Sleep-aiding recommendation method and device, mattress and storage medium

Abstract

The embodiment of the invention discloses a sleep-aiding recommendation method and device, a mattress and a storage medium, the method is applied to the mattress, and the method comprises the steps that sleep data corresponding to a plurality of analysis periods are acquired, and the analysis periods comprise the current analysis period and one or more historical analysis periods before the current analysis period; the sleep data comprises feature values corresponding to a plurality of sleep features; determining the stability of each sleep feature according to the feature value corresponding to each sleep feature in the plurality of analysis periods; according to the stability of each sleep feature, a target sleep-aiding scheme is determined, and the target sleep-aiding scheme refers to a measure for helping to improve the sleep quality of the user, so that a personalized sleep-aiding scheme can be provided for the user, and the user is helped to improve the sleep quality.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and in particular to a sleep aid recommendation method, device, mattress and storage medium. Background Art

[0002] As the pace of modern life accelerates, more and more people are facing sleep problems. Good sleep not only has a direct impact on the quality of daily life, but is also closely related to human health and the aging process. Scientific research shows that maintaining a stable circadian rhythm and consistent bedtimes can help form healthy sleep patterns, thereby improving overall health. However, current sleep detection solutions mainly focus on monitoring and reporting users' short-term sleep fluctuations, and lack practical means to help individual users improve their sleep quality. Summary of the invention

[0003] The embodiments of the present application disclose a sleep aid recommendation method, device, mattress and storage medium. The mattress can provide a personalized sleep aid plan for the user to help the user improve sleep quality.

[0004] In a first aspect, an embodiment of the present application discloses a sleep aid recommendation method, which is applied to a mattress, and the method comprises:

[0005] Acquire sleep data corresponding to a plurality of analysis cycles, wherein the plurality of analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data include characteristic values ​​corresponding to a plurality of sleep characteristics;

[0006] determining the stability of each sleep feature according to the feature values ​​respectively corresponding to each sleep feature in the plurality of analysis cycles;

[0007] According to the stability of each sleep characteristic, a target sleep-aiding solution is determined, where the target sleep-aiding solution refers to measures for helping to improve the sleep quality of the user.

[0008] As an optional implementation, in the first aspect of the embodiment of the present application, determining the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in the multiple analysis cycles includes:

[0009] calculating a stable range corresponding to the first sleep feature according to feature values ​​corresponding to the first sleep feature in the plurality of analysis cycles; the first sleep feature is any of the sleep features;

[0010] The stability corresponding to the first sleep feature is determined according to the feature value corresponding to the first sleep feature in the current analysis cycle and the stability range.

[0011] As an optional implementation, in the first aspect of the embodiment of the present application, determining the stability corresponding to the first sleep feature according to the feature value corresponding to the first sleep feature in the current analysis cycle and the stability range includes:

[0012] Calculate the standard deviation of the first sleep feature in the current analysis period according to the plurality of feature values ​​corresponding to the first sleep feature in the current analysis period;

[0013] If the standard deviation of the first sleep characteristic corresponding to the current analysis period is within the stable range, determining that the first sleep characteristic is stable;

[0014] If the standard deviation of the first sleep characteristic corresponding to the current analysis period is not within the stable range, it is determined that the first sleep characteristic is unstable.

[0015] As an optional implementation, in the first aspect of the embodiment of the present application, determining a target sleep-aiding plan based on the stability of each sleep characteristic includes:

[0016] Calculating the distance between the standard deviation corresponding to each unstable sleep feature in the current analysis period and the stable range corresponding to each unstable sleep feature;

[0017] The unstable target sleep feature with the largest corresponding distance is determined, and the target sleep-aiding scheme is determined according to the target sleep feature.

[0018] As an optional implementation, in the first aspect of the embodiment of the present application, the calculating the stable range corresponding to the first sleep feature according to the feature values ​​corresponding to the first sleep feature in the multiple analysis cycles respectively includes:

[0019] Calculating the standard deviation of the first sleep feature in each of the historical analysis cycles according to the feature values ​​of the first sleep feature in each of the historical analysis cycles;

[0020] Calculating an average value of standard deviations corresponding to the first sleep feature in each of the historical analysis cycles to obtain an average standard deviation corresponding to the first sleep feature;

[0021] Calculating the allowable deviation value corresponding to the first sleep feature according to the standard deviation of the first sleep feature corresponding to at least one selected target historical analysis period and the standard deviation of the first sleep feature corresponding to adjacent historical analysis periods of each target historical analysis period;

[0022] Based on the average standard deviation and the allowable deviation value, a stable range corresponding to the first sleep characteristic is determined.

[0023] As an optional implementation, in the first aspect of the embodiment of the present application, the stability of the sleep characteristics includes stable or unstable; and determining the target sleep-aiding scheme according to the stability of each sleep characteristic includes:

[0024] determining the sleep type according to the unstable sleep characteristics;

[0025] Determine a target sleep aid plan based on the sleep type;

[0026] The sleep type includes at least one of falling asleep too late, difficulty falling asleep, frequent awakenings, early awakenings, getting up too late, insufficient sleep, excessive sleep, etc.

[0027] As an optional implementation, in the first aspect of the embodiment of the present application, the mattress includes a physiological sign sensor, and before obtaining sleep data corresponding to a plurality of analysis cycles, the method further includes:

[0028] Acquiring physiological sign data collected by the physiological sign sensor during each sleep process;

[0029] Analyze the physiological sign data to obtain sleep stage data corresponding to each sleep process; the sleep stage data refers to the physiological sign data corresponding to each sleep stage of the user during the sleep process;

[0030] Feature values ​​corresponding to a plurality of sleep features during each sleep process are extracted according to the sleep stage data.

[0031] A second aspect of the present application discloses a sleep aid recommendation device, which is applied to a mattress. The device includes:

[0032] A data acquisition module, used to acquire sleep data corresponding to a plurality of analysis cycles, wherein the plurality of analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to a plurality of sleep characteristics;

[0033] a stability determination module, configured to determine the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in the plurality of analysis cycles;

[0034] The program determination module is used to determine a target sleep-aiding program according to the stability of each sleep characteristic, wherein the target sleep-aiding program refers to a measure used to help improve the sleep quality of the user.

[0035] A third aspect of the embodiment of the present application discloses a mattress, comprising:

[0036] A memory storing executable program code;

[0037] a processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the method described in any one of the above embodiments.

[0039] A fourth aspect of an embodiment of the present application discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor executes the method described in any one of the above embodiments.

[0040] Compared with the related art, the embodiments of the present application include at least the following beneficial effects:

[0041] The sleep aid recommendation method, device, mattress and storage medium disclosed in the embodiments of the present application obtain sleep data corresponding to multiple analysis cycles, the multiple analysis cycles include the current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to multiple sleep characteristics; the stability of each sleep characteristic is determined according to the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles; according to the stability of each sleep characteristic, a target sleep aid plan is determined, and the target sleep aid plan refers to measures used to help improve the user's sleep quality. In the embodiments of the present application, the mattress can obtain the sleep data corresponding to the current analysis cycle and one or more historical analysis cycles before the current analysis cycle, and by analyzing the sleep data of the current analysis cycle and each historical analysis cycle, determine the stability of each sleep characteristic, and give a corresponding sleep aid recommendation plan, so as to provide users with personalized sleep aid plans, help improve the user's sleep stability, and thus help users improve their sleep quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 This is a schematic diagram of an application scenario of a sleep aid recommendation method disclosed in an embodiment of the present application;

[0044] Figure 2 It is a flowchart of a sleep aid recommendation method disclosed in an embodiment of the present application;

[0045] Figure 3 It is a schematic diagram of a process of analyzing physiological sign data to obtain sleep data disclosed in an embodiment of the present application;

[0046] Figure 4 It is a flowchart of determining the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in multiple analysis cycles disclosed in an embodiment of the present application;

[0047] Figure 5 is a flow chart of determining the stability corresponding to the first sleep feature according to the characteristic value and the stability range corresponding to the first sleep feature in the current analysis cycle disclosed in the embodiment of the present application;

[0048] Figure 6 It is a flowchart of determining a target sleep-aiding plan according to the stability of each sleep characteristic disclosed in the embodiment of the present application;

[0049] Figure 7 is a flow chart of calculating a stable range corresponding to a first sleep feature according to characteristic values ​​corresponding to the first sleep feature in a plurality of analysis cycles disclosed in an embodiment of the present application;

[0050] Figure 8 It is a structural schematic diagram of a sleep aid recommendation device disclosed in an embodiment of the present application;

[0051] Fig. 9 It is a structural schematic diagram of a mattress disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present application and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0054] See also Figure 1 , Figure 1 Schematic diagram of an application scenario of a sleep aid recommendation method disclosed in an embodiment of the present application. Figure 1 As shown, the sleep aid recommendation method can be applied to a mattress 110 .

[0055] In some embodiments, a plurality of sensors may be provided in the mattress 110 , and the plurality of sensors may be used to collect the user's physiological sign data, pressure data, etc., so that the mattress 110 can analyze the user's sleep.

[0056] Optionally, the sensors in the mattress 110 may include a heart rate sensor, a breathing sensor, a temperature sensor, a pressure sensor, etc., which is not specifically limited in the embodiment of the present application.

[0057] In some embodiments, a controller and a memory are further provided in the mattress 110. The controller can be used to control the sensor to collect data during the user's sleep; and the memory can be used to store the data collected by the sensor during the user's sleep.

[0058] In some embodiments, the mattress 110 can establish a communication connection with the terminal device. Based on the communication connection, the mattress 110 can send the data of the user's sleep process to the terminal device, and the terminal device can analyze and process the data and output the analyzed and processed data.

[0059] In an embodiment of the present application, the mattress 110 can obtain sleep data corresponding to multiple analysis cycles, where the multiple analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle, and the sleep data includes characteristic values ​​corresponding to multiple sleep characteristics; the mattress 110 can determine the stability of each sleep characteristic based on the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles; and then determine a target sleep-aiding plan based on the stability of each sleep characteristic, which refers to measures used to help improve the user's sleep quality.

[0060] Please read further Figure 2 , Figure 2 1 is a flow chart of a sleep aid recommendation method disclosed in an embodiment of the present application, and the sleep aid recommendation method can be applied to the above-mentioned mattress 110. Figure 2 As shown, the sleep aid recommendation method may include the following steps:

[0061] Step 210, obtaining sleep data corresponding to a plurality of analysis cycles, the plurality of analysis cycles including a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to a plurality of sleep characteristics.

[0062] The analysis period may refer to a preset period of time, such as a period of time including a preset number of days. For example, the analysis period may be one week, half a month, or one month. If the analysis period is one week, every seven days is considered as an analysis period.

[0063] The current analysis period may refer to a preset period of time that is currently being analyzed. Furthermore, the current analysis period may be a preset period of time before the current moment, or a preset period of time including the current moment. For example, if the analysis period may be a week, the current analysis period may be a 7-day period before the current day, or a 7-day period including the current day.

[0064] The historical analysis period may refer to a preset period of time before the current analysis period.

[0065] In some embodiments, there is only one historical analysis cycle. The historical analysis cycle can be a preset period of time adjacent to the current analysis cycle before the current analysis cycle, or a preset period of time before the current analysis cycle and separated from the current analysis cycle by a preset period of time. Taking the analysis cycle as an example, the current analysis cycle can be a 7-day period before the current day, or a 7-day period including the current day, and the historical analysis cycle can be an adjacent 7-day period before the current analysis cycle, or a 7-day period before the current analysis cycle and separated from the current analysis cycle by a preset number of days or a preset number of weeks, for example, it can be a 7-day period before the current analysis cycle and separated from the current analysis cycle by 4 days, or it can be a 7-day period before the current analysis cycle and separated from the current analysis cycle by two 7-day periods, i.e., two weeks.

[0066] In some embodiments, the historical analysis period may be multiple.

[0067] As an optional implementation, multiple historical analysis periods can be divided according to preset interval periods. For example, the preset interval period can be 3 days and the analysis period can be one week. The current analysis period can be the 7-day period before the current day, or the 7-day period including the current day. Then, the mattress can divide the 7 days after every 3 days before the current analysis period into historical analysis periods.

[0068] As another optional implementation, multiple historical analysis periods may be randomly selected. For example, if the analysis period is 8 days, the current analysis period may be the 8-day period before the current day, or the 8-day period including the current day. Then, the multiple historical analysis periods may include an 8-day period with an interval of 3 days from the current analysis period, an 8-day period with an interval of 7 days from the current analysis period, an 8-day period with an interval of 12 days from the current analysis period, etc.

[0069] The sleep data corresponding to each analysis cycle may include characteristic values ​​corresponding to multiple sleep features, and the sleep features may refer to features used to reflect the user's sleep conditions. In some embodiments, the sleep features may include one or more of the time of falling asleep, the time of waking up, the proportion of awakening, the proportion of light sleep, and the proportion of deep sleep, etc., which are not specifically limited in the embodiments of the present application.

[0070] The sleeping time refers to the time when the user enters the sleeping state; the waking time refers to the time when the user ends the sleeping state. The mattress can obtain the user's total sleeping time based on the user's sleeping time and waking time.

[0071] The awakening ratio refers to the proportion of time the user is awake from the time the user goes to bed to get up. The awakening state refers to the state in which the user is awake. The higher the awakening ratio, the longer the user is awake, and the user may have frequent night awakening or early awakening problems.

[0072] The light sleep ratio refers to the proportion of time that the user is in the light sleep stage during the period of time when the user is in the sleep state. The light sleep stage refers to the stage when the user is in a light sleep state during the sleep process. The light sleep state refers to the state in which the body begins to relax, the breathing and heart rate gradually slow down, but the consciousness is not completely lost, and there is still a certain response to external stimuli.

[0073] The deep sleep ratio refers to the proportion of time that the user is in the deep sleep stage during the period of time when the user is in the sleeping state. The deep sleep stage refers to the stage when the user is in the deep sleep state during the sleep process. The deep sleep state refers to a state in which a person's heart rate, breathing, blood pressure and body temperature drop to the lowest level of the day, and the consciousness is almost completely disconnected from the outside world and is difficult to wake up.

[0074] In some embodiments, in the sleep data corresponding to each analysis cycle, each sleep feature may correspond to multiple feature values, and further, each sleep feature may have a corresponding feature value for each day in the analysis cycle. For example, if the analysis cycle is 7 days, each sleep feature may correspond to 7 feature values, and the 7 feature values ​​correspond one-to-one to the 7 days of the analysis cycle.

[0075] In some embodiments, the mattress can store sleep data corresponding to each day. When it is necessary to obtain sleep data corresponding to a historical analysis cycle, the mattress can divide all sleep data before the current moment according to a pre-set analysis cycle, determine the date range corresponding to each analysis cycle, and then query the sleep data corresponding to the historical analysis cycle from the memory according to the date range corresponding to the historical analysis cycle.

[0076] In some embodiments, the mattress can obtain the user's corresponding sleep data by analyzing the user's physiological sign data. Figure 3 As shown, before the step of acquiring sleep data corresponding to a plurality of analysis cycles, the method may further include the following steps:

[0077] Step 310: Acquire the physiological sign data collected by the physiological sign sensor during each sleep process.

[0078] The physiological sign sensor may be arranged on the surface or inside of the mattress to collect the physiological sign data of the user. Optionally, the physiological sign sensor may include one or more of a piezoelectric ceramic sensor, a piezoelectric film sensor, an optical fiber sensor, etc., which is not specifically limited in the present embodiment of the application.

[0079] The sleep process may refer to the time period from when the user enters the sleep state to when the user wakes up.

[0080] The physiological sign data can be used to record the physiological sign status of the user during sleep. Optionally, the physiological sign data may include heart rate data, breathing data, body movement data, etc., which is not specifically limited in the present embodiment of the application.

[0081] Among them, the heart rate data may refer to data recording the user's heart beat frequency. Optionally, the heart rate data may include maximum heart rate, average heart rate, heart rate variability (HRV), etc., which are not specifically limited in the embodiments of the present application. HRV refers to the variability of the time interval between consecutive heartbeats.

[0082] The respiratory data may refer to data recording the user's respiratory pattern and respiratory function. Optionally, the respiratory data may include respiratory frequency, inhalation time, exhalation time, etc., which is not specifically limited in the present embodiment of the application.

[0083] The body movement data may refer to data recording the user's activities and resting states. Optionally, the body movement data may include the number of times the user turns over during sleep, the number of times the user moves his legs, etc., which is not specifically limited in the present embodiment of the application.

[0084] The mattress may start collecting the user's physiological sign data when detecting that the user is on the mattress. In some embodiments, a pressure sensor is provided in the mattress, and the mattress may collect the pressure signal applied to the mattress when the user is on the mattress through the pressure sensor, and detect whether the user is on the mattress by determining whether the pressure signal is greater than a preset pressure threshold. If the pressure signal is greater than the preset pressure threshold, the mattress may determine that the user is on the mattress; if the pressure signal is less than the preset pressure threshold, the mattress may determine that the user is not on the mattress.

[0085] In some embodiments, the mattress may collect physiological sign data according to a preset collection period. For example, the mattress may collect physiological sign data every 2 seconds.

[0086] The physiological sign sensor has high sensitivity. In addition to the physiological sign data, the physiological sign sensor also collects other noises in the environment around the mattress. Therefore, after the mattress obtains the physiological sign data, it can clean the physiological sign data.

[0087] In some embodiments, the mattress can use a low-pass filter to filter and reduce noise on the physiological sign data. The mattress can pre-set a preset frequency threshold corresponding to the low-pass filter. When the physiological sign data passes through the low-pass filter, the low-pass filter can filter out the physiological sign data above the preset frequency threshold and allow the physiological sign data below the preset frequency threshold to pass. The low-pass filter can effectively filter out high-frequency interference caused by environmental noise, improve the signal-to-noise ratio in the physiological sign data, and ensure the accuracy of the sleep stage data obtained by subsequent analysis of the physiological sign data.

[0088] In some embodiments, the mattress can perform baseline drift correction on the collected physiological sign data to remove slow changes in the physiological sign data. Optionally, the baseline drift correction can include multiple means such as polynomial fitting, high-pass filtering, wavelet transform, adaptive filtering, sliding window averaging, etc.

[0089] Step 320, analyzing the physiological sign data to obtain sleep staging data corresponding to each sleep process; the sleep staging data refers to the physiological sign data corresponding to each sleep stage during the user's sleep process.

[0090] Sleep staging refers to dividing the user's sleep process into different stages based on the user's physiological signs data.

[0091] In some embodiments, sleep can be divided into non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), and the sleep staging may include five sleep stages, namely, wakefulness, N1 (NREM 1, light sleep), N2 (NREM 2, shallow sleep), N3 (NREM 3, deep sleep or slow wave sleep), and REM. Among them, N1 stage may refer to the transition stage between the user's wakefulness and sleep state; N2 stage may refer to the stage when the user formally enters the sleep state; N3 stage may refer to the stage when the user is in a deep sleep state that is difficult to wake up; REM stage may refer to the stage when the user is in a rapid eye movement and is almost awake.

[0092] In some embodiments, the mattress can divide the physiological sign data of the user during sleep according to preset time periods to obtain the physiological sign data corresponding to each preset time period, analyze the physiological sign data in each preset time period to obtain the physiological sign data corresponding to each sleep stage included in each preset time period, and then summarize the physiological sign data corresponding to each sleep stage included in all preset time periods during the user's sleep to obtain the physiological sign data corresponding to each sleep stage during the user's sleep, that is, sleep staging data.

[0093] In some embodiments, the mattress can analyze the waveform of the user's physiological sign data during sleep according to the waveform characteristics corresponding to each sleep stage, and obtain the physiological sign data corresponding to each sleep stage during sleep, that is, sleep staging data. Further, the mattress can divide the user's physiological sign data during sleep according to preset time periods, obtain the physiological sign data corresponding to each preset time period, analyze the waveform of the physiological sign data in each preset time period according to the waveform characteristics corresponding to each sleep stage, obtain the physiological sign data corresponding to each sleep stage contained in each preset time period, and then summarize the physiological sign data corresponding to each sleep stage contained in all preset time periods during sleep to obtain the sleep staging data during sleep.

[0094] In some embodiments, the mattress can analyze the physiological sign data through a sleep stage recognition model to obtain sleep stage data during each sleep process. The sleep stage recognition model has the ability to divide the various sleep stages during the user's sleep process. The sleep stage recognition model is obtained by training a training set, and the training set may include sample physiological sign data and sample sleep stage data corresponding to the sample physiological sign data. Optionally, the sleep stage recognition module may include a random forest model, a support vector machine model, and a neural network model, etc., which is not specifically limited in the embodiments of the present application.

[0095] Step 330 , extracting feature values ​​corresponding to a plurality of sleep features during each sleep process according to the sleep stage data.

[0096] In some embodiments, the mattress can determine the start and end time corresponding to each sleep stage according to the sleep stage data, and then determine the characteristic values ​​corresponding to multiple sleep characteristics during each sleep process according to the start and end time corresponding to each sleep stage, such as the user's sleep time, wake-up time, etc.

[0097] In some embodiments, the mattress can determine the duration corresponding to each sleep stage according to the sleep stage data, and then determine the characteristic values ​​corresponding to multiple sleep characteristics during each sleep process according to the ratio of the duration corresponding to each sleep stage to the total sleep time of the user during sleep. Exemplarily, the mattress can extract the awakening time from the sleep stage data, and then determine the awakening ratio during the user's sleep process according to the ratio of the awakening time to the total sleep time.

[0098] In the embodiment of the present application, the mattress can obtain the physiological sign data collected by the physiological sign sensor during each sleep process, analyze the physiological sign data, and obtain the sleep stage data corresponding to each sleep process. The sleep stage data refers to the physiological sign data corresponding to each sleep stage of the user's sleep process. The mattress can extract the characteristic values ​​corresponding to multiple sleep characteristics during each sleep process based on the sleep stage data during each sleep process. The physiological sign data of the user during each sleep process is collected by the built-in physiological sign sensor of the mattress, and the sleep stage data is analyzed to determine the sleep stage data, and then the characteristic values ​​corresponding to multiple sleep characteristics are extracted. Combined with multiple physiological sign data such as heart rate, breathing, and body movement, the accurate judgment of sleep stage is ensured, thereby improving the accuracy of the historical sleep rhythm analysis of the user.

[0099] Step 220 , determining the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in a plurality of analysis cycles.

[0100] The stability of a sleep feature may refer to the degree to which the characteristic value corresponding to the sleep feature remains consistent within a preset time period. Optionally, the stability of a sleep feature may be stable or unstable. The mattress may pre-set a change threshold, and determine a sleep feature whose characteristic value corresponding to the sleep feature changes less than or equal to the change threshold within the preset time period as stable; and determine a sleep feature whose characteristic value corresponding to the sleep feature changes more than the change threshold within the preset time period as unstable.

[0101] In some embodiments, the mattress can perform statistical analysis on the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles, obtain statistical parameters corresponding to each sleep characteristic, and determine the stability of each sleep characteristic according to the statistical parameters corresponding to each sleep characteristic. The statistical parameters may include one or more of the mean value, standard deviation, variance, coefficient of variation (CV), correlation coefficient, etc., which are not specifically limited in the embodiments of the present application.

[0102] For example, the mattress can calculate the standard deviation corresponding to each sleep feature according to the characteristic values ​​corresponding to each sleep feature in multiple analysis cycles, and determine the stability corresponding to each sleep feature according to whether the standard deviation corresponding to each sleep feature is less than a preset standard deviation threshold. If the standard deviation corresponding to the sleep feature is less than or equal to the preset standard deviation threshold, the sleep feature is determined to be stable; if the standard deviation corresponding to the sleep feature is greater than the preset standard deviation threshold, the sleep feature is determined to be unstable.

[0103] In some embodiments, the mattress can perform threshold judgment on the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles, and determine the stability corresponding to each sleep characteristic based on the relationship between the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles and the preset threshold.

[0104] For example, the mattress may pre-set a threshold range corresponding to each sleep feature, and the mattress may determine the stability of each sleep feature according to whether the feature values ​​corresponding to each sleep feature in multiple consecutive historical analysis cycles are all within the corresponding preset threshold range. If the feature values ​​corresponding to a sleep feature in multiple consecutive historical analysis cycles are all within the preset threshold range corresponding to the sleep feature, the sleep feature may be determined to be stable; if the feature values ​​corresponding to a sleep feature in multiple consecutive historical analysis cycles have a feature value that exceeds the preset threshold range corresponding to the sleep feature, the sleep feature may be determined to be unstable.

[0105] Step 230 : determining a target sleep-aiding solution based on the stability of each sleep characteristic, where the target sleep-aiding solution refers to measures for helping to improve the user's sleep quality.

[0106] Multiple sleep-aiding programs can be pre-stored in the mattress, and the corresponding sleep-aiding measures for each sleep-aiding program may be different. For example, when the sleep-aiding program is environmental control, the corresponding sleep-aiding measures may be temperature regulation, light simulation, etc. Among them, temperature regulation can automatically adjust the temperature of the mattress by building a heating or cooling system into the mattress to provide users with a comfortable sleeping temperature; light simulation can simulate the light changes of sunrise or sunset by integrating LED light strips around the mattress to help adjust the user's biological clock. When the sleep-aiding program is sound therapy, the corresponding sleep-aiding measures may be playing white noise, controlling the volume to fade, etc. For example, white noise, natural sound effects (such as rain, waves) or other soothing music can be played in the built-in speakers of the mattress to promote user sleep, and the volume can be gradually reduced within a set time to help users fall asleep smoothly.

[0107] In some embodiments, the stability of the sleep characteristics may include stable or unstable. The mattress may determine the unstable sleep characteristics according to the stability of each sleep characteristic, determine the sleep type according to the unstable sleep characteristics, and then determine the target sleep aid plan according to the sleep type.

[0108] Among them, the unstable sleep feature may refer to a feature in which the feature value shows significant fluctuation or change in multiple analysis cycles; the stable sleep feature may refer to a feature in which the feature value shows less fluctuation or change in multiple analysis cycles.

[0109] The mattress can analyze the changing characteristics of the characteristic values ​​corresponding to the unstable sleep characteristics and determine the corresponding sleep type. Taking the unstable sleep characteristic of the awakening ratio as an example, the mattress can analyze the changes in the awakening ratio. If the awakening ratio corresponding to the user is getting higher and higher, it means that the user is in a wakeful state for a longer and longer time, and the user may have a frequent awakening problem.

[0110] Optionally, the sleep type may include at least one of falling asleep too late, difficulty falling asleep, frequent awakenings, early awakenings, getting up too late, insufficient sleep, excessive sleep, etc.

[0111] For different sleep types, the corresponding target sleep-aiding solutions may be different. For example, if the user's corresponding awakening ratio is an unstable sleep characteristic, then the user's corresponding sleep type may be frequent awakenings. According to this sleep type, the target sleep-aiding solution may be to create a dark and quiet indoor space and adjust the indoor temperature to the optimal sleep temperature, etc.; if the user's corresponding sleeping time is an unstable sleep characteristic, then the user's corresponding sleep type may be to fall asleep too late. According to this sleep type, the target sleep-aiding solution may be to plan the user's bedtime every night and dim the indoor lights within the set time. The mattress can provide corresponding target sleep-aiding solutions according to different sleep types, and can make targeted improvements to the user's specific sleep problems, thereby helping the user improve their sleep quality.

[0112] In an embodiment of the present application, the mattress can obtain sleep data corresponding to multiple analysis cycles, the multiple analysis cycles include the current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to multiple sleep features; the mattress can determine the stability of each sleep feature according to the characteristic values ​​corresponding to each sleep feature in multiple analysis cycles; and then determine the target sleep-aiding solution according to the stability of each sleep feature, which refers to measures used to help improve the user's sleep quality. The mattress can obtain sleep data corresponding to the current analysis cycle and one or more historical analysis cycles before the current analysis cycle, and determine the stability of each sleep feature by analyzing the sleep data of the current analysis cycle and each historical analysis cycle, and give corresponding sleep-aiding recommendation solutions, so as to provide users with personalized sleep-aiding solutions, help improve the user's sleep stability, and thus help users improve their sleep quality.

[0113] In some embodiments, Figure 4 As shown, the step of determining the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in multiple analysis cycles may include the following steps:

[0114] Step 410, calculating a stable range corresponding to the first sleep feature according to feature values ​​corresponding to the first sleep feature in a plurality of analysis cycles; the first sleep feature is any sleep feature.

[0115] The first sleep feature may correspond to multiple feature values ​​in each analysis cycle. For example, if the analysis cycle is one week, the first sleep feature corresponds to one feature value for each day in the 7-day cycle, and each analysis cycle includes 7 feature values ​​corresponding to the first sleep feature.

[0116] The stable range corresponding to the first sleep characteristic may refer to a fluctuation interval for determining that the first sleep characteristic has stability.

[0117] In some embodiments, the mattress can determine the average standard deviation and the allowable deviation value corresponding to the first sleep characteristic according to the characteristic values ​​corresponding to the first sleep characteristic in multiple analysis cycles, and then calculate the stable range corresponding to the first sleep characteristic based on the average standard deviation and the allowable deviation value corresponding to the first sleep characteristic.

[0118] The average standard deviation corresponding to the first sleep feature may refer to an average value of standard deviations corresponding to the first sleep feature in multiple analysis cycles.

[0119] The allowable deviation value corresponding to the first sleep feature may refer to a maximum deviation amount that can be allowed for the standard deviation corresponding to the first sleep feature above and below the average standard deviation corresponding to the first sleep feature.

[0120] In some embodiments, the mattress may calculate the average value and standard deviation of the first sleep feature in the multiple analysis cycles according to the characteristic values ​​corresponding to the first sleep feature in the multiple analysis cycles, and then calculate the confidence interval corresponding to the first sleep feature based on the average value and the standard deviation, and use the confidence interval as the stable range corresponding to the first sleep feature. By determining the confidence interval corresponding to the first sleep feature as the stable range, the variability of the sleep data is taken into account, and the actual fluctuation of the characteristic value corresponding to the sleep feature can be more accurately reflected.

[0121] Step 420 : determining the stability corresponding to the first sleep feature according to the feature value and the stability range corresponding to the first sleep feature in the current analysis cycle.

[0122] The first sleep feature has multiple characteristic values ​​corresponding to the current analysis period. For example, the analysis period can be 10 days, and each day of the 10-day period corresponds to a characteristic value for the first sleep feature. Therefore, the first sleep feature has 10 characteristic values ​​corresponding to the current analysis period.

[0123] In some embodiments, Figure 5As shown, the step of determining the stability corresponding to the first sleep feature according to the characteristic value and the stability range corresponding to the first sleep feature in the current analysis cycle may include the following steps:

[0124] Step 510: Calculate the standard deviation of the first sleep feature in the current analysis period according to the multiple feature values ​​of the first sleep feature in the current analysis period.

[0125] In some embodiments, the mattress may calculate the mean value of the first sleep feature in the current analysis period based on multiple feature values ​​corresponding to the first sleep feature in the current analysis period, and then calculate the sum of squares of the differences between each feature value corresponding to the first sleep feature in the current analysis period and the mean value, and calculate the standard deviation of the first sleep feature in the current analysis period based on the sum of squares.

[0126] For example, the calculation formula of the standard deviation of the first sleep feature corresponding to the current analysis period can refer to the following formula (1):

[0127]

[0128] Wherein, σ may represent the standard deviation of the first sleep feature in the current analysis period; N may represent the number of days in the current analysis period; x i may represent the characteristic value of the first sleep feature corresponding to the i-th day; and μ may represent the mean value of the first sleep feature corresponding to the current analysis cycle.

[0129] Step 520 , determining whether the standard deviation of the first sleep characteristic corresponding to the current analysis period is within a stable range. If yes, executing step 530 ; if no, executing step 540 .

[0130] Step 530 , determining that the first sleep characteristic is stable.

[0131] Step 540 , determining that the first sleep characteristic is unstable.

[0132] The mattress can determine whether the standard deviation of the first sleep characteristic in the current analysis period is within a stable range. If the standard deviation of the first sleep characteristic in the current analysis period is within a stable range, the first sleep characteristic is stable; if the standard deviation of the first sleep characteristic in the current analysis period is not within a stable range, the first sleep characteristic is unstable.

[0133] In the embodiment of the present application, the mattress can calculate the standard deviation of the first sleep characteristic in the current analysis period according to the multiple characteristic values ​​corresponding to the first sleep characteristic in the current analysis period; determine whether the standard deviation of the first sleep characteristic in the current analysis period is within a stable range, if the standard deviation of the first sleep characteristic in the current analysis period is within a stable range, then the first sleep characteristic is stable; if the standard deviation of the first sleep characteristic in the current analysis period is not within a stable range, then the first sleep characteristic is unstable. The standard deviation of the first sleep characteristic in the current analysis period can accurately reflect the fluctuation of the characteristic value corresponding to the first sleep characteristic in the current analysis period, so that the stability of the first sleep characteristic can be accurately evaluated.

[0134] In the embodiment of the present application, the mattress can calculate the stability range corresponding to the first sleep feature according to the characteristic values ​​corresponding to the first sleep feature in multiple analysis cycles; the first sleep feature is any sleep feature; according to the characteristic value and the stability range corresponding to the first sleep feature in the current analysis cycle, the stability corresponding to the first sleep feature is determined. By analyzing the characteristic values ​​in multiple analysis cycles, calculating the stability range of the sleep feature, and determining the stability of the sleep feature according to the characteristic value in the current analysis cycle, the user's sleep quality can be evaluated more accurately.

[0135] Furthermore, the mattress can determine the target sleep aid solution according to the unstable sleep characteristics. Figure 6 As shown, the step of determining the target sleep-aiding program according to the stability of each sleep characteristic may include the following steps:

[0136] Step 610: Calculate the standard deviation of each unstable sleep feature in the current analysis period and the distance between the standard deviation and the stable range of each unstable sleep feature.

[0137] The mattress can calculate the standard deviation corresponding to each unstable sleep characteristic in the current analysis period and the difference between the stable range corresponding to each unstable sleep characteristic, and determine the absolute value of the difference as the distance between the standard deviation corresponding to each unstable sleep characteristic in the current analysis period and the stable range corresponding to each unstable sleep characteristic.

[0138] Step 620 , confirming the unstable target sleep feature with the largest corresponding distance, and determining a target sleep aid plan according to the target sleep feature.

[0139] The mattress can sort the standard deviation corresponding to each unstable sleep characteristic in the current analysis period and the distance between the stable range corresponding to each unstable sleep characteristic in order from large to small, confirm the unstable sleep characteristic ranked first as the unstable target sleep characteristic with the largest corresponding distance, and determine the target sleep aid plan based on the target sleep characteristic.

[0140] In the embodiment of the present application, the mattress can calculate the standard deviation corresponding to each unstable sleep feature in the current analysis cycle, and the distance between the standard deviation and the stable range corresponding to each unstable sleep feature, confirm the unstable target sleep feature with the largest corresponding distance, and determine the target sleep-aiding solution based on the target sleep feature. By determining the sleep feature with the largest distance among multiple unstable sleep features as the target sleep feature, and determining the target sleep-aiding solution based on the target sleep feature, the targeted nature of the target sleep-aiding solution is improved, so that a personalized sleep-aiding solution can be provided to the user to help the user improve sleep quality.

[0141] In some embodiments, Figure 7 As shown, the step of calculating the stable range corresponding to the first sleep feature according to the feature values ​​corresponding to the first sleep feature in multiple analysis cycles may include the following steps:

[0142] Step 710: Calculate the standard deviation of the first sleep feature in each historical analysis period according to the feature value of the first sleep feature in each historical analysis period.

[0143] Exemplarily, the mattress may calculate the mean value of the first sleep feature in the i-th historical analysis period based on the multiple feature values ​​corresponding to the first sleep feature in the i-th historical analysis period, and then calculate the sum of squares of the differences between each feature value corresponding to the first sleep feature in the i-th historical analysis period and the mean value, and calculate the standard deviation of the first sleep feature in the i-th historical analysis period based on the sum of squares. The calculation formula of the standard deviation of the first sleep feature in the i-th historical analysis period can refer to the following formula (2):

[0144]

[0145] Among them, σ i It can represent the standard deviation of the first sleep feature in the i-th historical analysis cycle; N i Can represent the number of days in the i-th historical analysis period; x ij It can represent the characteristic value of the first sleep characteristic corresponding to the jth day in the i-th historical analysis cycle; μ i It can represent the average value of the first sleep feature corresponding to the i-th historical analysis period.

[0146] Step 720 , calculating the average of the standard deviations corresponding to the first sleep feature in each historical analysis cycle, to obtain the average standard deviation corresponding to the first sleep feature.

[0147] For example, the mattress may calculate the sum of the standard deviations corresponding to the first sleep feature in each historical analysis period, and divide the sum by the number of selected historical analysis periods to obtain the average standard deviation corresponding to the first sleep feature. The calculation formula for the average standard deviation corresponding to the first sleep feature may refer to the following formula (3):

[0148]

[0149] Among them, σ a may represent the average standard deviation corresponding to the first sleep characteristic, and M may represent the number of selected historical analysis cycles; It can represent the sum of standard deviations corresponding to the first sleep feature in M ​​historical analysis cycles.

[0150] Step 730 , calculating the allowable deviation value corresponding to the first sleep characteristic according to the standard deviation of the first sleep characteristic corresponding to at least one selected target historical analysis period and the standard deviation of the first sleep characteristic corresponding to adjacent historical analysis periods of each target historical analysis period.

[0151] In some embodiments, the mattress can calculate the standard deviation of the first sleep characteristic corresponding to each selected target historical analysis period, and the difference between the standard deviation corresponding to the adjacent historical analysis period of each target historical analysis period, and calculate the allowable deviation value corresponding to the first sleep characteristic based on the absolute value of the difference.

[0152] For example, the calculation formula of the allowable deviation value corresponding to the first sleep characteristic may refer to the following formula (4):

[0153]

[0154] Wherein, D may represent the allowable deviation value corresponding to the first sleep characteristic; σ n1 It can represent the standard deviation corresponding to the target historical analysis period n1; σ n1-1 It can represent the standard deviation corresponding to the target historical analysis period n1-1 (i.e. the previous historical analysis period of the target historical analysis period n1); σ n2 It can represent the standard deviation corresponding to the target historical analysis period n2; σ n2-1 It can represent the standard deviation corresponding to the target historical analysis period n2-1 (that is, the historical analysis period before the target historical analysis period n2).

[0155] Step 740: Determine a stable range corresponding to the first sleep characteristic based on the average standard deviation and the allowable deviation value.

[0156] In some embodiments, the mattress may determine the difference between the average standard deviation and the allowable deviation value as the lower limit of the stable range corresponding to the first sleep characteristic; and may determine the sum of the average standard deviation and the allowable deviation value as the upper limit of the stable range corresponding to the first sleep characteristic.

[0157] For example, the mattress may a -D is used as the lower limit of the stable range corresponding to the first sleep feature, and σ a +D as the upper limit of the stable range corresponding to the first sleep feature, then the stable range corresponding to the first sleep feature can be [σ a -D,σ a +D] or (σ a -D,σ a +D).

[0158] In the embodiment of the present application, the mattress can calculate the standard deviation corresponding to the first sleep characteristic in each historical analysis cycle according to the characteristic value corresponding to the first sleep characteristic in each historical analysis cycle; calculate the average value of the standard deviation corresponding to the first sleep characteristic in each historical analysis cycle to obtain the average standard deviation corresponding to the first sleep characteristic; calculate the allowable deviation value corresponding to the first sleep characteristic according to the standard deviation corresponding to the first sleep characteristic in at least one selected target historical analysis cycle and the standard deviation corresponding to the adjacent historical analysis cycle of each target historical analysis cycle; determine the stable range corresponding to the first sleep characteristic based on the average standard deviation and the allowable deviation value. Determining the stable range corresponding to the first sleep characteristic by using the average standard deviation and the allowable deviation value takes into account the variability of the data, reduces the random error caused by a single measurement value, and improves the accuracy of the determined stable range.

[0159] See also Figure 8 , Figure 8 8 is a schematic diagram of a sleep aid recommendation device disclosed in an embodiment of the present application. The sleep aid recommendation device 800 can be applied to the mattress 110. Figure 7 As shown, the sleep aid recommendation device 800 may include: a data acquisition module 810 , a stability determination module 820 , and a solution determination module 830 .

[0160] The data acquisition module 810 is used to acquire sleep data corresponding to a plurality of analysis cycles, wherein the plurality of analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to a plurality of sleep characteristics.

[0161] The stability determination module 820 is used to determine the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in multiple analysis cycles.

[0162] The solution determination module 830 is used to determine a target sleep-aiding solution according to the stability of each sleep characteristic, where the target sleep-aiding solution refers to measures used to help improve the user's sleep quality.

[0163] In some embodiments, the stability determination module 820 may include a stability range calculation unit and a stability determination unit.

[0164] The stability range calculation unit is used to calculate the stability range corresponding to the first sleep feature according to the characteristic values ​​corresponding to the first sleep feature in multiple analysis cycles; the first sleep feature is any of the above sleep features.

[0165] The stability determination unit is used to determine the stability corresponding to the first sleep feature according to the characteristic value and the stability range corresponding to the first sleep feature in the current analysis cycle.

[0166] In some embodiments, the stability determination unit is further used to calculate the standard deviation of the first sleep characteristic in the current analysis cycle based on multiple characteristic values ​​corresponding to the first sleep characteristic in the current analysis cycle; if the standard deviation of the first sleep characteristic in the current analysis cycle is within the above-mentioned stable range, it is determined that the first sleep characteristic is stable; if the standard deviation of the first sleep characteristic in the current analysis cycle is not within the above-mentioned stable range, it is determined that the first sleep characteristic is unstable.

[0167] In some embodiments, the solution determination module 830 is also used to calculate the standard deviation corresponding to each unstable sleep characteristic in the current analysis period and the distance between the stable range corresponding to each unstable sleep characteristic; determine the unstable target sleep characteristic with the largest corresponding distance, and determine the target sleep aid solution based on the target sleep characteristic.

[0168] In some embodiments, the stable range calculation unit is also used to calculate the standard deviation of the first sleep characteristic in each historical analysis cycle based on the characteristic values ​​of the first sleep characteristic in each historical analysis cycle; calculate the average of the standard deviations of the first sleep characteristic in each historical analysis cycle to obtain the average standard deviation corresponding to the first sleep characteristic; calculate the allowable deviation value corresponding to the first sleep characteristic based on the standard deviation of the first sleep characteristic in at least one selected target historical analysis cycle and the standard deviation of the first sleep characteristic in adjacent historical analysis cycles of each target historical analysis cycle; determine the stable range corresponding to the first sleep characteristic based on the average standard deviation and the allowable deviation value.

[0169] In some embodiments, the plan determination module 830 is also used to determine the sleep type based on unstable sleep characteristics; determine the target sleep-aid plan based on the sleep type; wherein the sleep type includes at least one of falling asleep too late, difficulty falling asleep, frequent awakenings, early awakenings, getting up too late, insufficient sleep, and excessive sleep.

[0170] In some embodiments, the sleep aid recommendation device 800 further includes a data acquisition module, an analysis module, and an extraction module.

[0171] The data acquisition module is used to acquire the physiological sign data collected by the physiological sign sensor during each sleep process.

[0172] The analysis module is used to analyze the physiological sign data to obtain the sleep stage data corresponding to each sleep process; the sleep stage data refers to the physiological sign data corresponding to each sleep stage during the user's sleep process.

[0173] The extraction module is used to extract feature values ​​corresponding to multiple sleep features during each sleep process according to the sleep stage data.

[0174] In the embodiment of the present application, the mattress can obtain sleep data corresponding to multiple analysis cycles, including the current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to multiple sleep characteristics; the mattress can determine the stability of each sleep characteristic according to the characteristic values ​​corresponding to each sleep characteristic in multiple analysis cycles; and then determine the target sleep-aiding solution according to the stability of each sleep characteristic, which refers to measures used to help improve the user's sleep quality. The mattress collects the user's historical sleep data, analyzes the user's historical sleep rhythm through the historical sleep data, finds out the user's sleep stability, and gives corresponding sleep-aiding recommendation solutions, so as to provide users with personalized sleep-aiding solutions and improve the user's sleep quality.

[0175] See also Fig. 9 , Fig. 9 Schematic diagram of the structure of a mattress disclosed in the embodiment of the present application. Fig. 9 As shown, the mattress 900 may include:

[0176] A memory 910 storing executable program codes;

[0177] a processor 920 coupled to the memory 910;

[0178] The processor 920 calls the executable program code stored in the memory 910 to execute any one of the sleep aid recommendation methods disclosed in the embodiments of the present application.

[0179] The embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the above-mentioned processor, the above-mentioned processor implements any one of the sleep aid recommendation methods disclosed in the embodiment of the present application.

[0180] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for the present application.

[0181] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the above-mentioned processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0182] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0184] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the above-mentioned methods of various embodiments of the present application.

[0185] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0186] The above is a detailed introduction to a sleep aid recommendation method, device, mattress and storage medium disclosed in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those of ordinary skill in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A sleep aid recommendation method, characterized in that: Applied to a mattress, the method comprises: Acquire sleep data corresponding to a plurality of analysis cycles, wherein the plurality of analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data include characteristic values ​​corresponding to a plurality of sleep characteristics; determining the stability of each sleep feature according to the feature values ​​respectively corresponding to each sleep feature in the plurality of analysis cycles; According to the stability of each sleep characteristic, a target sleep-aiding solution is determined, where the target sleep-aiding solution refers to measures for helping to improve the sleep quality of the user.

2. The method according to claim 1, characterized in that The determining the stability of each sleep feature according to the feature values ​​respectively corresponding to each sleep feature in the plurality of analysis cycles includes: calculating a stable range corresponding to the first sleep feature according to feature values ​​corresponding to the first sleep feature in the plurality of analysis cycles; the first sleep feature is any of the sleep features; The stability corresponding to the first sleep feature is determined according to the feature value corresponding to the first sleep feature in the current analysis cycle and the stability range.

3. The method according to claim 2, characterized in that The determining, according to the characteristic value corresponding to the first sleep characteristic in the current analysis period and the stability range, the stability corresponding to the first sleep characteristic includes: Calculate the standard deviation of the first sleep feature in the current analysis period according to the plurality of feature values ​​corresponding to the first sleep feature in the current analysis period; If the standard deviation of the first sleep characteristic corresponding to the current analysis period is within the stable range, determining that the first sleep characteristic is stable; If the standard deviation of the first sleep characteristic corresponding to the current analysis period is not within the stable range, it is determined that the first sleep characteristic is unstable.

4. The method according to claim 3, characterized in that According to the stability of each sleep characteristic, a targeted sleep aid program is formulated, including: Calculating the distance between the standard deviation corresponding to each unstable sleep feature in the current analysis period and the stable range corresponding to each unstable sleep feature; The unstable target sleep feature with the largest corresponding distance is determined, and the target sleep-aiding scheme is determined according to the target sleep feature.

5. The method according to claim 2, characterized in that: The calculating, according to the characteristic values ​​respectively corresponding to the first sleep characteristic in the plurality of analysis cycles, a stable range corresponding to the first sleep characteristic, comprises: Calculating the standard deviation of the first sleep feature in each of the historical analysis cycles according to the feature values ​​of the first sleep feature in each of the historical analysis cycles; Calculating an average value of standard deviations corresponding to the first sleep feature in each of the historical analysis cycles to obtain an average standard deviation corresponding to the first sleep feature; Calculating the allowable deviation value corresponding to the first sleep feature according to the standard deviation of the first sleep feature corresponding to at least one selected target historical analysis period and the standard deviation of the first sleep feature corresponding to adjacent historical analysis periods of each target historical analysis period; Based on the average standard deviation and the allowable deviation value, a stable range corresponding to the first sleep characteristic is determined.

6. The method according to claim 1, characterized in that The stability of the sleep characteristics includes stable or unstable; and the target sleep-aiding scheme is determined according to the stability of each sleep characteristic, including: determining the sleep type according to the unstable sleep characteristics; Determine a target sleep aid plan based on the sleep type; The sleep type includes at least one of falling asleep too late, difficulty falling asleep, frequent awakenings, early awakenings, getting up too late, insufficient sleep, excessive sleep, etc.

7. The method according to claim 1, characterized in that The mattress includes a physiological sign sensor. Before acquiring sleep data corresponding to a plurality of analysis cycles, the method further includes: Acquiring physiological sign data collected by the physiological sign sensor during each sleep process; Analyze the physiological sign data to obtain sleep stage data corresponding to each sleep process; the sleep stage data refers to the physiological sign data corresponding to each sleep stage of the user during the sleep process; Feature values ​​corresponding to a plurality of sleep features during each sleep process are extracted according to the sleep stage data.

8. A sleep aid recommendation device, characterized in that: Applied to a mattress, the device comprises: A data acquisition module, used to acquire sleep data corresponding to a plurality of analysis cycles, wherein the plurality of analysis cycles include a current analysis cycle and one or more historical analysis cycles before the current analysis cycle; the sleep data includes characteristic values ​​corresponding to a plurality of sleep characteristics; a stability determination module, configured to determine the stability of each sleep feature according to the feature values ​​corresponding to each sleep feature in the plurality of analysis cycles; The program determination module is used to determine a target sleep-aiding program according to the stability of each sleep characteristic, wherein the target sleep-aiding program refers to a measure used to help improve the sleep quality of the user.

9. A mattress, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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