Cosmetic health information management system

Abstract

The invention discloses a beauty health information management system. According to the technical scheme, the beauty health information management system mainly comprises a user information management module, a data acquisition module, a health assessment module, a personalized scheme recommendation module, a reservation and service management module and a data security and privacy protection module. The user information management module comprises the following steps: S1, user file management: storing basic information of a user, and storing and recording dynamic health information of the user, such as a skin type, a body fat rate, blood pressure and an allergy history; s1.1, data input and update: supporting manual input and automatic synchronization of health data by an intelligent device, receiving data uploaded by a user or the device through a form or an API interface, and updating a database; and S1.2, querying and screening: quickly retrieving user information and historical data, supporting screening according to time and categories, and realizing a multi-condition screening function through SQL query.

Description

Technical Field

[0001] The present invention relates to the field of beauty and health information management, and in particular to a beauty and health information management system. Background Art

[0002] Facial skin is exposed to the air for many years. Ultraviolet radiation, dirt, dust, bacteria and other natural harmful substances in the air stimulate the skin surface. In addition, the oil, sweat, dead cells and other factors secreted by the skin itself will affect the normal function of the skin and even cause skin diseases such as acne, leading to premature aging of the skin. Skin care has become an indispensable part of our daily life. Proper skin care can remove dirt and skin secretions on the skin surface, keep the secretions of sweat glands and sebaceous glands unobstructed, and prevent bacterial infection. Skin cleansing can relax and rest the skin so as to give full play to the physiological functions of the skin and show youthful vitality. Skin cleansing can adjust the pH value of the skin to restore its normal pH and protect the skin. With the improvement of people's economic living standards, more and more people begin to pay attention to their skin care, and skin beauty care has become the most concerned area in the beauty industry.

[0003] However, in the existing technology, most systems mainly rely on manual input from users or simple health data collection, such as basic files, weight, and body data sources are relatively single, and the integration with smart devices is poor. They match and recommend beauty services based on simple user feature tags and lack dynamic adjustment capabilities. For this reason, we propose a beauty and health information management system. Summary of the invention

[0004] In view of the problems mentioned in the background technology, the purpose of the present invention is to provide a beauty and health information management system to solve the problems mentioned in the background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A beauty and health information management system includes the following steps: user information management module, data collection module, health assessment module, personalized plan recommendation module, appointment and service management module, data security and privacy protection module; the user information management module includes the following steps: S1: user profile management, storing basic information of users, health data storage and recording dynamic health information of users, such as skin type, body fat percentage, blood pressure, allergy history; S1.1: data input and update, supporting manual entry and automatic synchronization of health data by smart devices, receiving data uploaded by users or devices through forms or API interfaces, and updating data library; S1.2: query and filter, quickly retrieve user information and historical data, support filtering by time and category, and realize multi-condition filtering function through SQL query; S1.3: data analysis and visualization, analyze user data and generate statistical charts, such as health trend charts and skin condition distribution; S1.4: permission management, limit the access rights of different roles to user information, ensure data security, set up role permission table, associate with users, and divide them into administrators, beauticians and ordinary users; S1.5: data backup and recovery, regularly back up user data to prevent data loss due to system failure, and use database backup mechanism or cloud storage service.

[0007] Preferably, the data acquisition module includes the following steps: S2: data source definition, clarifying the supported data acquisition methods and sources, such as skin detectors, body fat scales, wearable devices, manually entering health data through forms or mobile APPs or from third parties; S2.1: data acquisition preparation, hardware device access realizes device connection through Bluetooth, Wi-Fi or USB interface, API interface definition, designing standardized interfaces for data interaction according to the documents provided by the third-party platform; S2.2: data field normalization, defining the field standards of health data, including data types integer, floating point, unit (such as % or mg / dL), timestamp; S2.3: data acquisition and transmission, real-time data acquisition through protocols (such as BLE or MQTT), real-time reception of health data of hardware devices, regular pulling or receiving of pushed data from third-party platforms, data verification to verify the integrity of collected data (fields are S2.4: Data storage: Temporary storage: temporarily stores the collected data in the cache to improve real-time performance; Persistent storage: writes the data in the cache to the database, and stores it in separate tables to improve query efficiency; Data archiving: archives data that exceeds a certain time period and stores it in low-cost cloud storage to reduce the pressure on the main database; S2.5: Data display and feedback: Visualization: Use the front-end chart library to display the collected health data in real time, provide data trend analysis, and user feedback: Users receive real-time health status update APP notifications and receive reminders for abnormal data; S2.6: Data security and privacy protection: Data encryption: Use AES to encrypt data during transmission and storage, and require users to explicitly authorize before authorizing data collection.

[0008] Preferably, the health assessment module includes the following steps: S3: data input preparation, obtaining user health data and verifying data integrity and accuracy; S3.1: data classification and preprocessing, classifying health data, organizing data according to different categories for subsequent analysis, skin health (moisture, oil, elasticity), body health (body fat rate, BMI, blood pressure) and lifestyle habits (sleep duration, water intake), feature extraction extracts key feature values ​​according to the analysis target; S3.2: health assessment algorithm, rule base matching defines assessment rules according to expert knowledge or industry standards (skin moisture <30%: dry skin, BMI>25: may be overweight, sleep <6 hours per night S3.3: Generation of evaluation results: Generate an evaluation report, which includes a health score (such as 0-100), data analysis (such as the skin moisture fluctuation curve in the past week) and a summary of suggestions (such as increasing the frequency of using moisturizer). The report format uses line charts and pie charts to display data. Text natural language generation technology generates personalized suggestions. Risk warnings are issued for high-risk data (such as dry skin or high BMI). S3.4: Data feedback and optimization: User feedback: Users can rate the evaluation results or make suggestions for improvement. User feedback is collected to optimize the evaluation rules and models.

[0009] Preferably, the personalized solution recommendation module includes the following steps: S4: data preparation, user data collection, user profile, basic information (such as age, gender), skin type, allergy history, real-time health data such as skin moisture, body fat percentage, user behavior data, browsing history, purchase history, ratings and feedback; S4.1: data cleaning and normalization, removal of duplicate or erroneous data, conversion of data from different sources into comparable numerical forms, labeling, generating feature labels for users based on data, static labels: such as "sensitive skin", "oily skin", dynamic labels: such as "skin moisture has decreased recently"; S4.2: data analysis and modeling, rule base matching, based on expert rules or industry standards, define recommendation rules for different user groups: if skin moisture is less than 30%, moisturizing products are recommended; if BMI is greater than 25, fat-reducing exercise plans are recommended; if "pollen" is included in the allergy history, products containing pollen ingredients are excluded; S4.2.1: recommendation algorithm selection, collaborative filtering algorithm, user-based collaborative filtering: recommend products that are similar to the user Like products, item-based collaborative filtering: recommend other products similar to the items selected by the user, content recommendation algorithm, based on user tag matching and item feature description, hybrid recommendation combines collaborative filtering with content recommendation, and integrates user preferences and product features; S4.3: Personalized solution generation, recommended content generation determines the type of recommended content, content priority sorting uses a scoring mechanism to sort the recommended content, and a diversified strategy avoids repeated recommendations and provides a variety of choices; S4.4: Recommendation display, recommendation list generation formats the generated recommendation solution, product pictures, names, function descriptions, interactive display in the user interface to display the recommendation solution, dynamic adjustment according to user behavior (such as browsing or purchasing), real-time adjustment of recommended content; S4.5: Effect tracking and optimization, data feedback collects users' actual usage data, uses these data to optimize the recommendation model or rules, model adjustment regularly retrains the machine learning model, updates the recommendation rule base, and recommends key indicators for effect monitoring: click-through rate (CTR), conversion rate (CVR), and user satisfaction score.

[0010] Preferably, the reservation and service management module includes the following steps: S5: data preparation and basic configuration, service item information configuration, beautician information management and user information management; S5.1: reservation process, user selects service. The user browses the service items on the front-end interface, selects the time and personnel to provide available time periods, filters out conflicting time periods, the system automatically assigns or the user manually selects a beautician, fills in the reservation information, the user fills in the necessary reservation information, notes information (special needs or allergy history), reservation verification, verifies whether the information filled in by the user is complete, confirms the reservation and sends a notification, notifies the user and the beautician, and the SMS / email notification of the successful reservation is sent before the reservation. Reminders are sent 24 hours a day; S5.2: Service management, service status update. On the day of the appointment, the service status can be divided into: waiting for service, in progress and completed. The service process management records the start and end time of the service process and user payment in the system; S5.3: User feedback, feedback form. After completing the service, the user can fill in the feedback form satisfaction score (1-5 stars) and feedback data storage. Store user reviews for beautician performance evaluation and service optimization, service optimization suggestions. Based on the feedback, the system generates service improvement suggestions or recommends related services; S5.4: Data visualization and statistical analysis, appointment data statistics, user habit analysis and performance analysis.

[0011] Preferably, the privacy protection module includes the following steps: S6: data collection and encrypted storage, data collection and encryption, sensitive information submitted by users (such as ID number, health data) is encrypted during transmission using the transport layer encryption protocol (TLS1.3) and database encryption storage; S6.1: data access permission control, user permission division, division of permissions according to user type (ordinary users, system administrators, beauticians), role-based access control to assign access permissions to different roles, and desensitize sensitive data; S6.2: data transmission security, using the security protocol, all API interfaces in the system use HTTPS to ensure data transmission encryption, prevent transmission data tampering, and use HMAC (hash message authentication code) to verify the integrity of transmission data; S6.3: logging and monitoring, sensitive operation log recording, log security storage, abnormal monitoring and alarm; S6.4: data sharing and third parties Service management, third-party service security review. All connected third-party services (such as payment platforms, SMS notification services) must meet security certification standards, minimize shared data and only provide necessary data to third parties to avoid redundant information leakage, and use end-to-end encryption technology (such as PGP) for encrypted sharing to ensure the security of data sharing with third parties; S6.5: Compliance assurance, data storage compliance ensures that user data is stored in servers that comply with regional laws, user data access and deletion rights provide user data management functions, and privacy policy display clearly displays the privacy policy (data usage description, data storage and processing methods) in the system; S6.6: Data backup and recovery, regular backup and disaster recovery drills; S6.7: Security assessment and update, regular security assessment, quarterly security audit, timely update and patch known vulnerabilities, regular update of system and database versions, and introduction of a security patch management system.

[0012] In summary, the present invention mainly has the following beneficial effects:

[0013] By adopting the beauty and health information management system, based on user health data and personalized needs, dynamic personalized recommendations are provided to meet user personalized needs. The one-stop function integration significantly improves the ease of use. Through health trend analysis, users can understand their own conditions more clearly, improve satisfaction, and enhance user experience. The all-round encryption and permission management mechanism ensures that user data is not leaked or tampered with. The system strictly abides by privacy regulations, gives users access to and management rights to their own data, and increases trust. Through continuous data collection and personalized suggestions, users can establish good health habits and increase the frequency of system use. Through intelligent scheduling algorithms, beautician and equipment resources are fully utilized to reduce idle time, improve service efficiency, reduce manual intervention, shorten the waiting time during appointments and services, and improve overall operational efficiency. By introducing automated management methods, the beauty and health industry is promoted from traditional manual management to digital and automated transformation. This technical solution significantly improves user experience, service efficiency and business decision-making capabilities, while promoting the digital transformation of the industry and the improvement of social health management levels. Its good effects include multiple dimensions such as improved user satisfaction, optimized operational efficiency, data value mining and privacy protection, which provide support for the future development of the beauty and health industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system schematic diagram of the present invention; DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] refer to Figure 1, a beauty and health information management system, including the following steps: user information management module, data collection module, health assessment module, personalized plan recommendation module, appointment and service management module, data security and privacy protection module, mainly six parts; the user information management module includes the following steps, S1: user profile management, storing the user's basic information, health data storage records the user's dynamic health information, such as skin type, body fat percentage, blood pressure, allergy history; S1.1: data input and update, support manual entry and automatic synchronization of health data by smart devices, receive data uploaded by users or devices through forms or API interfaces, update data Database; S1.2: Query and filter, quickly retrieve user information and historical data, support filtering by time and category, and realize multi-condition filtering function through SQL query; S1.3: Data analysis and visualization, analyze user data and generate statistical charts, such as health trend charts and skin condition distribution; S1.4: Permission management, limit the access rights of different roles to user information to ensure data security, set up role permission tables, associate with users, and divide them into administrators, beauticians and ordinary users; S1.5: Data backup and recovery, regularly back up user data to prevent data loss due to system failure, and use database backup mechanism or cloud storage service.

[0017] Preferably, the data acquisition module includes the following steps: S2: data source definition, clarifying the supported data acquisition methods and sources, such as skin detectors, body fat scales, wearable devices, manually entering health data through forms or mobile APPs or from third parties; S2.1: data acquisition preparation, hardware device access realizes device connection through Bluetooth, Wi-Fi or USB interface, API interface definition, designing standardized interfaces for data interaction according to the documents provided by the third-party platform; S2.2: data field normalization, defining the field standards of health data, including data types integer, floating point, unit (such as % or mg / dL), timestamp; S2.3: data acquisition and transmission, real-time data acquisition through protocols (such as BLE or MQTT), real-time reception of health data of hardware devices, regular pulling or receiving of pushed data from third-party platforms, data verification to verify the integrity of collected data (fields are S2.4: Data storage: Temporary storage: temporarily stores the collected data in the cache to improve real-time performance; Persistent storage: writes the data in the cache to the database, and stores it in separate tables to improve query efficiency; Data archiving: archives data that exceeds a certain time period and stores it in low-cost cloud storage to reduce the pressure on the main database; S2.5: Data display and feedback: Visualization: Use the front-end chart library to display the collected health data in real time, provide data trend analysis, and user feedback: Users receive real-time health status update APP notifications and receive reminders for abnormal data; S2.6: Data security and privacy protection: Data encryption: Use AES to encrypt data during transmission and storage, and require users to explicitly authorize before authorizing data collection.

[0018] As a further solution of the present invention, the health assessment module includes the following steps: S3: data input preparation, obtaining user health data and verifying data integrity and accuracy; S3.1: data classification and preprocessing, classifying health data, organizing data according to different categories for subsequent analysis, skin health (moisture, oil, elasticity), body health (body fat rate, BMI, blood pressure) and living habits (sleep duration, water intake), feature extraction extracts key feature values ​​according to the analysis target; S3.2: health assessment algorithm, rule base matching defines assessment rules according to expert knowledge or industry standards (skin moisture <30%: dry skin, BMI>25: may be overweight, sleep per night Sleep <6 hours: insufficient sleep), personalized assessment adjusts the assessment weight according to the user's health goals; S3.3: Generation of assessment results, generating an assessment report, which includes a health score (such as 0-100), data analysis (such as the skin moisture fluctuation curve in the past week) and a summary of suggestions (such as increasing the frequency of moisturizer use), report format, charts using line graphs and pie charts to display data, text natural language generation technology to generate personalized suggestions, risk warnings for high-risk data, issued reminders (such as skin is too dry or BMI is too high); S3.4: Data feedback and optimization, user feedback, users can rate the assessment results or make suggestions for improvement, collect user feedback, and optimize the assessment rules and models.

[0019] As a further solution of the present invention, the personalized solution recommendation module includes the following steps: S4: data preparation, user data collection, user profile, basic information (such as age, gender), skin type, allergy history, real-time health data such as skin moisture, body fat percentage, user behavior data, browsing history, purchase history, ratings and feedback; S4.1: data cleaning and normalization, removal of duplicate or erroneous data, conversion of data from different sources into comparable numerical forms, labeling, generating feature labels for users based on data, static labels: such as "sensitive skin", "oily skin", dynamic labels: such as "skin moisture has decreased recently"; S4.2: data analysis and modeling, rule base matching, based on expert rules or industry standards, define recommendation rules for different user groups: skin moisture is less than 30%, recommend moisturizing products, BMI is greater than 25, recommend fat loss exercise plans, allergy history contains "pollen", exclude products containing pollen ingredients; S4.2.1: recommendation algorithm selection, collaborative filtering algorithm, user-based collaborative filtering: recommend other similar users Products that are liked by people of different groups, item-based collaborative filtering: recommend other products that are similar to the items selected by the user, content recommendation algorithm, based on user tag matching and item feature description, hybrid recommendation combines collaborative filtering with content recommendation, and integrates user preferences and product features; S4.3: Personalized solution generation, recommended content generation determines the type of recommended content, content priority sorting uses a scoring mechanism to sort the recommended content, and a diversified strategy avoids repeated recommendations and provides a variety of choices; S4.4: Recommendation display, recommendation list generation formats the generated recommendation solution, product pictures, names, and function descriptions, interactively displays the recommendation solution in the user interface, and dynamically adjusts the recommended content in real time according to user behavior (such as browsing or purchasing); S4.5: Effect tracking and optimization, data feedback collects users' actual usage data, uses these data to optimize the recommendation model or rules, and adjusts the model to regularly retrain the machine learning model and update the recommendation rule library. Key indicators for monitoring recommendation effects: click-through rate (CTR), conversion rate (CVR), and user satisfaction score.

[0020] As a further solution of the present invention, the reservation and service management module includes the following steps: S5: data preparation and basic configuration, service item information configuration, beautician information management, and user information management; S5.1: reservation process, user selects service. The user browses the service items on the front-end interface, selects the time and personnel to provide available time periods, filters out conflicting time periods, the system automatically assigns or the user manually selects a beautician, fills in the reservation information, the user fills in the necessary reservation information, remarks information (special needs or allergy history), reservation verification, verifies whether the information filled in by the user is complete, confirms the reservation and sends a notification, notifies the user and the beautician, and the SMS / email notification of the successful reservation , a reminder will be sent 24 hours before the appointment; S5.2: Service management, service status update, on the day of the appointment, the service status can be divided into: waiting for service, in progress and completed, the service process management records the start and end time of the service process and user payment in the system; S5.3: User feedback, feedback form, after completing the service, the user can fill in the feedback form satisfaction score (1-5 stars), feedback data storage, store user evaluation, used for beautician performance evaluation and service optimization, service optimization suggestions, the system generates service improvement suggestions or recommends related services based on feedback; S5.4: Data visualization and statistical analysis, appointment data statistics, user habit analysis and performance analysis.

[0021] As a further solution of the present invention, the privacy protection module includes the following steps: S6: data collection and encrypted storage, data collection and encryption. Sensitive information submitted by users (such as ID number and health data) is encrypted and stored in the database during transmission using the transport layer encryption protocol (TLS1.3) and database encryption; S6.1: data access permission control, user permission division, and division of permissions according to user type (ordinary users, system administrators, beauticians). Role-based access control assigns access permissions to different roles and desensitizes sensitive data; S6.2: data transmission security, using the security protocol. All API interfaces in the system use HTTPS to ensure data transmission encryption and prevent tampering of transmitted data. HMAC (hash message authentication code) is used to verify the integrity of transmitted data; S6.3: logging and monitoring, sensitive operation log recording, log security storage, abnormal monitoring and alarm; S6.4: data sharing Management with third-party services, third-party service security review. All connected third-party services (such as payment platforms, SMS notification services) must meet security certification standards, minimize shared data and only provide necessary data to third parties to avoid redundant information leakage, and use end-to-end encryption technology (such as PGP) for encrypted sharing to ensure the security of data sharing with third parties; S6.5: Compliance assurance, data storage compliance ensures that user data is stored in servers that comply with regional laws, user data access and deletion rights provide user data management functions, and privacy policy display clearly displays the privacy policy (data usage description, data storage and processing methods) in the system; S6.6: Data backup and recovery, regular backup and disaster recovery drills; S6.7: Security assessment and update, regular security assessment, quarterly security audit, timely update and patch known vulnerabilities, regular update of system and database versions, and introduction of a security patch management system.

[0022] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A beauty and health information management system, characterized in that: The following steps are involved: User information management module, data collection module, health assessment module, personalized solution recommendation module, There are six main parts: appointment and service management module, data security and privacy protection module; The user information management module includes the following steps: S1: user profile management, storing basic information of the user, and health data storage recording the dynamic health information of the user, such as skin type, body fat percentage, blood pressure, and allergy history; S1.1: Data input and update, support manual entry and automatic synchronization of health data by smart devices, receive data uploaded by users or devices through forms or API interfaces, and update the database; S1.2: Query and filter, quickly retrieve user information and historical data, support filtering by time and category, and implement multi-condition filtering through SQL query; S1.3: Data analysis and visualization: Analyze user data and generate statistical charts, such as health trend charts and skin condition distribution; S1.4: Permission management, limit the access rights of different roles to user information, ensure data security, set up role permission table, associate with users, divided into administrators, beauticians and ordinary users; S1.5: Data backup and recovery. Regularly back up user data to prevent data loss due to system failures. Use database backup mechanisms or cloud storage services.

2. A beauty and health information management system according to claim 1, characterized in that: The data acquisition module The following steps are involved: S2: Data source definition, clearly define supported data collection methods and sources, such as skin detectors, body fat scales, wearable devices, manual entry of health data through forms or mobile apps, or from third parties; S2.1: Data collection preparation, hardware device access through Bluetooth, Wi-Fi or USB interface to achieve device connection, API interface definition, according to the documents provided by the third-party platform, design a standardized interface for data interaction; S2.2: Data field normalization, defining the field standards for health data, including data types integer, floating point, unit, and timestamp; S2.3: Data collection and transmission. Real-time data collection uses protocols to receive health data of hardware devices in real time. Regularly pull or receive pushed data from third-party platforms. Data verification verifies the integrity and correctness of collected data. Exception handling records missing or erroneous data and feedbacks to users or devices. Data transmission uses the HTTPS protocol to ensure data transmission security. S2.4: Data storage: temporary storage stores the collected data in the cache to improve real-time performance; persistent storage writes the data in the cache to the database, and stores it in separate tables to improve query efficiency; data archiving archives data that exceeds a certain time and stores it in low-cost cloud storage to reduce the pressure on the main database; S2.5: Data display and feedback, visualization uses the front-end chart library to display the collected health data in real time, provide data trend analysis, user feedback, users receive real-time health status update APP notifications and send reminders for abnormal data; S2.6: Data security and privacy protection, data encryption uses AES to encrypt data during transmission and storage, and users are required to explicitly authorize data collection before authorization.

3. A beauty and health information management system according to claim 1, characterized in that: The health assessment module includes the following steps: S3: data input preparation, obtaining user health data and verifying data integrity and accuracy; S3.1: Data classification and preprocessing,Classify health data, organize data into different categories for subsequent analysis,skin health,healthy body living habits,Feature extraction Extract key feature values ​​according to the analysis objectives; S3.2: Health assessment algorithm, rule base matching defines assessment rules based on expert knowledge or industry standards, and personalized assessment adjusts assessment weights based on user health goals; S3.3: Generation of assessment results: Generate an assessment report, which includes health scores, data analysis and recommendation summary, report format, and charts using line graphs and pie charts to display data. Text natural language generation technology generates personalized recommendations, and risk warnings are issued for high-risk data; S3.4: Data feedback and optimization, user feedback, users can rate the evaluation results or put forward suggestions for improvement, collect user feedback, and optimize the evaluation rules and models.

4. A beauty and health information management system according to claim 1, characterized in that: The personalized solution recommendation module The following steps are involved: S4: Data preparation, user data collection, user profile, basic information, skin type, allergy history, real-time health data such as skin moisture, body fat percentage, user behavior data, browsing history, purchase history, ratings and feedback; S4.1: Data cleaning and normalization, removing duplicate or erroneous data, converting data from different sources into comparable numerical forms, labeling, generating feature labels for users based on data, static labels: such as "sensitive skin", "oily skin", dynamic labels: such as "skin moisture has decreased recently"; S4.2: Data analysis and modeling, rule base matching, based on expert rules or industry standards, define recommendation rules for different user groups: if the skin moisture is less than 30%, moisturizing products are recommended; if the BMI is greater than 25, fat-reducing exercise plans are recommended; if the allergy history contains "pollen", products containing pollen ingredients are excluded; S4.2.1: Recommendation algorithm selection, collaborative filtering algorithm, user-based collaborative filtering: recommend products that are liked by other people similar to the user, item-based collaborative filtering: recommend other products similar to the items selected by the user, content recommendation algorithm, based on user tag matching and item feature description, hybrid recommendation combines collaborative filtering with content recommendation, and integrates user preferences and product features. S4.3: Personalized solution generation, recommended content generation determines the type of recommended content, content priority sorting uses a scoring mechanism to sort the recommended content, and a diversified strategy avoids duplicate recommendations and provides a variety of choices; S4.4: Recommendation display, generate a formatted recommendation list with product images, names, and function descriptions, interactively display the recommendation in the user interface, and dynamically adjust the recommendation content in real time based on user behavior; S4.5: Effect tracking and optimization, data feedback collects users' actual usage data, uses this data to optimize the recommendation model or rules, model adjustment regularly retrains the machine learning model, updates the recommendation rule library, and monitors key indicators of recommendation effects: click-through rate, conversion rate, and user satisfaction score.

5. A beauty and health information management system according to claim 1, characterized in that: The reservation and service management module The following steps are involved: S5: Data preparation and basic configuration, service item information configuration, beautician information management, and user information management; S5.1: Reservation process, user selects service. User browses service items on the front-end interface, selects time and personnel to provide available time slots, filters out conflicting time slots, the system automatically assigns or the user manually selects a beautician, fills in the reservation information, the user fills in the necessary reservation information, notes information, reservation verification, verifies whether the information filled in by the user is complete, confirms the reservation and sends a notification, notifies the user and beautician, SMS / email notification of successful reservation, and sends a reminder 24 hours before the appointment; S5.2: Service management, service status update. On the day of the appointment, the service status can be divided into: waiting for service, in progress and completed. The service process management records the start and end time of the service process and the user payment in the system; S5.3: User feedback, feedback form. After completing the service, the user can fill in the feedback form to rate the satisfaction and feedback data storage. The user evaluation is stored for beautician performance evaluation and service optimization, service optimization suggestions. The system generates service improvement suggestions or recommends related services based on the feedback; S5.4: Data visualization and statistical analysis, appointment data statistics, user habit analysis and performance analysis.

6. A beauty and health information management system according to claim 1, characterized in that: The privacy protection module The following steps are involved: S6: Data collection and encrypted storage, data collection encryption The sensitive information submitted by the user is encrypted during transmission using the transport layer encryption protocol and database encryption storage; S6.1: Data access permission control, user permission division, division of permissions according to user type, role-based access control to assign access permissions to different roles, and desensitization of sensitive data; S6.2: Data transmission is secure, using a secure protocol. All API interfaces in the system use HTTPS to ensure data transmission encryption and prevent data tampering. HMAC is used to verify the integrity of the data transmission. S6.3: Logging and monitoring, sensitive operation logging, log security storage, abnormal monitoring and alarm; S6.4: Data sharing and third-party service management, third-party service security review: All connected third-party services must meet security certification standards, share data to minimize and only provide necessary data to third parties, avoid redundant information leakage, and use end-to-end encryption technology to ensure data sharing security with third parties; S6.5: Compliance assurance, data storage location compliance ensures that user data is stored in servers that comply with regional laws, user data access and deletion rights provide user data management functions, and privacy policy display clearly displays the privacy policy in the system; S6.6: Data backup and recovery, regular backup and disaster recovery drills; S6.7: Security assessment and update, regular security assessment, conduct security audits every quarter, timely update and patch known vulnerabilities, regularly update system and database versions, and introduce a security patch management system.

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