Health monitoring management system and method based on intelligent bathtub

Abstract

The invention discloses a health monitoring management system and method based on an intelligent bathtub, and the method comprises the steps: when a user uses the intelligent bathtub, the system automatically starts a health monitoring module, collects the physiological data of body temperature, heart rate and blood pressure, analyzes the data through a data processing unit, combines historical health records and personal information, and carries out the health monitoring management. Generating personalized health assessment and suggestions; after the result is analyzed, the intelligent environment adjusting module automatically adjusts the water temperature and the water flow intensity of the bathtub; health data and suggestions are uploaded to the cloud through encryption, the system further provides remote monitoring and fault diagnosis functions, and the standby mode is automatically switched when a fault occurs; in addition, based on the health trend, the system periodically generates a health report and pushes personalized exercise, diet and work and rest suggestions; according to the health monitoring management system and method based on the intelligent bathtub, the existing defects are overcome by optimizing the monitoring technology, enhancing data safety, introducing a personalized analysis algorithm and adopting multiple backup and fault-tolerant mechanisms.

Description

Technical Field

[0001] The present invention belongs to the field of health monitoring and management based on intelligent bathtubs, and in particular relates to a health monitoring and management system and method based on intelligent bathtubs. Background Art

[0002] A smart bathtub-based health monitoring and management system integrates health monitoring functions into the bathtub using sensors, smart hardware, data analysis, and IoT technology. This allows for real-time monitoring and management of the user's physical condition. Smart sensors monitor the user's body temperature, heart rate, blood pressure, and other physiological indicators. By connecting to smart devices (such as smartwatches or mobile phones), the system analyzes the user's health data and provides personalized health advice or warnings. For example, if the user's heart rate or body temperature is abnormal, the system can automatically alert or directly contact a medical professional. Users can control the bathtub's temperature, massage mode, water flow intensity, and other functions through their mobile phone or voice assistant. The system also intelligently adjusts the water temperature based on the user's personal health data, such as automatically adjusting the water temperature to suit their physical condition. The system can provide customized health management recommendations based on the user's health data (such as age, gender, and health history), and help users develop healthy lifestyle habits. Through IoT technology, health data can be uploaded to the cloud, where users or doctors can remotely view and analyze the data. This is particularly important for patients with chronic diseases or the elderly, as it can help identify potential health issues in a timely manner.

[0003] However, although the health monitoring and management system based on smart bathtubs has many advantages, it also has some potential defects or limitations. The current smart bathtub health monitoring system generally relies on sensors to collect users' health data, such as body temperature, heart rate, blood pressure, etc. The existing system lacks personalized health analysis and cannot provide in-depth health management services. Users have low trust in smart technology, and equipment maintenance and updates are not timely. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a health monitoring and management system and method based on an intelligent bathtub, which solves the shortcomings of the existing technology by optimizing health data monitoring technology, enhancing data security, introducing personalized health analysis algorithms, and improving system reliability through multiple backup and fault-tolerant mechanisms.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A health monitoring and management system based on an intelligent bathtub, including:

[0007] Health monitoring module: Integrates multiple sensors to monitor the user's physical health in real time and generate multi-dimensional health data;

[0008] Data processing unit module: Based on advanced machine learning algorithms, it combines the user's historical health data, lifestyle habits, and environmental factors to conduct personalized health assessments;

[0009] Intelligent environment adjustment module: automatically adjusts the bathtub water temperature, water flow intensity and water quality based on health monitoring data and personalized health recommendations;

[0010] Data encryption and privacy protection module: uses end-to-end encryption technology to ensure the security of users' health data during transmission and storage;

[0011] Cloud data synchronization module: synchronizes health data and personalized recommendations to the cloud, allowing users to access historical records and health trends at any time through mobile devices;

[0012] Fault diagnosis and system fault tolerance mechanism module: The system adopts fault self-diagnosis and automatic repair functions, automatically switching to the backup system when the sensor or hardware fails to ensure uninterrupted health monitoring.

[0013] A health monitoring and management method based on an intelligent bathtub includes the following steps:

[0014] Step 1: When the user uses the smart bathtub, the system automatically activates the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters;

[0015] Step 2: The data processing unit analyzes the collected physiological data and combines it with the user's historical health records and personal information to generate personalized health assessments and recommendations;

[0016] Step 3: Based on the analysis results, the intelligent environment adjustment module automatically adjusts the water temperature and water flow intensity of the bathtub;

[0017] Step 4: Upload health data and personalized recommendations to the cloud in an encrypted manner;

[0018] Step 5: The system provides remote monitoring and fault diagnosis functions. In the event of sensor or hardware failure, the system can automatically switch to backup mode.

[0019] Step 6: Generate health reports regularly based on the user's health trends and push personalized exercise, diet, and lifestyle recommendations.

[0020] As a preferred method, when the user uses the smart bathtub, the system automatically starts the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters as follows:

[0021] Body temperature is usually monitored using thermocouples, infrared sensors, or temperature sensors;

[0022] Assume that the sensor returns the original voltage signal V of the temperature sensorraw , the temperature is calculated by the following formula:

[0023]

[0024] Where: T is the measured body temperature;

[0025] V raw The raw voltage value returned by the sensor;

[0026] V0 is the reference voltage value of the sensor;

[0027] S is the sensitivity of the sensor;

[0028] Heart rate is measured using photoplethysmography or an electrocardiogram sensor. Assuming the pulse wave signal collected by the sensor is P(t), where t is time, the peak interval of the pulse wave is detected using the autocorrelation function or the periodicity of the signal. The heart rate formula is:

[0029]

[0030] Where HR is heart rate;

[0031] T p is the pulse wave period, that is, the time interval from one pulse wave peak to the next pulse wave peak;

[0032] Blood pressure monitoring is carried out by combining airbags, sensors and algorithms. Assume that the pulse wave propagation speed υ is measured by the sensor. PW , combined with the user's age and weight information, blood pressure is estimated using the following formula:

[0033]

[0034] Among them, SBP is systolic blood pressure;

[0035] DBP is diastolic blood pressure;

[0036] υ PW is the pulse wave velocity;

[0037] R1 and R2 are coefficients estimated based on individual characteristics;

[0038] C1 and C2 are constants used to calibrate the formula;

[0039] These physiological parameters of body temperature, heart rate, and blood pressure are fused and further analyzed by the data processing unit. The real-time data collected by the sensors is input into a data fusion model to conduct multi-dimensional health status analysis;

[0040] Assuming X is a comprehensive health assessment indicator, the following weighted average model is used for calculation:

[0041] X=w1·T+w2·HR+w3·BP

[0042] Where X is the comprehensive health assessment score

[0043] T is the body temperature parameter;

[0044] HR is heart rate;

[0045] BP is blood pressure;

[0046] w1, w2, and w3 are weight coefficients representing body temperature, heart rate, and blood pressure, respectively, and are usually adjusted based on the user's health data or personalized age information.

[0047] Preferably, the method for analyzing the collected physiological data by the data processing unit and combining the user's historical health records and personal information to generate personalized health assessment and suggestions is as follows:

[0048] The model for personalized health assessment and recommendations consists of data collection, data preprocessing, physiological data analysis, personalized assessment, and health recommendation generation. Assuming there is a comprehensive health assessment index X, which combines body temperature T, heart rate HR, blood pressure, weight, and age factors, its calculation formula is:

[0049] X=w1·T+w2·HR+w3·SBP+w4·DBP+w5·BMI+w6·Age+w7·HistoricalData

[0050] Where, T is body temperature;

[0051] HR is heart rate;

[0052] SBP and DBP are blood pressure;

[0053] BMI is body mass index, and the calculation formula is:

[0054]

[0055] Age is the user's age;

[0056] HistoricalData is the comprehensive evaluation value of historical health data, obtained through a machine learning model. w1, w2, w3, w4, w5, w6, and w7 are the weight coefficients of each health indicator, which are adjusted according to the user's specific situation.

[0057] Historical health data has a great impact on health assessment. Therefore, we use machine learning algorithms to estimate the health status of individuals. Assume that the regression model of historical health data is:

[0058] Y=β0+β1·X1+β2·X2+…+β n ·X n +∈

[0059] Among them, Y is the health assessment index;

[0060] X1, X2, ..., X n Characteristics of historical health records;

[0061] β0 is the intercept term, β1, β2, ..., β n is the regression coefficient, which is obtained by training historical data;

[0062] ∈ is the error term;

[0063] Use personalized health assessment indicators to further calculate health risks, for the risk of common diseases such as heart disease. Assume that the heart disease risk score is calculated through historical data and physiological data, R heart , the formula is:

[0064] R heart =α1·SBP+α2·DBP+α3·HR+α4·BMI+α5·Age+α6·FamilyHistory

[0065] Among them, SBP, DBP, HR, BMI, and Age are basic health indicators;

[0066] FamilyHistory is the influence of the user's family medical history, which is a binary variable;

[0067] α1, α2, ..., α6 are regression coefficients obtained through training;

[0068] Based on the above health assessment and risk score, the system generates personalized health recommendations. Assuming that the comprehensive health assessment index X and the health risk score R are in a comprehensive health recommendation model, the following decision function is given:

[0069] S=f(X,R)=γ1·X-γ2·R

[0070] Among them, S is the health advice index;

[0071] X is the comprehensive health assessment index;

[0072] R is the health risk score;

[0073] γ1, γ2 are coefficients obtained through training.

[0074] Preferably, according to the analysis results, the intelligent environment adjustment module automatically adjusts the water temperature and water flow intensity of the bathtub in the following manner:

[0075] The goal of water temperature regulation is to set the bathtub water temperature based on the user's current body temperature, ambient temperature, and personal preferences. Assume that the target water temperature T target Calculated by the following formula:

[0076] T target =α1·T body +α2·T ambient +α3·H ambient +α4·P user +α5·T historieal

[0077] Among them, T body The user's current body temperature;

[0078] T ambicnt is the current ambient temperature of the bathroom;

[0079] H ambicnt is the current bathroom humidity;

[0080] P uscr For user personal preferences;

[0081] T historical The user's historical preferred water temperature;

[0082] α1, α2, α3, α4, and α5 are weight coefficients, which are determined based on user data and model training;

[0083] The adjustment of water flow intensity takes into account the user's current physical condition, ambient temperature and humidity, and the user's comfort preference. Assuming that the target water flow intensity F target Calculated by the following formula:

[0084] F target =β1·HR+β2·T body +β3·T ambieut +·β4·H ambient +β5·P user +β6·F historical

[0085] Among them, HR is the current heart rate;

[0086] T body The user's body temperature;

[0087] T ambient is the current ambient temperature;

[0088] H ambient is the current humidity;

[0089] P user For the user's personal preferences;

[0090] F historicalThe water flow intensity used historically by the user;

[0091] β1, β2, β3, β4, β5, and β6 are weight coefficients used to adjust the impact of various factors on water flow intensity;

[0092] To ensure that the intelligent adjustment module makes timely adjustments based on real-time data and user feedback, the system has a feedback mechanism. If the bathtub water temperature and water flow intensity deviate during the actual adjustment process, the intelligent system will correct it in the following ways:

[0093] T eurrent =T target +ΔT

[0094] F eurrent =F target +ΔF

[0095] Among them, T current The current set bathtub water temperature;

[0096] F current The current flow intensity is currently set;

[0097] ΔT and ΔF are dynamically adjusted based on the deviation between real-time data and target values;

[0098] Finally, the intelligent environment adjustment module adjusts the bathtub water temperature and water flow intensity in real time according to the above formula and feedback mechanism. The adjustment is:

[0099] T final =T target +ΔT

[0100] F final =F target +ΔF.

[0101] As a preferred method, the health data and personalized recommendations are uploaded to the cloud in an encrypted manner as follows:

[0102] Collect body temperature, heart rate, blood pressure health data and personalized recommendations. Assume that the health data is a vector D and the personalized recommendation is A, which can be expressed as:

[0103] D={D1,D2,…,D n}

[0104] A={A1,A2,…,A m}

[0105] Among them, D1, D2, ..., D n For various indicators of health data;

[0106] A1, A2, ..., A mTo personalize recommended content;

[0107] The data and suggestions are combined into a data package P by concatenating:

[0108] P=D||A

[0109] Among them, the symbol || represents the data splicing operation;

[0110] The encryption method uses symmetric encryption or asymmetric encryption. The following is the calculation formula based on symmetric encryption:

[0111] Set a key K to encrypt the data packet P. The encrypted data is C. The AES encryption operation is expressed as:

[0112] C=AES K (P)

[0113] Where C is the encrypted data;

[0114] AES K To use the key K to encrypt the data P using AES;

[0115] The security of the key K is extremely important. An asymmetric encryption algorithm is used to encrypt the key K and transmit it. The public key K of the recipient is used. pub Encryption key K:

[0116]

[0117] Among them, K' is the encrypted key, and only the recipient uses his private key K priv Decryption;

[0118] The encrypted data C and key K′ are uploaded to the cloud, and the transmission process is carried out via the HTTPS protocol:

[0119] Upload(C, K′)

[0120] After the cloud receives the encrypted data and encryption key, the data will be stored in a secure database, and the recipient uses his own private key K priv Decrypt the encryption key K′, and then use the decrypted key K to decrypt the data C:

[0121]

[0122] P=AES K 1 (C)

[0123] in, To decrypt the key using the private key;

[0124] AES K 1Use the key K to decrypt the data C.

[0125] Preferably, the system provides remote monitoring and fault diagnosis functions. In the event of a sensor or hardware failure, the system can automatically switch to a backup mode by:

[0126] The system first monitors the working status of each sensor and hardware, and uses health monitoring indicators to determine whether the equipment is working properly. Assume that sensor S i The state of the i ) indicates that H(S i ) is a value between 0 and 1, 1 indicates normal and 0 indicates fault;

[0127] H(S i )=f(S i )

[0128] Among them, S i is the i-th sensor or hardware device;

[0129] f(S i ) is a function that represents the health of a sensor or hardware, which usually depends on the quality of the data returned by the sensor;

[0130] In order to detect faults, the system sets a fault threshold θ. When the health level H(S i ) is lower than the threshold, the system considers that the sensor or hardware has failed. The fault detection formula is:

[0131]

[0132] Where, θ is the threshold for fault detection;

[0133] Fault_Detected(S i ) returns 1 if a fault is detected and 0 if no fault is detected;

[0134] Once a fault is detected, the system sends an alarm to the user through the remote monitoring function. The remote monitoring system sends alarm information via email, SMS, and App notification. The alarm conditions are:

[0135]

[0136] Where n is the number of sensors or hardware in the system;

[0137] If any sensor or hardware fails, the alarm system generates an alarm message;

[0138] When a fault is detected, the system automatically switches to backup mode. The triggering condition of backup mode is expressed by the following formula:

[0139]

[0140] Once the switch to backup mode is triggered, the system transfers the function of the failed device to the backup device. i In case of failure, the backup sensor Activated:

[0141]

[0142] At the same time, the system stops using the faulty sensor:

[0143] S i →Inactive

[0144] When the fault is repaired or the sensor returns to normal, the system performs a recovery operation. The recovery operation formula is:

[0145]

[0146] When all sensors return to normal operation, the system switches back to normal mode and stops standby mode.

[0147] As a preferred method, based on the user's health trends, health reports are regularly generated and personalized exercise, diet, and rest lifestyle recommendations are pushed as follows:

[0148] Health data includes body temperature, heart rate, blood pressure, weight, sleep quality, and exercise volume. Assume that each indicator X i (t) is the health data of the user at a certain time point t and the health trend of the user is analyzed by the time series of these data;

[0149] Assume that the health data is a multi-dimensional time series vector:

[0150] X(t)={X1(t),X2(t),...,X n (t)}

[0151] Among them, X i (t) is the value of the i-th health indicator at time t, and n is the number of health indicators;

[0152] Use time series analysis or machine learning methods to predict health trends in the future. Assume that the trend of user health data is obtained through some trend prediction method. Where k is the future time span;

[0153]

[0154] in, is the predicted value of the user's i-th health indicator at the next k moments;

[0155] f(·) is the trend prediction function;

[0156] Health reports are generated based on the user's health trends and analysis results. The report content includes the user's health status, changing trends, and related suggestions. The core content of the report is expressed by the following formula:

[0157] Assume that each health indicator X i (t) has a corresponding health score function, H i (X i (t)), the function gives a score based on the range and health standard of each indicator, ranging from 0 to 1, where 1 represents the best state and 0 represents the worst state;

[0158]

[0159] The total health score H(t) is calculated by the weighted average of each health indicator:

[0160]

[0161] Among them, w i is the weight of the i-th indicator, which is allocated according to the importance of the indicator;

[0162] H i (X i (t)) is the score of the i-th health indicator;

[0163] When generating a health report, the report content is output based on the health score, trend prediction results, and changes of each indicator:

[0164] Current health status: Give current health score and comments based on H(t);

[0165] Health Trends: Based on predict future trends;

[0166] Recommendations: Generate personalized recommendations based on predicted trends;

[0167] Based on health data trends and the user's health status, the system generates personalized exercise, diet, daily routine, and lifestyle recommendations. Assume that the recommendation generation process is divided into the following parts:

[0168] Personalized exercise recommendations are generated based on the user's activity level and health trends. Assume that exercise recommendation A cxcrcise (t) is based on the user’s health trend and current health score H(t):

[0169]

[0170] Where H(t) is the current health score;

[0171] The predicted value of the user's future movement trend;

[0172] X weight (t) is the user's current weight;

[0173] Dietary Recommendations dict (t) Generate according to the user's health status:

[0174] A dict (t)=diet function(H(t),H weight (t), X blood_sugar (t))

[0175] Where H(t) is the current health score;

[0176] X weight (t) is the user's current weight;

[0177] X blood_sugar (t) is the user’s current blood sugar level;

[0178] Work and rest suggestion A slccp (t) Generate based on the user's sleep quality and health status:

[0179] A slccp (t) = sleep function (H(t), X slccp (t), X strcss (t))

[0180] Among them, X slccp (t) is the user’s sleep quality;

[0181] X strcss (t) is the user's stress level;

[0182] Finally, all personalized recommendations are synthesized and pushed to the user's device. The push content includes exercise recommendations, diet recommendations, and daily routine recommendations. The comprehensive push formula is:

[0183] A(t)={A cxcrcisc (t), A dict (t), A slccp (t)}

[0184] The system regularly generates health reports and pushes personalized recommendations based on the user's health data and trends. Assuming that each period is P, the system generates reports based on the period:

[0185]

[0186] Among them, Report(P) is the health report of period P, including health score, trend forecast and personalized suggestions.

[0187] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a smart bathtub health monitoring and management system and method as described above.

[0188] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a health monitoring and management system and method based on an intelligent bathtub.

[0189] The beneficial effects of the present invention are:

[0190] By automatically collecting physiological data such as body temperature, heart rate, and blood pressure while using the smart bathtub, the user's physical condition can be monitored in real time, allowing potential health issues to be identified early. The intelligent environmental adjustment module automatically adjusts the bathtub's water temperature and flow intensity based on the user's physical condition. Health data and personalized recommendations are encrypted and uploaded to the cloud, ensuring the protection of the user's health information and preventing data leakage risks. The system provides real-time remote monitoring, allowing users to check their health status and the operating status of the bathtub via their mobile phone or computer, increasing ease of use and transparency. Based on the user's health trends, the system regularly generates health reports, providing a clear overview of their health data. The entire process is automated, requiring no manual intervention from the user. By continuously collecting and analyzing the user's health data, the system can create long-term health trend charts, helping users understand the trajectory of their health changes. By intelligently adjusting the system based on the user's physiological data and preferences, users can not only enjoy a comfortable bathing experience but also receive personalized services that meet their health needs. Through continuous health data monitoring and analysis, the system can help users transition from health management to preventive care. BRIEF DESCRIPTION OF THE DRAWINGS

[0191] Figure 1 This is a flow chart of a smart bathtub health monitoring and management system according to the present invention. DETAILED DESCRIPTION

[0192] The principles and features of the present invention are described below. The examples provided are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example. The advantages and features of the present invention will become more apparent from the following description and claims.

[0193] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0194] Example

[0195] The technical solution adopted by the present invention to solve its technical problem is:

[0196] A health monitoring and management system based on an intelligent bathtub, including:

[0197] Health monitoring module: Integrates multiple sensors to monitor the user's physical health in real time and generate multi-dimensional health data;

[0198] Data processing unit module: Based on advanced machine learning algorithms, it combines the user's historical health data, lifestyle habits, and environmental factors to conduct personalized health assessments;

[0199] Intelligent environment adjustment module: automatically adjusts the bathtub water temperature, water flow intensity and water quality based on health monitoring data and personalized health recommendations;

[0200] Data encryption and privacy protection module: uses end-to-end encryption technology to ensure the security of users' health data during transmission and storage;

[0201] Cloud data synchronization module: synchronizes health data and personalized recommendations to the cloud, allowing users to access historical records and health trends at any time through mobile devices;

[0202] Fault diagnosis and system fault tolerance mechanism module: The system adopts fault self-diagnosis and automatic repair functions, automatically switching to the backup system when the sensor or hardware fails to ensure uninterrupted health monitoring.

[0203] Through the cooperation of different sensors, the system can provide comprehensive physiological data, including body temperature, heart rate, blood pressure, etc., which helps to form a health record; by combining the user's historical data, living habits and environmental factors, the system can provide more accurate health recommendations to meet the user's personalized needs; by automatically adjusting the water temperature, water flow intensity and water quality, it provides a bathtub environment that is more in line with the user's health status and enhances the bathing experience; the user's health data is encrypted and protected, and there is no risk of leakage even during cloud storage or transmission; users can view health data at any time through mobile phones or other mobile devices, which is convenient for self-monitoring and adjustment; through automatic fault diagnosis and switching to the backup system, monitoring interruptions caused by hardware failure or sensor failure can be effectively avoided, ensuring the continuous and reliable operation of the system.

[0204] A health monitoring and management method based on an intelligent bathtub includes the following steps:

[0205] Step 1: When the user uses the smart bathtub, the system automatically activates the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters;

[0206] Step 2: The data processing unit analyzes the collected physiological data and combines it with the user's historical health records and personal information to generate personalized health assessments and recommendations;

[0207] Step 3: Based on the analysis results, the intelligent environment adjustment module automatically adjusts the water temperature and water flow intensity of the bathtub;

[0208] Step 4: Upload health data and personalized recommendations to the cloud in an encrypted manner;

[0209] Step 5: The system provides remote monitoring and fault diagnosis functions. In the event of sensor or hardware failure, the system can automatically switch to backup mode.

[0210] Step 6: Generate health reports regularly based on the user's health trends and push personalized exercise, diet, and lifestyle recommendations.

[0211] When the user uses the smart bathtub, the system automatically starts the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters. The method is as follows:

[0212] Body temperature is usually monitored using thermocouples, infrared sensors, or temperature sensors;

[0213] Assume that the sensor returns the original voltage signal V of the temperature sensor raw , the temperature is calculated by the following formula:

[0214]

[0215] Where: T is the measured body temperature;

[0216] V raw The raw voltage value returned by the sensor;

[0217] V0 is the reference voltage value of the sensor;

[0218] S is the sensitivity of the sensor;

[0219] Heart rate is measured using photoplethysmography or an electrocardiogram sensor. Assuming the pulse wave signal collected by the sensor is P(t), where t is time, the peak interval of the pulse wave is detected using the autocorrelation function or the periodicity of the signal. The heart rate formula is:

[0220]

[0221] Where HR is heart rate;

[0222] T p is the pulse wave period, that is, the time interval from one pulse wave peak to the next pulse wave peak;

[0223] Blood pressure monitoring is carried out by combining airbags, sensors and algorithms. Assume that the pulse wave propagation speed υ is measured by the sensor. PW , combined with the user's age and weight information, blood pressure is estimated using the following formula:

[0224]

[0225] Among them, SBP is systolic blood pressure;

[0226] DBP is diastolic blood pressure;

[0227] υ PW is the pulse wave velocity;

[0228] R1 and R2 are coefficients estimated based on individual characteristics;

[0229] C1 and C2 are constants used to calibrate the formula;

[0230] These physiological parameters of body temperature, heart rate, and blood pressure are fused and further analyzed by the data processing unit. The real-time data collected by the sensors is input into a data fusion model to conduct multi-dimensional health status analysis;

[0231] Assuming X is a comprehensive health assessment indicator, the following weighted average model is used for calculation:

[0232] X=w1·T+w2·HR+w3·BP

[0233] Where X is the comprehensive health assessment score

[0234] T is the body temperature parameter;

[0235] HR is heart rate;

[0236] BP is blood pressure;

[0237] w1, w2, and w3 are weight coefficients representing body temperature, heart rate, and blood pressure, respectively, and are usually adjusted based on the user's health data or personalized age information.

[0238] The data processing unit analyzes the collected physiological data and combines it with the user's historical health records and personal information to generate personalized health assessments and recommendations.

[0239] The model for personalized health assessment and recommendations consists of data collection, data preprocessing, physiological data analysis, personalized assessment, and health recommendation generation. Assuming there is a comprehensive health assessment index X, which combines body temperature T, heart rate HR, blood pressure, weight, and age factors, its calculation formula is:

[0240] X=w1·T+w2·HR+w3·SBP+w4·DBP+w5·BMI+w6·Age+w7·HistoricalData

[0241] Where, T is body temperature;

[0242] HR is heart rate;

[0243] SBP and DBP are blood pressure;

[0244] BMI is body mass index, and the calculation formula is:

[0245]

[0246] Age is the user's age;

[0247] HistoricalData is the comprehensive evaluation value of historical health data, obtained through a machine learning model. w1, w2, w3, w4, w5, w6, and w7 are the weight coefficients of each health indicator, which are adjusted according to the user's specific situation.

[0248] Historical health data has a great impact on health assessment. Therefore, we use machine learning algorithms to estimate the health status of individuals. Assuming that the regression model of historical health data is:

[0249] Y=β0+β1·X1+β2·X2+…+β n ·X n +∈

[0250] Among them, Y is the health assessment index;

[0251] X1, X2, ..., X n Characteristics of historical health records;

[0252] β0 is the intercept term, β1, β2, ..., β n is the regression coefficient, which is obtained by training historical data;

[0253] ∈ is the error term;

[0254] Use personalized health assessment indicators to further calculate health risks, for the risk of common diseases such as heart disease. Assume that the heart disease risk score is calculated through historical data and physiological data, R heart , the formula is:

[0255] R heart =α1·SBP+α2·DBP+α3·HR+α4·BMI+α5·Age+α6·FamilyHistory

[0256] Among them, SBP, DBP, HR, BMI, and Age are basic health indicators;

[0257] FamilyHistory is the influence of the user's family medical history, which is a binary variable;

[0258] α1, α2, ..., α6 are regression coefficients obtained through training;

[0259] Based on the above health assessment and risk score, the system generates personalized health recommendations. Assuming that the comprehensive health assessment index X and the health risk score R are in a comprehensive health recommendation model, the following decision function is given:

[0260] S=f(X,R)=γ1·X-γ2·R

[0261] Among them, S is the health advice index;

[0262] X is the comprehensive health assessment index;

[0263] R is the health risk score;

[0264] γ1, γ2 are coefficients obtained through training.

[0265] The user's health status is comprehensively assessed through multiple health indicators, avoiding the limitations of a single indicator; the weight coefficient and health assessment value are personalized according to each user's specific health status to ensure that the assessment results meet individual needs; by analyzing health indicators and family medical history, the risk of diseases such as heart disease can be accurately predicted and intervention can be carried out in advance; as new health data is continuously collected, the system can adjust the assessment model in real time and provide continuously optimized health management recommendations.

[0266] According to the analysis results, the intelligent environment adjustment module automatically adjusts the bathtub water temperature and water flow intensity in the following way:

[0267] The goal of water temperature regulation is to set the bathtub water temperature based on the user's current body temperature, ambient temperature, and personal preferences. Assume that the target water temperature T targct Calculated by the following formula:

[0268] T targct =α1·T bidy +α2·T anbicnt +α3·H ambicnt +α4·P uscr +α5·T historical

[0269] Among them, T body The user's current body temperature;

[0270] T ambicnt is the current ambient temperature of the bathroom;

[0271] T ambicnt is the current bathroom humidity;

[0272] P uscr For user personal preferences;

[0273] T historical The user's historical preferred water temperature;

[0274] α1, α2, α3, α4, and α5 are weight coefficients, which are determined based on user data and model training;

[0275] The adjustment of water flow intensity takes into account the user's current physical condition, ambient temperature and humidity, and the user's comfort preference. Assuming that the target water flow intensity F target Calculated by the following formula:

[0276] F target =β1·HR+β2·T body +β3·T ambicnt +β4·H ambient +β5·P user +β6·F historical

[0277] Among them, HR is the current heart rate;

[0278] T body The user's body temperature;

[0279] T ambicnt is the current ambient temperature;

[0280] H ambicnt is the current humidity;

[0281] P user For the user's personal preferences;

[0282] F historical The water flow intensity used historically by the user;

[0283] β1, β2, β3, β4, β5, and β6 are weight coefficients used to adjust the impact of various factors on water flow intensity;

[0284] To ensure that the intelligent adjustment module makes timely adjustments based on real-time data and user feedback, the system has a feedback mechanism. If the bathtub water temperature and water flow intensity deviate during the actual adjustment process, the intelligent system will correct it in the following ways:

[0285] T current =T target +ΔT

[0286] F current =F target +ΔF

[0287] Among them, T current The current set bathtub water temperature;

[0288] F current The current flow intensity is currently set;

[0289] ΔT and ΔF are dynamically adjusted based on the deviation between real-time data and target values;

[0290] Finally, the intelligent environment adjustment module adjusts the bathtub water temperature and water flow intensity in real time according to the above formula and feedback mechanism. The adjustment is:

[0291] T final =T target +ΔT

[0292] F final =F target +ΔF.

[0293] The method for uploading health data and personalized recommendations to the cloud in an encrypted manner is:

[0294] Collect body temperature, heart rate, blood pressure health data and personalized recommendations. Assume that the health data is a vector D and the personalized recommendation is A, which can be expressed as:

[0295] D={D1,D2,…,D n}

[0296] A={A1,A2,…,A m}

[0297] Among them, D1, D2, ..., D n For various indicators of health data;

[0298] A1, A2, ..., A m To personalize recommended content;

[0299] The data and suggestions are combined into a data package P by concatenating:

[0300] P=D||A

[0301] Among them, the symbol || represents the data splicing operation;

[0302] The encryption method uses symmetric encryption or asymmetric encryption. The following is the calculation formula based on symmetric encryption:

[0303] Set a key K to encrypt the data packet P. The encrypted data is C. The AES encryption operation is expressed as:

[0304] C=AES K (P)

[0305] Where C is the encrypted data;

[0306] AES K To use the key K to encrypt the data P using AES;

[0307] The security of the key K is extremely important. An asymmetric encryption algorithm is used to encrypt the key K and transmit it. The public key K of the recipient is used. pub Encryption key K:

[0308]

[0309] Among them, K' is the encrypted key, and only the recipient uses his private key K priv Decryption;

[0310] The encrypted data C and key K′ are uploaded to the cloud, and the transmission process is carried out via the HTTPS protocol:

[0311] Upload(C, K′)

[0312] After the cloud receives the encrypted data and encryption key, the data will be stored in a secure database, and the recipient uses his own private key K priv Decrypt the encryption key K′, and then use the decrypted key K to decrypt the data C:

[0313]

[0314] P=AES K 1 (C)

[0315] in, To decrypt the key using the private key;

[0316] AES K 1 Use the key K to decrypt the data C.

[0317] Symmetric encryption is used to protect users' health data and suggestions, while asymmetric encryption protects encryption keys, ensuring the security of data during storage and transmission; asymmetric encryption keys can be used to effectively manage keys and prevent them from being leaked during transmission; symmetric encryption algorithms (such as AES) are used for data encryption and decryption to ensure the efficiency of the encryption process, while asymmetric encryption algorithms are only used for encryption keys, ensuring the efficiency and security of the combination of the two; ensuring that only authorized recipients can decrypt users' health data and personalized suggestions, enhancing data privacy protection and complying with data protection regulations (such as GDPR, HIPAA, etc.); encrypted transmission through the HTTPS protocol ensures that data will not be interfered with or stolen by the outside world during transmission, enhancing the reliability of the entire system.

[0318] The system provides remote monitoring and fault diagnosis functions. In the event of a sensor or hardware failure, the system can automatically switch to backup mode by:

[0319] The system first monitors the working status of each sensor and hardware, and uses health monitoring indicators to determine whether the equipment is working properly. Assume that sensor S i The state of the i ) indicates that H(S i ) is a value between 0 and 1, 1 indicates normal and 0 indicates fault;

[0320] H(S i )=f(S i )

[0321] Among them, S i is the i-th sensor or hardware device;

[0322] f(S i ) is a function that represents the health of a sensor or hardware, which usually depends on the quality of the data returned by the sensor;

[0323] In order to detect faults, the system sets a fault threshold θ. When the health level H(S i ) is lower than the threshold, the system considers that the sensor or hardware has failed. The fault detection formula is:

[0324]

[0325] Where, θ is the threshold for fault detection;

[0326] Fault_Detected(S i ) returns 1 if a fault is detected and 0 if no fault is detected;

[0327] Once a fault is detected, the system sends an alarm to the user through the remote monitoring function. The remote monitoring system sends alarm information via email, SMS, and App notification. The alarm conditions are:

[0328]

[0329] Where n is the number of sensors or hardware in the system;

[0330] If any sensor or hardware fails, the alarm system generates an alarm message;

[0331] When a fault is detected, the system automatically switches to backup mode. The triggering condition of backup mode is expressed by the following formula:

[0332]

[0333] Once the switch to backup mode is triggered, the system transfers the function of the failed device to the backup device. i In case of failure, the backup sensor Activated:

[0334]

[0335] At the same time, the system stops using the faulty sensor:

[0336] S i →Inactive

[0337] When the fault is repaired or the sensor returns to normal, the system performs a recovery operation. The recovery operation formula is:

[0338]

[0339] When all sensors return to normal operation, the system switches back to normal mode and stops standby mode.

[0340] By monitoring the health of sensors and automatically switching to backup mode, the system is ensured to continue working in the event of a fault, greatly improving system availability; the system can automatically switch to backup equipment to avoid long downtime caused by a single sensor or hardware failure, shortening the fault recovery time; through remote monitoring functions, the system can immediately alarm when a fault occurs, helping users to quickly take repair measures and improving fault response speed; the system can automatically return to normal mode after the sensor returns to normal, reducing the need for manual intervention and improving the level of automation; through automated fault detection, alarms, backup mode switching and recovery operations, the workload of operation and maintenance personnel is reduced, reducing overall operation and maintenance costs.

[0341] Based on the user's health trends, regular health reports are generated and personalized exercise, diet, and lifestyle recommendations are pushed as follows:

[0342] Health data includes body temperature, heart rate, blood pressure, weight, sleep quality, and exercise volume. Assume that each indicator X i (t) is the health data of the user at a certain time point t and the health trend of the user is analyzed by the time series of these data;

[0343] Assume that the health data is a multi-dimensional time series vector:

[0344] X(t)={X1(t),X2(t),...,X n (t)}

[0345] Among them, X i (t) is the value of the i-th health indicator at time t, and n is the number of health indicators;

[0346] Use time series analysis or machine learning methods to predict health trends in the future. Assume that the trend of user health data is obtained through some trend prediction method. Where k is the future time span;

[0347]

[0348] in, is the predicted value of the user's i-th health indicator at the next k moments;

[0349] f(·) is the trend prediction function;

[0350] Health reports are generated based on the user's health trends and analysis results. The report content includes the user's health status, changing trends, and related suggestions. The core content of the report is expressed by the following formula:

[0351] Assume that each health indicator X i (t) has a corresponding health score function, H i (X i (t)), the function gives a score based on the range and health standard of each indicator, ranging from 0 to 1, where 1 represents the best state and 0 represents the worst state;

[0352]

[0353] The total health score H(t) is calculated by the weighted average of each health indicator:

[0354]

[0355] Among them, w iis the weight of the i-th indicator, which is allocated according to the importance of the indicator;

[0356] H i (X i (t)) is the score of the i-th health indicator;

[0357] When generating a health report, the report content is output based on the health score, trend prediction results, and changes of each indicator:

[0358] Current health status: Give current health score and comments based on H(t);

[0359] Health Trends: Based on predict future trends;

[0360] Recommendations: Generate personalized recommendations based on predicted trends;

[0361] Based on health data trends and the user's health status, the system generates personalized exercise, diet, daily routine, and lifestyle recommendations. Assume that the recommendation generation process is divided into the following parts:

[0362] Personalized exercise recommendations are generated based on the user's activity level and health trends. Assume that exercise recommendation A exercise (t) is based on the user’s health trend and current health score H(t):

[0363]

[0364] Where H(t) is the current health score;

[0365] The predicted value of the user's future movement trend;

[0366] X wcight (t) is the user's current weight;

[0367] Dietary Recommendations dict (t) Generate according to the user's health status:

[0368] A dict (t) = diet function (H(t), X weight (t), X blood_sugar (t))

[0369] Where H(t) is the current health score;

[0370] X weight (t) is the user's current weight;

[0371] X blood_sugar (t) is the user’s current blood sugar level;

[0372] Work and rest suggestion A sleep (t) Generate based on the user's sleep quality and health status:

[0373] A sleep (t) = sleep function (H(t), X sleep (t), X stress (t))

[0374] Among them, X sleep (t) is the user’s sleep quality;

[0375] X stress (t) is the user's stress level;

[0376] Finally, all personalized recommendations are synthesized and pushed to the user's device. The push content includes exercise recommendations, diet recommendations, and daily routine recommendations. The comprehensive push formula is:

[0377] A(t)={A excrcise (t), A dict (t), A sleep (t)}

[0378] The system regularly generates health reports and pushes personalized recommendations based on the user's health data and trends. Assuming that each period is P, the system generates reports based on the period:

[0379]

[0380] Among them, Report(P) is the health report of period P, including health score, trend forecast and personalized suggestions.

[0381] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the smart bathtub health monitoring and management system and method described above are implemented.

[0382] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the smart bathtub health monitoring and management system and method described above are implemented.

[0383] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0384] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0385] The above embodiments of the present invention are not intended to limit the scope of protection of the present invention, and the implementation methods of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, should fall within the scope of protection of the present invention.

Claims

1. A health monitoring and management system based on an intelligent bathtub, characterized in that: Includes: Health monitoring module: Integrates multiple sensors to monitor the user's physical health in real time and generate multi-dimensional health data; Data processing unit module: Based on advanced machine learning algorithms, it combines the user's historical health data, lifestyle habits, and environmental factors to conduct personalized health assessments; Intelligent environment adjustment module: automatically adjusts the bathtub water temperature, water flow intensity and water quality based on health monitoring data and personalized health recommendations; Data encryption and privacy protection module: uses end-to-end encryption technology to ensure the security of users' health data during transmission and storage; Cloud data synchronization module: synchronizes health data and personalized recommendations to the cloud, allowing users to access historical records and health trends at any time through mobile devices; Fault diagnosis and system fault tolerance mechanism module: The system adopts fault self-diagnosis and automatic repair functions, automatically switching to the backup system when the sensor or hardware fails to ensure uninterrupted health monitoring.

2. A health monitoring and management method based on an intelligent bathtub, characterized in that: The following steps are involved: Step 1: When the user uses the smart bathtub, the system automatically activates the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters; Step 2: The data processing unit analyzes the collected physiological data and combines it with the user's historical health records and personal information to generate personalized health assessments and recommendations; Step 3: Based on the analysis results, the intelligent environment adjustment module automatically adjusts the water temperature and water flow intensity of the bathtub; Step 4: Upload health data and personalized recommendations to the cloud in an encrypted manner; Step 5: The system provides remote monitoring and fault diagnosis functions. In the event of sensor or hardware failure, the system can automatically switch to backup mode. Step 6: Generate health reports regularly based on the user's health trends and push personalized exercise, diet, and lifestyle recommendations.

3. The health monitoring and management method based on the smart bathtub according to claim 2 is characterized in that: When the user uses the smart bathtub, the system automatically starts the health monitoring module to collect the user's body temperature, heart rate, and blood pressure physiological parameters. The method is as follows: Body temperature is usually monitored using thermocouples, infrared sensors, or temperature sensors; Assume that the sensor returns the original voltage signal V of the temperature sensor raw , the temperature is calculated by the following formula: Where: T is the measured body temperature; V raw The raw voltage value returned by the sensor; V0 is the reference voltage value of the sensor; S is the sensitivity of the sensor; Heart rate is measured using photoplethysmography or an electrocardiogram sensor. Assuming the pulse wave signal collected by the sensor is P(t), where t is time, the peak interval of the pulse wave is detected using the autocorrelation function or the periodicity of the signal. The heart rate formula is: Where HR is heart rate; T p is the pulse wave period, that is, the time interval from one pulse wave peak to the next pulse wave peak; Blood pressure monitoring is carried out by combining airbags, sensors and algorithms. Assume that the pulse wave propagation speed v is measured by the sensor. PW , combined with the user's age and weight information, blood pressure is estimated using the following formula: Among them, SBP is systolic blood pressure; DBP is diastolic blood pressure; v PW is the pulse wave velocity; R1 and R2 are coefficients estimated based on individual characteristics; C1 and C2 are constants used to calibrate the formula; These physiological parameters of body temperature, heart rate, and blood pressure are fused and further analyzed by the data processing unit. The real-time data collected by the sensors is input into a data fusion model to conduct multi-dimensional health status analysis; Assuming X is a comprehensive health assessment indicator, the following weighted average model is used for calculation: X=w1·T+w2·HR+w3·BP Where X is the comprehensive health assessment score T is the body temperature parameter; HR is heart rate; BP is blood pressure; w1, w2, and w3 are weight coefficients representing body temperature, heart rate, and blood pressure, respectively, and are usually adjusted based on the user's health data or personalized age information.

4. The health monitoring and management method based on the smart bathtub according to claim 3 is characterized in that: The data processing unit analyzes the collected physiological data and combines it with the user's historical health records and personal information to generate personalized health assessments and recommendations. The model for personalized health assessment and recommendations consists of data collection, data preprocessing, physiological data analysis, personalized assessment, and health recommendation generation. Assuming there is a comprehensive health assessment index X, which combines body temperature T, heart rate HR, blood pressure, weight, and age factors, its calculation formula is: X=w1·T+w2·HR+w3·SBP+w4·DBP+w5·BMI+w6·Age+w7·HistoricalData Where, T is body temperature; HR is heart rate; SBP and DBP are blood pressure; BMI is body mass index, and the calculation formula is: Age is the user's age; HistoricalData is the comprehensive evaluation value of historical health data, obtained through machine learning models. w1, w2, w3, w4, w5, w6, and w7 are weight coefficients of various health indicators, which are adjusted individually according to the specific circumstances of the user; Historical health data has a great impact on health assessment. Therefore, we use machine learning algorithms to estimate the health status of individuals. Assuming that the regression model of historical health data is: Y=β0+β1·X1+β2·X2+…+β n ·X n +∈ Among them, Y is the health assessment index; X1, X2, ..., X n Characteristics of historical health records; β0 is the intercept term, β1, β2, ..., β n is the regression coefficient, which is obtained by training historical data; ∈ is the error term; Use personalized health assessment indicators to further calculate health risks, for the risk of common diseases such as heart disease. Assume that the heart disease risk score is calculated through historical data and physiological data, R heart , the formula is: R heart＝ α1·SBP+α2·DBP+α3·HR+α4·BMI+α5·Age+α6·FamilyHistory Among them, SBP, DBP, HR, BMI, and Age are basic health indicators; FamilyHistory is the influence of the user's family medical history, which is a binary variable; α1, α2, ..., α6 are regression coefficients obtained through training; Based on the above health assessment and risk score, the system generates personalized health recommendations. Assuming that the comprehensive health assessment index X and the health risk score R are in a comprehensive health recommendation model, the following decision function is given: S=f(X,R)=γ1·X-γ2·R Among them, S is the health advice index; X is the comprehensive health assessment index; R is the health risk score; γ1, γ2 are coefficients obtained through training.

5. The health monitoring and management method based on the smart bathtub according to claim 4 is characterized in that: According to the analysis results, the intelligent environment adjustment module automatically adjusts the bathtub water temperature and water flow intensity in the following way: The goal of water temperature regulation is to set the bathtub water temperature based on the user's current body temperature, ambient temperature, and personal preferences. Assume that the target water temperature T targct Calculated by the following formula: T target =α1·T body +α2·T ambicnt +α3·H ambicnt +α4·P uscr +α5·T historical Among them, T body The user's current body temperature; T ambicnt is the current ambient temperature of the bathroom; H ambicnt is the current bathroom humidity; P uscr For user personal preferences; T historical The user's historical preferred water temperature; α1, α2, α3, α4, and α5 are weight coefficients, which are determined based on user data and model training; The adjustment of water flow intensity takes into account the user's current physical condition, ambient temperature and humidity, and the user's comfort preference. Assuming that the target water flow intensity F targct Calculated by the following formula: F targct =β1·HR+β2·T body +β3·T ambicent +β4.·H ambicnt +β5·P user +β6·F historical Among them, HR is the current heart rate; T body The user's body temperature; T ambient is the current ambient temperature; H ambicnt is the current humidity; P uscr For the user's personal preferences; F historical The water flow intensity used historically by the user; β1, β2, β3, β4, β5, and β6 are weight coefficients used to adjust the impact of various factors on water flow intensity; To ensure that the intelligent adjustment module makes timely adjustments based on real-time data and user feedback, the system has a feedback mechanism. If the bathtub water temperature and water flow intensity deviate during the actual adjustment process, the intelligent system will correct it in the following ways: T current =T target +△T F current =F target +ΔF Among them, T curret The current set bathtub water temperature; F current The current flow intensity is currently set; ΔT and ΔF are dynamically adjusted based on the deviation between real-time data and target values; Finally, the intelligent environment adjustment module adjusts the bathtub water temperature and water flow intensity in real time according to the above formula and feedback mechanism. The adjustment is: T final =T target +△T F final =F target +△F。 6. The health monitoring and management method based on the smart bathtub according to claim 5 is characterized in that: The method for uploading health data and personalized recommendations to the cloud in an encrypted manner is: Collect body temperature, heart rate, blood pressure health data and personalized recommendations. Assume that the health data is a vector D and the personalized recommendation is A, which can be expressed as: D={D1,D2,...,D n } <h2 style=";text-align:left;direction:ltr">A={A1,A2,...,A<h2 style=";text-align:left;direction:ltr"> m <h2 style=";text-align:left;direction:ltr">} Among them, D1, D2, ..., D n For various indicators of health data; A1, A2, ..., A m To personalize recommended content; The data and suggestions are combined into a data package P by concatenating: P=D||A Among them, the symbol || represents the data splicing operation; The encryption method uses symmetric encryption or asymmetric encryption. The following is the calculation formula based on symmetric encryption: Set a key K to encrypt the data packet P. The encrypted data is C. The AES encryption operation is expressed as: C=AES K (P) Where C is the encrypted data; AES K To use the key K to encrypt the data P using AES; The security of the key K is extremely important. An asymmetric encryption algorithm is used to encrypt the key K and transmit it. The public key K of the recipient is used. pub Encryption key K: Among them, K' is the encrypted key, and only the recipient uses his private key K priv Decryption; The encrypted data C and key K′ are uploaded to the cloud, and the transmission process is carried out via the HTTPS protocol: Upload(C,K′) After the cloud receives the encrypted data and encryption key, the data will be stored in a secure database, and the recipient uses his own private key K priv Decrypt the encryption key K′, and then use the decrypted key K to decrypt the data C: P=AES K 1 (C) in, To decrypt the key using the private key; AES K 1 Use the key K to decrypt the data C.

7. The health monitoring and management method based on the smart bathtub according to claim 6 is characterized in that: The system provides remote monitoring and fault diagnosis functions. In the event of a sensor or hardware failure, the system can automatically switch to backup mode by: The system first monitors the working status of each sensor and hardware, and uses health monitoring indicators to determine whether the equipment is working properly. Assume that sensor S i The state of the i ) indicates that H(S i ) is a value between 0 and 1, 1 indicates normal and 0 indicates fault; H(S i )=f(S i ) Among them, S i is the i-th sensor or hardware device; f(S i ) is a function that represents the health of a sensor or hardware, which usually depends on the quality of the data returned by the sensor; In order to detect faults, the system sets a fault threshold θ. When the health level H(S i ) is lower than the threshold, the system considers that the sensor or hardware has failed. The fault detection formula is: Where, θ is the threshold for fault detection; Fault_Detected(S i ) returns 1 if a fault is detected and 0 if no fault is detected; Once a fault is detected, the system sends an alarm to the user through the remote monitoring function. The remote monitoring system sends alarm information via email, SMS, and App notification. The alarm conditions are: Where n is the number of sensors or hardware in the system; If any sensor or hardware fails, the alarm system generates an alarm message; When a fault is detected, the system automatically switches to backup mode. The triggering condition of backup mode is expressed by the following formula: Once the switch to backup mode is triggered, the system transfers the function of the failed device to the backup device. i In case of failure, the backup sensor Activated: At the same time, the system stops using the faulty sensor: S i →Inactive When the fault is repaired or the sensor returns to normal, the system performs a recovery operation. The recovery operation formula is: When all sensors return to normal operation, the system switches back to normal mode and stops standby mode.

8. The health monitoring and management method based on the smart bathtub according to claim 7 is characterized in that: Based on the user's health trends, regular health reports are generated and personalized exercise, diet, and lifestyle recommendations are pushed as follows: Health data includes body temperature, heart rate, blood pressure, weight, sleep quality, and exercise volume. Assume that each indicator X i (t) is the health data of the user at a certain time point t and the health trend of the user is analyzed by the time series of these data; Assume that the health data is a multi-dimensional time series vector: X(t)={X1(t),X2(t),...,X n (t)} Among them, X i (t) is the value of the i-th health indicator at time t, and n is the number of health indicators; Use time series analysis or machine learning methods to predict health trends in the future. Assume that the trend of user health data is obtained through some trend prediction method. Where k is the future time span; in, is the predicted value of the user's i-th health indicator at the next k moments; f(·) is the trend prediction function; Health reports are generated based on the user's health trends and analysis results. The report content includes the user's health status, changing trends, and related suggestions. The core content of the report is expressed by the following formula: Assume that each health indicator X i (t) has a corresponding health score function, H i (X i (t)), the function gives a score based on the range and health standard of each indicator, ranging from 0 to 1, where 1 represents the best state and 0 represents the worst state; The total health score H(t) is calculated by taking the weighted average of each health indicator: Among them, w i is the weight of the i-th indicator, which is allocated according to the importance of the indicator; H i (X i (t)) is the score of the i-th health indicator; When generating a health report, the report content is output based on the health score, trend prediction results, and changes of each indicator: Current health status: Give current health score and comments based on H(t); Health Trends: Based on predict future trends; Recommendations: Generate personalized recommendations based on predicted trends; Based on health data trends and the user's health status, the system generates personalized exercise, diet, daily routine, and lifestyle recommendations. Assume that the recommendation generation process is divided into the following parts: Personalized exercise recommendations are generated based on the user's activity level and health trends. Assume that exercise recommendation A cxcrcise (t) is based on the user’s health trend and current health score H(t): Where H(t) is the current health score; The predicted value of the user's future movement trend; X weight (t) is the user's current weight; Dietary Recommendations dict (t) Generate according to the user's health status: A dict (t)=diet function(H(t),X weight (t),X blood_sugar (t)) Where H(t) is the current health score; X weight (t) is the user's current weight; X blood_sugar (t) is the user’s current blood sugar level; Work and rest suggestion A slccp (t) Generate based on the user's sleep quality and health status: A slecp (t)=sleep function(H(t),X slecp (t),X stress (t)) Among them, X slecp (t) is the user’s sleep quality; X stress (t) is the user's stress level; Finally, all personalized recommendations are synthesized and pushed to the user's device. The push content includes exercise recommendations, diet recommendations, and daily routine recommendations. The comprehensive push formula is: A(t)={A cxcrcise (t),A dict (t),A slecp (t)} The system regularly generates health reports and pushes personalized recommendations based on the user's health data and trends. Assuming that each period is P, the system generates reports based on the period: Among them, Report(P) is the health report of period P, including health score, trend forecast and personalized suggestions.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the health monitoring and management method based on the smart bathtub as claimed in any one of claims 2 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a health monitoring and management method based on an intelligent bathtub as described in any one of claims 2 to 8 is implemented.

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