Water quality automatic detection management system and management method based on intelligent bathtub

Abstract

The invention discloses an intelligent bathtub water quality automatic detection management system and method, and the method comprises the steps: the system regularly monitors the water quality of a bathtub, collects data through a sensor, uploads the data to a central control unit, and employs a data standardization technology for processing; according to user setting, the automatic control module adds aromatherapy, mineral micro-elements, mineral substances and ultraviolet rays, and adjustment is conducted in real time according to water quality changes and health requirements; microorganisms in water are detected by an ultraviolet technology, and ultraviolet output is automatically adjusted; mineral elements and minerals are dynamically adjusted according to requirements; based on a deep learning algorithm, analyzing water quality data, predicting a trend, generating a health report and providing personalized health suggestions; when the water quality does not reach the standard, the system automatically adjusts the water quality and generates a personalized bathing scheme according to health requirements; sensor self-calibration and fault diagnosis are carried out regularly, and real-time monitoring and maintenance reminding pushing are carried out; a user sets target water quality through an application program, checks a real-time report, feeds back requirements and adjusts components.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intelligent bathtub water quality automatic detection management, and specifically relates to an intelligent bathtub water quality automatic detection management system and a management method. BACKGROUND

[0002] An intelligent bathtub water quality automatic detection management system utilizes sensors and intelligent control technology to monitor water quality in real time and automatically adjust water treatment equipment (such as filtration, heating, or water replacement) based on water quality changes. Water quality sensors detect parameters such as pH, temperature, turbidity, dissolved oxygen, and chlorine content to ensure clean and safe water. By collecting sensor data, the system analyzes water quality and determines whether it meets health standards. If water quality is abnormal, the system automatically sends an alarm or adjusts relevant parameters. Based on detected water quality information, the system can automatically adjust water temperature and purify water, and even automatically replace water or start the filtration system when needed. Users can view water quality status through a mobile app, touchscreen, or voice assistant, manually intervene, or set automatic operation rules. If water quality is abnormal or there is a fault, the system can send alarm information to the user or automatically implement water quality improvement measures.

[0003] However, despite the many advantages of the intelligent bathtub water quality automatic detection management system in providing a convenient and healthy bathing experience, it still has some potential drawbacks and challenges, such as limited sensor accuracy, system misjudgment, high maintenance difficulty, excessive intervention, and waste of water resources and energy. SUMMARY

[0004] To address the deficiencies in the prior art, the present application aims to provide an intelligent bathtub water quality automatic detection management system and management method that monitors water quality in real time and adds health-promoting elements to ensure water safety while enhancing user health awareness and satisfaction.

[0005] The technical solution adopted by the present application to solve its technical problems is: An intelligent bathtub water quality automatic detection management system includes: A water quality monitoring module that uses sensors to detect pH, turbidity, dissolved oxygen, and temperature parameters in real time, uploads data to a central control unit, and interfaces with a health database. A health element management module that adds essential oils and mineral health-promoting ingredients and intelligently adjusts health elements in water based on user preferences. A data analysis and prediction module that uses deep learning algorithms to analyze water quality changes, predict potential problems, and automatically remind users to add health elements or adjust water quality. A dynamic water quality adjustment module for automatically adjusting water temperature, turbidity, and sterilization measures to ensure water quality compliance and generate intelligent recommendations for healthy soaking programs. A self-calibration and fault diagnosis module for periodic sensor self-calibration and initiating fault diagnosis procedures to generate user maintenance prompts. A user interaction platform module for real-time display of water quality data and processing through the application, while users manually intervene and adjust water quality parameters through the application.

[0006] An intelligent bathtub water quality automatic detection and management method includes: Periodically monitor the water quality of the bathtub, collect various water quality data through sensors, and upload them to the central control unit in real time, and process the input data through data standardization technology. According to user selection and setting, the system adds essential oils, mineral microelements, mineral components, and ultraviolet light to the bathtub water through the automatic control module, and dynamically adjusts them according to real-time changes in water quality and user health needs. The ultraviolet light technology detects the microbial content in the water through intelligent sensors and automatically adjusts the ultraviolet light output based on the detection results. Mineral microelements and mineral components are dynamically added according to user settings and water quality requirements, and the system intelligently controls their concentration. Using a machine learning algorithm based on deep learning, the collected data is analyzed in real time to identify water quality trends, predict future water quality conditions through data modeling, and generate health reports to assess the impact of water quality on user health, and provide personalized health recommendations based on user settings. When the water quality does not meet the standards, the system will automatically take adjustment measures, including using ultraviolet disinfection to treat waterborne microorganisms, adjusting the concentration of mineral microelements, and automatically adding mineral components according to water quality problems. The adjustment operation is based on user health needs and pre-set to generate personalized bathing programs that meet user health needs. Periodically calibrate sensors and perform fault diagnosis to monitor system operation in real time, generate and push user reminders through the intelligent maintenance module, and suggest user maintenance operations. Use a simple and easy-to-use application to provide real-time data visualization, with real-time chart updates, and support user feedback and manual parameter adjustment. Users can set target water quality parameters through the interface, view real-time water quality reports, provide feedback on health needs or dissatisfaction, and adjust ingredient settings based on feedback data.

[0007] As a preferred method, periodically monitor the water quality of the bathtub, collect various water quality data through sensors, and upload them to the central control unit in real time, and process the input data through data standardization technology. The Z-score standardization method converts data into a distribution with zero mean and unit standard deviation, with the formula: The original value of water quality data; The mean value of water quality data; The standard deviation of water quality data; The standardized data value; The Min-Max standardization method scales data to a specific range, the formula is: The original value of water quality data; The minimum value in water quality data; The maximum value in water quality data; The standardized data value.

[0008] As preferred, according to user selection and setting, the system adds aromatherapy, mineral microelements, mineral components and ultraviolet rays to the bathtub water through the automatic control module, and dynamically adjusts according to real-time changes of water quality and health needs of users. The ultraviolet technology detects the microbial content in the water through intelligent sensors, and automatically adjusts the ultraviolet output according to the detection results. Mineral microelements and mineral components are dynamically added according to user settings and water quality requirements. The method for the system to intelligently control the concentration is: The adjustment of ultraviolet output is based on the microbial content in the water. The sensor detects the concentration of microorganisms in the water , the output intensity of ultraviolet rays is dynamically adjusted by the following formula: The maximum output intensity coefficient of ultraviolet equipment; The real-time detected microbial concentration; The threshold value of microbial concentration in water quality, which will increase the intensity of ultraviolet rays when exceeding this threshold value; The output intensity of ultraviolet rays; If , the ultraviolet output is automatically increased; The amount of mineral microelements and mineral components added is dynamically adjusted according to water quality data and user-set requirements. If the user sets a target concentration of certain mineral , the concentration of a certain mineral in the actual water quality is , the amount of mineral added The amount of mineral added is calculated by the following formula: is the adjustment coefficient of the mineral adding device, representing the amount of mineral added per unit of concentration difference; is the target concentration set by the user; is the actual concentration of the mineral in the current water quality; is the amount of mineral component to be added; The concentration of the essential oil is controlled intelligently, adjusted according to the user's settings and changes in water quality, and the target value of the essential oil concentration is set to , the current concentration is , and the amount of essential oil added is The amount of essential oil added is calculated by the following formula: is the adjustment coefficient of the essential oil adding device, representing the amount of essential oil added per unit of concentration difference; is the target essential oil concentration set by the user; is the current essential oil concentration in the water; is the amount of essential oil to be added; When the concentrations of various components in the water are controlled, a weighted average method is used to set the comprehensive control target according to the control requirements and priorities of each component, and the comprehensive control adjustment amount is the combination of ultraviolet rays, minerals, and essential oil components, and the specific adjustment amount is expressed as: , , is the importance weight of each component control.

[0009] As an option, a machine learning algorithm based on deep learning is used to analyze the collected data in real time, identify water quality trends, predict future water quality conditions through data modeling, and generate a health report to assess the impact of water quality on user health, and provide personalized health recommendations according to user settings: Use a deep learning algorithm to model time series of water quality data to predict future water quality trends, set the collected data to include timestamps and multiple water quality parameters, and predict by the following steps: For time series data Standardization: For the time point Water quality data; And The mean and standard deviation of all data; Using LSTM neural network for time series prediction, the output of the model is the water quality prediction of the future time step : The step size of historical data; The predicted water quality at the future time ; Based on water quality data and user's health needs, evaluate the impact of water quality on health, health risk assessment formula: Set The risk assessment value of water quality on user health, the model is calculated by the following formula: , , , The value of water quality parameter at time ; , , , The weight coefficient of the model; Health risk classification: According to Value classification, for: Low risk: Less than a certain threshold; Moderate risk: In a certain range; High risk: Exceeds a certain threshold; Based on the output of the health risk assessment model , generate a health report, including the potential impact of water quality on user health, the health report includes the following content: Evaluation of current water quality; According to the user's health history; Generate health suggestions for predicted water quality changes; Personalized health suggestions are based on user settings and generated using recommendation system algorithms. If a certain component in the water is too high or too low, the following suggestions are generated: According to the influence of water quality on health, combined with the user's health settings, generate personalized health recommendations: For personalized health recommendations; For the user's health settings; For the function mapping according to the health assessment and user needs, output personalized health recommendations.

[0010] As preferred, when the water quality does not meet the standard, the system will automatically take adjustment measures, specific measures including using ultraviolet disinfection to treat microorganisms in water, adjusting the concentration of mineral microelements and automatically adding mineral components according to water quality problems, adjusting the operation according to the user's health needs and preset, generating personalized bathing scheme that meets the user's health needs: The purpose of ultraviolet disinfection is to kill microorganisms in water. If the concentration of microorganisms in water Exceeds the standard threshold , the ultraviolet intensity will be automatically adjusted according to the concentration of microorganisms ; Ultraviolet intensity adjustment formula: For the real-time detected concentration of microorganisms in water; For the threshold of the concentration of microorganisms in water, exceeding this value will be disinfected; For the maximum allowed value of the concentration of microorganisms; For the maximum output intensity coefficient of the ultraviolet disinfection equipment; If , the ultraviolet disinfection intensity Will be dynamically adjusted according to the deviation of the concentration of microorganisms; The concentration of mineral microelements is adjusted according to the water quality problems and the user's health needs settings. When the concentration of a certain mineral in water Does not match the target concentration , adjustment will be made. The mineral adjustment formula is: For the amount of mineral components to be added; For the adjustment coefficient of the mineral addition equipment, indicating the amount of mineral mass added per unit concentration difference; For the target mineral concentration set by the user; Concentration of minerals in current water quality; For water quality problems, the system automatically adds specific mineral ingredients according to real-time water quality data. If the water is deficient in calcium, magnesium, potassium minerals, the system automatically adds the required ingredients according to these deficient elements. The formula for adding minerals is: The amount of a certain mineral element to be added; The adjustment coefficient of the mineral element adding device; The target mineral element concentration; The concentration of the mineral element in the current water quality; The generation of personalized bathing programs is based on the user's health needs, combined with water quality analysis results to provide health recommendations. The user sets the demand for aromatherapy and mineral concentration ingredients. The personalized health recommendations are calculated by the following formula. The personalized bathing program formula is: The personalized bathing program includes health recommendations, recommended water quality ingredients, and concentrations; The health risk output by the health assessment model; The ultraviolet disinfection intensity is adjusted according to the microbial concentration; The amount of mineral ingredients to be added according to water quality requirements; The amount of elements to be automatically added according to the lack of minerals; The user's health settings; Function Combining health assessment, water quality adjustment, and user setting factors, a complete personalized bathing program is provided for the user, which includes: Ultraviolet disinfection intensity setting; Suitable mineral and element concentrations; Health recommendations based on water quality.

[0011] As an option, regular sensor self-calibration and fault diagnosis are performed, and the system operation state is monitored in real time. Through the intelligent maintenance module, user reminders are generated and pushed, and the method for suggesting user maintenance operations is: Self-calibration is a process to ensure sensor accuracy and stability. At each calibration, the system detects the error value of the sensor and automatically adjusts it; Sensor error calibration formula: For sensor error; For standard calibration value; For sensor current reading; If , the system will automatically calibrate and adjust the sensor output; The system monitors the running state of each sensor and device in real time, detects whether there is a fault, and the fault diagnosis is based on the sensor reading and the normal range of device operation to judge. Fault diagnosis formula: Fault diagnosis flag, 1 indicates fault, 0 indicates normal; Maximum allowed reading of sensor; Minimum allowed reading of sensor; Current sensor reading; If , trigger fault alarm and start fault repair process; Real-time monitoring, the system will continuously collect sensor data and evaluate the overall system running state through the monitoring model. System running state evaluation formula: System overall running state evaluation based on real-time data of all sensors; Number of sensors; Real-time data of the th sensor; Through the formula, the system obtains an overall health status score , so as to evaluate the running health of the system; The intelligent maintenance module generates maintenance reminders based on the system running state, sensor readings and fault diagnosis results, and pushes them to the user. The generation of reminders is based on the following algorithm: Maintenance requirement flag, 1 indicates that maintenance is required, 0 indicates that maintenance is not required; System health status score; System health status threshold; 1 represents failure, 0 represents normal, for fault diagnosis flag; and are weight coefficients, representing the degree of influence of health status and failure on maintenance demand; If , the system generates a maintenance reminder and notifies the user to maintain through push notification; Once the maintenance demand is generated, the system sends a maintenance reminder to the user through the push module, and the push content includes maintenance suggestions, operation steps, and recommended maintenance time. The push notification generation formula is: is the content of the push notification; is the maintenance demand flag, which determines whether to send a notification; is the user's settings, including push preferences and notification receiving time.

[0012] As a preferred, a simple and easy-to-use application is adopted to provide real-time data visualization display, and the chart is updated in real time. At the same time, user feedback and manual parameter adjustment are supported. The user sets the target water quality parameters through the interface, views the real-time water quality report, feeds back the health demand or unsatisfactory part, and adjusts the component setting according to the feedback data. The method is: Real-time data display and chart update are based on sensor collected data, which is updated and displayed on the chart in real time. The water quality data update formula is: is the real-time water quality data at time ; is the real-time data collected by the sensor; is the system preset water quality parameter; The user can input the target water quality parameter through the interface, and the system adjusts and displays the water quality report according to the target parameter. The user target water quality setting formula is: is the user-set target water quality parameter set , ,..., is the specific value of the target water quality; The user feeds back its health demand or unsatisfactory part through the interface, and the system receives and adjusts according to the feedback data. The user feedback processing formula is: Feedback set for user , ,..., Specific feedback item; According to user feedback and target parameters, the system automatically adjusts water quality ingredients or filtration settings to match target water quality standards, adjusting ingredient setting formula: Adjusted water quality ingredients configuration; User-set target water quality parameters; User feedback content, including health needs or dissatisfaction; Real-time water quality data; The system generates water quality reports regularly for users to view and optimize adjustments based on feedback, water quality report generation formula: Water quality report content, showing current water quality, target water quality and adjusted ingredients; Real-time water quality data; User target water quality parameters; Adjusted water quality ingredients; Manual adjustment formula: User manual adjustment of ingredients or operations; Current water quality ingredient configuration; User input manual parameters; The entire system will form a closed loop according to real-time data, user targets and feedback, continuously adjusting water quality, closed loop adjustment formula: Feedback loop, indicating the closed loop of data flow and adjustment process; Including real-time data, target water quality, user feedback, adjusted ingredients and manually input parameters.

[0013] Another technical problem to be solved by the present application is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the system and method for automatic detection and management of water quality of a smart bathtub according to any of the above.

[0014] Another technical problem to be solved by the present application is to provide a computer-readable storage medium having a computer program stored thereon, wherein the program is executable by a processor to implement the system and method for automatic detection and management of water quality of a smart bathtub.

[0015] The present application has the following advantages: The system not only adds aromatherapy and minerals according to user settings, but also uses precise control of ultraviolet technology, mineral microelements and other components to achieve real-time and accuracy of water quality adjustment; uses deep learning algorithms to analyze, predict and evaluate water quality, generates personalized health reports and bathing programs to achieve precise health management; combines ultraviolet disinfection technology with real-time feedback from sensors to automatically adjust ultraviolet intensity according to water quality and microorganisms to ensure water quality meets safety standards and meets health needs; generates personalized bathing programs based on water quality testing and user health needs, unlike traditional single water quality adjustment methods, provides multiple intelligent adjustment measures to meet individual needs; regularly performs sensor self-calibration to ensure system accuracy and stability, and has fault diagnosis function to reduce human intervention; users can manually adjust, receive data feedback and make personalized settings through a simple application, enhancing system usability and interactivity. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A flowchart of the system and method for automatic detection and management of water quality of a smart bathtub according to the present application. DETAILED DESCRIPTION

[0017] The principles and features of the present application are described below, and the examples are used only to illustrate the present application and are not intended to limit the scope of the present application. The present application is described in more detail in the following paragraphs by way of example. The advantages and features of the present application will be more apparent from the following description and claims.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terminology used in the specification of the present application is only for the purpose of describing specific embodiments and is not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more related listed items. EMBODIMENTS

[0019] The technical scheme adopted by the present application to solve its technical problems is: An intelligent bathtub water quality automatic detection management system, comprising: A water quality monitoring module for real-time detection of pH, turbidity, dissolved oxygen, and temperature parameters in water using sensors, uploading data to a central control unit, and interfacing with a health database; A health element management module for adding essential oils, mineral health-promoting ingredients, and intelligently adjusting health ingredients in water according to user preferences; A data analysis and prediction module for applying deep learning algorithms to analyze water quality changes, predict potential problems, and automatically generate reminders for users to add health elements or adjust water quality; A dynamic water quality adjustment module for automatically adjusting water temperature, turbidity, and sterilization measures to ensure water quality compliance and generate intelligent recommendations for healthy soaking programs; A self-calibration and fault diagnosis module for regular sensor self-calibration and starting fault diagnosis procedures to generate user maintenance prompts; A user interaction platform module for real-time display of water quality data and processing through an application, and manual intervention and adjustment of water quality parameters by users through the application.

[0020] Through real-time monitoring, automatic adjustment, and intelligent recommendations, the system ensures that water quality always meets health standards; according to user health needs and preferences, health elements are intelligently added to improve user experience; through deep learning algorithms, potential problems are identified in advance to reduce health risks; users can view water quality data in real time and intervene through the application, making it convenient and efficient.

[0021] An intelligent bathtub water quality automatic detection management method, comprising: Regularly monitor the water quality of the bathtub, collect various water quality data through sensors, and upload them to the central control unit in real time, and process the input data through data standardization technology; According to user selection and setting, the system adds essential oils, mineral microelements, mineral ingredients, and ultraviolet light to the bathtub water through an automatic control module, and dynamically adjusts according to real-time changes in water quality and user health needs; ultraviolet technology detects the microbial content in the water through intelligent sensors and automatically adjusts the ultraviolet output according to the detection results; mineral microelements and mineral ingredients are dynamically added according to user settings and water quality requirements, and the system intelligently controls their concentration; Using deep learning-based machine learning algorithms, the collected data is analyzed in real time to identify water quality trends, predict future water quality conditions through data modeling, generate health reports to assess the impact of water quality on user health, and provide personalized health recommendations based on user settings; When the water quality does not meet the standards, the system will automatically take adjustment measures, including using ultraviolet disinfection to treat microorganisms in water, adjusting mineral microelement concentration, and automatically adding mineral components according to water quality problems. The operation is generated according to the user's health needs and preset, and the personalized bathing scheme that meets the user's health needs is generated; Regularly calibrate the sensor and implement fault diagnosis to monitor the system operation state in real time. Through the intelligent maintenance module, the user is reminded and suggested to perform maintenance operations; Use a simple and easy-to-use application to provide real-time data visualization. The chart is updated in real time, and user feedback and manual parameter adjustment are supported. The user sets the target water quality parameters through the interface, views the real-time water quality report, feeds back the health needs or dissatisfaction, and adjusts the component settings according to the feedback data.

[0022] Through standardized data processing, the water quality monitoring of the system has higher accuracy and consistency, avoiding improper water quality adjustment due to data errors; the automatic control system intelligently adjusts according to water quality changes and user needs to ensure that the water quality continuously meets the health standards; according to the needs of different users, add essential oils, mineral components, etc. to provide users with a unique healthy bathing experience; based on the prediction ability of machine learning, potential changes can be identified in advance before water quality problems occur, avoiding the impact of sudden water quality problems on users; according to the water quality change trend and user's health needs, provide targeted health suggestions to help users maintain the best physical condition; when the water quality is abnormal, the system will automatically take corrective measures to avoid users using water that does not meet the health standards; regular self-calibration ensures the accuracy of water quality monitoring to prevent equipment errors from affecting water quality management; real-time fault diagnosis and maintenance reminders help users discover and solve problems in a timely manner, extending the service life of the system; users can use the application to understand the water quality in real time and adjust it according to their own needs, so that users can flexibly adjust the water quality parameters according to their individual needs; the application interface is friendly, users can easily view the water quality report and adjust the water quality settings through the feedback mechanism, enhancing interactivity and operability.

[0023] Regularly monitor the water quality of the bathtub, collect various water quality data through sensors, upload them to the central control unit in real time, and process the input data through data standardization technology: The Z-score standardization method converts data into a distribution with zero mean and unit standard deviation, and the formula is: The original value of the water quality data is; The mean value of the water quality data is; The standard deviation of the water quality data; The normalized data value; The Min-Max normalization method scales the data to a specific range, with the formula: The original value of the water quality data; The minimum value in the water quality data; The maximum value in the water quality data; The normalized data value.

[0024] In the water quality monitoring system, different types of water quality data usually have different units and dimensions; the Z-score normalization method can convert the data into a standard normal distribution, which has strong applicability in data comparison, anomaly detection, and subsequent machine learning algorithms; the Min-Max normalization method ensures that different water quality data has the same order of magnitude in subsequent operations by scaling the data to a unified range (such as 0 to 1); through these normalization methods, the system can convert various water quality data into an easy-to-analyze and process form, and then analyze water quality trends, predict future changes, and automatically adjust water quality components through intelligent algorithms; the normalized data enables the system to more accurately understand and control water quality, enhancing the accuracy and reliability of the system, and users can obtain personalized water quality adjustment schemes through unified data display and health reports, improving the overall bathing experience.

[0025] According to user selection and settings, the system adds aromatherapy, mineral microelements, mineral components, and ultraviolet light to the bathtub water through the automatic control module, and dynamically adjusts according to real-time changes in water quality and user health needs; the ultraviolet light technology detects the microbial content in the water through intelligent sensors and automatically adjusts the ultraviolet light output based on the detection results; mineral microelements and mineral components are dynamically added according to user settings and water quality needs, and the system intelligently controls their concentration through the following method: The adjustment of ultraviolet light output is based on the microbial content in the water, and a sensor is set to detect the concentration of microorganisms in the water The output intensity of ultraviolet light is dynamically adjusted through the following formula: The maximum output intensity coefficient of the ultraviolet light device; The real-time detected microbial concentration; Threshold of microorganism concentration in water quality, above which the UV intensity will be increased; UV output intensity; If , the UV output is automatically increased; The amount of mineral microelement and mineral component added is dynamically adjusted according to water quality data and user-set requirements, and a target concentration of certain mineral set by the user is established , the actual concentration of a certain mineral in the water is , the amount of mineral added is calculated by the following formula: is the adjustment coefficient of the mineral adding device, indicating the amount of mineral added per unit concentration difference; is the target concentration set by the user; is the actual concentration of the mineral in the current water quality; is the amount of mineral component to be added; The concentration of essential oil is intelligently controlled and adjusted according to user settings and changes in water quality, and a target value of essential oil concentration is set as , the current concentration is , and the amount of essential oil added is calculated by the following formula: is the adjustment coefficient of the essential oil adding device, indicating the amount of essential oil added per unit concentration difference; is the target essential oil concentration set by the user; is the current essential oil concentration in the water; is the amount of essential oil to be added; When the concentrations of various components in the water quality are controlled, a weighted average method is used to set a comprehensive control target according to the control requirements and priorities of each component, and a comprehensive control adjustment amount is established is the combination of UV, mineral, and essential oil components, and the specific adjustment amount is expressed as: , , is the importance weight of each component control.

[0026] By dynamically adjusting the concentrations of ultraviolet rays, minerals, and aromatherapy, the system can optimize water quality in real-time to adapt to different users' health needs and environmental changes. The intensity of ultraviolet output is adjusted according to the concentration of microorganisms to ensure the hygiene and safety of the water quality. The adjustment of mineral and aromatherapy concentrations provides a more personalized and comfortable bathing experience. The system not only considers the microbial contamination of water quality but also further enhances the user's health experience by precisely controlling the concentrations of minerals and aromatherapy. The system achieves automated water quality adjustment through intelligent sensors and control algorithms, without the need for user manual intervention. Through the weighted average method, the system can flexibly adjust the water quality according to the control requirements and priorities of different components, providing customized water quality experience for each user. Through the automatic control module and intelligent adjustment algorithm, the system can accurately adjust each component according to the actual water quality requirements, avoiding excessive addition, thereby saving energy and resources, achieving environmental protection and high efficiency.

[0027] The method uses a deep learning-based machine learning algorithm to analyze the collected data in real-time, identify water quality trends, predict future water quality conditions through data modeling, and generate a health report to assess the impact of water quality on user health, and provide personalized health recommendations based on user settings: The deep learning algorithm is used to model the time series of water quality data to predict future trends. The collected data includes timestamps and multiple water quality parameters. The following steps are used for prediction: For time series data Standardization: For water quality data at time point And are the mean and standard deviation of all data; LSTM neural network is used for time series prediction. The output of the model is the water quality prediction at future time steps : is the step size of historical data; is the predicted water quality at future time Based on water quality data and user health needs, the impact of water quality on health is evaluated, and the health risk assessment formula is: Set as the risk assessment value of water quality on user health. The model calculates it through the following formula: ,​​ , , is the value of the water quality parameter at time ; , , , is the weight coefficient of the model; Health risk classification: According to value classification, for: Low risk: Less than a certain threshold; Moderate risk: Within a certain range; High risk: Exceeds a certain threshold; Based on the output of the health risk assessment model , generate a health report containing the potential impact of water quality on user health, the health report includes the following: Evaluation of current water quality conditions; According to the user's health history; Generate health recommendations for predicted changes in water quality; Personalized health recommendations are based on user settings and generated using recommendation system algorithms, if a certain component in the water is too high or too low, the following recommendations are generated: Generate personalized health recommendations based on the impact of water quality on health and the user's health settings: Personalized health recommendations; User health settings; Function mapping according to health assessment and user needs, output personalized health recommendations.

[0028] The LSTM neural network in deep learning is used to model the time series of water quality, which can accurately predict the future trend of water quality change based on historical data. This prediction can help users understand the possible future water quality conditions in advance, so that they can take timely measures; by considering the user's health history and combining water quality data, the system can accurately assess the impact of water quality on different users' health, generate personalized health reports and risk assessments; the system can analyze water quality changes in real time and generate health reports based on prediction results. The report not only assesses the impact of current water quality on health, but also predicts possible future health risks and provides targeted health recommendations to help users make appropriate adjustments; this scheme uses the combination of deep learning and recommendation system algorithms to automatically adjust water quality components through intelligent decision-making to optimize users' health; through accurate prediction and health assessment of the deep learning model, users can obtain more accurate and timely health management information, and personalized health recommendations can help improve users' health management efficiency, so that each user's health needs can be best met.

[0029] When the water quality does not meet the standard, the system will automatically take adjustment measures, including using ultraviolet disinfection to treat microorganisms in water, adjusting the concentration of mineral microelements, and automatically adding mineral components according to water quality problems. The adjustment is based on the user's health needs and the preset to generate a personalized bathing scheme that meets the user's set health needs. The purpose of ultraviolet disinfection is to kill microorganisms in water. If the concentration of microorganisms in water exceeds the standard threshold , the ultraviolet intensity will be automatically adjusted according to the concentration of microorganisms . Ultraviolet intensity adjustment formula: is the real-time detected concentration of microorganisms in water is the threshold value of the concentration of microorganisms in water quality. If this value is exceeded, disinfection will be performed is the maximum allowed value of the concentration of microorganisms is the maximum output intensity coefficient of the ultraviolet disinfection device If , the ultraviolet disinfection intensity will be dynamically adjusted according to the deviation of the concentration of microorganisms The concentration of mineral microelements is adjusted according to water quality problems and user-set health needs. When the concentration of a certain mineral in water does not match the target concentration , adjustment will be made. The mineral adjustment formula is:​ Amount of mineral component to be added; Adjustment factor of mineral adding device, representing the amount of mineral to be added per unit of concentration difference; Target mineral concentration set by user; Mineral concentration in current water quality; For water quality problems, the system automatically adds specific mineral components according to real-time water quality data. If the water is deficient in calcium, magnesium, potassium minerals, the system automatically adds the required components according to these deficient elements. The formula for the amount of mineral to be added is: Amount of a certain mineral element to be added; Adjustment factor of mineral element adding device; Target mineral element concentration; Mineral element concentration in current water quality; The generation of personalized bathing program is based on the health needs of the user, combined with the analysis results of water quality to provide health advice. The user sets the demand for aromatherapy, mineral concentration components, and the personalized health advice is calculated through the following formula. The formula for personalized bathing program is: Personalized bathing program, including health advice, recommended water quality components, and concentration; Health risk output by health assessment model; Ultraviolet disinfection intensity, adjusted according to microbial concentration; Amount of mineral component to be added, according to water quality requirements; Amount of element to be automatically added according to deficient mineral; User's health settings; Function A complete personalized bathing program will be provided to the user by combining health assessment, water quality adjustment, and user setting factors. The program includes: Setting of ultraviolet disinfection intensity; Appropriate mineral and element concentration; Health recommendations based on water quality.

[0030] This solution can identify water quality problems in real time and automatically take measures to ensure that water quality always meets health standards by monitoring water quality data in real time; the intensity of ultraviolet disinfection and the amount of mineral addition are dynamically adjusted according to real-time water quality conditions and user needs; this automatic and precise adjustment method can effectively improve water quality, reduce manual intervention, and improve water treatment efficiency; the system not only processes water quality problems but also generates personalized bathing programs based on user health needs (such as aromatherapy and mineral concentration), so that each user can obtain customized water quality and health recommendations based on their health status and preferences; by combining user-set health needs and real-time water quality data, the system can provide customized health recommendations and optimize user health management by adjusting water quality components and disinfection intensity; for example, if a user has a high demand for calcium or magnesium, the system will automatically supplement the corresponding minerals to ensure that the water quality meets the user's health needs; the personalized bathing program provided by this solution not only ensures water quality hygiene during bathing but also provides additional health benefits, such as helping skin health through mineral adjustment or eliminating potential microbial hazards through ultraviolet disinfection; the system's intelligence and personalization enable each user to enjoy the best water quality and health management experience.

[0031] Regularly perform sensor self-calibration and implement fault diagnosis, monitor system operation status in real time, and generate and push user reminders through the intelligent maintenance module, the method for suggesting user maintenance operations is: Self-calibration is a process to ensure sensor accuracy and stability, during each calibration, the system detects the error value of the sensor and automatically adjusts it; Sensor error calibration formula: is the sensor error; is the standard calibration value; is the current reading of the sensor; If , the system will automatically calibrate and adjust the sensor output; The system monitors the operation status of each sensor and device in real time, detects whether a fault has occurred, and diagnoses the fault based on the sensor readings and the normal range of device operation, the fault diagnosis formula is: is the fault diagnosis flag, 1 indicates a fault, and 0 indicates normal; is the maximum allowed reading of the sensor; is the minimum allowed reading for the sensor; is the current reading of the sensor; If , a fault alarm is triggered and a fault repair process is initiated; Real-time monitoring is performed, and the system continuously collects data from the sensors and evaluates the overall system operation state through the monitoring model. The system operation state evaluation formula is: is the overall system operation state evaluation based on real-time data from all sensors; is the number of sensors; is the real-time data of the sensor; Through this formula, the system obtains an overall health status score , thereby evaluating the operation health of the system; The intelligent maintenance module generates maintenance reminders based on the system operation state, sensor readings, and fault diagnosis results, and pushes them to the user. The generation of reminders is based on the following algorithm: is the maintenance requirement flag, 1 indicating the need for maintenance and 0 indicating no need; is the system health status score; is the system health status threshold; is the fault diagnosis flag, 1 indicating a fault and 0 indicating normal; and are weight coefficients, indicating the degree of influence of health status and fault on maintenance requirements; If , the system generates a maintenance reminder and notifies the user to perform maintenance through a push notification; Once a maintenance requirement is generated, the system sends a maintenance reminder to the user through the push module. The push content includes maintenance suggestions, operation steps, and recommended maintenance time. The push notification generation formula is: is the content of the push notification; is the maintenance requirement flag, determining whether to send a notification; is the user's settings, including push preferences and notification receiving time.

[0032] By regularly performing sensor self-calibration, the system can ensure the accuracy and stability of the sensors. Each time the calibration is performed, the system automatically detects the error of the sensor and adjusts it, avoiding false judgments and unstable system operation due to sensor errors; the system can monitor the running state of the sensors and equipment in real time, discover abnormalities in time and perform fault diagnosis, when the sensor reading exceeds the normal range, the system will automatically trigger a fault alarm and start the repair process, reducing the impact of faults on the normal operation of the system; by integrating the real-time data of each sensor, the system can evaluate the health status of the entire system, which enables the system to actively discover potential problems and make adjustments and maintenance in advance, thereby prolonging the service life of the system and ensuring efficient operation of the system; the system intelligently generates maintenance demand signs based on the health status evaluation and fault diagnosis results. When the system needs maintenance, it automatically pushes detailed maintenance suggestions and operation steps to the user, through push notifications, the user can perform maintenance at the right time, thereby avoiding equipment failure or performance degradation; this scheme makes the system more stable and reliable through real-time monitoring, automatic adjustment, intelligent maintenance and other functions, users can operate in time through the maintenance reminders of the system to avoid problems caused by neglecting maintenance, in addition, personalized push notification settings and operation suggestions improve the user experience, making maintenance work more convenient and intelligent.

[0033] A simple and easy-to-use application is adopted to provide real-time data visualization display, real-time updating of charts, and simultaneous support for user feedback and manual parameter adjustment. The user sets target water quality parameters through the interface, views real-time water quality reports, feeds back health needs or dissatisfaction, and adjusts the component setting according to the feedback data: Real-time data display and chart updating are based on sensor-collected data, which is updated and displayed on the chart in real time. The water quality data update formula is: To display real-time water quality data in time The user can input target water quality parameters through the interface, and the system adjusts and displays water quality reports according to the target parameters. The user target water quality setting formula is: The user sets a set of target water quality parameters , ,..., ​​​Specific numerical values for target water quality; Users provide feedback on their health needs or dissatisfaction through the interface. The system receives and adjusts based on feedback data. User feedback processing formula: Feedback collection for users , ,..., Specific feedback items; Based on user feedback and target parameters, the system automatically adjusts water quality components or filtration settings to match target water quality standards. Adjustment component setting formula: Adjusted water quality component configuration; User-set target water quality parameters; User feedback content, including health needs or dissatisfaction; Real-time water quality data; The system generates water quality reports regularly for users to view and optimize adjustments based on feedback. Water quality report generation formula: Water quality report content, showing current water quality, target water quality and adjusted components; Real-time water quality data; User target water quality parameters; Adjusted water quality components; Manual adjustment formula: User manual adjustment of components or operations; Current water quality component configuration; User input manual parameters; The entire system will form a closed loop based on real-time data, user targets and feedback, continuously adjusting water quality. Closed loop adjustment formula: Feedback loop, indicating the closed loop of data flow and adjustment process; Real-time data, target water quality, user feedback, adjusted ingredients, and manually input parameters.

[0034] Real-time data display and chart updates can provide users with intuitive water quality conditions, ensuring that users can always understand the trend of water quality changes. The system collects data from sensors in real time and updates it to the chart, helping users monitor water quality in real time. Users can set target water quality parameters according to their own needs, and the system will adjust the water quality according to these target parameters. Through user feedback on health needs or dissatisfaction, the system can flexibly adjust water quality ingredients to better match user needs. The automatic adjustment mechanism makes water quality optimization more accurate and efficient, reducing the need for manual intervention. Users can fine-tune water quality ingredients through a manual adjustment interface, providing users with greater control space and meeting their specific water quality requirements. The system generates water quality reports regularly to help users understand the current water quality, target water quality, and system adjustment results. The transparency of the report allows users to track the effectiveness of water quality adjustments, further enhancing user trust and satisfaction. Through a closed-loop adjustment mechanism, the system can continuously optimize from multiple dimensions such as real-time data, target water quality, user feedback, and manual adjustments, ensuring that the water quality is always in the best state and can be dynamically adjusted as user needs change.

[0035] The embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize the above-mentioned smart bathtub water quality automatic detection management system and management method.

[0036] The embodiment also provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to realize the above-mentioned smart bathtub water quality automatic detection management system and management method.

[0037] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present 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. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), 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), etc.

[0038] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the above-mentioned functions.

[0039] The above-mentioned embodiments of the present application are not a limitation on the protection scope of the present application, and the embodiments of the present application are not limited thereto. According to the above-mentioned content of the present application, other various forms of modifications, replacements or changes of the above-mentioned structure of the present application can be made according to ordinary technical knowledge and conventional means in the art without departing from the above-mentioned basic technical idea of the present application, which should fall within the protection scope of the present application.

Claims

1. An automatic detection and management system for water quality in intelligent bathtubs, characterized in that: Includes: The water quality monitoring module uses sensors to detect pH, turbidity, dissolved oxygen, and temperature parameters in water in real time, uploads the data to the central control unit, and connects to the health database; The health element management module is used to intelligently adjust the health ingredients in water based on user preferences by adding aromatherapy essential oils and mineral health-promoting ingredients; A data analysis and prediction module that applies deep learning algorithms to analyze water quality changes, predict potential problems, and automatically generate reminders for users to add healthy elements or adjust water quality; Dynamic water quality adjustment module, which automatically adjusts water temperature, turbidity, and sterilization measures to ensure water quality compliance and generates intelligent recommendations for healthy soaking plans; The self-calibration and fault diagnosis module is used to perform regular sensor self-calibration, start the fault diagnosis program, and generate prompts for users to perform maintenance; The user interaction platform module is used to display water quality data and treatment status in real time through the application, while users can manually intervene and adjust water quality parameters through the application.

2. A method for automatic detection and management of water quality in a smart bathtub, characterized in that: Includes: Regularly monitor bathtub water quality, collect various water quality data through sensors, upload them to the central control unit in real time, and process the input data through data standardization technology; Based on user selections and settings, the system adds aromatherapy, mineral trace elements, mineral components, and ultraviolet light to the bathtub water through an automatic control module, and dynamically adjusts according to real-time changes in water quality and the user's health needs. Ultraviolet technology uses intelligent sensors to detect the microbial content in the water and automatically adjusts the UV output based on the test results. Mineral trace elements and mineral components are dynamically added according to user settings and water quality requirements, and the system intelligently controls their concentrations. Using a deep learning-based machine learning algorithm, the system analyzes collected data in real time, identifies water quality trends, predicts future water quality conditions through data modeling, generates health reports, assesses the impact of water quality on user health, and provides personalized health recommendations based on user settings. When water quality does not meet standards, the system will automatically take corrective measures, including using ultraviolet disinfection to treat microorganisms in the water, adjusting the concentration of mineral trace elements, and automatically adding mineral components based on water quality issues. Adjustment operations are based on the user's health needs and presets, generating a personalized bathing plan that meets the user's set health needs; Regularly perform sensor self-calibration and fault diagnosis, monitor system operation status in real time, generate and push user reminders through the intelligent maintenance module, and recommend maintenance operations to users; It uses a simple and easy-to-use application to provide real-time data visualization, real-time chart updates, and supports user feedback and manual parameter adjustment. Users can set target water quality parameters through the interface, view real-time water quality reports, feedback health needs or dissatisfaction, and adjust ingredient settings based on feedback data.

3. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 2, characterized in that: Regularly monitor the bathtub water quality, collect various water quality data through sensors, upload them to the central control unit in real time, and process the input data through data standardization technology as follows: The Z-score normalization method transforms the data into a distribution with zero mean and unit standard deviation, as follows: is the original value of water quality data; is the mean of water quality data; is the standard deviation of water quality data; is the standardized data value; The Min-Max normalization method scales the data to a specific range, and the formula is: is the original value of water quality data; is the minimum value in the water quality data; is the maximum value in the water quality data; is the normalized data value.

4. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 3, characterized in that: Based on user selections and settings, the system adds aromatherapy, mineral trace elements, mineral components, and ultraviolet light to the bathtub water through an automatic control module, and dynamically adjusts according to real-time changes in water quality and the user's health needs. Ultraviolet technology uses intelligent sensors to detect the microbial content in the water and automatically adjusts the ultraviolet output based on the test results. Mineral trace elements and mineral components are dynamically added according to user settings and water quality requirements. The system intelligently controls their concentrations in the following ways: The UV output is adjusted according to the microbial content in the water, and a sensor is set up to detect the concentration of microorganisms in the water. , UV output intensity Dynamic adjustment is made through the following formula: is the maximum output intensity coefficient of the UV device; is the real-time detected microbial concentration; is the threshold value of microbial concentration in water quality, exceeding which the UV intensity will increase; is the UV output intensity; like , UV output Automatic height adjustment; The amount of mineral trace elements and mineral components added is dynamically adjusted according to water quality data and user-set requirements, and the user sets the target concentration of certain minerals. , the actual concentration of a certain mineral in water is , mineral addition Calculated by the following formula: The adjustment coefficient for the mineral addition equipment, which indicates the amount of mineral added per unit concentration difference; Target concentration set by the user; is the actual concentration of the mineral in the current water quality; is the amount of mineral components that should be added; The concentration of the aromatherapy is controlled intelligently and adjusted according to the user's settings and changes in water quality. The target value of the aromatherapy concentration is set to , the current concentration is , the amount of aromatherapy added Calculated by the following formula: The adjustment coefficient for the aromatherapy adding device indicates the amount of aromatherapy required to be added per unit concentration difference; Target aroma concentration set by the user; is the current aroma concentration in the water; is the amount of aromatherapy that should be added; When controlling the concentration of each component in the water quality, a weighted average method is used to set a comprehensive control target based on the control requirements and priorities of each component. The comprehensive control adjustment amount is set as a combination of ultraviolet rays, minerals, and aromatherapy components. The specific adjustment amount is expressed as: , , Importance weights for each component control.

5. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 4, characterized in that: Using a machine learning algorithm based on deep learning, the collected data is analyzed in real time to identify water quality trends. Future water quality conditions are predicted through data modeling. Health reports are generated to assess the impact of water quality on user health. The personalized health recommendations are provided based on user settings. Use deep learning algorithms to model water quality data in time series and predict future water quality trends. The collected data includes timestamps and multiple water quality parameters, and predictions are made through the following steps: For time series data To standardize: For time point water quality data; and is the mean and standard deviation of all data; LSTM neural network is used for time series prediction, and the output of the model is the water quality prediction for the future time step : is the step size of historical data; For the future time Predicted water quality at all times; Based on water quality data and user health needs, the impact of water quality on health is assessed using the health risk assessment formula: set up The risk assessment value of water quality to user health is calculated using the following formula: , , , is the water quality parameter at time The value of , , , is the weight coefficient of the model; Health risk classification: according to Value categories are: Low risk: Less than a certain threshold; Medium risk: within a certain range; High risk: exceeds a certain threshold; Based on the output of the health risk assessment model , generate a health report containing the potential impact of water quality on user health. The health report includes the following: Assessment of current water quality conditions; Based on the user’s health history; Generate health advisories based on predicted changes in water quality; Personalized health recommendations are generated based on user settings using a recommendation system algorithm. If a component in the water quality is too high or too low, the following recommendations will be generated: Generate personalized health recommendations based on the impact of water quality on health and the user's health settings: for personalized health recommendations; Set for the user's health; Output personalized health recommendations based on function mapping between health assessment and user needs.

6. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 5, characterized in that: When the water quality does not meet the standards, the system will automatically take adjustment measures, including using ultraviolet disinfection to treat microorganisms in the water, adjusting the concentration of mineral trace elements, and automatically adding mineral components according to water quality issues. The adjustment operation is based on the user's health needs and presets to generate a personalized bathing plan that meets the user's set health needs. The method is as follows: The purpose of ultraviolet disinfection is to kill microorganisms in water. If the concentration of microorganisms in water is Exceeding the standard threshold , the UV intensity is automatically adjusted according to the concentration of microorganisms ; UV intensity adjustment formula: It is the real-time detection of microbial concentration in water; The threshold value of microbial concentration in water quality, above which disinfection will be required; is the maximum permissible concentration of microorganisms; is the maximum output intensity coefficient of the ultraviolet disinfection equipment; like , UV disinfection intensity Dynamic adjustments will be made based on the degree of deviation in microbial concentration; The concentration of mineral trace elements is adjusted according to water quality problems and health needs set by users, and the concentration of certain minerals in water is set. With target concentration If there is inconsistency, adjustments will be made. The mineral adjustment formula is: is the amount of mineral ingredients to be added; The adjustment coefficient for the mineral addition equipment, which indicates the amount of mineral added per unit concentration difference; Target mineral concentrations set for the user; is the mineral concentration in the current water quality; For water quality issues, the system automatically adds specific mineral components based on real-time water quality data. If the water lacks calcium, magnesium, or potassium minerals, the system automatically adds the required components based on these missing elements. The formula for mineral addition is: The amount of a certain mineral element to be added; Adjustment coefficients for equipment adding mineral elements; is the target mineral element concentration; is the concentration of mineral elements in the current water quality; The generation of personalized bathing plans is based on the user's health needs and provides health recommendations based on the results of water quality analysis. The user sets their requirements for aromatherapy and mineral concentration components, and the personalized health recommendations are calculated using the following formula. The personalized bathing plan formula is: Personalized bathing plan, including health advice, recommended water ingredients and concentration; Health risks as output of health assessment models; The intensity of ultraviolet disinfection is adjusted according to the concentration of microorganisms; The amount of mineral components to be added depends on the water quality requirements; The amount of elements automatically added according to the mineral deficiency; Set for the user's health; function We will combine health assessment, water quality adjustment, and user-defined factors to provide users with a complete personalized bathing plan, which includes: Setting of UV disinfection intensity; appropriate mineral and element concentrations; Health recommendations based on water quality.

7. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 6, characterized in that: Regularly perform sensor self-calibration and fault diagnosis, monitor system operation status in real time, and generate and push user reminders through the intelligent maintenance module, suggesting maintenance operations for users: Self-calibration is a process to ensure the accuracy and stability of the sensor. During each calibration, the system detects the error value of the sensor and automatically adjusts it. Sensor error calibration formula: is the sensor error; is the standard calibration value; is the current reading of the sensor; like , the system will automatically calibrate and adjust the sensor output; The system monitors the operating status of each sensor and device in real time to detect whether a fault has occurred. Fault diagnosis is based on the sensor readings and the normal operating range of the device. The fault diagnosis formula is: It is a fault diagnosis flag, 1 indicates fault, 0 indicates normal; is the maximum allowable reading of the sensor; is the minimum permissible reading of the sensor; is the current sensor reading; like , then a fault alarm is triggered and the fault repair process is started; For real-time monitoring, the system will continuously collect sensor data and evaluate the overall operating status of the system through the monitoring model. The system operating status evaluation formula is: Assess the overall system operating status based on real-time data from all sensors; is the number of sensors; For the Real-time data from sensors; Through this formula, the system derives an overall health status score , thereby evaluating the operational health of the system; The intelligent maintenance module generates maintenance reminders based on the system's operating status, sensor readings, and fault diagnosis results, and pushes them to the user. The reminder generation is based on the following algorithm: Maintenance requirement flag, 1 means maintenance is required, 0 means no maintenance is required; Score the system health status; is the system health status threshold; It is a fault diagnosis flag, 1 indicates fault, 0 indicates normal; and are weight coefficients, indicating the degree of influence of health status and fault on maintenance demand; like , the system generates maintenance reminders and informs users of maintenance via push notifications; Once a maintenance requirement is generated, the system sends a maintenance reminder to the user through the push module. The push content includes maintenance suggestions, operation steps, and recommended maintenance time. The push notification generation formula is: The content of the push notification; To maintain the demand flag, decide whether to send a notification; Settings for users, including push preferences and the time to receive notifications.

8. The method for automatic detection and management of water quality based on an intelligent bathtub according to claim 7, characterized in that: A simple and easy-to-use application provides real-time data visualization and real-time chart updates. It also supports user feedback and manual parameter adjustment. Users can set target water quality parameters through the interface, view real-time water quality reports, report health needs or dissatisfaction, and adjust ingredient settings based on the feedback data. Real-time data display and chart updates are based on data collected by sensors, and real-time updates are displayed on the chart. The water quality data update formula is: For in time Real-time water quality data; Real-time data collected by sensors; Water quality parameters preset for the system; The user can input the target water quality parameters through the interface, and the system will adjust and display the water quality report according to the target parameters. The user's target water quality setting formula is: A set of target water quality parameters set by the user , ,..., is the specific value of the target water quality; Users use the interface to provide feedback on their health needs or dissatisfaction. The system receives and adjusts based on the feedback data. The user feedback processing formula is: Collection of user feedback , ,..., is a specific feedback item; Based on user feedback and target parameters, the system automatically adjusts water composition or filtration settings to match the target water quality standards and adjusts the composition setting formula: It is the configuration of the adjusted water composition; Target water quality parameters set by the user; Provide user feedback, including health needs or dissatisfaction; For real-time water quality data; The system regularly generates water quality reports for users to review and makes optimization adjustments based on feedback. The water quality report generation formula is: The water quality report shows the current water quality, target water quality and adjusted composition; For real-time water quality data; Target water quality parameters for users; is the water quality composition after adjustment; Manually adjust the formula: Ingredients or operations that are manually adjusted by the user; Configure for current water quality composition; Manual parameters entered by the user; The entire system forms a closed loop based on real-time data, user goals, and feedback to continuously adjust water quality. The closed-loop adjustment formula is: It is a feedback loop, which represents the closed loop of data flow and adjustment process; Includes real-time data, target water quality, user feedback, adjusted composition and manually entered parameters.

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 method for automatic detection and management of water quality based on a smart bathtub as described 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, an automatic detection and management method for water quality of a smart bathtub according to any one of claims 2 to 8 is implemented.

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