Cooperative treatment and analysis system for beauty and skin care industry chain

Abstract

The invention belongs to the technical field of information processing and industry chain digital management, and discloses a skincare industry chain collaborative governance and analysis system, which comprises a terminal layer, a cloud platform layer and a collaborative governance layer, and is characterized in that the terminal layer collects user data and store data in real time through intelligent equipment and sensors of a user terminal and a store terminal; the collected data is pre-cleaned and then transmitted back to the cloud platform layer; the cloud platform layer is used for receiving the multi-source heterogeneous data returned by the terminal layer and analyzing and integrating the multi-source heterogeneous data; and the collaborative governance layer is used for carrying out business management by using the analysis result based on the cloud platform layer. Aiming at the problems of data integration and intelligent management of the whole industry chain of the beauty industry, closed-loop management from data acquisition to decision support is realized by utilizing an intelligent terminal, a sensor and an AI analysis technology. The system not only gets through various heterogeneous data, but also can generate personalized service and operation strategies according to different user and store requirements, and improves service quality, operation efficiency and decision scientificity.

Description

Technical Field

[0001] This invention belongs to the field of information processing and digital management technology of the industrial chain, specifically relating to a collaborative governance and analysis system for the beauty and skincare industrial chain. Background Technology

[0002] In the current development of the beauty and skincare industry, digital transformation and intelligent governance have become key aspects for improving efficiency and competitiveness.

[0003] The traditional beauty and skincare industry chain includes multiple links such as raw material supply, production and processing, brand sales, store services, and user usage. The data flow and business collaboration between these links mostly rely on manual processes, paper records, or fragmented information systems, resulting in significant silo effects and management gaps. For example, key information such as store operation data, changes in user skin type, and product efficacy feedback often cannot form a closed loop with the production and supply chain, making it impossible for companies to adjust product formulas or production plans in a timely manner according to changes in user needs. Issues such as raw material fluctuations and inventory backlogs in the upstream supply chain are also difficult to detect and optimize through traditional systems.

[0004] In summary, existing data flow and business collaboration methods have the following problems: 1. Lack of systematic data collection: At present, most beauty and skin care companies have not yet established a unified data collection system. There is a lack of interconnection and interoperability between various smart terminals, skin detection equipment and operating systems, making it impossible to achieve comprehensive collection and management of user skin characteristics, product usage effects, store operation status and supply chain information. 2. Insufficient depth of data analysis: Existing systems mostly remain at the level of static reports or simple statistical analysis, and cannot use artificial intelligence algorithms to dynamically model and predict user needs, product efficacy and market trends, thus failing to support refined decision-making and proactive optimization; 3. Insufficient supply chain collaboration capabilities: The lack of unified data standards and collaboration mechanisms among suppliers, manufacturers, sales terminals and users leads to information delays, low response efficiency, and an inability to achieve collaborative governance and resource scheduling based on real-time data. 4. Lack of intelligent risk control: There is no mature intelligent identification and automatic early warning mechanism in terms of product quality traceability, inventory anomaly monitoring, supply interruption early warning and user feedback analysis, and the enterprise's risk prevention and control capabilities are limited.

[0005] Based on the above reasons, a collaborative governance and analysis system for the beauty and skincare industry chain is provided to realize the connection and real-time dynamic analysis of upstream and downstream data, promote the digital and intelligent management of the entire process of the beauty and skincare industry, provide enterprises with high-precision decision support and risk warning, and improve the overall level of product development, supply chain scheduling and user service. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this invention is to provide a collaborative governance and analysis system for the beauty and skincare industry chain. This system employs a layered architecture, consisting of a terminal layer, a cloud platform layer, and a collaborative governance layer from bottom to top. Addressing the data integration and intelligent management challenges across the entire beauty industry chain, this invention utilizes smart terminals, sensors, and AI analytics to achieve closed-loop management from data collection to decision support. This system not only integrates various heterogeneous data sources but also generates personalized service and operational strategies based on the needs of different users and stores, improving service quality, operational efficiency, and the scientific basis of decision-making.

[0007] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows: A collaborative governance and analysis system for the beauty and skincare industry chain includes a terminal layer, a cloud platform layer, and a collaborative governance layer. The terminal layer is used to collect user data and store data in real time through smart devices and sensors on user terminals and store terminals, and then transmit the collected data back to the cloud platform layer after pre-cleaning. The cloud platform layer is used to receive and analyze heterogeneous data from multiple sources transmitted back from the terminal layer. The collaborative governance layer uses the analysis results based on the cloud platform layer for business management.

[0008] In this invention, the user terminal refers to a smart beauty instrument and its associated sensors used by a personal user. These devices continuously collect skin feature data, product usage data, and user behavior data during use. The skin feature data is acquired through a variety of high-precision sensors, including skin type, skin color, humidity, oil content, sensitivity, blackheads, pores, acne, wrinkles, and texture indicators; The product usage data includes usage cycle, dosage, and effect feedback; The user behavior data includes usage habits, feedback records, and interaction logs, such as device power-on / off time, usage mode, and usage duration. Store terminals refer to smart devices deployed in offline scenarios such as beauty salons and counters. Among them, the skin care devices can continuously collect skin feature data, product usage data, and user behavior data during use. Among them, the management devices are responsible for collecting store operation data and supply chain status data. The store operation data includes customer traffic, new and returning customer data, sales records, and employee attendance; the supply chain status data includes inventory levels and logistics tracking. After the terminal layer collects data, it performs pre-cleaning (such as filtering obviously invalid outliers, performing preliminary data format standardization, compression, etc., to reduce network transmission pressure and cloud processing burden), and then transmits it back to the cloud platform layer via the Internet. The cloud platform layer is used to receive and analyze the multi-source heterogeneous data returned by the terminal layer. It includes six core modules: data processing module, distributed storage module, AI analysis module, risk warning module, security operation and maintenance module, and data service module. The data processing module is used to clean and standardize multi-source heterogeneous data. First, it buffers and processes massive amounts of real-time data through a data receiving queue. Then, it performs cleaning and fusion operations, including data verification, data denoising, and format unification. It also associates data from different terminals and different structures to form high-quality unified data that can be used for analysis. The distributed storage module includes a relational database, unstructured storage, cache index, and replica management, used for data storage, supporting high availability, fast access, and off-site backup of data. Specifically, to cope with massive amounts of multi-source data, the distributed storage module includes: First, relational databases: used to store structured business data, such as user information, equipment information, consumable information, model data, order data, etc. Second, unstructured storage: used to store unstructured data such as log files, images, and video streams; 3. Caching and Indexing: Utilize in-memory databases to establish caching mechanisms and efficient indexes to support the rapid data query needs of the AI ​​analysis module and data service module; 4. Replica Management: Through distributed backup, snapshots, disaster recovery and synchronization mechanisms, ensure high availability of data and business continuity, and prevent data loss caused by single point of failure; The AI ​​analysis module establishes individual and group demand prediction models based on cleaned multi-source heterogeneous data, evaluates product efficacy and market response, and pushes its own products in a targeted manner by combining RAG technology and demand models. It also provides quantitative analysis models based on the store operation data collected from store terminals. These results are written back to the distributed storage module through the back-storage channel for other modules to call. Specifically, the AI ​​analysis module plays the following roles: First, user profile building: By analyzing historical and real-time skin data and usage behavior data, a dynamic skin type model and usage behavior model are built for each user to form an accurate user profile; 2. Product evaluation and recommendation: By linking changes in users' skin condition before and after using the product, the efficacy of the product is quantitatively evaluated. Combined with user profiles and RAG technology, relevant information is accurately retrieved from the product knowledge base. Then, a highly personalized product push plan is generated through the demand model, recommending the system's own products to form a marketing closed loop. 3. Store operation analysis: Analyze customer flow data, sales data, and employee attendance data at the store, and build customer flow analysis model, attendance analysis model, and sales data analysis model to provide quantitative basis for refined store operation; The risk warning module is used to monitor abnormal system operations and supply risks, and generate warning information. Specifically, it provides real-time business risk monitoring. Once a risk is detected, the module immediately generates a warning and pushes it to the collaborative governance layer for visual alerts via the data service module. Its main functions include: 1. Abnormal operation monitoring: Monitor whether the equipment is in an abnormal working state, whether the user is using it improperly, and whether the system has unauthorized access logs, etc. 2. Supply monitoring: Monitor inventory levels and consumable usage, predict and provide timely warnings of potential stockout risks; The security operation and maintenance module runs through the entire cloud platform layer, ensuring system stability and data security, including... 1. Network security: Deploy firewalls and intrusion detection systems to prevent external attacks; Second, data protection: Encrypt sensitive data during storage and transmission; Third, access control: establish a strict permission management system to ensure that different roles can only access data and functions within their authorized scope; It ensures data security for multi-terminal operations, provides hierarchical access control and data encryption transmission mechanisms for collected user data and store data, ensures that the access permissions of users with different roles are controlled, and prevents the leakage of sensitive information; The data service module is used to integrate the data generated by the AI ​​analysis module and provide the necessary business interfaces to the collaborative governance layer. Specifically, it encapsulates the integrated analysis data generated by the AI ​​analysis module and provides it to upper-layer applications in the form of standardized business API interfaces, enabling various functions of the collaborative governance layer to easily obtain the required data and achieving decoupling of front-end and back-end businesses. The collaborative governance layer uses the analysis results based on the cloud platform layer for business management. It is the command and display center of the entire system, providing managers with comprehensive business management capabilities and data visualization, including two parts: the management backend and the large screen data overview module. The management backend is a comprehensive business operation platform that integrates various data from user terminals, store terminals, and cloud platforms to achieve multi-dimensional collaborative business management, including monitoring management, instrument and consumable management, product information management, user management, process work orders, and multi-dimensional report generation. in, 1. Monitoring and Management: Real-time viewing of monitoring device status, distribution, and detailed information, and online access to monitoring images; 2. Instrument and Consumables Management: Manage basic information, inventory data, and logistics data for instruments and consumables; 3. Product data management: Maintain product information, ingredients, efficacy descriptions, etc.; 4. User Management: Manage user profiles, membership levels, service records, etc. 5. Work Order Process: Processes reports, repair requests, and service applications from stores or users, enabling streamlined tracking; 6. Multi-dimensional report generation: Display various business analysis results in chart form from multiple dimensions, and provide a brief overview on the backend homepage; The large-screen data overview module constructs a panoramic map of the industrial chain based on the analysis data output from the cloud platform layer, used to visually display the operational data of the industrial chain; the operational data of the industrial chain visualized by the large-screen data overview module includes, Real-time dashboard: Real-time updates of core operational metrics; Sales trend chart: Displays sales trends, user growth trends, etc. User distribution and store heat map: Display user distribution, store distribution and business status on electronic maps in the form of heat maps, and expand them in ascending order of administrative region level; Equipment / Instrument Energy Efficiency Comparison Panel; Summary of inventory levels and replenishment recommendations; and, Risk Alerts: Centrally displays real-time alert information for the entire system; The large-screen data overview module supports multi-level filtering, linked data retrieval, and report export based on time intervals, administrative regions, and individual stores.

[0009] In some optional instances, the smart devices used in the terminal layer include any one or more of the following: skin analyzer, smart facial cleansing device, photon skin rejuvenation device, radio frequency beauty device, ultrasonic import device, smart skin care sprayer, smart facial mask machine, smart scalp analyzer, smart body fat scale, consumable NFC tag, smart NVR system, beauty salon front desk terminal, and tablet sales guide device.

[0010] In some optional instances, the sensors used in the terminal layer include any one or more of the following: a smart camera, a skin moisture sensor, a sebum sensor, a temperature sensor, a humidity sensor, a texture and wrinkle scanning sensor, a pore detection sensor, a spectral analysis sensor, an infrared skin imaging sensor, and a pressure and touch sensor.

[0011] The beneficial effects of this invention are: 1. Data integration and fusion across the entire industry chain: Breaking down data silos among users, products, stores, and the supply chain to form a unified, high-quality data asset pool, providing a foundation for precise analysis and collaborative decision-making; 2. Personalized Service and Marketing Enhancement: Based on dynamic user profiles and RAG technology, we can achieve personalized product and service recommendations to each user, thereby improving user satisfaction and brand loyalty. 3. Refined Operations and Scientific Decision-Making: Provides multi-dimensional quantitative analysis and real-time data views to support strategy adjustments, resource allocation, and performance evaluation, thereby improving operational efficiency; 4. Risk Foresight and Proactive Prevention: An independent risk early warning module monitors equipment malfunctions, operational violations, and inventory shortages in real time, shifting management from passive response to proactive intervention; 5. Secure and reliable data governance: We build a three-tiered defense system encompassing network, data, and access control to protect user privacy and the security of core business data; 6. Efficient multi-terminal collaboration and visualized command: The unified platform supports access from multiple terminals, allowing users to quickly grasp the dynamics of the industrial chain through an intuitive and visualized interface, thereby improving collaboration efficiency and emergency response capabilities. Attached Figure Description

[0012] The present invention can be further illustrated by the non-limiting embodiments given in the accompanying drawings; Figure 1 This is a system overall architecture diagram according to an embodiment of the present invention; Figure 2 This is a flowchart of the data acquisition and preprocessing process according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the working principle of the AI ​​analysis module in an embodiment of the present invention. Figure 4 This is a schematic diagram of the management backend interface according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the large-screen data overview interface according to an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the present invention will be described in detail below with reference to specific embodiments and accompanying drawings. The embodiments described herein are specific implementations of the present invention, used to illustrate the concept of the present invention; these descriptions are explanatory and exemplary, and should not be construed as limiting the implementation methods or the scope of protection of the present invention. In addition to the embodiments described herein, those skilled in the art can employ other obvious technical solutions based on the content disclosed in the claims and specification of this application. These technical solutions include those that make any obvious substitutions and modifications to the embodiments described herein.

[0014] Regarding the attached diagram, the following additional explanation is provided: Figure 1 Show the structure and data flow of the terminal layer, cloud platform layer, and collaborative governance layer; Figure 2 The process from terminal data acquisition to pre-cleaning and data transmission is shown in detail. Figure 3 It demonstrates the process of building and outputting models from raw data to user profiles, product evaluation, and operational analysis; Figure 4 The simulation displays core visual elements such as a brief overview, monitoring management, equipment management, consumables management, and user management. Figure 5 It simulates and displays core visualization elements such as real-time dashboards, GIS heat maps, and sales trend charts.

[0015] This invention provides a collaborative governance and analysis system for the beauty and skincare industry chain, comprising a terminal layer, a cloud platform layer, and a collaborative governance layer, wherein... The terminal layer is used to collect user data and store data in real time through smart devices and sensors on user terminals and store terminals. The collected data is pre-cleaned and then transmitted back to the cloud platform layer. The collected user data includes skin feature data, product usage data, and user behavior data; the collected store data includes store operation data and supply chain status data. Skin characteristic data, including skin type, skin tone, moisture, oil content, sensitivity, blackheads, pores, acne, wrinkles, and texture indicators; Product usage data, including usage cycle, dosage, and effect feedback; User behavior data, including usage habits, feedback records, and interaction logs; Store operation data, including customer traffic, new and returning customer data, sales records, and employee attendance; Supply chain status data, including inventory levels and logistics tracking; The cloud platform layer is used to receive and analyze heterogeneous data from multiple sources transmitted back from the terminal layer. The cloud platform layer includes a data processing module, a distributed storage module, an AI analysis module, a risk warning module, a security operation and maintenance module, and a data service module. in, The data processing module is used for cleaning and standardizing multi-source heterogeneous data; The distributed storage module includes relational databases, unstructured storage, cache indexes, and replica management, which are used for data storage and support high availability, fast access, and off-site backup of data. The AI ​​analysis module builds individual and group demand prediction models based on cleaned multi-source heterogeneous data, evaluates product efficacy and market response, and pushes proprietary products in a targeted manner by combining RAG technology and demand models. It also provides quantitative analysis models based on the store operation data collected from store terminals. The risk warning module is used to monitor abnormal system operations and supply risks, and generate warning information. The data service module is used to integrate the data generated by the AI ​​analysis module and provide the necessary business interfaces to the collaborative governance layer; The smart devices used at the terminal layer include any one or more of the following: skin analyzer, smart facial cleansing device, photon skin rejuvenation device, radio frequency beauty device, ultrasonic import device, smart skin care sprayer, smart facial mask machine, smart scalp analyzer, smart body fat scale, consumable NFC tags, smart NVR system, beauty salon front desk terminal and tablet sales guide device; The sensors used in the terminal layer include any one or more of the following: smart camera, skin moisture sensor, sebum sensor, temperature sensor, humidity sensor, texture and wrinkle scanning sensor, pore detection sensor, spectral analysis sensor, infrared skin imaging sensor, pressure and touch sensor; The security operations module includes network security, data protection and access control, ensuring data security for multi-terminal operations. It provides hierarchical access control and data encryption transmission mechanisms for collected user data and store data, ensuring that the access permissions of users with different roles are controlled and preventing the leakage of sensitive information. The collaborative governance layer uses the analysis results based on the cloud platform layer for business management, including the management backend and the large screen data overview module; The management backend includes monitoring management, instrument and consumables management, product information management, user management, workflow and work order generation, and multi-dimensional report generation. The large-screen data overview module constructs a panoramic map of the industry chain based on the analytical data output from the cloud platform layer, which is used to visualize the operation data of the industry chain. The industry chain operation data visualized by the large-screen data overview module includes real-time dashboards, sales trend charts, user distribution and store heat maps, equipment / instrument energy efficiency comparison panels, inventory levels and replenishment suggestion summaries, and risk alarms. The large-screen data overview module supports multi-level filtering, linked data retrieval, and report export based on time intervals, administrative regions, and individual stores.

[0016] Example 1 (System Construction and Personalized Skincare Closed Loop Implementation) Regarding terminal layer system hardware deployment: Step 1: At the user's home end, a smart beauty device integrating a multispectral imaging sensor, a capacitive moisture sensor, and a sebaceous gland impedance sensor is provided. This device has a built-in Wi-Fi / 4G communication module for data uploading and command reception. At the beauty salon end, a high-precision facial skin detector is deployed to collect data such as pore density, skin condition, and wrinkle depth. Step 2: Embed NFC tags on the packaging of accompanying consumables (such as serum ampoules and face masks). The tags contain information such as unique ID, ingredients, batch information, compatible instruments, and suitable working modes. Step 3: Equip beauticians with tablet-based sales guide devices and connect them to smart beauty instruments, store back-end data, and historical customer analysis data; Step 4: Cloud Platform Layer: Adopting a hybrid cloud architecture, elastic computing services are rented on the public cloud for deploying modules that require elastic scaling, such as AI analysis and data processing; core user privacy data and commercial data are stored in a local private distributed database to ensure data sovereignty and security. Step 5: Collaborative Governance Layer: Deploy a visual dashboard at the brand headquarters; administrators' PCs and mobile devices securely access the management backend application deployed on the private cloud via VPN or dedicated line.

[0017] Taking a complete "store testing - home care" linkage scenario as an example, the data flow is explained as follows: Step 1: In-store skin test triggered. Customers use a facial skin analyzer in the store to complete a skin test. The device collects multi-dimensional data including skin type, skin tone, moisture, oil content, sensitivity, blackheads, pores, acne, wrinkles, and texture indicators. Step 2: Pre-cleaning and local formatting. The scanner's built-in preprocessing program verifies the validity of the data and packages the numerical data and image metadata (detection time, device ID, working status information, etc.) into an encrypted data packet following the JSON format. Unstructured image data is sliced ​​and the slices are encrypted. Step 3: Secure transmission. Data packets are transmitted via HTTPS protocol and encrypted with TLS 1.3, passing through the store gateway to the cloud platform's data receiving queue. Step 4: Data access and cleaning integration. The cloud platform's data processing module consumes data from the data queue; executes cleaning rules and data integration, for example, determining whether the current value has undergone a physiologically impossible mutation based on the customer's historical data; and associating the test data with information such as the customer's historical records and membership level. Step 5: Distributed storage. The standardized data records are simultaneously written to the MySQL database and the Elasticsearch search engine, while the 3D image tile files are uploaded to object storage. Step Six: AI Analysis and Model Update. After new skin data is written, the AI ​​analysis pipeline is triggered. The pipeline performs the following tasks: 1. Update user skin model data: The pipeline calls the trained skin type classification model and updates the customer's skin type to "combination with oily skin and mild sensitivity" based on the latest data; 2. Product Efficacy Evaluation: The system retrieves "Repair Essence B" used by the customer within the past 28 days and compares the average change in "Redness Index" before and after use. If the statistical significance test shows a significant decrease in the Redness Index, then the "Soothing and Repairing Efficacy Score" of Product B will be improved in the global product database. 3. Personalized Recommendations: Using RAG technology, based on the customer's profile of "combination to oily skin with mild sensitivity," the system retrieves all proprietary products tagged with "soothing," "oil control," and "barrier repair" from a vectorized product knowledge base, and sorts them by semantic relevance. Taking into account the customer's purchase history, product efficacy scores, and inventory status, the system selects the optimal Refreshing Repair Lotion C from the search results and generates a recommendation reason: "For your recent oiliness and sensitivity, we recommend using Refreshing Repair Lotion C, whose ingredient X effectively soothes and maintains the water-oil balance." 4. Data Writeback: The results of this analysis (updated user profile, new efficacy score for product B, and recommendation scheme) are written back to the database; Step 7: Management backend operation. Log in to the management backend and you can view the product B efficacy quantification report automatically generated by the system on the "Product Evaluation" page. Step 8: Overview of large screen data. On the visualization screen at headquarters, the "Today's total number of skin tests nationwide" increases by 1 in real time. In the detailed GIS overview, the color of the area where the store is located, which represents the "density of oily skin users", is slightly darkened. The system is running smoothly and there are no abnormal alarms on the risk monitoring panel. Through the above process, the system achieves a complete closed loop from data collection and intelligent analysis to precise recommendation and management.

[0018] Example 2 (Risk Warning and Consumables Collaborative Management Process) This embodiment details the specific implementation of the system in terms of risk warning and supply chain collaboration.

[0019] Step 1: Risk detection triggered. A beautician at a store uses a radio frequency device to serve a customer. When the device is activated, it reads its own NFC tag and the NFC tag of the consumable ampoule, uploading data such as the device ID, consumable ID, working time, and energy level of this service to the cloud platform. Step Two: Abnormal Operation Monitoring and Risk Warning Module. After receiving the data, the module analyzes it in real time. The rule engine detects that the energy level used by the device exceeds the recommended safety threshold for the customer's skin type (sensitive) and immediately generates a P2 level (medium priority) warning: "Device [ID:xxx] may have used too high an energy level on a customer with sensitive skin. Please verify immediately." Step 3: Supply monitoring. The inventory management system records that one ampoule of this consumable has been used. The "Supply Monitoring" submodule of the risk warning module predicts that the consumable will fall below the safety stock threshold in 2 days by comparing real-time inventory with the historical consumption rate model, generating a P1 level (high priority) warning. Step 4: The two warning messages are pushed in real time to the "Warning Center" list in the management backend, the WeChat work groups of the store manager and regional manager, and the "Risk Monitoring" area on the headquarters screen through the interface of the data service module. Step 5: The store manager receives the alert on the tablet, immediately contacts the beautician to verify the situation, and records the handling result in the work order: "Confirmed with the beautician, it was an operational error. The employee has been retrained, and the customer had no adverse reaction." Immediately create a "Consumables Emergency Replenishment Request" work order in the "Work Order" section of the management backend. The work order automatically links the alert information and inventory data. Step Six: Supply chain managers can see the work order in the backend, check the regional central warehouse inventory through the system, and complete the approval and transfer instructions online; Step 7: The status of the entire processing ("Pending", "Approved", "Outbound", "Closed") is updated in real time to form a closed loop for risk management; Step 8: Security Implementation. Throughout the process, from terminal transmission encryption to database column-level encryption, and then to RBAC-based access control (such as beauticians only being able to see data from their own store, and regional managers being able to view data within their jurisdiction), end-to-end data security and privacy protection are ensured.

[0020] This embodiment demonstrates, through specific equipment operation monitoring and consumable management scenarios, how the system achieves a complete closed loop from risk perception and intelligent early warning to collaborative handling. It highlights the deep integration of the risk early warning module with the business process system. Through multi-level early warning mechanisms, intelligent handling suggestions, and strict access control, it effectively improves equipment usage safety and supply chain response efficiency, providing technical support for the standardization and safety management of beauty and skincare services.

[0021] Example 3 (Dynamic Optimization of Skincare Solution) This embodiment demonstrates how the system can automatically evaluate and optimize personalized skincare plans tailored for users by continuously tracking user data.

[0022] Step 1: Solution Generation and Device Synchronization. A user diagnosed by the system as having "combination to oily skin with noticeable pores in the T-zone" receives an initial skincare plan generated by the AI ​​analysis module in the management backend. This plan includes: "Use the 'deep cleansing' mode of the smart beauty device 3 times a week, and use 'oil-control essence A' in conjunction with it." This plan is synchronized to the user's smart beauty device and the tablet device of the beauty therapist in the store through the data service module. Step Two: Data Collection and Solution Execution. Users use the beauty device at home according to the solution. Each time the device is used, the duration and intensity of the "deep cleansing" mode, as well as data such as the amount of oil produced and the smoothness of the skin collected by the sensors, are collected and uploaded by the user's terminal. The NFC code of the "Oil Control Essence A" purchased by the user is scanned and linked to their personal profile to confirm that the solution has been executed. Step 3: AI Analysis and Effect Evaluation. The AI ​​analysis module at the cloud platform layer continuously receives user data. After 21 days of implementation, the module initiates an effect evaluation analysis. Positive feedback shows that the real-time oil production data curve of the user's T-zone shows that the average oil production has decreased by 15%, indicating a bottleneck. However, the improvement in the pore enlargement index has not met expectations, and the moisture data of the facial U-zone (cheeks) shows a slight downward trend. The system determines that the current solution is effective in controlling oil, but may be slightly irritating to the U-zone and insufficient in improving pores. Step 4: Dynamic optimization and collaborative notification of the plan. The AI ​​analysis module automatically generates an optimization plan: "Focus the 'Deep Cleansing' mode on the T-zone and reduce the frequency to twice a week; add a 'Moisturizing Repair' mode for the entire face, twice a week; it is recommended to alternate between 'Oil Control Essence A' and 'Repair Essence B'." Step 5: This new solution is immediately pushed to user terminals and store terminals through the data service module. After the plan is manually reviewed and the product is ordered, the user's smart beauty device and mobile app terminal automatically receive, update and execute the treatment program.

[0023] This embodiment fully demonstrates how the system forms a closed loop of "evaluation-optimization-execution". It does not rely on any marketing tools, but is based on device data and AI models. It avoids the illusion error of AI models through human review, and realizes continuous iteration of personalized skin care solutions to ensure that the service is always accurate and effective.

[0024] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A collaborative governance and analysis system for the beauty and skincare industry chain, characterized in that: It includes the terminal layer, cloud platform layer, and collaborative governance layer, among which, The terminal layer is used to collect user data and store data in real time through smart devices and sensors on user terminals and store terminals, and then transmit the collected data back to the cloud platform layer after pre-cleaning. The cloud platform layer is used to receive and analyze heterogeneous data from multiple sources transmitted back from the terminal layer. The collaborative governance layer uses the analysis results based on the cloud platform layer for business management.

2. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 1, characterized in that: The collected user data includes skin feature data, product usage data, and user behavior data; in, The skin characteristic data includes skin type, skin tone, humidity, oil content, sensitivity, blackheads, pores, acne, wrinkles, and texture indicators; The product usage data includes usage cycle, dosage, and effect feedback; The user behavior data includes usage habits, feedback records, and interaction logs.

3. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 1, characterized in that: The collected store data includes store operation data and supply chain status data; in, The store operation data includes customer traffic, new and returning customer data, sales records, and employee attendance. The supply chain status data includes inventory levels and logistics tracking.

4. A collaborative governance and analysis system for the beauty and skincare industry chain according to any one of claims 1 to 3, characterized in that: The intelligent devices used in the terminal layer include any one or more of the following: skin analyzer, intelligent facial cleansing device, photon skin rejuvenation device, radio frequency beauty device, ultrasonic import device, intelligent skin care sprayer, intelligent facial mask machine, intelligent scalp analyzer, intelligent body fat scale, consumable NFC tag, intelligent NVR system, beauty salon front desk terminal and tablet sales guide device.

5. A collaborative governance and analysis system for the beauty and skincare industry chain according to any one of claims 1 to 3, characterized in that: The sensors used in the terminal layer include any one or more of the following: smart camera, skin moisture sensor, sebum sensor, temperature sensor, humidity sensor, texture and wrinkle scanning sensor, pore detection sensor, spectral analysis sensor, infrared skin imaging sensor, and pressure and touch sensor.

6. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 1, characterized in that: The cloud platform layer includes a data processing module, a distributed storage module, an AI analysis module, a risk warning module, a security operation and maintenance module, and a data service module. in, The data processing module is used to clean and standardize multi-source heterogeneous data; The distributed storage module includes a relational database, unstructured storage, cache index, and replica management, which are used for data storage and support high availability, fast access, and off-site backup of data. The AI ​​analysis module establishes individual and group demand prediction models based on cleaned multi-source heterogeneous data, evaluates product efficacy and market response, and combines RAG technology and demand models to push proprietary products in a targeted manner. It also provides quantitative analysis models based on the store operation data collected from store terminals. The risk warning module is used to monitor abnormal system operations and supply risks, and generate warning information. The data service module is used to integrate the data generated by the AI ​​analysis module and provide the necessary business interfaces to the collaborative governance layer.

7. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 6, characterized in that: The security operation and maintenance module includes network security, data protection and access control, which ensures the data security of multi-terminal operations. It provides hierarchical access control and data encryption transmission mechanism for collected user data and store data, ensuring that the access permissions of users with different roles are controlled and preventing the leakage of sensitive information.

8. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 1, characterized in that: The collaborative governance layer includes a management backend and a large-screen data overview module; The management backend includes monitoring management, instrument and consumables management, product information management, user management, workflow and work order generation, and multi-dimensional report generation. The large-screen data overview module constructs a panoramic map of the industrial chain based on the analysis data output by the cloud platform layer, which is used to visualize and display the operational data of the industrial chain.

9. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 8, characterized in that: The large-screen data overview module visualizes the industry chain operation data, including real-time dashboards, sales trend charts, user distribution and store heat maps, equipment / instrument energy efficiency comparison panels, inventory levels and replenishment suggestion summaries, and risk alarms.

10. The collaborative governance and analysis system for the beauty and skincare industry chain according to claim 8, characterized in that: The large-screen data overview module supports multi-level filtering, linked data retrieval, and report export based on time intervals, administrative regions, and individual stores.

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