Sleep health degree evaluation method and device based on load data

Abstract

The invention relates to the field of bedding load data processing, in particular to a sleep health degree evaluation method and device based on load data, and the method comprises the steps: obtaining the load data of each load detection position in each control period; inputting the load data of all the load detection positions obtained in the total sleep time into a sleep health degree evaluation algorithm for calculation, including: calculating a change gradient and an overall fluidity value of the load data of each load detection position corresponding to the current control period; calculating the concentration degree according to the load data of all the load detection positions corresponding to the current control period; calculating the cycle sleep health degree corresponding to the current control cycle according to the overall fluidity value and the concentration degree; the average value is calculated according to the cycle sleep health degrees corresponding to all the control cycles in the total sleep time, the total sleep health degree is obtained, the problem that a monitoring and analysis method for the bedding and the sleep posture is insufficient in connection is solved, and data support is provided for intelligence of the bedding and improvement of the sleep quality.

Description

Technical Field

[0001] The present invention relates to the field of bedding load data processing, and in particular to a sleep health evaluation method and device based on load data. Background Art

[0002] Sleeping posture and sleep quality are closely related to human health. Incorrect sleeping posture or maintaining a certain sleeping posture for a long time can easily cause problems such as muscle stiffness and obstructed blood circulation, which in turn affects sleep quality and may even lead to the occurrence of diseases such as cervical spondylosis. If you are in a certain state of compression for a long time during sleep, it may cause difficulty breathing, further affecting the depth and duration of sleep. Bedding is an important external factor affecting sleep quality. Currently, intelligent bedding is a major development trend. Intelligent bedding can improve the user's sleeping posture and thus improve the user's sleep quality. In the existing technology, research on intelligent bedding requires research on the user's sleeping posture and movement behavior in order to develop a more humane bedding design.

[0003] The current methods for monitoring and analyzing sleep posture mainly include polysomnography, actigraphy, and videography. Polysomnography can only be performed in a laboratory and requires placing multiple electrodes on the human body. Actigraphy requires wearing motion recording devices on multiple parts of the human body, both of which will have a certain degree of impact on sleep. Although the videography method can monitor sleep posture and movement behaviors during sleep without disturbing sleep, this method does not respect the privacy of the subjects and cannot accurately monitor subtle movements and movements covered by bedding. In addition, the above-mentioned sleep posture monitoring and analysis methods cannot link sleep posture and movement behaviors with the mechanical properties of bedding, and cannot provide data support for the intelligentization of bedding and the improvement of sleep quality. Summary of the Invention

[0004] The purpose of this application is to propose a sleep health evaluation method and device based on load data to address the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a method for evaluating sleep health based on load data, comprising the following steps: Obtain the load data of each load detection position once in each control cycle; The load data of all load detection positions obtained during the total sleep time are input into the sleep health evaluation algorithm to calculate the total sleep health, specifically including: Calculate the change gradient of the load data at each load detection position corresponding to the current control cycle, and calculate the overall fluidity value based on the load data and their change gradients at all load detection positions corresponding to the current control cycle; Calculate the concentration based on the load data of all load detection positions corresponding to the current control period; Calculate the sleep health of the cycle corresponding to the current control cycle based on the overall mobility value and concentration; The average value of the sleep health of each cycle corresponding to all control cycles in the total sleep time is calculated to obtain the total sleep health.

[0006] Preferably, calculating the change gradient of the load data at each load detection position corresponding to the current control period specifically includes: Calculate the change value of the load data of each load detection position in the current control cycle and the previous control cycle, and calculate the ratio of the change value to the control cycle to obtain the change gradient of the load data of each load detection position corresponding to the current control cycle. The formula is as follows: ; Among them, all load detection positions are arranged in an array of m rows and n columns, (i, j) represents the load detection position located in the i-th row and j-th column, , ; Indicates the gradient of load data change, in units of ; T represents the control period, the unit is s; Represents the load data of the load detection position at row i and column j acquired during the current control cycle, in units of N; Represents the load data at the load detection position located at the i-th row and j-th column acquired in the previous control cycle, in units of N.

[0007] Preferably, the overall fluidity value is calculated based on the load data and their change gradients at all load detection positions corresponding to the current control period, specifically including: In the current control cycle, the single flow value of each load detection position is calculated based on the load data and its change gradient at each load detection position. The formula is as follows: ; in, Indicates the single liquidity value, the unit is N / s; represents the liquidity component corresponding to the load data, A represents the first weight parameter, represents the mobility component corresponding to the change gradient of the load data, and B represents the second weight parameter; In the current control cycle, the overall mobility value is calculated based on the single mobility values ​​of all load detection positions. The formula is as follows: ; ; ; in, Indicates the row liquidity value, Represents the column liquidity value, U represents the overall liquidity value, and the unit is ; Indicates summation, Indicates the longitudinal distance between two adjacent load detection positions in each column of load detection positions. Indicates the horizontal distance between two adjacent load detection positions in each row of load detection positions.

[0008] Preferably, the concentration is calculated based on the load data of all load detection positions corresponding to the current control period, and the formula is as follows: ; Among them, R represents the concentration, C represents the third weight parameter, and D represents the load data threshold. Indicates the average value of the load data at all load detection locations.

[0009] As a preference, the sleep health of the cycle corresponding to the current control cycle is calculated based on the overall mobility value and concentration, and the formula is as follows: ; in, Indicates the periodic sleep health corresponding to the w-th control cycle.

[0010] Preferably, the average sleep health value corresponding to all control cycles in the total sleep time is calculated to obtain the total sleep health value, and the formula is as follows: ; in, represents the total sleep health, and W is the number of all control cycles in the total sleep time.

[0011] In a second aspect, the present invention provides a sleep health evaluation device based on load data, comprising: A data acquisition module is configured to acquire load data of each load detection position once in each control cycle; The sleep health calculation module is configured to input the load data of all load detection positions obtained during the total sleep time into the sleep health evaluation algorithm to calculate the total sleep health, specifically including: Calculate the change gradient of the load data at each load detection position corresponding to the current control cycle, and calculate the overall fluidity value based on the load data and their change gradients at all load detection positions corresponding to the current control cycle; Calculate the concentration based on the load data of all load detection positions corresponding to the current control period; Calculate the sleep health of the cycle corresponding to the current control cycle based on the overall mobility value and concentration; The average value of the sleep health of each cycle corresponding to all control cycles in the total sleep time is calculated to obtain the total sleep health.

[0012] In a third aspect, the present invention provides an electronic device comprising one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0014] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the method described in any implementation manner in the first aspect when the computer program is executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This application proposes a sleep health evaluation method based on load data. By analyzing the fluidity and concentration of load data, it provides an evaluation standard for sleep health to evaluate the user's sleep condition and sleep quality, thereby providing data support for users to conduct sleep analysis, improve sleep quality, and intelligent bedding design. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a method for evaluating sleep health based on load data according to an embodiment of the present application; Figure 2 This is a schematic structural diagram of the bedding according to an embodiment of the present application; Figure 3 This is a structural schematic diagram of a user lying on bedding in an embodiment of the present application; Figure 4 Schematic diagram of a sleep health assessment device based on load data according to an embodiment of the present application; Figure 5 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0019] Figure 1 A sleep health evaluation method based on load data provided in an embodiment of the present application is shown, comprising the following steps: S1, acquiring load data of each load detection position once in each control cycle.

[0020] S2: Input the load data of all load detection positions obtained during the total sleep time into the sleep health evaluation algorithm to calculate the total sleep health. Specifically, it includes: Calculate the change gradient of the load data at each load detection position corresponding to the current control cycle, and calculate the overall fluidity value based on the load data and their change gradients at all load detection positions corresponding to the current control cycle; Calculate the concentration based on the load data of all load detection positions corresponding to the current control period; Calculate the sleep health of the cycle corresponding to the current control cycle based on the overall mobility value and concentration; The average value of the sleep health of each cycle corresponding to all control cycles in the total sleep time is calculated to obtain the total sleep health.

[0021] In a specific embodiment, calculating the change gradient of the load data at each load detection position corresponding to the current control period specifically includes: Calculate the change value of the load data of each load detection position in the current control cycle and the previous control cycle, and calculate the ratio of the change value to the control cycle to obtain the change gradient of the load data of each load detection position corresponding to the current control cycle. The formula is as follows: ; Among them, all load detection positions are arranged in an array of m rows and n columns, (i, j) represents the load detection position located in the i-th row and j-th column, , ; Indicates the gradient of load data change, in units of ; T represents the control period, the unit is s; Represents the load data of the load detection position at row i and column j acquired during the current control cycle, in units of N; Represents the load data at the load detection position located at the i-th row and j-th column acquired in the previous control cycle, in units of N.

[0022] In a specific embodiment, the overall fluidity value is calculated based on the load data and their change gradients at all load detection positions corresponding to the current control period, specifically including: In the current control cycle, the single flow value of each load detection position is calculated based on the load data and its change gradient at each load detection position. The formula is as follows: ; in, Indicates the single liquidity value, the unit is N / s; represents the liquidity component corresponding to the load data, A represents the first weight parameter, represents the mobility component corresponding to the change gradient of the load data, and B represents the second weight parameter; In the current control cycle, the overall mobility value is calculated based on the single mobility values ​​of all load detection positions. The formula is as follows: ; ; ; in, Indicates the row liquidity value, Represents the column liquidity value, U represents the overall liquidity value, and the unit is ; Indicates summation, Indicates the longitudinal distance between two adjacent load detection positions in each column of load detection positions. Indicates the horizontal distance between two adjacent load detection positions in each row of load detection positions.

[0023] Specifically, It reflects the weight of the interaction between the load detection position located in the i-th row and the j-th column and the user. The higher the value of the load data, the stronger the interaction between the load detection position located in the i-th row and the j-th column and the user. The greater the change gradient of the load data, This reflects that the stronger the mobility of the load detection position at the i-th row and j-th column, the smaller the change gradient of the load data. The load detection position located in the i-th row and the j-th column has weaker fluidity. The first weight parameter A and the second weight parameter B can be adjusted based on experimental results. In the embodiment of the present application, all load detection positions are arranged in an array of m rows and n columns. The single fluidity value of each load detection position is calculated, and then the row and column fluidity values ​​are calculated based on the single fluidity values ​​of all load detection positions.

[0024] In the calculation process of the row mobility value, the single mobility value of each load detection position in each row is calculated. With vertical spacing After multiplication, row summation is performed to obtain the row summation result. In the row summation result, the vertical spacing The smaller the value, the denser the detection. Finally, the average value of the sum of all rows is calculated to get the final row mobility value. .

[0025] In the calculation process of the column mobility value, the single mobility value of each load detection position in each column is calculated. and horizontal spacing After multiplication, the column sum is performed to obtain the column sum result. In the column sum result, the horizontal spacing The smaller the value, the denser the detection. Finally, the average value of the sum of all columns is calculated to get the final column mobility value. .

[0026] The overall liquidity value U is the liquidity value of the bank and column liquidity values Perform trigonometric operations.

[0027] In a specific embodiment, the concentration is calculated based on the load data of all load detection positions corresponding to the current control period, and the formula is as follows: ; Among them, R represents the concentration, C represents the third weight parameter, and D represents the load data threshold. Indicates the average value of the load data at all load detection locations.

[0028] Specifically, because the forces act mutually, the pressure distribution on the bedding reflects the pressure distribution experienced by the user. Therefore, calculating the concentration based on the load data from all load detection locations can indicate whether the user has a sleeping posture characterized by concentrated pressure. In the embodiments of the present application, the concentration is calculated by combining a load data threshold D with a variance calculation. When the load data falls below the load data threshold D, indicating that the load detection location from which the load data was obtained is not covered by the user, the load data is ignored in the concentration calculation to improve the accuracy of the concentration.

[0029] In a specific embodiment, the sleep health of the cycle corresponding to the current control cycle is calculated based on the overall mobility value and the concentration, and the formula is as follows: ; in, Indicates the periodic sleep health corresponding to the w-th control cycle.

[0030] In a specific embodiment, the average value of the sleep health corresponding to all control cycles within the total sleep time is calculated to obtain the total sleep health, and the formula is as follows: ; in, represents the total sleep health, and W is the number of all control cycles in the total sleep time.

[0031] Specifically, refer to Figure 2 and Figure 3 The bedding 11 has 6 rows and 3 columns with a total of 18 load detection positions 12. The longitudinal spacing between two adjacent load detection positions 12 in each column is Set to 0.25 meters, indicating the horizontal distance between two adjacent load detection positions 12 in each row of load detection positions Set to 0.18 meters. Figure 3 The user lies on bedding 11. A load detection unit is provided in load detection position 12 to detect the magnitude of the interaction force between the user and bedding 11, thereby obtaining load data. If the user sleeps for 8 hours and the control cycle is 10 seconds, a total of 2880 load data points are obtained during the total sleep time. Load data for 20 users sleeping on the aforementioned bedding 11 is obtained, with the following parameters: A=0.2, B=0.5, C=0.3, and T=10 seconds. Each user's load data is input into the sleep health evaluation algorithm to calculate the overall fluidity value, concentration, and overall sleep health, as shown in Table 1.

[0032] Table 1 User sleep health evaluation table According to the analysis in the table above, the overall mobility value U can reflect the dynamic characteristics of the load data when the user uses the bedding. The larger the overall mobility value U, the more frequent the user's movements during sleep, and the lower the sleep health. The concentration R can reflect whether the user has a sleeping posture with concentrated pressure. The larger the concentration R value, the more frequent the user's sleeping posture with concentrated pressure, and the lower the sleep health. The overall sleep health The smaller the value, the lower the user's sleep health.

[0033] The present application proposes a method for evaluating sleep health based on load data. By collecting load data on bedding, analyzing the fluidity and concentration of the load data, and providing evaluation criteria for sleep health, the method is used to evaluate the user's sleep condition and sleep quality, thereby providing data support for users to conduct sleep analysis, improve sleep quality, and design intelligent bedding, thereby solving the problem of insufficient connection between bedding and sleeping posture monitoring and analysis methods.

[0034] Further references Figure 4 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a sleep health evaluation device based on load data. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0035] The present invention provides a sleep health evaluation device based on load data, including: The data acquisition module 1 is configured to acquire the load data of each load detection position once in each control cycle; The sleep health calculation module 2 is configured to input the load data of all load detection positions obtained during the total sleep time into the sleep health evaluation algorithm to calculate the total sleep health, specifically including: Calculate the change gradient of the load data at each load detection position corresponding to the current control cycle, and calculate the overall fluidity value based on the load data and their change gradients at all load detection positions corresponding to the current control cycle; Calculate the concentration based on the load data of all load detection positions corresponding to the current control period; Calculate the sleep health of the cycle corresponding to the current control cycle based on the overall mobility value and concentration; The average value of the sleep health of each cycle corresponding to all control cycles in the total sleep time is calculated to obtain the total sleep health.

[0036] Figure 5 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present invention. Figure 5 As shown, the electronic device of this embodiment includes: a processor 501 and a memory 502; wherein the memory 502 is used to store computer-executable instructions; and the processor 501 is used to execute the computer-executable instructions stored in the memory to implement the various steps performed by the electronic device in the above embodiment. For details, please refer to the relevant description of the above method embodiment.

[0037] Optionally, the memory 502 may be independent or integrated with the processor 501 .

[0038] When the memory 502 is independently provided, the electronic device further includes a bus 503 for connecting the memory 502 and the processor 501 .

[0039] An embodiment of the present invention further provides a computer storage medium, in which computer-executable instructions are stored. When the processor 501 executes the computer-executable instructions, the above method is implemented.

[0040] An embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by the processor 501, the above method is implemented.

[0041] In the embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or module, which may be electrical, mechanical or other forms.

[0042] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to implement the solution of this embodiment based on actual needs.

[0043] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The aforementioned modular units may be implemented in the form of hardware or hardware plus software functional units.

[0044] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or processor 501 to perform some steps of the methods of various embodiments of the present application.

[0045] It should be understood that the processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASIC). A general-purpose processor may be a microprocessor, or the processor 501 may be any conventional processor 501. The steps of the method disclosed in the present invention may be directly implemented by the hardware processor 501, or implemented by a combination of hardware and software modules in the processor 501.

[0046] The memory 502 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

[0047] Bus 503 can be an Industry Standard Architecture (ISA), a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Bus 503 can be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the bus 503 in the drawings of this application is not limited to a single bus 503 or a single type of bus 503.

[0048] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0049] An exemplary storage medium is coupled to the processor 501, so that the processor 501 can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor 501. The processor 501 and the storage medium can be located in an application-specific integrated circuit (ASIC). Of course, the processor 501 and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0050] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sleep health evaluation method based on load data, characterized in that: The following steps are involved: Obtain the load data of each load detection position once in each control cycle; The load data of all load detection positions obtained during the total sleep time are input into the sleep health evaluation algorithm to calculate the total sleep health, specifically including: Calculating a change gradient of the load data of each load detection position corresponding to the current control period, and calculating an overall fluidity value based on the load data and their change gradients of all the load detection positions corresponding to the current control period; Calculating the concentration degree according to the load data of all the load detection positions corresponding to the current control period; Calculating the periodic sleep health corresponding to the current control period according to the overall mobility value and the concentration; An average value of the cycle sleep health corresponding to all control cycles within the total sleep time is calculated to obtain the total sleep health.

2. The sleep health evaluation method based on load data according to claim 1, characterized in that: The calculating of the change gradient of the load data of each load detection position corresponding to the current control period specifically includes: Calculate the change value of the load data of each load detection position in the current control cycle and the previous control cycle, and calculate the ratio of the change value to the control cycle to obtain the change gradient of the load data of each load detection position corresponding to the current control cycle. The formula is as follows: ； Wherein, all the load detection positions are arranged in an array of m rows and n columns, (i, j) represents the load detection position located in the i-th row and j-th column, , ; Indicates the gradient of load data change, in units of ; T represents the control period, the unit is s; Represents the load data of the load detection position at row i and column j acquired during the current control cycle, in units of N; Represents the load data at the load detection position located at the i-th row and j-th column acquired in the previous control cycle, in units of N.

3. The sleep health evaluation method based on load data according to claim 2, characterized in that: The calculating of the overall fluidity value according to the load data and the change gradient of all the load detection positions corresponding to the current control period specifically includes: In the current control cycle, the single flow value of each load detection position is calculated according to the load data and its change gradient at each load detection position, and the formula is as follows: ； in, Indicates single liquidity value, unit is N / s; represents the liquidity component corresponding to the load data, A represents the first weight parameter, represents the mobility component corresponding to the change gradient of the load data, and B represents the second weight parameter; In the current control cycle, the overall mobility value is calculated based on the single mobility values ​​of all the load detection positions, and the formula is as follows: ； ； ； in, Indicates the row liquidity value, Represents the column liquidity value, U represents the overall liquidity value, and the unit is ; Indicates summation, Indicates the longitudinal distance between two adjacent load detection positions in each column of load detection positions. Indicates the horizontal distance between two adjacent load detection positions in each row of load detection positions.

4. The sleep health evaluation method based on load data according to claim 3, characterized in that: The concentration is calculated based on the load data of all the load detection positions corresponding to the current control period, and the formula is as follows: ； Among them, R represents the concentration, C represents the third weight parameter, and D represents the load data threshold. It represents the average value of the load data of all the load detection positions.

5. The sleep health evaluation method based on load data according to claim 4, characterized in that: The sleep health degree corresponding to the current control period is calculated based on the overall mobility value and the concentration, and the formula is as follows: ； in, Indicates the periodic sleep health corresponding to the w-th control cycle.

6. The sleep health evaluation method based on load data according to claim 5, characterized in that: The average value of the sleep health degree corresponding to all control cycles within the total sleep time is calculated to obtain the total sleep health degree, and the formula is as follows: ； in, represents the total sleep health, and W is the number of all control cycles in the total sleep time.

7. A sleep health evaluation device based on load data, characterized in that: include: A data acquisition module is configured to acquire load data of each load detection position once in each control cycle; The sleep health calculation module is configured to input the load data of all the load detection positions obtained during the total sleep time into the sleep health evaluation algorithm to calculate the total sleep health, specifically including: Calculating a change gradient of the load data of each load detection position corresponding to the current control period, and calculating an overall fluidity value based on the load data and their change gradients of all the load detection positions corresponding to the current control period; Calculating the concentration degree according to the load data of all the load detection positions corresponding to the current control period; Calculating the periodic sleep health corresponding to the current control period according to the overall mobility value and the concentration; An average value of the cycle sleep health corresponding to all control cycles within the total sleep time is calculated to obtain the total sleep health.

8. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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