Intelligent heating method and system based on sleep state of user and mattress

Abstract

The invention relates to an intelligent heating method and system based on a sleep state of a user and a mattress. An existing control method is single in heating mode. The mattress comprises a heating assembly with built-in heating pieces and a power supply for supplying power to the heating assembly, and the power supply comprises a portable power supply and is used for data collection, collection of real-time physiological data of a user and obtaining of the maximum heating piece operation number Heatmax capable of being supported within the rated power of the power supply; during use, the sleep state of a user is judged according to real-time physiological data of the user, and the heating assembly adjusts the running state according to the changing real-time running number Heatflag of the heating pieces. The decision basis is provided for the heating assembly by monitoring the real-time physiological parameters of the user, it is ensured that the heating assembly can provide the corresponding heating service according to the sleep state of the user, electric energy can be effectively saved, the service time of the heating assembly is prolonged, and the use experience is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of bedding, and particularly to an intelligent heating method, system and mattress based on the sleep state of a user. Background Art

[0002] The quality of sleep is directly related to health. There are many reasons for poor sleep quality. Research shows that environmental temperature has a great impact on sleep quality. Especially in cold winters, people usually adopt various methods to drive away the cold to improve sleep quality. Currently, the commonly used heating methods mainly include air-conditioning heating, floor heating, electric blanket or electric mattress heating, etc. Among them, electric mattresses have attracted the attention of the public due to advantages such as low power consumption, integration with the mattress, and convenient use.

[0003] Electric mattresses are mainly used when we sleep to increase the temperature of the mattress surface for the purpose of heating, and can also be used for dehumidifying the mattress. At the same time, for people with rheumatism or lumbar diseases, it can also play a role in accelerating blood circulation and conditioning rheumatism and lumbar diseases. Its heating effect can bring us a more comfortable and natural feeling. Especially for people working and living outdoors, the outdoor environment is humid and cold, and heating is more needed to improve sleep quality and effectively remove moisture. Existing electric mattresses generate heat in a single energy consumption mode by connecting to an external power supply, neither considering the limited power supply in the outdoor environment, resulting in too short a duration, nor considering the impact of a single heating mode on the user's sleep quality, affecting the use experience. Summary of the Invention

[0004] To solve the deficiencies of the prior art, the present invention provides an intelligent heating method, system and mattress based on the sleep state of a user, which provides a decision-making basis for the heating component by monitoring the real-time physiological parameters of the user, not only ensuring that the heating component can provide corresponding heating services according to the sleep state of the user, but also effectively saving electric energy, thereby extending the service life of the heating component and effectively improving the use experience.

[0005] The present invention is achieved in the following way: An intelligent heating method based on the sleep state of a user, for a mattress, the mattress includes a heating component with an embedded heating sheet and a power supply for supplying power to the heating component, and the power supply includes a portable power supply:

[0006] Data collection: Collect the real-time physiological data of the user to obtain the maximum number of heating sheets Heat_max that can be supported within the rated power of the power supply;

[0007] During use, determine the sleep state of the user according to the real-time physiological data of the user, and the heating component adjusts its operating state according to the changing real-time number of operating heating sheets Heat_flag:

[0008] When in the waking state, set Heat_flag to Heat_max to guide the user to transition to the light sleep state by rapidly increasing the temperature;

[0009] When in the light sleep state, adjust Heat_flag through the sleep-aiding algorithm to guide the user to transition from the light sleep state to the deep sleep state;

[0010] When in the deep sleep state, set Heat_flag ≤ 2 to enable the user to maintain the deep sleep state.

[0011] Bind the operating decision trigger conditions of the heating component when the user is in different sleep states to the physiological data that the user is more sensitive to in the corresponding sleep states. This can not only utilize the sensitive physiological data to improve the sensitivity of determining the trigger of the operating decision, ensure that the heating component can execute the corresponding operating decision when the user switches to different sleep states, and thus provide a comfortable heating experience for the entire sleep process of the user, but also reduce the monitoring difficulty by selecting physiological data that is convenient to monitor, effectively improve the monitoring accuracy, and thus facilitate the determination of the user's sleep state. Moreover, when the user switches from the waking state to the light sleep state and from the light sleep state to the deep sleep state, by implementing the strategy of gradually reducing the real-time operation quantity Heat_flag of the heating sheet, the power consumption of the heating component can be gradually reduced, effectively extending the operable duration of the heating component. By appropriately reducing the temperature, it can guide the user to switch to and maintain the deep sleep state, extending the time the user is in the deep sleep state and improving the sleep quality.

[0012] Preferably, the real-time physiological data includes the heart rate data HR and the body movement frequency data BMF. Summarize the real-time physiological data within a preset duration in the waking state to obtain the initial heart rate data HR1 and the initial body movement frequency data BMF1, and calculate the heart rate threshold HR_TH and the body movement frequency threshold BMF_TH for judging that the user enters the light sleep state. When HR < HR_TH and BMF < BMF_TH, the user enters the light sleep state. When the user switches from the waking state to the light sleep state, the heart rate data HR and the body movement frequency data BMF of the user will show a larger change amplitude compared to other physiological parameters. Using the heart rate data HR and the body movement frequency data BMF as the basis for judging whether the user enters the light sleep state is not only convenient for monitoring, effectively reducing the monitoring difficulty, but also effectively improving the monitoring accuracy, ensuring the matching and correlation between the operating state of the heating component and the sleep state of the user.

[0013] Preferably, HR_TH = K * HR1 + ΔHR, where K is an adjustment coefficient, K = 0.85 - 0.95, and ΔHR is a dynamically adjusted value obtained based on the user's historical heart rate data, with an initial value of 0. The heart rate threshold HR_TH is calculated using the monitored initial heart rate data HR1 of the user. When the user switches from the awake state to the light sleep state, the heart rate data decreases. By setting a reasonable adjustment coefficient K and a dynamically adjusted value ΔHR, the size of HR_TH is adjusted, thereby adjusting the tightness of determining whether to enter the light sleep state, and ensuring that the operation change of the heating component is synchronously matched with the change of the user's sleep state.

[0014] Preferably, BMF_TH = L * BMF1 + ΔBMF, where L is an adjustment coefficient, L = 0.3 - 0.6, and ΔBMF is a dynamically adjusted value obtained based on the user's historical body movement frequency data, with an initial value of 0. The body movement frequency threshold BMF_TH is calculated using the monitored initial body movement frequency data BMF1 of the user. When the user switches from the awake state to the light sleep state, the body movement frequency data decreases. By setting a reasonable adjustment coefficient L and a dynamically adjusted value ΔBMF, the size of BMF_TH is adjusted, thereby adjusting the tightness of determining whether to enter the light sleep state, and ensuring that the operation change of the heating component is synchronously matched with the change of the user's sleep state.

[0015] Preferably, the sleep assistance algorithm adjusts the parameter Heat_flag in real time according to the change of the obtained real-time physiological data. Among them, w1 is the weight coefficient of the heart rate, and w2 is the weight coefficient of the body movement frequency. If Heat_flag is a decimal, it is rounded to an integer. When the user enters the light sleep state and has not entered the deep sleep state, the real-time running number Heat_flag of the heating elements of the heating component is variable. Since HR < HR_TH and BMF < BMF_TH, it makes and both less than 1. By setting the matching weight coefficients w1 and w2, it makes The value of is always less than 1, which not only ensures that the real-time running number Heat_flag of the heating elements is variable and always less than the maximum running number value Heat_max of the heating elements, ensuring the safe operation of the heating component, but also can utilize the characteristics that the heart rate data HR and the body movement frequency data BMF gradually decrease when the user switches to the deep sleep state to control the real-time running number Heat_flag of the heating elements to decrease synchronously. By reducing the number of running heating elements, the user's temperature is reduced, which is conducive to the user's switching from the light sleep state to the deep sleep state.

[0016] Preferably, the real-time physiological data includes respiratory variability data BV and body movement intensity data BMI. The real-time physiological data within a preset duration in the waking state is summarized to obtain initial respiratory variability data BV1 and initial body movement intensity data BMI1, and a respiratory variability threshold BV_TH and a body movement intensity threshold BMI_TH for judging that the user enters the deep sleep state are calculated. When BV < BV_TH and BMI < BMI_TH, the user enters the deep sleep state. When the user switches from the light sleep state to the deep sleep state, the respiratory variability data BV and the body movement intensity data BMI of the user will have a larger change range compared with other physiological parameters. Using the respiratory variability data BV and the body movement intensity data BMI as the basis for judging whether the user enters the deep sleep state is not only convenient for monitoring, effectively reduces the monitoring difficulty, but also effectively improves the monitoring accuracy, ensuring that the operating state of the heating component is matched and associated with the sleep state of the user.

[0017] Preferably, BV_TH = M * BV1 + ΔBV, where M is a regulation coefficient, M = 0.2 - 0.5, and ΔBV is a dynamically adjusted value obtained based on the user's historical respiratory variability data, with an initial value of 0. The respiratory variability threshold BV_TH is calculated using the monitored initial respiratory variability data BV1 of the user. When the user switches from the light sleep state to the deep sleep state, the respiratory duration of the user will be relatively constant, and the respiratory variability data will decrease. By setting a reasonable regulation coefficient M and a dynamically adjusted value ΔBV to adjust the size of BV_TH, the tightness of judging whether to enter the deep sleep state is further adjusted, ensuring that the operating change of the heating component is synchronously matched with the change of the user's sleep state.

[0018] BMI_TH = N * BMI1 + ΔBMI, where N is a regulation coefficient, N = 0.2 - 0.5, and ΔBMI is a dynamically adjusted value obtained based on the user's historical body movement intensity data, with an initial value of 0. The body movement intensity threshold BMI_TH is calculated using the monitored initial body movement intensity data BMI1 of the user. When the user switches from the light sleep state to the deep sleep state, the body movement intensity of the user will weaken, and the body movement intensity data will decrease. By setting a reasonable regulation coefficient N and a dynamically adjusted value ΔBMI to adjust the size of BMI_TH, the tightness of judging whether to enter the deep sleep state is further adjusted, ensuring that the operating change of the heating component is synchronously matched with the change of the user's sleep state.

[0019] Preferably, set the total heating duration T. When the cumulative running duration of the heating component reaches T, turn off the heating component and set Heat_flag to 1. By setting the total heating duration T to control the running duration of the heating component, it not only ensures that the heating component can provide heating service for the preset duration to the user, ensuring that the user enters and effectively maintains the deep sleep state, but also can reduce power consumption by limiting the running duration of the heating component, thereby ensuring the running duration of the heating component when powered by a mobile power supply and facilitating multiple uses. Setting Heat_flag to 1 facilitates subsequent reuse.

[0020] Preferably, set the alarm time. When the heating component reaches the total heating duration T or the alarm time, turn off the heating component and set Heat_flag to 1. The heating component is turned off when it reaches the alarm time, effectively saving power consumption and preventing the heating component from consuming power due to continuous operation after being separated from the user.

[0021] A system includes a detection module, a control module, a storage module, and a heating module. The detection module collects real-time physiological data of the user and transmits it to the control module; the control module receives the real-time physiological data from the detection module, determines the user's sleep state, calculates and adjusts the parameter Heat_N, and controls the operation of the heating component module; the storage module is used to store the operation program in the control module, the real-time physiological data from the detection module, and the parameters and instructions generated during the operation of the program; the heating module includes several independently set heating units, and the heating units receive instructions from the control module and operate independently.

[0022] Each module works in cooperation to form an intelligent solution for controlling the operation of the heating component, which not only ensures that the operation strategy of the heating component is matched and associated with the user's sleep state, provides comfortable heating for the user, and guides the user to convert to the deep sleep state, but also can reasonably utilize the electric energy in the mobile power supply in the outdoor environment, ensuring that the heating component can meet the requirement of providing comfortable heating to the user by setting a reasonable operation plan when the electric energy in the external power supply is limited.

[0023] A mattress, comprising a mattress body, wherein a heating component is arranged at the top of the mattress body, and the heating component includes a waist heating area and a leg heating area formed by splicing a plurality of heating sheets. The mattress body serves as a carrier for installing the heating component, which not only ensures the relative positions of the heating sheets in the heating component are fixed, but also facilitates the disassembly and assembly of the mattress body for use, and further facilitates carrying when going out. The heating component includes a waist heating area and a leg heating area formed by splicing a plurality of heating sheets, which can respectively provide heating services to the user's waist and legs by using the waist heating area and the leg heating area, thereby improving the heat utilization efficiency by finely dividing the heating area, reducing heat loss, and preventing the heat generated by the heating component from being dissipated and wasted due to the inability to accurately transfer to the target area, and improving the electric energy utilization efficiency by reducing the electric energy loss.

[0024] Preferably, since the real-time operation quantity Heat_flag of the heating sheets is a variable, when Heat_flag is an even number, the waist heating area and the leg heating area have the same operation quantity of heating sheets; when Heat_flag is an odd number, the operation quantity of heating sheets in the leg heating area is 1 more than that in the waist heating area, ensuring that the leg heating area obtains electric energy supply prior to the waist heating area, and further providing more heat for the legs.

[0025] The beneficial effects of the present invention: Binding the operation decision trigger conditions of the heating component when the user is in different sleep states to the physiological data that the user is more sensitive to in the corresponding sleep states can not only use the sensitive physiological data to improve the sensitivity of determining the operation decision trigger, ensure that the heating component can execute the corresponding operation decision when the user switches to different sleep states, and thus provide a comfortable heating experience for the entire sleep process of the user, but also reduce the monitoring difficulty by selecting physiological data that is convenient to monitor, effectively improve the monitoring accuracy, and thus facilitate determining the user's sleep state. Moreover, when the user switches from the awake state to the light sleep state and from the light sleep state to the deep sleep state, by implementing the strategy of gradually reducing the real-time operation quantity Heat_flag of the heating sheets, the electric energy consumption of the heating component can be gradually reduced, effectively prolonging the operable duration of the heating component, guiding the user to switch to and maintain the deep sleep state by appropriately reducing the temperature, and prolonging the time the user is in the deep sleep state, thereby improving the sleep quality. Description of the Drawings

[0026] Figure 1 It is the decision diagram of the intelligent control method described in Embodiment 1;

[0027] Figure 2 It is the decision diagram of the intelligent control method described in Embodiment 2;

[0028] Figure 3 It is the structure diagram of the system described in Embodiment 3;

[0029] Figure 4Schematic diagram of the disassembly structure of the mattress described in Embodiment 4;

[0030] In the figure: 1, heating pad; 2, heating sheet; 3, waist heating area; 4, leg heating area; 5, support pad; 6, outer cover. Detailed implementation manners

[0031] The following further describes the substantial features of the present invention in conjunction with the accompanying drawings of the specification and the detailed implementation manners.

[0032] Embodiment 1:

[0033] This embodiment provides an intelligent heating method based on the user's sleep state.

[0034] As Figure 1 shown in the method for a mattress, the mattress includes a heating component with an in-built heating sheet and a power source for supplying power to the heating component. The power source includes a portable power source. During data collection, real-time physiological data of the user is collected to obtain the maximum number of heating sheets Heat_max that can be supported within the rated power of the power source. During use, the user's sleep state is determined based on the real-time physiological data of the user, and the heating component adjusts its operating state according to the changing real-time number of heating sheets Heat_flag. Binding the operating decision trigger conditions of the heating component when the user is in different sleep states to the physiological data that the user is more sensitive to in the corresponding sleep state can not only use the sensitive physiological data to improve the sensitivity of determining the operating decision trigger, ensure that the heating component can execute the corresponding operating decision when the user switches to different sleep states, and thus provide a comfortable heating experience for the entire sleep process of the user, but also reduce the monitoring difficulty by selecting physiological data that is convenient to monitor, effectively improve the monitoring accuracy, and thus facilitate the determination of the user's sleep state. Moreover, when the user switches from the awake state to the light sleep state and from the light sleep state to the deep sleep state, by implementing the strategy of gradually reducing the real-time number of heating sheets Heat_flag, the power consumption of the heating component can be gradually reduced, effectively extending the operable duration of the heating component. By appropriately reducing the temperature, the user can be guided to switch to and maintain the deep sleep state, extending the time the user is in the deep sleep state and improving the sleep quality.

[0035] In this embodiment, the maximum number of heating sheets Heat_max that can be supported within the rated power of the power source is obtained by detecting the power source, and intelligent heating is implemented through the following steps:

[0036] S113. When the user is in the awake state, set Heat_flag according to the latest saved maximum number of heating sheets Heat_max, turn on the intelligent heating operation, and jump to S114;

[0037] S114, use the heart rate threshold HR_TH and the body movement frequency threshold BMF_TH to determine the heart rate data HR and the body movement frequency data BMF respectively. When HR < HR_TH and BMF < BMF_TH, it indicates that the user enters the light sleep state and jumps to S115; otherwise, it indicates that the user is still in the awake state and jumps to S113;

[0038] S115, the user is in the light sleep state. Adjust Heat_flag through the sleep aid algorithm, and perform intelligent control on the waist heating pad and the leg heating pad to guide the user to enter the deep sleep state faster. After completing the adjustment of the parameter Heat_flag, jump to S116;

[0039] S116, use the breathing variability threshold BV_TH and the body movement intensity threshold BMI_TH to determine the breathing variability data BV and the body movement intensity data BMI respectively. When BV < BV_TH and BMI < BMI_TH, it indicates that the user enters the deep sleep state and jumps to S117; otherwise, it indicates that the user is still in the light sleep state and jumps to S115;

[0040] S117, the user is in the deep sleep state. Perform intelligent control on the leg heating pad and the waist heating pad to help the user maintain the deep sleep state and jump to S118;

[0041] S118, determine whether the user has set an alarm before going to bed. If an alarm is set, jump to S120; if no alarm is set, jump to S119;

[0042] S119, when any of the following situations occurs: whether the running time of the heating component reaches the set total heating duration T, the heating switch of the mattress is turned off, and the power supply is disconnected, jump to S122; otherwise, jump to S117;

[0043] S120, whether the set heating duration is greater than the set alarm time. If so, jump to S121; if not, jump to S119;

[0044] S121, when any of the following situations occurs: when the time reaches the set alarm time, the heating switch of the mattress is turned off, and the power supply is disconnected, jump to S122; otherwise, jump to S117;

[0045] S122, set the parameter Heat_flag to 1, turn off the heating component, and end the intelligent operation.

[0046] In this embodiment, the real-time running number of heating pads Heat_flag is a variable and is correlated with the sleep state of the user. Specifically:

[0047] When in the waking state, set Heat_flag to Heat_max, and guide the user to transition to the light sleep state through rapid heating. The heating component starts to operate. By setting Heat_flag to Heat_max, the maximum number of heating elements in operation can be obtained. Then, the heating component raises the user's body temperature through rapid heating, which plays a role in promoting blood circulation and relaxation, and thus is conducive to the user's transition from the waking state to the light sleep state.

[0048] When in the light sleep state, adjust Heat_flag through the sleep aid algorithm to guide the user to transition from the light sleep state to the deep sleep state. The sleep aid algorithm adjusts the parameter Heat_flag in real time according to the changes in the obtained real-time physiological data. Among them, w1 is the weight coefficient of the heart rate, and w2 is the weight coefficient of the body movement frequency. If Heat_flag has a decimal, round it to the nearest integer. Set the heart rate data HR and the body movement frequency data BMF as the key indicators for evaluating the degree of the light sleep state. It can not only determine the sleep state according to the monitored heart rate data HR and body movement frequency data BMF and use them to calculate and adjust the real-time operation number of the heating elements Heat_flag, providing a comfortable and suitable heating service for the user and effectively improving the sleep experience, but also utilize the characteristics that the heart rate data HR and the body movement frequency data BMF change significantly when the user is in the light sleep state to improve the correlation accuracy between the user's sleep state and the operation decision of the heating component by increasing the amplitude of the numerical change. As the user's sleep state transitions to the deep sleep state, the real-time operation number of the heating elements Heat_flag will decrease accordingly. Then, by reducing the number of operating heating elements, the environmental temperature of the user can be lowered, which is conducive to the change of the sleep state.

[0049] When in the deep sleep state, set Heat_flag ≤ 2 to keep the user in the deep sleep state. Provide heating for the user in the deep sleep state by setting a constant real-time operation number of the heating elements Heat_flag. This not only ensures that the user's body temperature can be maintained within the temperature range suitable for maintaining the deep sleep state for a long time, improving the sleep quality by extending the deep sleep state time, but also can reasonably utilize the limited electric energy in the mobile power supply by reducing the power consumption of the heating component, and thus facilitate the usage times and duration of the heating component in the outdoor environment.

[0050] In this embodiment, when the user switches from the waking state to the light sleep state and from the light sleep state to the deep sleep state, both can be indirectly observed and judged through physiological parameters with significant change amplitudes. This not only improves the accuracy of sleep state evaluation by selecting sensitive physiological parameters, but also reduces the monitoring difficulty by choosing easily monitored physiological data, and thus facilitates the use and operation, which is more convenient to operate compared to physiological parameters such as brain waves that require wearing professional detection equipment.

[0051] In this embodiment, when the user switches from the waking state to the light sleep state, the heart rate data HR and the body movement frequency data BMF have a greater variation range compared to other physiological parameters. When the heating component is turned on, it will detect the user's heart rate data HR and body movement frequency data BMF. By summarizing the real-time physiological data of the user for a preset duration in the waking state, the initial heart rate data HR1 and the initial body movement frequency data BMF1 are obtained, and the heart rate threshold HR_TH and the body movement frequency threshold BMF_TH for judging that the user enters the light sleep state are calculated. When HR < HR_TH and BMF < BMF_TH, it indicates that both the user's heart rate data HR and body movement frequency data BMF are less than the corresponding thresholds, which means the user enters the light sleep state.

[0052] In this embodiment, when the user switches from the light sleep state to the deep sleep state, the respiratory variability data BV and the body movement intensity data BMI have a greater variation range compared to other physiological parameters. The real-time physiological data includes the respiratory variability data BV and the body movement intensity data BMI. By summarizing the real-time physiological data within a preset duration in the waking state, the initial respiratory variability data BV1 and the initial body movement intensity data BMI1 are obtained, and the respiratory variability threshold BV_TH and the body movement intensity threshold BMI_TH for judging that the user enters the deep sleep state are calculated. When BV < BV_TH and BMI < BMI_TH, it indicates that both the user's respiratory variability data BV and body movement intensity data BMI are less than the corresponding thresholds, which means the user enters the deep sleep state.

[0053] In this embodiment, HR_TH = K * HR1 + ΔHR, where K is an adjustment coefficient, K = 0.85 - 0.95, and ΔHR is a dynamically adjusted value obtained based on the user's historical heart rate data, with an initial value of 0; BMF_TH = L * BMF1 + ΔBMF, L is an adjustment coefficient, L = 0.3 - 0.6, and ΔBMF is a dynamically adjusted value obtained based on the user's historical body movement frequency data, with an initial value of 0; BV_TH = M * BV1 + ΔBV, where M is an adjustment coefficient, M = 0.2 - 0.5, and ΔBV is a dynamically adjusted value obtained based on the user's historical respiratory variability data, with an initial value of 0; BMI_TH = N * BMI1 + ΔBMI, N is an adjustment coefficient, N = 0.2 - 0.5, and ΔBMI is a dynamically adjusted value obtained based on the user's historical body movement intensity data, with an initial value of 0. The thresholds corresponding to the heart rate data HR, body movement frequency data BMF, respiratory variability data BV, and body movement intensity data BMI are all calculated through the corresponding formulas. Among them, the adjustment coefficient is a variable value used to adjust the tightness of the conversion, and it varies from person to person. The appropriate value for the corresponding person can be obtained through large-scale electroencephalogram, sleep state, and physiological parameter data tests, which is convenient for users to set according to their own situations to ensure that the sleep state determination matches the actual situation of the user. The adjustment coefficient is a first-level adjustment, which is divided and set according to the applicable group of people with the same associated factors. The dynamically adjusted value is a second-level adjustment, which is personalized according to the user's personal historical data to ensure that each threshold can effectively improve the accuracy of judging the user's sleep state.

[0054] In this embodiment, the total heating duration T is set. When the cumulative running duration of the heating component reaches T for a single time, the heating component is turned off. The alarm time is set. When the heating component reaches the total heating duration T or the alarm time, the heating component is turned off. When either condition is triggered, the heating component is turned off, which not only effectively improves the power utilization efficiency, ensures that the electric energy will not be wasted, but also ensures that the user can obtain the heating duration that meets their usage requirements and improves the sleep quality.

[0055] Embodiment 2:

[0056] Compared with Embodiment 1, this embodiment provides another method.

[0057] As Figure 2 shown, by detecting the external power supply and the user's physiological data, parameter support is provided for the intelligent control of the heating component, so as to ensure that the intelligent control method can not only meet the user's usage requirements, improve the heating comfort and sleep quality, but also ensure the safe operation of the power supply and effectively extend the heating duration. Specifically, the operation is realized through the following steps:

[0058] S101. Turn on the external power supply and connect it to the heating component. Read the recorded operating value of the heating element, Heat_flag. If there is no record, assign the original value of 1. Record and read the operating value of the heating element, Heat_flag, to establish an associated interaction between steps, effectively determine the type of external power supply, and ensure the long-term effective operation of the heating component.

[0059] S102. Perform a voltage detection on the external power supply to obtain the voltage VDD1, and determine the type of the external power supply. If 5V ≤ VDD1 ≤ 20V, determine that the external power supply is a mobile power supply and proceed to S103; if VDD1 > 20V, determine that the external power supply is a normal power supply and jump to S104; if VDD1 < 5V, determine that the external power supply is not effectively connected and jump to S101. Performing a voltage detection on the external power supply can not only determine the type of the external power supply based on the voltage level, facilitating the distinction between a normal power supply generated by the mains or a generator with a voltage exceeding 20V and a mobile power supply that is convenient to carry and has a voltage between 5 - 20V, but also determine whether the external power supply is reliably connected by whether there is voltage. When the voltage is lower than 5V, jump to S101 to reconnect the external power supply, ensuring that the subsequent steps are carried out on the premise of a reliable connection of the external power supply.

[0060] S103. When Heat_flag = 1, jump to S105; when Heat_flag > 1, jump to S106. Make a determination and decision by reading the operating value of the heating element, Heat_flag. When Heat_flag = 1, it indicates that the voltage detection step has been completed for the first time in the detection stage, the distinction between the normal power supply and the mobile power supply has been completed, and the type of the mobile power supply needs to be further distinguished; when Heat_flag > 1, it indicates that the detection stage has completed step S107 and the power supply input has been shut off. The external power supply is a portable over - load shutdown type mobile power supply, and it is necessary to reconnect the external power supply through S101 and proceed with subsequent use.

[0061] S104. The external power supply is a normal power supply. Increase the parameter Heat_flag to the preset operating quantity value and use it as the maximum operating quantity value of the heating element, Heat_max, and enter the usage stage. Determine that the external power supply is a normal power supply. A normal power supply can meet any energy consumption requirements of the heating component. Therefore, increase the parameter Heat_flag to the preset operating quantity value and use it as the maximum operating quantity value of the heating element, Heat_max, to meet the user's usage requirements.

[0062] S105, increment the current parameter Heat_flag by 1, turn on an additional heating element, and jump to S107. The external power supply is a mobile power source, and it is necessary to detect the load capacity of the mobile power source. By increasing the number of heating elements turned on, the load requirement for the external power supply is increased, and then jump to S107 to determine the load capacity of the external power supply.

[0063] S106, if the external power supply is a portable overload shutdown type mobile power source, decrement the current parameter Heat_flag by 1, record it, and then jump to S112. It is determined that the external power supply is a portable overload shutdown type mobile power source, and running the heating elements corresponding to the current parameter Heat_flag simultaneously will cause the mobile power source to shut down due to overload. To ensure the normal operation of the heating component, it is necessary to decrement the current parameter Heat_flag by 1 and obtain the number of heating elements that can ensure the normal power supply of the portable overload shutdown type mobile power source.

[0064] S107, perform a voltage detection on the external power supply. When the power supply input voltage remains stable, jump to S108; when the power supply input voltage is turned off, jump to S101. When the power supply input voltage remains stable, it indicates that the external power supply can stably maintain the normal operation of the heating elements corresponding to the current parameter Heat_flag, and then jump to S108. When the power supply input voltage is turned off, it indicates that the external power supply can no longer maintain the normal operation of the heating elements corresponding to the current parameter Heat_flag, which means that the energy supply limit of the external power supply has been reached. End the detection of the external power supply type and energy supply limit. Reconnect and start the external power supply by jumping to S101, and use a parameter Heat_flag greater than 1 in S103 to jump to S106, and then prepare to enter the usage stage through S112.

[0065] S108, determine the current parameter Heat_flag. When the parameter Heat_flag reaches the preset operating quantity value, jump to S109; when the parameter Heat_flag does not reach the preset operating quantity value, jump to S110. Since the user has set a preset operating quantity value, when the parameter Heat_flag reaches the preset operating quantity value, it indicates that the external power supply can meet the user's usage requirements, and there is no need to detect the energy supply limit of the external power supply. When the parameter Heat_flag does not reach the preset operating quantity value, it means that neither the energy supply limit of the external power supply has been detected nor the user's expected usage requirements have been met. Jump to S110 to continue the subsequent detection.

[0066] S109. The external power supply is a portable mobile power supply and can normally supply power to the heating elements with the preset number of operating units. Jump to S112. Since the parameter Heat_flag reaches the preset number of operating units, it indicates that the external power supply can already meet the user's usage expectations. The detection of the type of external power supply and the upper limit of energy supply is ended, and S112 is used to prepare for entering the usage stage.

[0067] S110. Detect the output current. When the output current changes in proportion to the parameter Heat_flag, jump to S105. When the output current does not change in proportion to the parameter Heat_flag, jump to S111. The output current of the external power supply is proportional to the number of loaded heating elements. When the output current changes in proportion to the parameter Heat_flag, it indicates that the number of loaded heating elements has not reached the load upper limit of the external power supply at this time. Jump to S105 to detect the load upper limit of the external power supply by gradually increasing the number of loaded heating elements. When the output current does not change in proportion to the parameter Heat_flag, it indicates that the external power supply has reached the output power upper limit. The detection of the type of external power supply and the upper limit of energy supply is ended, and jump to S111 to prepare for entering the usage stage.

[0068] S111. The external power supply is a portable mobile power supply with overloaded maximum power output. Decrease the current parameter Heat_flag by 1, record it, and then jump to S112. Since the external power supply can still deliver current when it reaches the output upper limit, it is determined that the external power supply is a portable mobile power supply with overloaded maximum power output. And running the heating elements with the current parameter Heat_flag simultaneously will cause potential safety hazards due to overload of the mobile power supply, and each heating element cannot obtain the electric energy required for operation. To ensure the normal operation of the heating component, it is necessary to decrease the current parameter Heat_flag by 1 and obtain the number of heating elements that can ensure the normal power supply of the portable mobile power supply with overloaded maximum power output.

[0069] S112. Use the current parameter Heat_flag as the maximum number of heating elements in operation value Heat_max, enter the usage stage, turn on the mattress heating function, turn on the sleep sensing belt for sleep monitoring, obtain the user's physiological state data, and jump to S113.

[0070] S113. When the user is awake, set Heat_flag according to the latest saved maximum number of heating elements in operation value Heat_max, turn on the intelligent heating operation, and jump to S114;

[0071] S114, determine the heart rate data HR and body movement frequency data BMF using the heart rate threshold HR_TH and body movement frequency threshold BMF_TH respectively. When HR < HR_TH and BMF < BMF_TH, it indicates that the user enters the light sleep state and jumps to S115. Otherwise, it indicates that the user is still in the awake state and jumps to S113;

[0072] S115, when the user is in the light sleep state, adjust Heat_flag through the sleep assistance algorithm, perform intelligent control on the waist heating pad and leg heating pad, and guide the user to enter the deep sleep state faster. After completing the adjustment of the parameter Heat_flag, jump to S116;

[0073] S116, determine the respiratory variability data BV and body movement intensity data BMI using the respiratory variability threshold BV_TH and body movement intensity threshold BMI_TH respectively. When BV < BV_TH and BMI < BMI_TH, it indicates that the user enters the deep sleep state and jumps to S117. Otherwise, it indicates that the user is still in the light sleep state and jumps to S115;

[0074] S117, when the user is in the deep sleep state, perform intelligent control on the leg heating pad and waist heating pad to help the user maintain the deep sleep state and jump to S118;

[0075] S118, determine whether the user has set an alarm before going to bed. If an alarm is set, jump to S120. If no alarm is set, jump to S119;

[0076] S119, when any of the following situations occurs: the running time of the heating component reaches the set total heating duration T, the heating switch of the mattress is turned off, and the power is disconnected, jump to S122. Otherwise, jump to S117;

[0077] S120, determine whether the set heating duration is greater than the set alarm time. If it is, jump to S121. If not, jump to S119;

[0078] S121, when any of the following situations occurs: the time reaches the set alarm time, the heating switch of the mattress is turned off, and the power is disconnected, jump to S122. Otherwise, jump to S117;

[0079] S122, set the parameter Heat_flag to 1, turn off the heating component, and end the intelligent operation.

[0080] The specific features and effects of the method described in this embodiment are the same as those in Embodiment 1 and will not be elaborated here.

[0081] Embodiment 3:

[0082] Compared with Embodiment 1, this embodiment provides a system.

[0083] As Figure 3 shown, a system includes a detection module, a control module, a storage module, and a heating module. Each module cooperates with each other and provides an intelligent heating experience for users according to the method. The control module collects data through the detection module and provides a heating function for users through the heating module.

[0084] In this embodiment, the detection module collects real-time physiological data of users and transmits it to the control module. The monitored physiological data includes heart rate data HR, body movement frequency data BMF, respiratory variability data BV, and body movement intensity data BMI. It not only has the characteristics of convenient monitoring, but also can provide data support for sleep state determination and calculation of the real-time operation quantity Heat_flag of the heating sheet.

[0085] In this embodiment, the control module receives real-time physiological data from the detection module, determines the sleep state of the user, calculates and adjusts the parameter Heat_N, and controls the operation of the heating component module. The control module is used to formulate the operation control scheme of the heating module, and ensure that the operation control scheme not only needs to consider the heating expected demand given by the user to effectively improve the heating experience, but also needs to consider the load capacity of the external power supply to ensure receiving electrical energy from various external power supplies and ensuring the safety of the external power supply, and also needs to provide comfortable heating services for users, and is used to guide the user to transform into a deep sleep state and improve sleep quality.

[0086] In this embodiment, the storage module is used to store the operation program in the control module, real-time physiological data from the detection module, and parameters and instructions generated during the operation of the program. The parameters include but are not limited to the parameter Heat_N and various physiological data and corresponding thresholds.

[0087] In this embodiment, the heating module includes a number of independently arranged heating units. The heating units receive instructions from the control module and operate independently.

[0088] The specific features and effects of the method in this embodiment are the same as those in Embodiment 1 and will not be elaborated here.

[0089] Embodiment 4:

[0090] Compared with Embodiment 1, this embodiment provides a mattress.

[0091] As Figure 4 shown, a mattress includes a mattress body 1. A heating component is arranged on the top of the mattress body 1. The heating component includes a waist heating area 3 and a leg heating area 4 formed by splicing a number of heating sheets 2. The heating component can operate intelligently based on the method to ensure that the heating pad can provide comfortable heating services for users.

[0092] In this embodiment, since the real-time running quantity Heat_flag of the heating sheet 2 is a variable, when Heat_flag is an even number, the heating sheet 2 running quantities of the waist heating area 3 and the leg heating area 4 are the same. When Heat_flag is an odd number, the heating sheet 2 running quantity of the leg heating area 4 is 1 more than that of the waist heating area 3, ensuring that the leg heating area 4 obtains power supply prior to the waist heating area 3, thereby providing more heat to the legs.

[0093] In this embodiment, a support pad 5 is provided below the pad body 1, and the pad body 1 completely covers the top surface of the support pad 5. The pad body 1 is detachably arranged on the support pad 5, which can not only be used in combination with the support pad 5, but also be disassembled and carried out for use when going out, effectively expanding the usage scenarios of the pad body 1 and improving the usage experience. The pad body 1 is stacked above the support pad 5 and is wrapped and positioned by an outer cover 6.

[0094] The specific features and effects of the method in this embodiment are the same as those in Embodiment 1 and will not be elaborated here.

Claims

1. An intelligent heating method based on the user's sleep state, which is used for a mattress. The mattress includes a heating component with built-in heating sheets and a power supply for powering the heating component. The power supply includes a portable power supply. It is characterized in that Data collection: Collect the user's real-time physiological data to obtain the maximum number of heating sheets Heat_max that can be supported within the rated power of the power supply. During use, determine the user's sleep state according to the user's real-time physiological data, and the heating component adjusts its operating state according to the changing real-time number of operating heating sheets Heat_flag: In the awake state, set Heat_flag to Heat_max to guide the user to transition to the light sleep state through rapid heating. In the light sleep state, adjust Heat_flag through a sleep aid algorithm to guide the user to transition from the light sleep state to the deep sleep state. In the deep sleep state, set Heat_flag ≤ 2 to keep the user in the deep sleep state.

2. The intelligent heating method based on the user's sleeping state according to claim 1, characterized in that: The real-time physiological data includes heart rate data HR and body movement frequency data BMF. Summarize the real-time physiological data within a preset duration in the awake state to obtain initial heart rate data HR1 and initial body movement frequency data BMF1, and calculate the heart rate threshold HR_TH and body movement frequency threshold BMF_TH for judging that the user enters the light sleep state. When HR < HR_TH and BMF < BMF_TH, the user enters the light sleep state.

3. The intelligent heating method based on the user's sleeping state according to claim 2 is characterized in that: HR_TH = K * HR1 + △HR, where K is an adjustment coefficient, K = 0.85 - 0.95, and △HR is a dynamically adjusted value obtained according to the user's historical heart rate data, with an initial value of 0; or BMF_TH = L * BMF1 + △BMF, L is an adjustment coefficient, L = 0.3 - 0.6, and △BMF is a dynamically adjusted value obtained according to the user's historical body movement frequency data, with an initial value of 0.

4. The intelligent heating method based on the user's sleeping state according to claim 2 is characterized in that: The sleep aid algorithm adjusts the parameter Heat_flag in real time according to the changes in the real-time physiological data obtained. Among them, w1 is the weight coefficient of heart rate, w2 is the weight coefficient of body movement frequency, if Heat_flag is a decimal, it is rounded off.

5. The intelligent heating method based on the user's sleeping state according to claim 1, characterized in that: The real-time physiological data includes respiratory variability data BV and body movement intensity data BMI. Summarize the real-time physiological data within a preset duration in the awake state to obtain initial respiratory variability data BV1 and initial body movement intensity data BMI1, and calculate the respiratory variability threshold BV_TH and body movement intensity threshold BMI_TH for judging that the user enters the deep sleep state. When BV < BV_TH and BMI < BMI_TH, the user enters the deep sleep state.

6. The intelligent heating method based on the user's sleeping state according to claim 5, characterized in that: BV_TH = M * BV1 + △BV, where M is an adjustment coefficient, M = 0.2 - 0.5, and △BV is a dynamically adjusted value obtained according to the user's historical respiratory variability data, with an initial value of 0; or BMI_TH = N * BMI1 + △BMI, N is an adjustment coefficient, N = 0.2 - 0.5, and △BMI is a dynamically adjusted value obtained according to the user's historical body movement intensity data, with an initial value of 0.

7. An intelligent heating method based on user's sleeping state according to any one of claims 1 to 6, characterized in that: Set the total heating duration T. When the single cumulative operating duration of the heating component reaches T, turn off the heating component, and set Heat_flag to 1.

8. The intelligent heating method based on the user's sleeping state according to claim 7, characterized in that: Set the alarm time. When the heating component reaches the total heating duration T or reaches the alarm time, turn off the heating component, and set Heat_flag to 1.

9. A system for the method according to any one of claims 1 to 8, characterized in that: It includes: The detection module collects real-time physiological data of the user and transmits it to the control module; The control module receives the real-time physiological data from the detection module, determines the user's sleep state and calculates and adjusts the parameter Heat_N, and controls the operation of the heating component module; A storage module, used to store the operating program in the control module, the real-time physiological data from the detection module, and the parameters and instructions generated when the program is running; The heating module includes a plurality of independently arranged heating units, and the heating units receive instructions from the control module and operate independently.

10. A mattress using the method according to any one of claims 1 to 8, comprising a mattress body, characterized in that: A heating component is arranged on the top of the cushion body, and the heating component comprises a waist heating area and a leg heating area formed by splicing a plurality of heating sheets.

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