Automatic mattress temperature adjusting method and system based on physiological data

Abstract

The invention relates to an automatic mattress temperature adjusting method and system based on physiological data. According to the method, heart rate, body movement, respiratory rate and body temperature data of a user are collected in real time through a physiological parameter sensor in the intelligent mattress, and sleep stages of the user are identified on the basis of the data and comprise a falling asleep stage, a light sleep stage, a deep sleep stage and an awakening stage. And establishing an optimal sleep temperature database of the user, recording a corresponding relationship between physiological data of the user in different sleep stages and the mattress temperature, calculating a sleep quality score based on the body movement frequency and the deep sleep duration, and recording a compensation coefficient of the environment temperature and the mattress temperature at the same time. And determining a target adjusting temperature according to the current sleep stage of the user and the database information, and correcting the target adjusting temperature in combination with the environment temperature and the compensation coefficient to obtain a final adjusting temperature. The temperature of the mattress is dynamically adjusted to the corrected target adjusting temperature through a water circulation temperature control system arranged in the mattress, so that personalized sleep temperature control is provided.

Description

Technical Field

[0001] The present application relates to the technical field of mattress temperature control, and in particular to a mattress automatic temperature control method and system based on physiological data. Background Art

[0002] In the prior art, smart mattresses are gradually being used to improve the sleep quality of users. Such mattresses are usually equipped with physiological parameter sensors to detect the user's physiological data, such as heart rate, body movement and body temperature. These data are used to analyze the user's sleep state and provide a relatively comfortable sleeping environment by adjusting parameters such as temperature and hardness. Some mattresses are also equipped with a temperature control system that can automatically adjust the mattress temperature based on the detected environment and body temperature data.

[0003] However, there are still some problems with existing smart mattress systems. Most systems can only detect a single or limited physiological parameter, and it is difficult to provide comprehensive sleep stage identification and temperature control. At the same time, these systems lack the ability to adjust the temperature of the sleep stage, and it is difficult to provide the best temperature in different sleep stages, resulting in insufficient user experience.

[0004] Therefore, it is necessary to develop a new mattress automatic temperature control method based on physiological data. Summary of the invention

[0005] The present application provides a mattress automatic temperature adjustment method and system based on physiological data to improve the user's sleep quality.

[0006] The present application provides a mattress automatic temperature control method based on physiological data, comprising:

[0007] The real-time physiological data of the user is collected by the physiological parameter sensor arranged in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, breathing frequency and body temperature data;

[0008] Based on the real-time physiological data, identifying the current sleep stage of the user, wherein the sleep stage includes a falling asleep stage, a light sleep stage, a deep sleep stage, and a waking stage;

[0009] Establishing a user's optimal sleeping temperature database, wherein the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; recording the user's sleep quality score at the mattress temperature, wherein the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; and recording the compensation coefficient between the ambient temperature and the mattress temperature;

[0010] Determining a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database;

[0011] According to the current ambient temperature, the target adjustment temperature is corrected in combination with the compensation coefficient recorded in the optimal sleeping temperature database to obtain a corrected target adjustment temperature;

[0012] The mattress temperature is dynamically adjusted to the corrected target adjustment temperature through the water circulation temperature control system built into the mattress.

[0013] Furthermore, the physiological parameter sensor installed in the smart mattress collects the user's real-time physiological data, including:

[0014] A photoelectric volumetric pulse wave sensor is arranged at the corresponding position of the chest of the mattress. The photoelectric volumetric pulse wave sensor obtains a pulse signal by detecting changes in blood flow in the user's chest area, thereby calculating the user's real-time heart rate; auxiliary photoelectric volumetric pulse wave sensors are arranged in other areas of the user's body to further improve the accuracy of heart rate detection and reduce errors caused by posture changes;

[0015] An acceleration sensor is installed inside the mattress to collect body motion data by detecting the slight vibration and movement of the user's body on the mattress surface;

[0016] A thin film pressure sensor is arranged on the surface of the mattress to determine the initial breathing frequency by detecting the small pressure fluctuation caused by the user's breathing; according to the initial breathing frequency, signal processing and correction are performed in combination with the acceleration sensor data to obtain stable breathing frequency data;

[0017] An infrared temperature sensor is placed on the contact surface of the mattress to measure body temperature data by receiving thermal radiation from different parts of the user's body in a contactless manner.

[0018] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0019] When it is detected that the user has entered the sleeping stage, the target adjustment temperature of the mattress temperature is set to the mattress temperature corresponding to the highest sleep quality score in the user's historical sleeping state according to the optimal sleeping temperature database.

[0020] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0021] When it is detected that the user enters the light sleep stage, the target adjustment temperature of the mattress temperature is set to the temperature corresponding to the minimum body temperature fluctuation amplitude in the user's historical light sleep state according to the optimal sleep temperature database.

[0022] Furthermore, the identifying the current sleep stage of the user based on the real-time physiological data includes:

[0023] According to the following formula (1), the user's dynamic comprehensive sleep index is calculated:

[0024]

[0025] Among them, DSI is the user's dynamic comprehensive sleep index; HRV represents heart rate variability, which is used to reflect autonomic nervous activity; θ and μ are attenuation coefficients; RF represents respiratory frequency; RF avg represents the user's average breathing rate; MV represents body movement frequency; T var represents the rate of change of body temperature; σ is the stability factor; w5, w6, w7, w8 are weight coefficients;

[0026] When the DSI value is greater than the first preset threshold θ1, the user is determined to be in the wakefulness stage; when the DSI value is between the second preset threshold θ2 and θ1, the user is determined to be in the light sleep stage; when the DSI value is between the third preset threshold θ3 and θ2, the user is determined to be in the falling asleep stage; when the DSI value is lower than the third preset threshold θ3, the user is determined to be in the deep sleep stage; wherein θ1>θ2>θ3;

[0027] Heart rate variability HRV is calculated using the following formula (2):

[0028]

[0029] Where n is the heartbeat interval sequence {RR1,RR2,…,RR n};RR i+1 Indicates the i+1th heartbeat interval; RR i Represents the i-th heartbeat interval.

[0030] Furthermore, the mattress automatic temperature adjustment method based on physiological data further includes:

[0031] According to the following formula (3), the data sampling frequency f of the physiological parameter sensor is dynamically adjusted sample :

[0032]

[0033] Among them, f base is the basic sampling frequency; τ and κ are adjustment factors; DSI is the user's dynamic comprehensive sleep index; is the adjustment threshold; dHR / dt represents the heart rate change rate; dRF / dt represents the respiratory rate change rate; dMV / dt represents the body movement frequency change rate; dT / dt represents the body temperature change rate; w1, w2, w3, w4 are weight coefficients.

[0034] Furthermore, the step of establishing the user's optimal sleeping temperature database includes:

[0035] Record the user's physiological data at different sleep stages and the corresponding mattress temperature;

[0036] The user's comprehensive sleep quality score SQ is calculated and recorded according to the following formula (4):

[0037]

[0038] Among them, S MV S is the body movement frequency score; DS Score the duration of deep sleep; S HRV T is the heart rate variability score; var is the user's body temperature change rate; T var,avg is the average temperature change rate of the user; θ and η are the adjustment coefficients;

[0039] The body movement frequency score S MV Calculated by the following formula (5):

[0040]

[0041] Where γ is the attenuation coefficient, and α are the adjustment coefficients; MV is the body movement frequency;

[0042] The deep sleep duration score S DS Calculated by the following formula (6):

[0043]

[0044] Among them, T DS Indicates the duration of deep sleep; T DS,avg represents the average deep sleep duration of the user; δ and β are adjustment coefficients; k is the attenuation factor;

[0045] The heart rate variability score S HRV Calculated by the following formula (7):

[0046]

[0047] Among them, HRV avg is the user's average heart rate variability; λ and v are adjustment coefficients; HRV is the user's heart rate variability;

[0048] According to the following formula (8), calculate and record the compensation coefficient C between ambient temperature and mattress temperature: comp :

[0049]

[0050] Among them, SQ env At ambient temperature T env The comprehensive sleep quality score under SQ base is the reference ambient temperature T base The overall sleep quality score is given below.

[0051] The present application provides a mattress automatic temperature control system based on physiological data, comprising:

[0052] A collection unit, used to collect real-time physiological data of the user through a physiological parameter sensor disposed in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, respiratory rate and body temperature data;

[0053] an identification unit, configured to identify a current sleep stage of the user based on the real-time physiological data, wherein the sleep stage includes a falling asleep stage, a light sleep stage, a deep sleep stage, and a waking stage;

[0054] An establishment unit is used to establish an optimal sleeping temperature database for a user, wherein the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; record the user's sleep quality score at the mattress temperature, wherein the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; and record the compensation coefficient between the ambient temperature and the mattress temperature;

[0055] a determination unit, configured to determine a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database;

[0056] A correction unit, configured to correct the target adjustment temperature according to the current ambient temperature and the compensation coefficient recorded in the optimal sleeping temperature database to obtain a corrected target adjustment temperature;

[0057] The regulating unit is used to dynamically regulate the temperature of the mattress to the corrected target regulating temperature through the water circulation temperature control system built into the mattress.

[0058] The technical solution proposed in this application has the following beneficial technical effects:

[0059] (1) Based on the user's physiological data and the optimal sleep temperature database, the present invention can accurately identify the user's current sleep stage and dynamically adjust the mattress temperature accordingly, ensuring that the user enjoys the most suitable temperature in different sleep stages (such as falling asleep, light sleep, deep sleep and waking up), significantly improving sleep quality. (2) By collecting the user's heart rate, body movement, breathing rate and body temperature data in real time, the present invention realizes comprehensive monitoring of the user's sleep status, can respond to changes in physiological status in a timely manner, and dynamically adjust the mattress temperature, which helps to improve the system's response speed and adjustment accuracy to the user's status. (3) The present invention designs a compensation mechanism for ambient temperature and mattress temperature, which can correct the target adjustment temperature under different ambient temperature conditions, so that the mattress temperature is more in line with the user's actual needs, and improves the system's adaptability and comfort in various external environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a mattress automatic temperature adjustment method based on physiological data provided in the first embodiment of the present application.

[0061] Figure 2 Schematic diagram of a mattress automatic temperature control system based on physiological data provided in the second embodiment of the present application. DETAILED DESCRIPTION

[0062] Many specific details are described in the following description to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0063] The first embodiment of the present application provides a mattress automatic temperature adjustment method based on physiological data. Figure 1 , which is a schematic diagram of the first embodiment of the present application. Figure 1 A first embodiment of the present application provides a method for automatically adjusting the temperature of a mattress based on physiological data, which is described in detail.

[0064] Step S101: collecting the user's real-time physiological data through the physiological parameter sensor set in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, breathing frequency and body temperature data.

[0065] In step S101, the user's real-time physiological data is collected through various physiological parameter sensors configured in the smart mattress, including heart rate, body movement data, respiratory rate and body temperature data. In order to achieve comprehensive and accurate data collection, it is necessary to embed multiple types of sensors in different areas of the mattress and arrange them reasonably to ensure that the sensors can reliably obtain the user's physiological signals. Data linkage can be achieved between the sensors to reduce the impact of single sensor errors on the overall data quality.

[0066] First, in order to collect the user's heart rate data, a photoplethysmography sensor (PPG) can be set near the chest area of ​​the mattress. The sensor calculates the pulse signal by detecting changes in blood flow under the skin to obtain heart rate information. The specific working principle of the PPG sensor is to use a light source and a photodetector. After the light source emits light to the surface of the user's skin, part of the light is absorbed depending on the blood flow, and the rest of the light is reflected back to the photodetector to generate a pulse waveform signal. The system can obtain heart rate data per minute by analyzing this waveform change. In order to avoid affecting the measurement accuracy of the heart rate due to changes in the user's posture, one or more auxiliary PPG sensors can be arranged in other areas of the user's body (such as the arms or back) to further improve the accuracy of heart rate detection.

[0067] Secondly, it is recommended to use an accelerometer as a sensor for collecting body motion data, and distribute it in multiple areas of the mattress to obtain the user's body motion data on the surface of the mattress. The accelerometer can sense the user's subtle movements, such as turning over, raising hands, etc. Specifically, the vibration and movement data sensed by the accelerometer are converted into numerical values ​​of movement frequency, amplitude, and direction through the internal signal processing unit to determine the user's body motion frequency and amplitude. These body motion data can reflect changes in the user's physical state and provide support for subsequent sleep stage identification.

[0068] Thirdly, in order to collect the user's breathing rate, a thin film pressure sensor can be placed on the upper surface of the mattress to ensure that it can sense the user's tiny pressure fluctuations without contacting the user's skin. The thin film pressure sensor is extremely sensitive to the pressure changes caused by the user's breathing, and the initial breathing rate data is obtained by capturing the subtle fluctuations caused by each breath. To improve the stability of the data, the initial breathing rate data can be combined with the body motion data of the acceleration sensor, and the noise can be removed through the signal processing algorithm to obtain a more accurate breathing rate.

[0069] Finally, the collection of body temperature data is completed through infrared temperature sensors. Infrared temperature sensors can detect the temperature of various parts of the user's body in real time without contact. The working principle of the sensor is to use the infrared radiation emitted by the human body, automatically receive and convert it into a temperature value. The sensor can be distributed around the user's body contact area to detect the user's overall body temperature distribution. Multiple infrared temperature sensors can work together to identify subtle changes in temperature and generate a user's temperature change data stream to provide a basis for subsequent analysis and temperature adjustment.

[0070] Through the physiological data collected and transmitted in real time by the above sensors, the system can continuously monitor the user's heart rate, body movement, breathing rate and body temperature changes. The data collected by each sensor will enter the data processing module for integration, providing detailed and accurate physiological data support for the next step of identifying the user's sleep stage.

[0071] Furthermore, the physiological parameter sensor installed in the smart mattress collects the user's real-time physiological data, including:

[0072] A photoelectric volumetric pulse wave sensor is arranged at the corresponding position of the chest of the mattress. The photoelectric volumetric pulse wave sensor obtains a pulse signal by detecting changes in blood flow in the user's chest area, thereby calculating the user's real-time heart rate; auxiliary photoelectric volumetric pulse wave sensors are arranged in other areas of the user's body to further improve the accuracy of heart rate detection and reduce errors caused by posture changes;

[0073] An acceleration sensor is installed inside the mattress to collect body motion data by detecting the slight vibration and movement of the user's body on the mattress surface;

[0074] A thin film pressure sensor is arranged on the surface of the mattress to determine the initial breathing frequency by detecting the small pressure fluctuation caused by the user's breathing; according to the initial breathing frequency, signal processing and correction are performed in combination with the acceleration sensor data to obtain stable breathing frequency data;

[0075] An infrared temperature sensor is placed on the contact surface of the mattress to measure body temperature data by receiving thermal radiation from different parts of the user's body in a contactless manner.

[0076] In this embodiment, a variety of physiological parameter sensors are embedded in the smart mattress to collect real-time physiological data of the user, including heart rate, body movement data, respiratory rate and body temperature data. First, a photoelectric volumetric pulse wave sensor (PPG) is arranged at the corresponding position of the chest of the mattress. The PPG sensor uses a combination of a light source and a photodetector to detect changes in blood flow in the chest area of ​​the user to obtain a pulse signal. Specifically, when light is irradiated on the user's skin, the flow of blood absorbs part of the light, and the remaining light is reflected back to the photodetector. By analyzing this change in light absorption and reflection, the system can calculate the user's heart rate in real time. In order to improve the accuracy of detection, the system arranges auxiliary PPG sensors in other areas of the user's body. These auxiliary sensors are used to provide additional detection data when the user's posture changes, reduce errors caused by posture changes, and thus ensure the accuracy and reliability of heart rate monitoring.

[0077] At the same time, an accelerometer is embedded inside the mattress to collect the user's body movement data. The accelerometer senses the user's subtle vibrations and movements on the mattress surface. By detecting these subtle changes in movement frequency and amplitude, the system can monitor the user's body movement in real time. This data can be used to analyze the user's sleep stability, determine whether the user is in a deep or light sleep state, and provide a reference for the automatic temperature adjustment of the mattress.

[0078] In addition, thin-film pressure sensors are arranged on the surface of the mattress to detect tiny pressure fluctuations caused by the user's breathing. When the user breathes, the body rises and falls slightly, causing small changes in the pressure on the mattress surface. The thin-film pressure sensor can detect these subtle pressure fluctuations to obtain the user's initial breathing rate data. In order to further improve the stability and accuracy of the breathing rate data, the system combines the initial breathing rate data with the data from the acceleration sensor, corrects the data through signal processing technology, removes the influence of noise, and finally obtains relatively stable breathing rate data.

[0079] Finally, an infrared temperature sensor is embedded in the contact surface of the mattress. The infrared temperature sensor measures body temperature data in a contactless manner by receiving thermal radiation from different parts of the user's body. This contactless temperature measurement method ensures the user's comfort while avoiding temperature changes caused by body contact with the sensor. Infrared temperature sensors are arranged in multiple contact areas to obtain temperature information from different parts of the user, thereby generating more comprehensive body temperature data. This data provides an accurate temperature reference for the temperature control of the mattress, enabling the system to adjust the mattress temperature according to the user's body temperature during different sleep stages, thereby improving sleep comfort.

[0080] In summary, through the coordinated work of photoplethysmography sensors, acceleration sensors, thin-film pressure sensors, and infrared temperature sensors, the system can collect the user's heart rate, body movement, breathing rate, and body temperature data in real time and comprehensively. These data provide rich and accurate physiological information for the automatic temperature adjustment function of the mattress, ensuring that the mattress temperature can match the user's sleep state and physiological needs, thereby improving sleep quality.

[0081] Step S102: Based on the real-time physiological data, identify the user's current sleep stage, wherein the sleep stage includes a falling asleep stage, a light sleep stage, a deep sleep stage, and a waking stage.

[0082] In step S102, based on the real-time physiological data collected from step S101, the system identifies the user's sleep stage. This process relies on the analysis of heart rate, body movement data, respiratory rate and body temperature data, thereby dividing the user's sleep state into falling asleep, light sleep, deep sleep and wakefulness stages.

[0083] First, the system analyzes the heart rate variability. Generally, heart rate variability is low in the deep sleep stage and relatively high in the light sleep or wakefulness stage. Therefore, by counting the amplitude and frequency of heart rate variability, the system can preliminarily judge the user's sleep depth. If the heart rate variability is low and stable, the system identifies it as a possible deep sleep state, and when the heart rate variability fluctuates greatly, it is more likely to be a light sleep or wakefulness state.

[0084] After acquiring the body motion data, the system further analyzes the user's movement frequency and amplitude. Generally, there is less body movement in the deep sleep stage, while there is more body movement in the light sleep or wakefulness stage. The system will determine whether the user has entered a quiet deep sleep state, or is in a light sleep and wakefulness state with more frequent activities by calculating the body motion frequency and amplitude. This process uses signal processing algorithms to perform frequency and time domain analysis on the body motion data to derive the user's body motion frequency and amplitude in a specific time period, thereby providing a more accurate basis for stage identification.

[0085] For the analysis of breathing frequency, the system will assist in identifying the sleep stage by detecting the regularity and frequency changes of breathing. Generally speaking, the breathing frequency is slower and more stable in the deep sleep stage, while in the light sleep or awake stage, the breathing frequency is relatively fast and more volatile. By analyzing the changing trend of breathing frequency, the system can further confirm whether the user is in a stable deep sleep state or is about to enter the awakening stage.

[0086] Finally, the system also analyzes the user's body temperature data. Body temperature is usually more stable and relatively low in the deep sleep stage, while it may fluctuate greatly in the light sleep and wakefulness stages. The system compares and analyzes body temperature data with time and other physiological indicators to more accurately identify the current sleep stage.

[0087] Through comprehensive analysis of the above physiological data, the system finally divides the user's sleep state into four stages: falling asleep, light sleep, deep sleep and awakening. This recognition result will be used in subsequent temperature adjustment operations to provide a personalized temperature control experience that meets the user's current sleep stage needs.

[0088] Furthermore, the identifying the current sleep stage of the user based on the real-time physiological data includes:

[0089] According to the following formula (1), the user's dynamic comprehensive sleep index is calculated:

[0090]

[0091] Among them, DSI is the user's dynamic comprehensive sleep index; HRV represents heart rate variability, which is used to reflect autonomic nervous activity; θ and μ are attenuation coefficients; RF represents respiratory frequency; RF avg represents the user's average breathing rate; MV represents body movement frequency; T var represents the rate of change of body temperature; σ is the stability factor; w5, w6, w7, w8 are weight coefficients;

[0092] When the DSI value is greater than the first preset threshold θ1, the user is determined to be in the wakefulness stage; when the DSI value is between the second preset threshold θ2 and θ1, the user is determined to be in the light sleep stage; when the DSI value is between the third preset threshold θ3 and θ2, the user is determined to be in the falling asleep stage; when the DSI value is lower than the third preset threshold θ3, the user is determined to be in the deep sleep stage; wherein θ1>θ2>θ3;

[0093] Heart rate variability HRV is calculated using the following formula (2):

[0094]

[0095] Where n is the heartbeat interval sequence {RR1,RR2,…,RR n};RR i+1 Indicates the i+1th heartbeat interval; RR i Represents the i-th heartbeat interval.

[0096] In this embodiment, the system identifies the user's current sleep stage by calculating the user's dynamic comprehensive sleep index (DSI). The user's sleep stage is divided into four stages according to the DSI value, namely, the wakefulness stage, the light sleep stage, the falling asleep stage, and the deep sleep stage. This division is based on the different sleep cycles and physiological states of the human body, and the purpose is to provide the user with the most suitable temperature regulation support at each stage.

[0097] Formula (1) is used to calculate the DSI value, and its specific form is as follows:

[0098]

[0099] The meaning and acquisition method of each item are as follows:

[0100] HRV (heart rate variability): HRV is used to reflect the activity of the autonomic nervous system, especially the role of the parasympathetic nervous system. A higher HRV value usually indicates that the user is in a more relaxed state, while a lower HRV value may mean deep sleep or a lighter sleep state. HRV is calculated using formula (2), which is:

[0101]

[0102] Where n represents the number of collected heartbeat interval sequences, RR i+1 and RR i are the intervals between the i+1th and ith heartbeats, respectively. The user's pulse signal is collected through the built-in photoplethysmography (PPG) sensor in the mattress, and the heartbeat interval is calculated to obtain the HRV value. In Formula 1, the calculation of HRV includes the attenuation coefficient θ, which is used to control its contribution to DSI.

[0103] RF (Respiratory Frequency): RF represents the user's real-time respiratory frequency, reflecting the rhythmicity and depth of breathing. During light sleep or when drunk, the respiratory frequency is usually high and unstable, while during deep sleep, the respiratory frequency is lower and more regular. RF is determined by the pressure fluctuations sensed by the thin film pressure sensor and a stable value is obtained after signal processing. RF avg is the user's average breathing rate, which is used to measure the degree of deviation of the current RF. In Formula 1, the attenuation coefficient μ controls the impact of this term on DSI, helping to identify the depth of sleep corresponding to respiratory rate fluctuations.

[0104] MV (body movement frequency): MV is used to indicate the user's body movement frequency per unit time. Generally speaking, the body movement frequency is higher during wakefulness and light sleep, and significantly decreases during deep sleep. Therefore, the body movement frequency is a key parameter for distinguishing between light sleep and deep sleep. The value of MV detects the user's subtle vibrations and movements on the surface of the mattress through the accelerometer in the mattress, and counts the frequency of body movement through a signal processing algorithm. The DSI formula uses the square root form to process the body movement frequency, making the effect of body movement on DSI smoother and avoiding the impact of fluctuations in body movement data on the recognition of sleep stages.

[0105] T var (Body temperature change rate): T var Indicates the rate of change of the user's body temperature. Body temperature is relatively stable during deep sleep, but usually fluctuates more during light sleep or wakefulness. The system can calculate the rate of change of body temperature by detecting the temperature of different parts of the user through the infrared temperature sensor in the mattress. This term in the DSI formula uses the denominator form, which is calculated by the stability factor σ and The combination of temperature changes can smooth the impact of body temperature changes on DSI and reduce the interference of abnormal temperature fluctuations on sleep stage judgment.

[0106] Each parameter has a weight coefficient w5, w6, w7, and w8, which is used to balance the contribution of different physiological data to DSI. By setting these coefficients, the system can identify the depth of the user's sleep at different stages and accurately determine the user's sleep state by combining the characteristics of each physiological parameter.

[0107] According to the numerical range of DSI, the system divides the user's current state into four stages. If the DSI value is higher than the first preset value θ1, the user is in the wakefulness stage; when the DSI value is between θ2 and θ1, the system identifies it as the light sleep stage; when the DSI value is between θ3 and θ2, the user is judged to be in the falling asleep stage; if the DSI value is lower than the third preset threshold θ3, the user enters the deep sleep stage. The division of the four stages is based on the user's physiological characteristics, helping the system to monitor the user's sleep state in real time and provide an accurate reference for subsequent automatic temperature adjustment.

[0108] Furthermore, the mattress automatic temperature adjustment method based on physiological data further includes:

[0109] According to the following formula (3), the data sampling frequency f of the physiological parameter sensor is dynamically adjusted sample :

[0110]

[0111] Among them, f base is the basic sampling frequency; τ and κ are adjustment factors; DSI is the user's dynamic comprehensive sleep index; is the adjustment threshold; dHR / dt represents the heart rate change rate; dRF / dt represents the respiratory rate change rate; dMV / dt represents the body movement frequency change rate; dT / dt represents the body temperature change rate; w1, w2, w3, w4 are weight coefficients.

[0112] In this embodiment, the system uses formula (3) to dynamically adjust the data sampling frequency f of the physiological parameter sensor. sample , to ensure that the sensor can obtain the user's physiological data at an appropriate sampling frequency in different sleep states. The structure of the formula allows the sampling frequency to be adaptively adjusted according to the user's dynamic comprehensive sleep index (DSI) and the rate of change of physiological parameters, thereby reducing the sampling frequency in static states such as deep sleep to save energy, and increasing the sampling frequency during wakefulness or drastic physiological changes to ensure data accuracy.

[0113] In formula (3), f sample The calculation process includes the following items:

[0114] Basic sampling frequency f base :This is the sampling frequency benchmark of the sensor when there is no significant physiological change, which is used to maintain basic physiological data collection. The basic sampling frequency is a system preset value to ensure that basic physiological data can still be obtained when the user is in a relatively stable sleep stage, but it will not consume too much power.

[0115] First adjustment This item is used to adjust the sampling frequency according to the Dynamic Comprehensive Sleep Index (DSI) to adapt to different sleep depths. Specifically, DSI represents the user's overall sleep state. When the DSI value is high (for example, in the wakeful state), the system automatically increases the sampling frequency to monitor the user's physiological changes more frequently; when the DSI value is low (for example, in the deep sleep state), the sampling frequency is reduced to reduce energy consumption. This adjustment item uses a DSI-based Sigmoid function to ensure the smoothness of the frequency adjustment. The parameter τ is the frequency adjustment factor, which is used to control the impact of DSI on the sampling frequency; κ controls the steepness of the Sigmoid function so that the frequency adjustment gradually transitions when the DSI changes; φ is the adjustment interval value, which sets the baseline value of DSI in different sleep stages.

[0116] Second adjustment This item adjusts the sampling frequency based on the rate of change of the user's physiological parameters, including the rate of change of heart rate, breathing rate, body movement rate and body temperature. The rate of change of each data item is adjusted by the corresponding weight coefficient to reflect the contribution of different physiological data to the sampling frequency.

[0117] Heart rate change rate dHR / dt: indicates the rate of change of heart rate, which is obtained by calculating the change amplitude of heart rate per unit time. If the heart rate fluctuates greatly, it usually indicates that the user is in a non-deep sleep state (such as awake or light sleep). At this time, the sampling frequency should be increased to track the heart rate dynamics more accurately. The weight coefficient w1 is used to adjust the impact of the heart rate change rate on the sampling frequency.

[0118] Respiratory frequency change rate dRF / dt: indicates the rate of change of respiratory frequency, and is obtained by analyzing the change of respiratory frequency over time. If the fluctuation of respiratory frequency increases, it may also indicate that the user is about to enter or is in a light sleep state. The weight coefficient w2 is used to balance the impact of respiratory frequency changes on the sampling frequency.

[0119] Body movement frequency change rate dMV / dt: indicates the rate of change of the user's body movement. A higher body movement frequency usually indicates that the user is in a light sleep or awake state. By detecting the frequency of body movement changes through the acceleration sensor, the system can identify the user's activity state. The weight coefficient w3 adjusts the contribution of this frequency change to the sampling frequency.

[0120] Body temperature change rate dT / dt: The body temperature change rate is calculated by the change of body temperature data detected by the infrared temperature sensor. A large change in body temperature usually means that the user is about to enter a light sleep or wake-up phase, while a relatively stable body temperature indicates that the user is in a deep sleep state. The impact of body temperature changes appears in the denominator, which is mainly used to smooth the sampling frequency adjustment caused by changes in other physiological parameters. The weight coefficient w4 is used to control the inhibitory effect of the body temperature change rate on the frequency adjustment.

[0121] Through the above two adjustments, formula (3) realizes the adjustment of dynamic sampling frequency, so that the system can flexibly adjust the sampling frequency according to the user's current sleep state and the fluctuation of physiological parameters. This adaptive adjustment of sampling frequency not only ensures the efficiency of data collection in the deep sleep stage, but also ensures the accuracy and timeliness of data when physiological changes are significant, thereby optimizing the energy efficiency and performance of the system under different sleep states.

[0122] Step S103: Establishing an optimal sleeping temperature database for the user, wherein the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; recording the user's sleep quality score at the mattress temperature, wherein the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; and recording the compensation coefficient of the ambient temperature and the mattress temperature.

[0123] In step S103, the system establishes a user's optimal sleeping temperature database for storing and associating the user's physiological data, mattress temperature, sleep quality score, and compensation coefficient at different sleep stages. The establishment of the database involves data collection, processing, and storage to provide a precise temperature control basis in the subsequent temperature adjustment process.

[0124] First, after the system identifies the user's sleep stage, it records the user's real-time physiological data and mattress temperature. This record includes the user's heart rate, body movement frequency, breathing rate, and body temperature in the four stages of falling asleep, light sleep, deep sleep, and waking up. For each sleep stage, the system will store the physiological data corresponding to the corresponding mattress temperature, thereby establishing a specific temperature configuration basis for each stage. These data may be different for different users, so the system forms a personalized data set based on the user's physiological response, making the temperature database individual-specific.

[0125] On this basis, the system also scores the user's sleep quality at a specific temperature and stores the score in the database. The calculation of the sleep quality score is based on two core parameters: body movement frequency and deep sleep duration. Generally speaking, lower body movement frequency and longer deep sleep duration represent higher sleep quality. By statistically analyzing and summarizing the sleep quality scores at different temperatures, the system can determine the temperature range that best suits the user and generate a corresponding relationship between the sleep quality scores for each stage.

[0126] In addition, the system also records the compensation coefficients of ambient temperature and mattress temperature in the optimal sleep temperature database. The compensation coefficient is used to dynamically correct the target adjustment temperature to adapt to changes in the external ambient temperature. The system calculates the adaptability of the mattress temperature under a specific ambient temperature by analyzing the sleep quality score under different ambient temperature conditions, thereby generating a compensation coefficient. These compensation coefficients are recorded in the database so that the target temperature can be adjusted during the subsequent temperature adjustment process to ensure that users can still get the best sleep experience in different seasons or ambient temperature changes.

[0127] By storing the above information in the optimal sleep temperature database, the system can provide personalized temperature adjustment basis for different sleep stages, so that the mattress temperature adjustment can not only adapt to the user's physiological needs, but also adapt to changes in ambient temperature.

[0128] Furthermore, the step of establishing the user's optimal sleeping temperature database includes:

[0129] Record the user's physiological data at different sleep stages and the corresponding mattress temperature;

[0130] The user's comprehensive sleep quality score SQ is calculated and recorded according to the following formula (4):

[0131]

[0132] Among them, S MV S is the body movement frequency score; DS Score the duration of deep sleep; S HRV T is the heart rate variability score; var is the user's body temperature change rate; T var,avg is the average temperature change rate of the user; θ and η are the adjustment coefficients;

[0133] The body movement frequency score S MV Calculated by the following formula (5):

[0134]

[0135] Where γ is the attenuation coefficient, and α are the adjustment coefficients; MV is the body movement frequency;

[0136] The deep sleep duration score S DS Calculated by the following formula (6):

[0137]

[0138] Among them, T DS Indicates the duration of deep sleep; T DS,avg represents the average deep sleep duration of the user; δ and β are adjustment coefficients; k is the attenuation factor;

[0139] The heart rate variability score S HRV Calculated by the following formula (7):

[0140]

[0141] Among them, HRV avg is the user's average heart rate variability; λ and v are adjustment coefficients; HRV is the user's heart rate variability;

[0142] According to the following formula (8), calculate and record the compensation coefficient C between ambient temperature and mattress temperature: comp :

[0143]

[0144] Among them, SQ env At ambient temperature T env The comprehensive sleep quality score under SQ base is the reference ambient temperature T base The overall sleep quality score is given below.

[0145] In this embodiment, the system establishes a user's optimal sleeping temperature database, which not only records the user's physiological data and the corresponding mattress temperature at different sleep stages, but also calculates and stores the user's comprehensive sleep quality score (SQ) to help the system provide more personalized temperature regulation support for the user in future sleep. Formula (4) is used to calculate the comprehensive sleep quality score, which generates a value that can reflect the user's sleep quality through comprehensive analysis of the body movement frequency score, deep sleep duration score, heart rate variability score and body temperature change rate.

[0146] In formula (4), the definitions and acquisition methods of each term are as follows:

[0147]

[0148] S MV (Body Movement Frequency Score): This item is used to reflect the user's body movement frequency during sleep. A high body movement frequency usually indicates light sleep or unstable sleep, while a low body movement frequency indicates a deeper and more stable sleep. Body movement frequency score S MV The calculation formula is:

[0149]

[0150] Among them, MV is the body movement frequency, which is obtained by measuring the user's movement frequency through the acceleration sensor in the mattress. The parameter γ is the attenuation coefficient of the body movement frequency, which is used to reduce the negative impact of the body movement frequency on the sleep quality score; and α are adjustment coefficients to ensure that the body movement frequency score can more accurately reflect the user's actual sleep state.

[0151] S DS (Deep sleep duration score): This item is used to evaluate the user's deep sleep quality. Longer deep sleep duration usually means better recovery effect. Deep sleep duration score S DS The calculation formula is:

[0152]

[0153] Among them, T DS is the actual deep sleep duration of the user, T DS,avg is the average deep sleep duration of the user, which is obtained through the accumulation of physiological parameter monitoring data of the smart mattress. Parameters δ and β are adjustment coefficients used to control the impact of deep sleep duration on the score; and k is the attenuation factor, which makes the response of the score to the duration have certain nonlinear characteristics, thereby more accurately reflecting the contribution of deep sleep time to sleep quality.

[0154] S HRV(Heart rate variability score): This item indicates the impact of the user's heart rate variability (HRV) on sleep quality. Generally, a higher HRV indicates that the user's body is more relaxed and the parasympathetic nerve activity is stronger, which is conducive to restorative sleep. Heart rate variability score S HRV The calculation formula is:

[0155]

[0156] Among them, HRV is the user's current heart rate variability, which is obtained by obtaining sensor data through the photoelectric volume pulse wave sensor of the smart mattress and calculated using Formula 2. avg is the user's average HRV, reflecting the user's long-term heart rate variability level. Parameters λ and v are adjustment coefficients used to balance the impact of HRV on sleep quality scores and make the scores more in line with individual physiological characteristics.

[0157] (Body temperature change rate influence item): This item is used to evaluate the impact of the user's body temperature change rate on sleep quality. Body temperature change rate T var Indicates the degree of fluctuation of the user's body temperature, measured by an infrared temperature sensor. The more stable the temperature changes, the deeper the sleep. var,avg It represents the average temperature change rate of the user, which is used as a reference. θ and η are adjustment coefficients, which are used to adjust the impact of the temperature change rate on the overall score.

[0158] After calculating and recording the user's comprehensive sleep quality score SQ, the system further records the compensation coefficient C between the ambient temperature and the mattress temperature. comp , so that the mattress temperature can be effectively adjusted under different environmental conditions. The compensation coefficient is calculated by formula (8):

[0159]

[0160] Among them, SQ env At a specific ambient temperature T env The sleep quality score is calculated based on the base At the reference ambient temperature T base Through this compensation factor, the system can adjust the mattress temperature according to the ambient temperature to ensure that the optimal sleeping temperature is maintained under various environmental conditions.

[0161] Combining all the above, the system establishes an optimal sleeping temperature database, which can not only provide precise temperature control support based on physiological data, but also dynamically respond to changes in ambient temperature to ensure that users are provided with a comfortable and personalized sleeping experience.

[0162] Step S104: determining a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database.

[0163] In step S104, the system determines the target adjustment temperature suitable for the user based on the currently identified user sleep stage and the optimal sleep temperature database. First, when the user's real-time physiological data indicates that they have entered a specific sleep stage, such as falling asleep, light sleep, deep sleep or waking up, the system retrieves the temperature information corresponding to the sleep stage from the optimal sleep temperature database. This database records the optimal temperature range of users in different sleep stages to ensure that the most suitable temperature setting can be provided for each stage.

[0164] When the user enters a specific sleep stage, the system first retrieves the correspondence between the historical temperature data and the physiological state in that stage, and finds the mattress temperature corresponding to the user's best physiological indicators in that stage. For example, if the user is currently in a deep sleep stage, the system will extract the temperature with the lowest body movement frequency in that stage from the database as the basis for target temperature adjustment. The system will select the best temperature that helps maintain or improve the sleep quality of that stage based on the performance of the sleep quality score at different temperatures, so as to help the user maintain a stable sleep state.

[0165] For the selection of target temperature adjustment, the system will also make fine adjustments according to the physiological needs of different stages. For example, in the stage of falling asleep, a moderate decrease in temperature can help accelerate falling asleep, while in the stage of waking up, a moderate increase in temperature can help the user gradually wake up. Therefore, the system will not only extract temperature data from the database, but also comprehensively consider the physiological needs of the current stage to ensure that the target temperature adjustment meets both the individual needs of the user and the changing laws of the sleep stages.

[0166] Through this temperature selection based on sleep stages and database, the system can provide users with the best temperature support in specific sleep stages, and provide precise temperature targets for subsequent automatic temperature adjustment operations to achieve the best sleep experience.

[0167] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0168] When it is detected that the user has entered the sleeping stage, the target adjustment temperature of the mattress temperature is set to the mattress temperature corresponding to the highest sleep quality score in the user's historical sleeping state according to the optimal sleeping temperature database.

[0169] In this embodiment, when the system detects that the user has entered the sleeping stage, it uses the optimal sleeping temperature database to determine the target adjustment temperature of the mattress. First, the system will identify the state of the user entering the sleeping stage, which is usually accompanied by specific changes in heart rate, breathing rate, body movement frequency and body temperature, such as lower body movement frequency and gradually slowing heart rate. By analyzing the trends of these physiological data in real time, the system can accurately identify the user's sleeping time.

[0170] Once it is determined that the user has entered the sleeping stage, the system immediately accesses the optimal sleeping temperature database and retrieves the temperature value that has obtained the highest sleep quality score in the historical sleeping stage. In the optimal sleeping temperature database, the system records the associated data of the user's temperature and sleep quality score in multiple sleeping stages. These sleep quality scores are calculated based on factors such as body movement frequency and deep sleep duration, and can reflect the impact of specific temperatures on the user's sleep quality. Therefore, by selecting the temperature value with the highest score in the historical data, the system can select the most suitable temperature to provide the user with the best temperature support during the sleeping stage.

[0171] The system sets this temperature value as the current target adjustment temperature to ensure that the user maintains a suitable body temperature during sleep. The target adjustment temperature setting enables the mattress to reach a comfortable temperature environment when the user just falls asleep, thereby helping the user to enter deep sleep faster. This personalized temperature setting is based on the user's historical sleep data, ensuring the accuracy and adaptability of temperature control.

[0172] Through this temperature setting process, the system not only improves the user's speed of falling asleep, but also lays the foundation for temperature regulation in subsequent sleep stages, allowing users to smoothly enter and maintain deep sleep in an environment with the most suitable temperature.

[0173] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0174] When it is detected that the user enters the light sleep stage, the target adjustment temperature of the mattress temperature is set to the temperature corresponding to the minimum body temperature fluctuation amplitude in the user's historical light sleep state according to the optimal sleep temperature database.

[0175] In this embodiment, when the system detects that the user has entered the light sleep stage, the target adjustment temperature of the mattress will be set to the mattress temperature corresponding to the user's historical light sleep stage when the body temperature fluctuation is the smallest based on the data in the optimal sleep temperature database. Specifically, the system identifies the moment when the user transitions from the sleeping state to the light sleep stage by analyzing the user's real-time physiological data, including heart rate, body movement frequency, and breathing frequency. This stage is usually characterized by less body movement and more stable breathing, but the user's physiological data still maintains a certain degree of activity.

[0176] Once the system recognizes that the user has entered the light sleep stage, it immediately accesses the optimal sleep temperature database to find historical light sleep stage data. In the database, the system records the user's temperature information during multiple light sleep states and the body temperature fluctuation amplitude associated with the temperature. When the body temperature fluctuation amplitude is small, it usually means that the temperature is more suitable for the user's needs at this stage, which can help the user maintain stable sleep quality during the light sleep stage. The system extracts the temperature with the smallest body temperature fluctuation amplitude from these historical data and uses it as the optimal temperature setting for the current light sleep stage.

[0177] The system then sets this temperature as the target temperature to ensure that the user can enjoy the best temperature support in the light sleep stage. By selecting the temperature with the smallest body temperature fluctuation as the temperature reference for the light sleep stage, the system can effectively reduce the interference of temperature on the user's sleep state and help maintain the continuity and quality of light sleep. While the precise temperature setting improves the user's overall sleep quality, it also provides an ideal transition for the subsequent deep sleep stage temperature regulation, making the temperature regulation of the entire sleep cycle more consistent and adaptable.

[0178] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0179] When it is detected that the user is in a deep sleep stage, the target adjustment temperature of the mattress temperature is set to the temperature corresponding to the lowest body movement frequency in the user's historical deep sleep state according to the optimal sleep temperature database.

[0180] In this embodiment, when the system detects that the user has entered the deep sleep stage, it will obtain historical deep sleep state data from the optimal sleep temperature database to set the target adjustment temperature that is most suitable for this stage. Specifically, the system first uses the change characteristics of physiological parameters to identify that the user has entered the deep sleep stage. In the deep sleep stage, it usually exhibits low heart rate variability, stable breathing rate and almost no body movement. Therefore, the system can determine the beginning of this state through a comprehensive analysis of the real-time heart rate, body movement frequency and breathing rate.

[0181] Once the system determines that the user is in a deep sleep state, the system will access the optimal sleep temperature database and retrieve the temperature data of the user's historical deep sleep stage. In the database, the temperature setting in the deep sleep stage is associated with the user's body movement frequency. Generally, a lower body movement frequency means a deeper level of sleep quality. In the database, the system has recorded the correspondence between the temperature values ​​and body movement frequencies in multiple deep sleep stages. The system analyzes historical data to find the temperature with the lowest body movement frequency as the most suitable temperature for the user's current deep sleep.

[0182] The system then sets this temperature as the target adjustment temperature for the current deep sleep stage, ensuring that the user can enjoy the best temperature support during deep sleep. The temperature with the lowest body movement frequency is selected to reduce the increase in body movement frequency caused by temperature discomfort during deep sleep, helping the user maintain a stable sleep state during deep sleep. This temperature setting for the deep sleep stage not only improves the user's sleep quality, but also reduces the interference of external temperature fluctuations on deep sleep, allowing the system to provide consistent and effective temperature support throughout the sleep cycle.

[0183] Furthermore, the determining the target adjustment temperature according to the different sleep stages of the user and the optimal sleep temperature database includes:

[0184] When it is detected that the user is about to enter the awakening stage, a temperature range suitable for gradual awakening is determined in the optimal sleeping temperature database according to the preset awakening time;

[0185] Within a specified time before the preset wake-up time, the mattress temperature is gradually increased, so that the user gradually leaves the deep sleep state and enters the light sleep or wakefulness state during the temperature adjustment process.

[0186] In this embodiment, when the system detects that the user is about to enter the awakening stage, it determines the temperature range suitable for gradual awakening by referring to the preset awakening time and combining the data in the optimal sleep temperature database. First, the system calculates an advance awakening preparation time period according to the awakening time set by the user. During this time period, the system begins to gradually adjust the temperature, with the aim of allowing the user to gradually transition from a deep sleep state to a light sleep state, and finally reach a natural awake state.

[0187] Once the wake-up preparation time is reached, the system will retrieve the temperature range suitable for wake-up from the optimal sleep temperature database. This temperature range is usually slightly higher than the temperature in the deep sleep stage, and is intended to stimulate the user's physiological state to gradually transition from deep sleep to light sleep through gentle temperature changes. The slow rise in temperature triggers changes in the human body's physiological rhythm, allowing the user to gradually get rid of the deep sleep state and enter a light sleep or near-wake-up state before the preset wake-up time, thereby creating a comfortable temperature environment for natural wake-up.

[0188] Before the preset wake-up time is reached, the system will gradually increase the temperature of the mattress. The temperature control system will accurately control the amplitude and speed of the temperature rise to ensure that the temperature change will not be too sudden and affect the user's sleep quality. As the temperature slowly rises, the system adjusts the wake-up temperature so that the user can experience a warm and comfortable environment, thereby causing the user's sleep depth to gradually become shallower and closer to awakening. Through this gradual adjustment of the temperature, the system ensures that the user can wake up naturally within the set wake-up time, avoiding the sudden awakening caused by a traditional alarm clock, improving the comfort and naturalness of the wake-up process, and also helping the user to maintain a good mental state after waking up.

[0189] Step S105: According to the current ambient temperature, the target adjustment temperature is corrected in combination with the compensation coefficient recorded in the optimal sleep temperature database to obtain a corrected target adjustment temperature.

[0190] In step S105, after determining the target adjustment temperature, the system adjusts it according to the current ambient temperature to obtain the final corrected target adjustment temperature. The system first collects the current ambient temperature of the space where the mattress is located through the temperature sensor. This ambient temperature may vary greatly due to the season, room ventilation or air conditioning operation. The change in ambient temperature directly affects the user's perceived temperature, so the target adjustment temperature needs to be corrected accordingly to ensure that the user can obtain a suitable sleeping temperature under different environmental conditions.

[0191] The system calibrates the target adjustment temperature by combining the compensation coefficients of the ambient temperature and mattress temperature stored in the optimal sleep temperature database. The compensation coefficient is a parameter calculated based on historical data, which indicates the extent to which the mattress temperature needs to be adjusted to maintain the user's comfort at a specific ambient temperature. For example, at lower ambient temperatures, the system may need to increase the mattress temperature moderately to offset the discomfort caused by the cold; at higher ambient temperatures, the system may appropriately reduce the mattress temperature to prevent the user from overheating.

[0192] To implement this correction process, the system inputs the target adjustment temperature and the current ambient temperature into the compensation model. The model uses the compensation coefficient to calculate a temperature offset value that adapts to the current environment. The system then multiplies this offset value with the initial target adjustment temperature to obtain a final adjustment temperature that is corrected for the ambient temperature. This temperature value can better meet the user's physical needs based on the current environmental conditions while maintaining personalized sleep support.

[0193] Through this ambient temperature correction process, the system achieves intelligent temperature compensation, ensuring that users can always enjoy the appropriate mattress temperature under different ambient temperature conditions, providing effective protection for improving sleep quality.

[0194] Step S106: dynamically adjusting the mattress temperature to the corrected target adjustment temperature through the water circulation temperature control system built into the mattress.

[0195] In step S106, the system dynamically adjusts the mattress temperature to the corrected target temperature calculated in the previous step through the water circulation temperature control system built into the mattress. The structure of the water circulation temperature control system generally includes a water tank, a temperature control heater, a cooling device, a circulation pump, and water circulation pipes distributed inside the mattress. The system controls the temperature and flow rate of the water to accurately adjust the temperature distribution of the water flow in the mattress, thereby affecting the temperature of the mattress surface.

[0196] First, the system heats or cools the water in the tank to the desired temperature. The temperature-controlled heater or cooling device processes the water according to the corrected target adjustment temperature. When the corrected target adjustment temperature is higher than the current water temperature, the heater heats the water to the target temperature; conversely, when the corrected target adjustment temperature is lower than the current water temperature, the cooling device starts to lower the water temperature. The temperature sensor in the system continuously monitors the water temperature and inputs the feedback data into the control module to ensure that the water temperature can quickly reach the set value.

[0197] After the water temperature reaches the set temperature, the circulation pump starts to push the water to flow in the water circulation pipe inside the mattress. The pipe is designed to be evenly distributed in the key areas of the mattress to ensure that the temperature can be evenly transmitted to the user's contact surface. The circulation of water in the mattress takes away or releases heat, thereby adjusting the temperature of the mattress surface to the corrected target adjustment temperature. The flow rate of the circulation pump can be adjusted dynamically to control the speed of temperature change. The system will monitor the surface temperature of the mattress in real time during the temperature control process to ensure that the temperature is always maintained near the corrected target adjustment temperature to avoid affecting the user's sleep quality due to temperature fluctuations.

[0198] The water circulation temperature control system can not only accurately control the temperature, but also has good heat conduction and heat dissipation performance, so that the mattress temperature can quickly respond to the user's physiological needs and ambient temperature changes at any time, realizing truly personalized and dynamic temperature control. Through this series of water circulation operations, the system ensures that users can enjoy appropriate temperature support at different sleep stages, thereby improving overall sleep quality.

[0199] In the above-mentioned embodiment, a mattress automatic temperature adjustment method based on physiological data is provided. Correspondingly, the present application also provides a mattress automatic temperature adjustment system based on physiological data. Since this embodiment, i.e., the second embodiment, is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiment. The embodiment described below is only illustrative.

[0200] The second embodiment of the present application provides a mattress automatic temperature control system based on physiological data, comprising:

[0201] The collecting unit 201 is used to collect the real-time physiological data of the user through the physiological parameter sensor arranged in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, respiratory rate and body temperature data;

[0202] The identification unit 202 is used to identify the current sleep stage of the user based on the real-time physiological data, wherein the sleep stage includes the falling asleep stage, the light sleep stage, the deep sleep stage and the awakening stage;

[0203] Establishing unit 203, used to establish an optimal sleeping temperature database for the user, the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; record the user's sleep quality score at the mattress temperature, the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; record the compensation coefficient of the ambient temperature and the mattress temperature;

[0204] A determination unit 204, configured to determine a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database;

[0205] The correction unit 205 is used to correct the target adjustment temperature according to the current ambient temperature and the compensation coefficient recorded in the optimal sleep temperature database to obtain a corrected target adjustment temperature;

[0206] The regulating unit 206 is used to dynamically regulate the temperature of the mattress to the corrected target regulating temperature through the water circulation temperature control system built into the mattress.

[0207] Although the present application is disclosed as above in the form of a preferred embodiment, it is not intended to limit the present application. Any technical personnel in this field may make possible changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

Claims

1. A mattress automatic temperature control method based on physiological data, characterized in that: include: The real-time physiological data of the user is collected by the physiological parameter sensor arranged in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, breathing frequency and body temperature data; Based on the real-time physiological data, identifying the current sleep stage of the user, wherein the sleep stage includes a falling asleep stage, a light sleep stage, a deep sleep stage, and a waking stage; Establishing a user's optimal sleeping temperature database, wherein the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; recording the user's sleep quality score at the mattress temperature, wherein the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; and recording the compensation coefficient between the ambient temperature and the mattress temperature; Determining a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database; According to the current ambient temperature, the target adjustment temperature is corrected in combination with the compensation coefficient recorded in the optimal sleeping temperature database to obtain a corrected target adjustment temperature; The mattress temperature is dynamically adjusted to the corrected target adjustment temperature through the water circulation temperature control system built into the mattress.

2. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The physiological parameter sensors installed in the smart mattress collect the user's real-time physiological data, including: A photoelectric volumetric pulse wave sensor is arranged at the corresponding position of the chest of the mattress. The photoelectric volumetric pulse wave sensor obtains a pulse signal by detecting changes in blood flow in the user's chest area, thereby calculating the user's real-time heart rate; auxiliary photoelectric volumetric pulse wave sensors are arranged in other areas of the user's body to further improve the accuracy of heart rate detection and reduce errors caused by posture changes; An acceleration sensor is installed inside the mattress to collect body motion data by detecting the slight vibration and movement of the user's body on the mattress surface; A thin film pressure sensor is arranged on the surface of the mattress to determine the initial breathing frequency by detecting the small pressure fluctuation caused by the user's breathing; according to the initial breathing frequency, signal processing and correction are performed in combination with the acceleration sensor data to obtain stable breathing frequency data; An infrared temperature sensor is placed on the contact surface of the mattress to measure body temperature data by receiving thermal radiation from different parts of the user's body in a contactless manner.

3. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The step of determining the target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database includes: When it is detected that the user has entered the sleeping stage, the target adjustment temperature of the mattress temperature is set to the mattress temperature corresponding to the highest sleep quality score in the user's historical sleeping state according to the optimal sleeping temperature database.

4. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The step of determining the target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database includes: When it is detected that the user enters the light sleep stage, the target adjustment temperature of the mattress temperature is set to the temperature corresponding to the minimum body temperature fluctuation amplitude in the user's historical light sleep state according to the optimal sleep temperature database.

5. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The step of determining the target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database includes: When it is detected that the user is in a deep sleep stage, the target adjustment temperature of the mattress temperature is set to the temperature corresponding to the lowest body movement frequency in the user's historical deep sleep state according to the optimal sleep temperature database.

6. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The step of determining the target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database includes: When it is detected that the user is about to enter the awakening stage, a temperature range suitable for gradual awakening is determined in the optimal sleeping temperature database according to the preset awakening time; Within a specified time before the preset wake-up time, the mattress temperature is gradually increased, so that the user gradually leaves the deep sleep state and enters the light sleep or wakefulness state during the temperature adjustment process.

7. The mattress automatic temperature control method based on physiological data according to claim 1, characterized in that: The identifying the current sleep stage of the user based on the real-time physiological data includes: According to the following formula (1), the user's dynamic comprehensive sleep index is calculated: Among them, DSI is the user's dynamic comprehensive sleep index; HRV represents heart rate variability, which is used to reflect autonomic nervous activity; θ and μ are attenuation coefficients; RF represents respiratory frequency; RF avg represents the user's average breathing rate; MV represents body movement frequency; T var represents the rate of change of body temperature; σ is the stability factor; w5, w6, w7, w8 are weight coefficients; When the DSI value is greater than the first preset threshold θ1, the user is determined to be in the wakefulness stage; when the DSI value is between the second preset threshold θ2 and θ1, the user is determined to be in the light sleep stage; when the DSI value is between the third preset threshold θ3 and θ2, the user is determined to be in the falling asleep stage; when the DSI value is lower than the third preset threshold θ3, the user is determined to be in the deep sleep stage; wherein θ1>θ2>θ3; Heart rate variability HRV is calculated using the following formula (2): Where n is the number of the user's heartbeat interval sequences {RR1,RR2,…,RRn} collected by the photoelectric volumetric pulse wave sensor in the smart mattress; RR i+1 Indicates the i+1th heartbeat interval; RR i Represents the i-th heartbeat interval.

8. The mattress automatic temperature control method based on physiological data according to claim 7, characterized in that: Also includes: According to the following formula (3), the data sampling frequency f of the physiological parameter sensor is dynamically adjusted sample : Among them, f base is the basic sampling frequency; τ and κ are adjustment factors; DSI is the user's dynamic comprehensive sleep index; is to adjust the threshold; dHR / dt represents the rate of change of heart rate; dRF / dt represents the rate of change of respiratory frequency; dMV / dt represents the rate of change of body movement frequency; Indicates the rate of change of body temperature; w1, w2, w3, w4 are weight coefficients.

9. The mattress automatic temperature control method based on physiological data according to claim 8, characterized in that: The step of establishing the user's optimal sleeping temperature database includes: Record the user's physiological data at different sleep stages and the corresponding mattress temperature; The user's comprehensive sleep quality score SQ is calculated and recorded according to the following formula (4): Among them, S MV S is the body movement frequency score; DS Score the duration of deep sleep; S HRV T is the heart rate variability score; var is the user's body temperature change rate; T var,avg is the average temperature change rate of the user; θ and η are the adjustment coefficients; The body movement frequency score S MV Calculated by the following formula (5): S MV =e ―γ·MV +log(1+ζ·MV α ) (5) Among them, γ is the attenuation coefficient, ζ and α are the adjustment coefficients; MV is the body movement frequency; The deep sleep duration score S DS Calculated by the following formula (6): Among them, T DS Indicates the duration of deep sleep; T DS,avg represents the average deep sleep duration of the user; δ and β are adjustment coefficients; k is the attenuation factor; The heart rate variability score S HRV Calculated by the following formula (7): Among them, HRV avg is the user's average heart rate variability; λ and v are adjustment coefficients; HRV is the user's heart rate variability; According to the following formula (8), calculate and record the compensation coefficient C between ambient temperature and mattress temperature: comp : Among them, SQ env At ambient temperature T env The comprehensive sleep quality score under SQ base is the reference ambient temperature T base The overall sleep quality score is given below.

10. A mattress automatic temperature control system based on physiological data, characterized in that: include: A collection unit, used to collect real-time physiological data of the user through a physiological parameter sensor disposed in the smart mattress, wherein the real-time physiological data includes heart rate, body movement data, respiratory rate and body temperature data; an identification unit, configured to identify a current sleep stage of the user based on the real-time physiological data, wherein the sleep stage includes a falling asleep stage, a light sleep stage, a deep sleep stage, and a waking stage; An establishment unit is used to establish an optimal sleeping temperature database for a user, wherein the optimal sleeping temperature database is used to record the corresponding relationship between the user's physiological data and the mattress temperature at different sleep stages; record the user's sleep quality score at the mattress temperature, wherein the sleep quality score is calculated based on the body movement frequency and the deep sleep duration; and record the compensation coefficient between the ambient temperature and the mattress temperature; a determination unit, configured to determine a target adjustment temperature according to different sleep stages of the user and the optimal sleep temperature database; A correction unit, configured to correct the target adjustment temperature according to the current ambient temperature and the compensation coefficient recorded in the optimal sleeping temperature database to obtain a corrected target adjustment temperature; The regulating unit is used to dynamically regulate the temperature of the mattress to the corrected target regulating temperature through the water circulation temperature control system built into the mattress.

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