the above-mentioned embodiment of the method, and the specific functions of the apparatus can be referred to the above description, and the detailed description is appropriately omitted here to avoid redundancy. The device includes at least one software function that can be stored in memory in the form of software or firmware (firmware) or solidified in the Operating System (OS) of the device.
Please refer to fig. 4 for a schematic structural diagram of an electronic device according to an embodiment of the present application. An electronic device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing machine-readable instructions executable by the processor 410, the machine-readable instructions when executed by the processor 410 performing the method as above.
The embodiment of the present application further provides a storage medium 430, where the storage medium 430 stores a computer program, and the computer program is executed by the processor 410 to perform the sleep evaluation method as above.
The storage medium 430 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an alternative embodiment of the embodiments of the present application, but the scope of the embodiments of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present application, and all the changes or substitutions should be covered by the scope of the embodiments of the present application.
Claims (13)
1. A sleep evaluation method, comprising:
acquiring sleep state data;
determining sleep index data according to the sleep state data;
calculating the sleep index data by using a sleep quality model to obtain sleep information, wherein the sleep information represents quality information of sleep, and the sleep quality model comprises: sleep level, sleep duration, sleep regularity, and sleep breathing.
2. The method of claim 1, wherein the sleep information comprises: sleep quality score and/or sleep grade; after the obtaining sleep information, further comprising:
and sending the sleep quality score and/or the sleep grade to the first terminal equipment.
3. The method of claim 1, further comprising, after the obtaining sleep information:
and generating a control command according to the sleep information, and sending the control command to a sleep-assisting device, wherein the control command is used for enabling the sleep-assisting device to assist a user in sleeping.
4. The method of any of claims 1-3, wherein the obtaining sleep state data comprises:
receiving the sleep state data sent by a data acquisition device, wherein the sleep state data is acquired by using at least one sensor of the data acquisition device.
5. The method of any of claims 1-3, wherein the sleep state data comprises: sleep type data; the acquiring sleep state data includes:
and counting data of a preset sleep type questionnaire measuring table to obtain the sleep type data.
6. The method of claim 5, wherein the sleep state data further comprises: the sleep index data comprises the following data, namely the sleep time, the getting-up time, the sleep heart rate and the sleep breathing rate: the sleep time length of the first sleep degree, the sleep time length of the second sleep degree, the sleep time length of the third sleep degree, the sleep time length, the proportion parameter of a preset time range and the average sleep time length in preset days; the determining sleep index data according to the sleep state data includes:
determining the duration of the first sleep degree, the duration of the second sleep degree, the duration of the third sleep degree, the length of the falling asleep, the proportional parameter of the preset time range and the average sleep duration in the preset days according to the sleep type data, the falling asleep moment, the getting-up moment, the sleep heart rate and the sleep respiration rate; wherein the first sleep level is greater than the second sleep level, and the second sleep level is greater than the third sleep level.
7. The method of any of claims 1-3, further comprising, after said obtaining sleep information:
and analyzing the sleep information by using the sleep quality model to obtain a sleep problem.
8. The method of claim 7, further comprising, after said deriving a sleep problem:
determining a sleep improvement recommendation from the sleep information using the sleep quality model.
9. The method of claim 8, wherein determining a sleep improvement recommendation from the sleep information using the sleep quality model comprises:
obtaining a recommendation data repository comprising a plurality of sleep improvement recommendations having priority levels;
and screening out a sleep improvement suggestion with the highest priority level from the suggestion data warehouse according to the sleep information by using the sleep quality model.
10. The method of claim 8, further comprising:
sending the sleep problem and/or the sleep improvement suggestion to a second terminal device.
11. A sleep evaluation device, comprising:
the state data acquisition module is used for acquiring sleep state data;
the index data determining module is used for determining sleep index data according to the sleep state data;
a sleep information obtaining module, configured to calculate the sleep index data by using a sleep quality model to obtain sleep information, where the sleep information represents quality information of sleep, and the sleep quality model includes: sleep level, sleep duration, sleep regularity, and sleep breathing.
12. An electronic device, comprising: a processor and a memory, the memory storing machine-readable instructions executable by the processor, the machine-readable instructions, when executed by the processor, performing the method of any of claims 1 to 10.
13. A storage medium, having stored thereon a computer program which, when executed by a processor, performs the method of any one of claims 1 to 10.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112190419A (en) * | 2020-09-11 | 2021-01-08 | 深圳数联天下智能科技有限公司 | Sleep management method and device |
| CN112535464A (en) * | 2020-12-21 | 2021-03-23 | 安徽华米智能科技有限公司 | PAI-based state evaluation method, PAI-based state evaluation device, PAI-based state evaluation equipment and storage medium |
| CN112561468A (en) * | 2020-12-04 | 2021-03-26 | 珠海格力电器股份有限公司 | Sleep-in reminding method and device, readable storage medium and computer equipment |
| CN112890829A (en) * | 2021-01-15 | 2021-06-04 | 褚明礼 | Sleep state evaluation method based on electroencephalogram information |
| CN113742397A (en) * | 2021-08-30 | 2021-12-03 | 慕思健康睡眠股份有限公司 | Sleep monitoring method and device, monitoring equipment and storage medium |
| CN113926045A (en) * | 2021-11-22 | 2022-01-14 | 紫罗兰家纺科技股份有限公司 | Intelligent control method and system for sleep-assisting home textile product |
| CN114929319A (en) * | 2020-06-22 | 2022-08-19 | 华为技术有限公司 | Method and device for updating sleep-aid audio signal |
| CN115154831A (en) * | 2022-06-14 | 2022-10-11 | 北京百度网讯科技有限公司 | Sleep-assisting method, device, electronic equipment, storage medium and program product |
| CN115430003A (en) * | 2022-10-24 | 2022-12-06 | 慕思健康睡眠股份有限公司 | Sleep assisting method and device, intelligent mattress and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130205A1 (en) * | 2002-10-09 | 2012-05-24 | Compumedics Limited | Method and Apparatus For Maintaining and Monitoring Sleep Quality During Therapeutic Treatments |
| US20120245437A1 (en) * | 2003-08-18 | 2012-09-27 | Lovett Eric G | Sleep State Classification |
| CN105748043A (en) * | 2016-02-17 | 2016-07-13 | 安徽华米信息科技有限公司 | Sleep quality mentoring method and device and wearable device |
| CN106419841A (en) * | 2016-09-13 | 2017-02-22 | 深圳市迈迪加科技发展有限公司 | Method, device and system for evaluating sleep |
| CN108309233A (en) * | 2017-12-21 | 2018-07-24 | 速眠创新科技（深圳）有限公司 | Sleep quality monitoring method, system, computer equipment and storage medium |
| CN109085872A (en) * | 2018-08-31 | 2018-12-25 | 安徽天鹅科技实业（集团）有限公司 | A kind of sleeping cloud service platform and its application method and its application method |
| CN109730658A (en) * | 2018-12-29 | 2019-05-10 | 北京工业大学 | A kind of artificial intelligence mattress system |
| CN110464303A (en) * | 2019-08-15 | 2019-11-19 | 深圳和而泰家居在线网络科技有限公司 | Sleep quality appraisal procedure and device |
| CN110491468A (en) * | 2019-07-18 | 2019-11-22 | 广州柏颐信息科技有限公司 | A kind of processing method and system of sleep quality report |
| CN110558934A (en) * | 2019-08-02 | 2019-12-13 | 杭州连帆科技有限公司 | sleep quality evaluation method and device, electronic equipment and medium |
-
2020
- 2020-02-04 CN CN202010080172.XA patent/CN111281341A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130205A1 (en) * | 2002-10-09 | 2012-05-24 | Compumedics Limited | Method and Apparatus For Maintaining and Monitoring Sleep Quality During Therapeutic Treatments |
| US20120245437A1 (en) * | 2003-08-18 | 2012-09-27 | Lovett Eric G | Sleep State Classification |
| CN105748043A (en) * | 2016-02-17 | 2016-07-13 | 安徽华米信息科技有限公司 | Sleep quality mentoring method and device and wearable device |
| CN106419841A (en) * | 2016-09-13 | 2017-02-22 | 深圳市迈迪加科技发展有限公司 | Method, device and system for evaluating sleep |
| CN108309233A (en) * | 2017-12-21 | 2018-07-24 | 速眠创新科技（深圳）有限公司 | Sleep quality monitoring method, system, computer equipment and storage medium |
| CN109085872A (en) * | 2018-08-31 | 2018-12-25 | 安徽天鹅科技实业（集团）有限公司 | A kind of sleeping cloud service platform and its application method and its application method |
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