Adjusting control system of AI bed

Abstract

The invention relates to the technical field of AI bed adjustment and control, and discloses an AI bed adjustment and control system which comprises a data acquisition module, a data processing module and a data analysis module. According to the adjustment control system of the AI bed, a real-time temperature value, a real-time snore value, a real-time pressure value and a real-time heart rate value form corresponding real-time data sets through a data acquisition module, and the real-time snore data set and the real-time heart rate data set are preprocessed through a data processing module; the real-time snore data and the real-time heart rate data after cleaning are respectively obtained, the real-time snore data after cleaning, the real-time heart rate data after cleaning, the temperature data fluctuation value and the pressure data fluctuation value are respectively compared with corresponding threshold values or standard values, and judgment results are sent to the control module or the early warning module through a network. And according to the corresponding numerical value abnormal signals, control adjustment or early warning is carried out respectively, so that problems occurring in the use process of a user can be solved in time.

Description

Technical Field

[0001] The present invention relates to the technical field of AI bed adjustment and control, and in particular to an AI bed adjustment and control system. Background Art

[0002] The AI bed is a sleep product that incorporates artificial intelligence technology, designed to improve the user's sleep quality and experience through intelligent means. This technology integrates advanced sensors, algorithms, and data analysis to monitor and analyze the user's sleep patterns in real time, providing personalized sleep recommendations and adjustments to help users achieve better sleep quality.

[0003] The AI bed's control system integrates multiple sensors and intelligent algorithms to automatically adjust the mattress to meet the user's individual needs. The core of this system is real-time data monitoring, intelligent algorithm decision-making, automatic control and adjustment, and interactive feedback with the user. As technology advances, the control system of AI beds will become increasingly intelligent, better adapting to users' sleep habits and health needs.

[0004] Although AI beds have certain data analysis capabilities, due to the limitations of computing resources and algorithm complexity, their data processing accuracy and speed still have room for improvement. This may lead to lower accuracy of monitoring data and affect the system's adjustment effect. In addition, different users have different requirements for the softness, hardness, temperature, etc. of mattresses, and the current AI beds have limited adjustment capabilities in this regard. This may cause some users to be dissatisfied with the comfort of the mattress, affecting their sleep quality, and causing problems encountered during use to not be resolved in a timely manner, affecting their satisfaction. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an adjustment and control system for an AI bed, which compares the cleaned real-time snoring data SHSsj, the cleaned real-time heart rate data SXLsj, the temperature data fluctuation value WDbd, and the pressure data fluctuation value YLbdz with corresponding thresholds or standard values, determines whether an abnormality occurs, and sends the judgment result to the control module or the early warning module via the network. The control module or the early warning module performs control modulation or issues an early warning according to the corresponding abnormal signal, thereby ensuring that problems encountered by the user during use can be resolved in a timely manner, etc., thereby solving the above-mentioned problems.

[0007] (2) Technical solution

[0008] To achieve the above-mentioned object, the present invention provides the following technical solutions: an adjustment and control system for an AI bed, comprising a data acquisition module, a data processing module, a data analysis module, a control module, and an early warning module;

[0009] The data processing module includes a temperature data monitoring unit, a snoring data monitoring unit, a pressure data monitoring unit and a heart rate data monitoring unit;

[0010] The temperature data monitoring unit is used to collect the user's real-time temperature value WDz, the snoring data collection unit is used to collect the user's real-time snoring value HSz, the pressure data collection unit is used to collect the user's real-time pressure value YLz, and the heart rate data monitoring unit is used to collect the user's real-time heart rate value XLz;

[0011] The data acquisition module collects multiple real-time temperature values WDz, real-time snoring values HSz, real-time pressure values YLz, and real-time heart rate values XLz according to the time period to form a real-time temperature data set, a real-time snoring data set, a real-time pressure data set, and a real-time heart rate data set, respectively. The data acquisition module connects these data sets to the data processing module through a network;

[0012] The data processing module pre-processes the real-time snoring data set and the real-time heart rate data set to obtain cleaned real-time snoring data SHSsj and real-time heart rate data SXLsj, respectively. The data processing module calculates the temperature data fluctuation value WDbd and the pressure data fluctuation value YLbdz according to the algorithm formula, and sends these data to the data analysis module via the network;

[0013] The data analysis module is preset with a snoring data standard value HSbzz, a heart rate data standard value XLbzz, a temperature data fluctuation value threshold WDbdyz, and a pressure data fluctuation value threshold YLbdyz. The data analysis module compares the cleaned real-time snoring data SHSsj, the cleaned real-time heart rate data SXLsj, the temperature data fluctuation value WDbd, and the pressure data fluctuation value YLbdz with the snoring data standard value HSbzz, the heart rate data standard value XLbzz, the temperature data fluctuation value threshold WDbdyz, and the pressure data fluctuation value threshold YLbdyz, respectively, to determine whether an abnormality occurs, and sends the determination result to the control module or the early warning module via the network;

[0014] The control module is used to control the automatic adjustment of the cooling device or heating device of the AI bed, the control module is used to control the automatic adjustment of the mattress hardness and support points, and the control module is used to control the intelligent vibration reminder;

[0015] When the warning module receives the corresponding abnormal signal, the fault warning module sends a corresponding fault warning signal according to the corresponding abnormal signal, and feeds back the fault warning signal to the user.

[0016] Preferably, the real-time temperature data set expression is {WDz1, WDz2, WDz3, ..., WDz n}, the real-time snoring data set is expressed as {HSz1, HSz2, HSz3, ..., HSz n}, the real-time pressure data set expression is {YLz1, YLz2, YLz3, ..., YLz n}, the real-time heart rate data set expression is {XLz1, XLz2, XLz3, ..., XLz n}, the number of data in each data set is the same, the collection period is the same, the time interval between each data in the data set is the same, and the subscript in each data set indicates that there are n data in each data set.

[0017] Preferably, the temperature data fluctuation value WDbd is calculated as follows:

[0018]

[0019] In the formula, WDz max Represents the highest temperature in the real-time temperature collection interval, EDz min Represents the lowest temperature in the real-time temperature collection interval. The n in the denominator represents that the data set has n real-time temperature values. max -EDz min Represents the difference between the highest and lowest temperatures in the data set. The temperature data fluctuation value WDbd is calculated by dividing the difference between the highest and lowest temperatures in the data set by the total number of data sets.

[0020] Preferably, the pressure data fluctuation value YLbdz is calculated as follows:

[0021]

[0022] In the formula, n represents the number of real-time pressure values in the data set. i=1 means starting from the first value in the real-time pressure data set, i.e., YLz1, and n means ending with the nth value in the data set, i.e., YLz n Finish, Represents the square of the difference between each data point and the average value in the collection interval, Represents the sum of the squared differences between each data point and the mean value in the collection interval. Represents the mean value of pressure data fluctuation in the acquisition interval.

[0023] Preferably, the calculation formula of the cleaned real-time snoring sound data SHSsj is as follows:

[0024]

[0025] In the formula, represents the mean of the real-time snoring data in the collection interval, n represents the number of real-time snoring values in the data set, HSz n Represents the last real-time snoring value collected in the real-time data set. Represents the absolute deviation between each real-time snoring value and the average value during the collection period. It represents the sum of the absolute deviations between each real-time snoring value and the average value in the collection interval. The cleaned real-time snoring data SHSsj is calculated by dividing the total absolute deviation by the total number of real-time snoring data sets.

[0026] Preferably, the calculation formula of the cleaned real-time heart rate data SXLsj is as follows:

[0027]

[0028] In the formula, middle, Represents the absolute deviation between each real-time heart rate value and the average heart rate in the collection interval. i=1 means starting from the first real-time heart rate value in the real-time heart rate data set, i.e., XLz1. n means ending with the nth real-time heart rate value in the data set, i.e., XLz n Finish, Represents the sum of all absolute deviation values in the collection interval. The cleaned real-time heart rate data SXLsj is calculated by dividing the sum of all absolute deviation values in the collection interval by the total number of collection intervals.

[0029] Preferably, the data analysis module compares the cleaned real-time snoring data SHSsj with the snoring data standard value HSbzz. If the real-time snoring data SHSsj exceeds the snoring data standard value HSbzz, it means that the snoring value is abnormal, and the data analysis module sends an intelligent vibration reminder instruction to the control module.

[0030] Preferably, the data analysis module compares the temperature data fluctuation value WDbd with the temperature data fluctuation value threshold WDbdyz. When the temperature data fluctuation value WDbd exceeds the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal, and the data analysis module issues an instruction to the control module to automatically adjust the cooling equipment of the AI bed. When the temperature data fluctuation value WDbd is lower than the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal, and the data analysis module issues an instruction to the control module to automatically adjust the heating equipment of the AI bed.

[0031] Preferably, the data analysis compares the cleaned real-time heart rate data SXLsj with the heart rate data standard value XLbzz. If the cleaned real-time heart rate data SXLsj exceeds the heart rate data standard value XLbzz, it means that the user's heart rate value is abnormal. The data analysis module sends a heart rate data abnormality signal to the early warning module and feeds back to the user.

[0032] Preferably, the data analysis module compares the pressure data fluctuation value YLbdz with the pressure data fluctuation value threshold YLbdyz. When the pressure data fluctuation value YLbdz exceeds the pressure data fluctuation value threshold YLbdyz, it indicates that the pressure value is abnormal. The data output module sends an instruction to the control module to automatically adjust the mattress hardness and support point.

[0033] Compared with the prior art, the present invention provides an adjustment and control system for an AI bed, which has the following beneficial effects:

[0034] 1. The data acquisition module collects multiple real-time temperature values and real-time pressure values periodically to form real-time temperature data sets and real-time pressure data sets respectively. The real-time temperature value is the body temperature of the human body on the AI bed collected in real time by the body temperature sensor. The real-time pressure value is the pressure intensity of different parts of the body on the AI bed collected by the pressure sensor inside the AI bed. The data processing module calculates the temperature data fluctuation value and pressure data fluctuation value according to the algorithm formula, and then compares the temperature data fluctuation value and pressure data fluctuation value with the temperature data fluctuation value threshold and pressure data fluctuation value preset in the data analysis module to determine whether the temperature data fluctuation value or pressure data fluctuation value is abnormal. The control module adjusts and controls the AI bed in a timely manner according to the corresponding abnormal signal, so that the user can improve the sleep comfort and sleep quality during the experience of the AI bed.

[0035] 2. The data acquisition module collects multiple real-time snoring values and real-time heart rate values collected periodically to form a real-time snoring data set and a real-time heart rate data set respectively. The real-time snoring value uses the sound sensor inside the AI bed to capture the real-time snoring value of the user when sleeping. The data processing module pre-processes the collected real-time snoring data set to extract snoring and other breathing sounds or environmental noise to obtain the snoring signal during sleep. The real-time heart rate value uses the optical heart rate sensor inside the AI bed to monitor the heart rate. The data processing module pre-processes the collected heart rate data set to obtain heart rate and breathing information. The data analysis module According to the algorithm formula, real-time snoring data and real-time heart rate data are calculated respectively. The real-time snoring data and real-time heart rate data are compared with the standard snoring data values and heart rate data standard values preset in the data processing module respectively to determine whether the snoring value and heart rate value are abnormal. When the heart rate value is abnormal, the data analysis module sends a heart rate data abnormality signal to the early warning module and feeds back to the user, which can prevent and diagnose potential health problems early and provide users with accurate medical advice. If the snoring value is abnormal, the control module controls and adjusts the AI bed according to the abnormal snoring value signal, which can improve the user's sleep experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] See also Figure 1 , an AI bed adjustment and control system, including a data acquisition module, a data processing module, a data analysis module, a control module and an early warning module. The data processing module includes a temperature data monitoring unit, a snoring data monitoring unit, a pressure data monitoring unit and a heart rate data monitoring unit.

[0039] The temperature data monitoring unit is used to collect the user's real-time temperature value WDz, and monitor the user's body temperature changes in real time through the body temperature sensor. When the user's body temperature is detected to be high, the AI bed receives a signal of abnormal temperature value and automatically adjusts the cooling equipment of the AI bed. If the user's temperature is detected to be low, the AI bed automatically adjusts the heating equipment of the AI bed according to the abnormal temperature value signal, and can adjust the temperature of the AI bed to improve the user's sleep comfort. The snoring data collection unit is used to collect the user's real-time snoring value HSz. The snoring data is monitored by the sound sensor during sleep snoring, and after the collection is completed, the sound signal is cleaned and pre-processed to obtain the snoring signal. In the case of abnormal snoring signal, when the AI bed receives the abnormal snoring value signal, the AI bed will perform intelligent vibration reminder to remind the user to change sleeping posture or Adjust the user's sleep breathing and improve the user's sleep quality. The pressure data acquisition unit is used to collect the user's real-time pressure value YLz. Pressure sensors are evenly distributed inside the AI bed. It can monitor the pressure distribution of various parts of the body on the bed when the human body lies on the bed. When the AI receives an abnormal pressure value, the AI bed automatically adjusts the mattress hardness and support points to improve the user's sleep comfort. The heart rate data monitoring unit is used to collect the user's real-time heart rate value XLz and uses an optical heart rate sensor for heart rate monitoring. The data processing module pre-processes the collected heart rate data set to obtain heart rate and breathing information. When the heart rate value is abnormal, the data analysis module sends a heart rate data abnormality signal to the early warning module and feeds back to the user, which can prevent and diagnose potential health problems early and provide users with accurate medical advice.

[0040] The data acquisition module collects multiple real-time temperature values WDz, real-time snoring values HSz, real-time pressure values YLz, and real-time heart rate values XLz according to the time period to form a real-time temperature dataset, a real-time snoring dataset, a real-time pressure dataset, and a real-time heart rate dataset. It should be noted that there are several of these datasets, and the expressions of each dataset are as follows:

[0041] The real-time temperature data set expression is {WDz1, WDz2, WDz3, ..., WDz n};

[0042] The real-time snoring data set is expressed as {HSz1, HSz2, HSz3, ..., HSz n};

[0043] The real-time pressure data set expression is {YLz1, YLz2, YLz3, ..., YLz n};

[0044] The expression of real-time heart rate dataset is {XLz1, XLz2, XLz3, ..., XLz n};

[0045] In the above data sets, the number of data in each data set is the same, the acquisition period is the same, and the time interval of each data in the data set is the same. The subscript in each data set indicates that there are n data in each data set. Among them, the same number of data, the same acquisition period, and the same time interval of each data in the data set can ensure the consistency of the data, which is beneficial to subsequent processing and analysis. At the same time, no additional data alignment processing is required, thereby simplifying data processing.

[0046] The data collection module sends these data sets to the data processing module through the network.

[0047] The data processing module preprocesses the real-time snoring data set to obtain the cleaned real-time snoring data SHSsj. The calculation formula for the cleaned real-time snoring data SHSsj is as follows:

[0048]

[0049] In the formula, Represents the mean of the real-time snoring data in the collection interval, N represents the number of real-time snoring values in the data set, HSz n Represents the last real-time snoring value collected in the real-time data set. Represents the absolute deviation between each real-time snoring value and the average value during the collection period. It represents the sum of the absolute deviations between each real-time snoring value and the average value in the collection interval. The cleaned real-time snoring data SHSsj is calculated by dividing the total absolute deviation by the total number of real-time snoring data sets.

[0050] The data processing module preprocesses the real-time heart rate data set to obtain the cleaned real-time heart rate data SXLsj. The calculation formula for the cleaned real-time heart rate data SXLsj is as follows:

[0051]

[0052] In the formula, middle, Represents the absolute deviation between each real-time heart rate value and the average heart rate in the collection interval. i=1 means starting from the first real-time heart rate value in the real-time heart rate data set, i.e., XLz1. n means ending with the nth real-time heart rate value in the data set, i.e., XLz n Finish, Represents the sum of all absolute deviation values in the collection interval. The cleaned real-time heart rate data SXLsj is calculated by dividing the sum of all absolute deviation values in the collection interval by the total number of collection intervals.

[0053] The data processing module calculates the temperature data fluctuation value WDbd according to the algorithm formula. The calculation formula of the temperature data fluctuation value WDbd is as follows:

[0054]

[0055] In the formula, WDz max Represents the highest temperature in the real-time temperature collection interval, WDz min Represents the lowest temperature in the real-time temperature collection interval. The n in the denominator represents that the data set has n real-time temperature values. max -WDz min Represents the difference between the highest and lowest temperatures in the data set. The temperature data fluctuation value WDbd is calculated by dividing the difference between the highest and lowest temperatures in the data set by the total number of data sets.

[0056] The data processing module calculates the pressure data fluctuation value YLbdz according to the algorithm formula. The calculation formula of the pressure data fluctuation value YLbdz is as follows:

[0057]

[0058] In the formula, n represents the number of real-time pressure values in the data set. i=1 means starting from the first value in the real-time pressure data set, i.e., YLz1, and n means ending with the nth value in the data set, i.e., YLz n Finish, Represents the square of the difference between each data point and the average value in the collection interval, Represents the sum of the squared differences between each data point and the mean value in the collection interval. Represents the mean value of pressure data fluctuation in the acquisition interval.

[0059] The data processing module sends these data to the data analysis module through the network.

[0060] The data analysis module is preset with the snoring data standard value HSbzz, the heart rate data standard value XLbzz, the temperature data fluctuation value threshold WDbdyz and the pressure data fluctuation value threshold YLbdyz. The data analysis module compares the cleaned real-time snoring data SHSsj, the cleaned real-time heart rate data SXLsj, the temperature data fluctuation value WDbd and the pressure data fluctuation value YLbdz with the snoring data standard value HSbzz, the heart rate data standard value XLbzz, the temperature data fluctuation value threshold WDbdyz and the pressure data fluctuation value threshold YLbdyz respectively.

[0061] When the real-time snoring data SHSsj exceeds the snoring data standard value HSbzz, it means that the snoring value is abnormal. The data analysis module sends an intelligent vibration reminder instruction to the control module. The user changes the sleeping position according to the vibration reminder, reducing sleep interruptions caused by snoring and improving the sleeping experience.

[0062] When the temperature data fluctuation value WDbd exceeds the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal. The data analysis module sends an instruction to the control module to automatically adjust the cooling equipment of the AI bed. When the temperature data fluctuation value WDbd is lower than the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal. The data analysis module sends an instruction to the control module to automatically adjust the heating equipment of the AI bed. Automatically adjusting the temperature of the AI bed can create a personalized sleeping environment for the user, help to speed up falling asleep and improve sleep quality.

[0063] When the real-time heart rate data SXLsj after cleaning exceeds the heart rate data standard value XLbzz, it means that the user's heart rate value is abnormal. The data analysis module sends a heart rate data abnormality signal to the early warning module and feeds back to the user, which can prevent and diagnose potential health problems early and provide users with accurate medical advice.

[0064] This system uses the data acquisition module to form a real-time temperature data set and a real-time pressure data set respectively from multiple real-time temperature values and real-time pressure values collected in a periodic manner. The real-time temperature value is the real-time collection of the human body temperature on the AI bed by the body temperature sensor. The real-time pressure value is the pressure intensity of different parts of the body on the AI bed collected by the pressure sensor inside the AI bed. The data processing module calculates the temperature data fluctuation value and the pressure data fluctuation value respectively according to the algorithm formula, and then compares the temperature data fluctuation value and the pressure data fluctuation value with the temperature data fluctuation value threshold and pressure data fluctuation value pressure value preset in the data analysis module to determine whether the temperature data fluctuation value or the pressure data fluctuation value is abnormal. The control module adjusts and controls the AI bed in time according to the corresponding abnormal signal, so that the user can improve the sleep comfort and sleep quality in the process of experiencing the AI bed. The data acquisition module forms a real-time snoring data set and a real-time heart rate data set respectively from multiple real-time snoring values and real-time heart rate values collected in a periodic manner. The real-time snoring value is calculated by using the internal AI bed The AI bed's sound sensor captures the user's real-time snoring sound value while sleeping. The data processing module pre-processes the collected real-time snoring sound data set, extracts the snoring sound from other breathing sounds or environmental noise, and obtains the snoring signal during sleep. The real-time heart rate value is monitored by the optical heart rate sensor inside the AI bed. The data processing module pre-processes the collected heart rate data set to obtain heart rate and breathing information. The data analysis module calculates the real-time snoring data and real-time heart rate data respectively according to the algorithm formula. The real-time snoring data and real-time heart rate data are compared with the preset snoring data standard values and heart rate data standard values inside the data processing module to determine whether the snoring value and heart rate value are abnormal. When the heart rate value is abnormal, the data analysis module sends a heart rate data abnormality signal to the early warning module and feedback to the user, which can prevent and diagnose potential health problems early and provide users with accurate medical advice. If the snoring value is abnormal, the control module controls and adjusts the AI bed according to the snoring value abnormality signal, which can improve the user's sleep experience.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An adjustment and control system for an AI bed, characterized by: It includes data acquisition module, data processing module, data analysis module, control module and early warning module; The data processing module includes a temperature data monitoring unit, a snoring data monitoring unit, a pressure data monitoring unit and a heart rate data monitoring unit; The temperature data monitoring unit is used to collect the user's real-time temperature value WDz, the snoring data collection unit is used to collect the user's real-time snoring value HSz, the pressure data collection unit is used to collect the user's real-time pressure value YLz, and the heart rate data monitoring unit is used to collect the user's real-time heart rate value Xlz; The data acquisition module collects multiple real-time temperature values WDz, real-time snoring values HSz, real-time pressure values YLz, and real-time heart rate values XLz according to the time period to form a real-time temperature data set, a real-time snoring data set, a real-time pressure data set, and a real-time heart rate data set, respectively. The data acquisition module connects these data sets to the data processing module through a network; The data processing module pre-processes the real-time snoring data set and the real-time heart rate data set to obtain cleaned real-time snoring data SHSsj and real-time heart rate data SXLsj, respectively. The data processing module calculates the temperature data fluctuation value WDbd and the pressure data fluctuation value YLbdz according to the algorithm formula, and sends these data to the data analysis module via the network; The data analysis module is preset with a snoring data standard value HSbzz, a heart rate data standard value XLbzz, a temperature data fluctuation value threshold WDbdyz, and a pressure data fluctuation value threshold YLbdyz. The data analysis module compares the cleaned real-time snoring data SHSsj, the cleaned real-time heart rate data SXLsj, the temperature data fluctuation value WDbd, and the pressure data fluctuation value YLbdz with the snoring data standard value HSbzz, the heart rate data standard value XLbzz, the temperature data fluctuation value threshold WDbdyz, and the pressure data fluctuation value threshold YLbdyz, respectively, to determine whether an abnormality occurs, and sends the determination result to the control module or the early warning module via the network; The control module is used to control the automatic adjustment of the cooling device or heating device of the AI bed, the control module is used to control the automatic adjustment of the mattress hardness and support points, and the control module is used to control the intelligent vibration reminder; When the warning module receives the corresponding abnormal signal, the fault warning module sends a corresponding fault warning signal according to the corresponding abnormal signal, and feeds back the fault warning signal to the user.

2. The AI bed adjustment and control system according to claim 1, characterized in that: The real-time temperature data set expression is {WDz1, WDz2, WDz3, ..., WDz n }, the real-time snoring data set is expressed as {HSz1, HSz2, HSz3, ..., HSz n }, the real-time pressure data set expression is {YLz1, YLz2, YLz3, ..., YLz n ], the real-time heart rate data set expression is {XLz1, XLz2, XLz3, ..., XLz n }, the number of data in each data set is the same, the collection period is the same, the time interval between each data in the data set is the same, and the subscript in each data set indicates that there are n data in each data set.

3. The AI bed adjustment and control system according to claim 2, characterized in that: The calculation formula of the temperature data fluctuation value WDbd is as follows: In the formula, WDz max Represents the highest temperature in the real-time temperature collection interval, WDz min Represents the lowest temperature in the real-time temperature collection interval. The n in the denominator represents that the data set has n real-time temperature values. max -WDz min Represents the difference between the highest and lowest temperatures in the data set. The temperature data fluctuation value WDbd is calculated by dividing the difference between the highest and lowest temperatures in the data set by the total number of data sets.

4. The AI bed adjustment and control system according to claim 3, characterized in that: The calculation formula of the pressure data fluctuation value YLbdz is as follows: In the formula, n represents the number of real-time pressure values in the data set. i=1 means starting from the first value in the real-time pressure data set, i.e., YLz1, and n means ending with the nth value in the data set, i.e., YLz n Finish, Represents the square of the difference between each data point and the average value in the collection interval, Represents the sum of the squared differences between each data point and the mean value in the collection interval. Represents the mean value of pressure data fluctuation in the acquisition interval.

5. The AI bed adjustment and control system according to claim 4, characterized in that: The calculation formula of the cleaned real-time snoring sound data SHSsj is as follows: In the formula, represents the mean of the real-time snoring data in the collection interval, n represents the number of real-time snoring values in the data set, HSz n Represents the last real-time snoring value collected in the real-time data set. Represents the absolute deviation between each real-time snoring value and the average value during the collection period. It represents the sum of the absolute deviations between each real-time snoring value and the average value in the collection interval. The cleaned real-time snoring data SHSsj is calculated by dividing the total absolute deviation by the total number of real-time snoring data sets.

6. The AI bed adjustment and control system according to claim 5, characterized in that: The calculation formula of the cleaned real-time heart rate data SXLsj is as follows: In the formula, middle, Represents the absolute deviation between each real-time heart rate value and the average heart rate in the collection interval. i=1 means starting from the first real-time heart rate value in the real-time heart rate data set, i.e., XLz1. n means ending with the nth real-time heart rate value in the data set, i.e., XLz n Finish, Represents the sum of all absolute deviation values in the collection interval. The cleaned real-time heart rate data SXLsj is calculated by dividing the sum of all absolute deviation values in the collection interval by the total number of collection intervals.

7. The AI bed adjustment and control system according to claim 6, characterized in that: The data analysis module compares the cleaned real-time snoring data SHSsj with the snoring data standard value HSbzz. If the real-time snoring data SHSsj exceeds the snoring data standard value HSbzz, it means that the snoring value is abnormal, and the data analysis module sends an intelligent vibration reminder instruction to the control module.

8. The AI bed adjustment and control system according to claim 7, characterized in that: The data analysis module compares the temperature data fluctuation value WDbd with the temperature data fluctuation value threshold WDbdyz. When the temperature data fluctuation value WDbd exceeds the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal. The data analysis module issues an instruction to the control module to automatically adjust the cooling equipment of the AI bed. When the temperature data fluctuation value WDbd is lower than the temperature data fluctuation value threshold WDbdyz, it indicates that the temperature value is abnormal. The data analysis module issues an instruction to the control module to automatically adjust the heating equipment of the AI bed.

9. The AI bed adjustment and control system according to claim 8, characterized in that: The data analysis module compares the cleaned real-time heart rate data SXLsj with the heart rate data standard value XLbzz. If the cleaned real-time heart rate data SXLsj exceeds the heart rate data standard value XLbzz, it means that the user's heart rate value is abnormal. The data analysis module sends a heart rate data abnormality signal to the early warning module and feeds back to the user.

10. The AI bed adjustment and control system according to claim 9, characterized in that: The data analysis module compares the pressure data fluctuation value YLbdz with the pressure data fluctuation value threshold YLbdyz. When the pressure data fluctuation value YLbdz exceeds the pressure data fluctuation value threshold YLbdyz, it means that the pressure value is abnormal. The data output module sends an instruction to the control module to automatically adjust the mattress hardness and support point.

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