A medical beauty management system based on a smart organization framework and a control method thereof

Abstract

This invention discloses a medical aesthetics management system and its control method based on a smart organizational architecture. The system adopts a smart organizational architecture, including a manager control center, multiple capability center modules, and components such as a data snapshot and feedback iteration mechanism and an MCP multi-agent collaborative protocol interface. The manager control center, as the AI ​​decision-making core, intelligently directs and schedules the collaborative work of each capability center module to achieve globally optimized business decisions. Each capability center module is divided into areas such as operations, consulting, marketing, data, and product, and is modularly deployed to support functional expansion. The system periodically collects and stores business data through a data snapshot module and combines it with an AI analysis engine to achieve real-time prediction and decision optimization. Simultaneously, a data feedback learning mechanism is included to continuously optimize the AI ​​model based on customer feedback and operational results, ensuring rapid iterative improvement of the system's intelligence level.

Description

Technical Field

[0001] This invention relates to an application of an artificial intelligence system, and more specifically, to a medical aesthetics management system and its control method based on a smart organizational architecture. Background Technology

[0002] As the medical aesthetics industry enters the AI ​​era, it places higher demands on the intelligence and autonomy of operational decision-making. Currently, some medical aesthetics institutions have introduced customer management systems, appointment systems, and simple AI recommendation tools, achieving a certain degree of process automation and data-driven decision-making. However, these systems are mostly partial functions, lacking overall intelligent coordination, and cannot meet the needs of the "super intelligent medical aesthetics manager" in the Smart Medical Aesthetics 5.0 stage. Smart Medical Aesthetics 5.0 (a fully automated operational management intelligent decision support system) requires the construction of a comprehensive AI hub capable of autonomous learning, real-time decision-making, and dynamic optimization, providing professional support to managers and clients while coordinating overall resources. Existing solutions have not yet formed an AI-centric intelligent organizational structure, i.e., an AI hub similar to a CEO directing the collaborative operation of various business modules. Furthermore, traditional systems struggle to achieve global optimization in areas such as cross-departmental resource allocation, personalized customer service, and real-time strategy adjustments. In addition, how to efficiently integrate multi-source business data and continuously improve AI decision-making capabilities through feedback mechanisms is also a challenge for existing technologies. In view of the above shortcomings, it is necessary to provide a new technical solution to build a medical aesthetics super manager system, realize intelligent management of the whole business under the central command of AI, and meet the development needs of smart medical aesthetics 5.0 (fully automated operation and management intelligent decision support system). Summary of the Invention

[0003] One of the objectives of this invention is to address the aforementioned shortcomings by providing a medical aesthetics management system and its control method based on a smart organizational architecture. This aims to solve the technical problems of existing systems being unable to efficiently integrate multi-source business data and continuously improve AI decision-making capabilities through feedback mechanisms, thus failing to meet the needs of the "super intelligent medical aesthetics manager" in the smart medical aesthetics 5.0 stage.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: This invention provides a medical aesthetics super manager system based on an intelligent organizational architecture, including a manager control center. The manager control center is connected to multiple capability center modules, namely an operations center module, a consultation center module, a marketing center module, a data center module, and a product center module. The operations center module, consultation center module, marketing center module, data center module, and product center module are all connected to a data service layer through an AI capability layer. The data service layer is connected to a data storage layer. The data service layer is used to obtain real-time data from the medical aesthetics business system, preprocess the real-time data, and then store it in the data storage layer. Simultaneously, it periodically generates snapshot data from the real-time data and also stores it in the data storage layer. The manager... The control center is used to call the AI ​​capability layer to integrate and analyze snapshot data and real-time data in the data storage layer, and then, based on the analysis results, to formulate business optimization decisions using the AI ​​autonomous decision-making model. The manager center is also used to convert the business optimization decisions into specific task instructions, and to issue them to the operation center module, consultation center module, marketing center module, data center module, and product center module respectively. The operation center module, consultation center module, marketing center module, data center module, and product center module are used to execute the tasks in the task instructions, and after the tasks are completed, they transmit feedback data to the feedback learning unit. The manager control center is also used to update and train the AI ​​autonomous decision-making model using the data in the feedback learning unit.

[0005] The aforementioned data storage layer includes a business database and a snapshot database; the business database is used to store the real-time data; and the snapshot database is used to store the snapshot data, so as to achieve separate storage of real-time data and historical snapshot data.

[0006] As a preferred embodiment, a further technical solution is as follows: the operation center module, consultation center module, marketing center module, data center module, and product center module are used to call one or more intelligent agents through the MCP protocol interface to collaboratively complete the tasks in the task instruction when needed. The aforementioned MCP protocol interface is used to interact and collaborate with one or more external intelligent agents according to the MCP protocol. When executing the task instruction, the MCP protocol interface calls one or more external intelligent agents to collaboratively complete the sub-tasks in the task instruction according to the task type or computing resource strategy, and sends the results returned by the external intelligent agents back to the manager's control center or the corresponding capability center module. The aforementioned task instruction includes at least one or more of the following fields: task type identifier, task target object identifier, execution constraint parameters, expected output format, execution priority, and deadline; the capability center module group generates corresponding execution actions according to the fields in the task instruction, and generates the feedback data containing the execution results and key indicators.

[0007] As described above, the manager control center is used to call the AI ​​capability layer to integrate and analyze the snapshot data and real-time data in the data storage layer, and generate business optimization decisions based on the analysis results using the AI ​​autonomous decision-making model. The manager control center is also used to convert the business optimization decisions into task instructions and distribute them to the operation center module, consultation center module, marketing center module, data center module, and product center module respectively; the operation center module, consultation center module, marketing center module, data center module, and product center module are used to execute the corresponding task instructions, and generate feedback data after the task is completed and send it to the feedback learning unit; the manager control center is also used to update and train the AI ​​autonomous decision-making model based on the feedback data in the feedback learning unit.

[0008] The aforementioned feedback learning unit constructs training samples or reward signals for updating and training the AI ​​autonomous decision-making model based on the feedback data; the update training is triggered according to a preset period or according to event triggering conditions, the event triggering conditions including at least one or more of the following conditions: indicator deviates from threshold, task execution is completed, user feedback arrives, or strategy effect evaluation is completed.

[0009] The aforementioned Operations Center module is used for monitoring, aggregating, analyzing, managing targets, and adjusting operational strategies. The aforementioned Consulting Center module is used for customer lifecycle management, customer segmentation, follow-up reminders, and communication support. The aforementioned Marketing Center module is used for marketing content generation, outreach strategy execution, and channel effectiveness analysis. The aforementioned Data Center module is used for data collection, data governance, snapshot generation scheduling, and data API services. The aforementioned Product Center module is used for product structure analysis, project insights, and product recommendation strategy generation.

[0010] A further technical solution is that the system also includes an interaction touchpoint layer. This layer connects to the operation center module, consultation center module, marketing center module, data center module, and product center module. The interaction touchpoint layer has multiple data interfaces, which are used to provide interactive interfaces with the AI ​​capability layer, AI autonomous decision-making model, and external intelligent agents to various users, and to receive user input and business feedback. The aforementioned data interfaces include any one or more of the following: web interface interface, mobile application interface, WeChat Work interface, OpenAPI interface, and smart hardware interface.

[0011] A further technical solution is that the data interface is any one or more of the following: Web interface interface, mobile application interface, WeChat Work interface, and smart hardware device interface.

[0012] A further technical solution is as follows: the operation center module, consultation center module, marketing center module, data center module, and product center module execute the tasks in the task instructions, which involves integrating and analyzing the snapshot data in the data storage layer through the AI ​​capability layer, generating corresponding feedback data, and transmitting it to the feedback learning unit. The aforementioned snapshot data includes at least one or more of the following snapshot types: performance snapshot, customer snapshot, consultant snapshot, doctor snapshot, item / product snapshot, and employee behavior snapshot; the snapshot data includes a snapshot time identifier to support historical backtracking and trend analysis. The aforementioned AI capability layer includes at least one or more of the following AI engines: analysis and inference engine, content generation engine, intent recognition engine, and prediction engine. The AI ​​capability layer can provide intelligent analysis results, content generation results, prediction results, or intent recognition results to the manager control center and the capability center module group.

[0013] The aforementioned data service layer includes at least one or more of the following sub-services: real-time query service, snapshot service, data API service, and caching service; wherein the real-time query service is used to provide a query interface for the real-time data, and the snapshot service is used to provide a query interface for the snapshot data.

[0014] The system of the present invention also includes a security and access control unit, which is used to perform access control and auditing records on the access to the interaction touch layer, the data service layer, the MCP protocol interface and the data storage layer, so as to restrict the access scope of different roles to data and task instructions.

[0015] Another aspect of the present invention provides a control method for the above-mentioned system, comprising the following steps: Step A: The data service layer obtains real-time data from the medical aesthetics business system, preprocesses the real-time data, and then stores it in the data storage layer; at the same time, it periodically generates snapshot data from the real-time data and also stores it in the data storage layer.

[0016] Step B: The manager's control center calls the AI ​​capability layer to integrate and analyze the snapshot data and real-time data in the data storage layer, and then uses the AI ​​autonomous decision-making model to formulate business optimization decisions based on the analysis results.

[0017] Step C: The central manager converts business optimization decisions into specific task instructions and distributes them to the Operations Center Module, Consulting Center Module, Marketing Center Module, Data Center Module, and Product Center Module respectively.

[0018] Step D, the Operations Center Module, Consulting Center Module, Marketing Center Module, Data Center Module, and Product Center Module are used to execute the tasks in the task instructions, and after the tasks are completed, they transmit the feedback data to the Feedback Learning Unit.

[0019] Step E: The manager control center is also used to update and train the AI ​​autonomous decision-making model using the data in the feedback learning unit.

[0020] As a preferred embodiment, a further technical solution is as follows: In step D, the operation center module, consultation center module, marketing center module, data center module, and product center module are used to call one or more intelligent agents through the MCP protocol interface to collaboratively complete the task when needed.

[0021] A further technical solution includes step F, where the interaction touchpoint layer provides each user with an interactive interface to the AI ​​capability layer, the AI ​​autonomous decision-making model, and the external intelligent agent through multiple data interfaces.

[0022] A further technical solution is as follows: In step D, the operation center module, consultation center module, marketing center module, data center module, and product center module execute the tasks in the task instructions, which are to integrate and analyze the snapshot data in the data storage layer through the AI ​​capability layer, and generate corresponding feedback data, which are then transmitted to the feedback learning unit.

[0023] A further technical solution is to continuously cycle through steps A to E during system operation.

[0024] Compared with existing technologies, one of the beneficial effects of this invention is that by realizing the overall intelligent and automated operation and management of medical aesthetic institutions through the system, the efficiency and accuracy of operational decisions can be improved, and through snapshot accumulation and continuous learning, core competitiveness driven by data and algorithms can be built. Attached Figure Description

[0025] Figure 1 This is a system architecture diagram used to illustrate an embodiment of the present invention.

[0026] Figure 2 This is a technical architecture diagram used to illustrate an embodiment of the present invention.

[0027] Figure 3 This is a flowchart illustrating another embodiment of the present invention. Detailed Implementation

[0028] This invention aims to address the lack of a global AI-powered intelligent decision-making center in the operation of medical aesthetic institutions. It provides a "Medical Aesthetic Super Manager System" that organically integrates multiple business capability modules through a smart organizational architecture, with a unified command and dispatch system controlled by an AI manager, thereby achieving cross-departmental collaborative optimization decision-making. Simultaneously, it addresses the issues of data silos and low learning efficiency in existing systems by using snapshot and feedback iteration mechanisms to effectively accumulate business data and continuously optimize and upgrade the AI ​​model. Furthermore, by introducing the MCP multi-agent collaborative protocol, the system can integrate external large-scale models and other intelligent agent resources, further enhancing the intelligence and adaptability of decision-making.

[0029] Based on the aforementioned technical objectives, this invention provides multiple hardware modules in a medical aesthetics super-manager system based on a smart organizational architecture. Specifically, these modules include: The manager's control center, equivalent to the CEO in an intelligent organizational structure, is the core AI decision-making module of the system. It coordinates the operational data and resources of the medical aesthetics institution globally, intelligently directing and scheduling various business modules. This control center has a built-in autonomous decision-making engine that analyzes and judges real-time data based on large-scale pre-trained models and reinforcement learning algorithms, formulating optimal operational strategies and dynamically adjusting business objectives. As a strategy maker and scheduler, the manager's control center acts as both the "global brain" and the "strategist."

[0030] Multiple capability center modules correspond to various business director departments within the smart organizational architecture, such as the Operations Center, Consulting Center, Marketing Center, Data Center, and Product Center modules. Each capability center module undertakes functional responsibilities in a specific area: for example, the Operations Center module is responsible for business data analysis and target management; the Consulting Center module is responsible for customer management and communication support; the Marketing Center module is responsible for marketing content generation and channel placement; the Data Center module is responsible for data collection, governance, and service provision; and the Product Center module is responsible for project and inventory analysis. These capability center modules are deployed in a modular architecture, decoupled from each other, and communicate with the central control unit through a unified interface, supporting continuous expansion and upgrades of system functions. In the smart organizational architecture, they are similar to the "directors" of each business line, working collaboratively under the overall coordination of the central control unit.

[0031] The data acquisition and snapshot module, integrated into the data service layer, automatically collects business data from the organization's existing business systems (such as appointment systems, POS systems, and electronic medical record systems) and generates data snapshots at a set frequency. This module includes a data acquisition unit at the ODS (Operational Data Store) layer and a snapshot unit at the DWD (Detailed Data Store) layer, used to convert source business data into analytically friendly structured snapshot data. Through the snapshot mechanism, the system records the business status at each point in time, ensuring that subsequent analysis and decision-making are based on a unified historical data view.

[0032] The data service and AI analytics module includes sub-modules such as the data service layer and the AI ​​capability layer. The data service layer interfaces with the snapshot data, providing real-time querying, data APIs, and caching capabilities to support efficient data access for upper-layer AI analytics and business applications. The AI ​​capability layer integrates multiple intelligent engines, such as an analytical inference engine, a content generation engine, an intent recognition engine, and predictive models, for in-depth analysis and intelligent processing of business data. In its implementation, this layer's functionality can be supported by self-developed AI models or third-party AI services (such as the large-scale language model GPT and multimodal recognition models). Through this module, the management control center can automatically analyze, predict trends, and generate content from massive amounts of business data, providing a basis for decision-making.

[0033] The MCP (Multi-Agent Collaboration) module enables communication and collaboration between the administrator control center and multiple agents. The MCP protocol, serving as the system's agent communication interface, integrates the internal AI engine with external intelligent services (such as the conversational large-scale models ChatGPT and Claude). Through the MCP protocol, the administrator control center can invoke external AI agents to perform complex natural language analysis and decision support tasks, or collaborate with multiple internal sub-AI agents to complete tasks, realizing an "agent collaboration system (intelligent organization)." For example, when generating personalized marketing content, the control center can request a content generation engine (such as the Gemini model) to create copy through the MCP interface; when complex decisions are required, it can also collaborate with external expert-level AI models to improve the accuracy and reliability of decisions.

[0034] The multi-touchpoint interaction module provides a multi-channel interactive interface between the system and users (including managers, employees, and customers). This module covers various touchpoints such as web interface, mobile application, WeChat Work interface, and smart hardware devices. For example, managers can view operational data and AI suggestions through web / mobile dashboards, and the system can also push customer follow-up reminders or daily operational reports to consultants via WeChat Work; smart hardware such as large screens and smart mirrors can be used to display data in real time or provide customers with interactive experiences. The multi-touchpoint module ensures that system decisions and services can reach relevant personnel in the right scenarios and in the right ways, forming a closed-loop intelligent operation. For example, the Consulting Director module, combined with the WeChat Work robot, prompts consultants with customer acquisition strategies; the Marketing Director module automatically publishes marketing content through the content platform; and the Operations Director module generates operational reports on the management end, all through this interaction module.

[0035] A security and access control module (optional) is used to ensure the security and compliance of the system and data. This module implements multi-layered access control, data security auditing, and privacy protection mechanisms to ensure that AI decision-making and data use comply with medical industry regulations. This part is an additional design in this invention and may be part of the security director module in some implementations.

[0036] Based on the deployment and design of the above hardware modules, refer to Figure 1 As shown, in one embodiment of the present invention, the medical aesthetics super manager system adopts an intelligent organizational architecture design, with its core including a manager control center and several capability center modules. The manager control center is equivalent to the intelligent operation "brain" of the hospital, responsible for overall decision-making and scheduling. The capability center modules include multiple modules such as operations, consulting, marketing, data, and products, with the following functions: the operations module is used for monitoring operational indicators and adjusting strategies; the consulting module is used for customer relationship management and service process optimization; the marketing module is used for market analysis and promotional content generation; the data module is responsible for data governance and providing data services; and the product module is responsible for project management and supply chain analysis. Each capability center module communicates with the manager control center through a predefined data interface, receives instructions issued by the control center, executes corresponding business operations, and simultaneously feeds back the execution results and data to the control center.

[0037] In actual deployment, each capability center module runs independently as a microservice, and the modules are integrated in a loosely coupled manner. This modular deployment approach ensures that the system has good scalability and maintainability: when a new function is needed, only the corresponding module needs to be added and registered with the control center, without modifying other modules. For example, when introducing the "Hardware Director" module to manage IoT devices, it can be integrated into the system framework as a new capability center module.

[0038] Based on the above ideas, combined with Figure 2 As shown, in another embodiment of the present invention, the system's technical architecture layering and data flow process are specifically described. The top layer of the system is the interaction touchpoint layer, which includes various interaction channels such as the web front-end, mobile applications, WeChat Work interface, MCP interface, smart hardware terminals, and OpenAPI. These touchpoint layer components enable different users and intelligent agents to interact with the system: for example, administrators access the management interface through a web browser, doctors or consultants receive reminders through a mobile app or WeChat Work, third-party applications obtain data through APIs, and external AI agents access the system through the MCP protocol, etc.

[0039] Below the interaction touchpoint layer is the capability center layer, corresponding to various business capability center modules (operations, consulting, marketing, data, product, etc.). Below that is the AI ​​capability layer, containing intelligent components such as analytical reasoning, content generation, intent recognition, and predictive models to support the intelligent analysis needs of the upper-layer businesses. For example, the analytical reasoning component is used for complex data analysis, the content generation component is used to automatically write marketing content, intent recognition is used to understand the instructions of users or employees, and predictive models are used for performance and trend forecasting. The components in the AI ​​capability layer can be implemented using internal algorithms or can interface with external AI services (such as NLP cloud services and large model APIs), and collaborate with the control center through the MCP interface.

[0040] Below the AI ​​capability layer is the data service layer, which includes sub-modules such as real-time query service, snapshot service, data API service, and caching. The data service layer is responsible for retrieving and processing business data from the data storage layer, providing a unified data access interface to the upper layers. For example, the real-time query service provides quick access to operational metrics, the snapshot service provides historical data views, the data API is available for front-end and third-party use, and caching improves the response speed for frequently accessed data.

[0041] At the bottom layer is the data storage layer, which includes a business database (such as MySQL, codenamed "neuron" to store business data) and a snapshot database (such as PostgreSQL to store historical snapshot data). The business database records raw data from daily operations (appointment records, cashier records, medical records, etc.), while the snapshot database stores time-series copies of key operational data generated by the snapshot module. The combination of the two ensures that both real-time data can be obtained and historical trends can be traced.

[0042] The data flow within the system is as follows: First, data from various business systems enters the data acquisition module and is written to the business database. Then, the snapshot module extracts data from the business database to generate snapshots and saves them to the snapshot database. Next, the data service layer extracts the required data from the business database and the snapshot database, processes it, and provides it to the AI ​​capability layer and capability center layer. For example, daily sales data is first captured by the snapshot module, and the operations module obtains yesterday's complete data through the data service layer the following day to generate the daily operations report. The AI ​​analysis component also uses this data for trend analysis. During business execution, the manager's control center subscribes to key data changes (such as real-time performance and customer feedback), triggering corresponding AI analysis processes or decision adjustment processes to achieve data-driven real-time closed-loop control.

[0043] refer to Figure 3 As shown, another embodiment of the present invention is a control method for the above-described system. In this embodiment, the aforementioned method is performed according to the following steps: Step S301: Data Acquisition and Snapshot Generation. Real-time operational data, including customer information, appointment schedules, treatment records, and financial data, is collected from various business systems of the medical aesthetic institution. After preprocessing, the collected data is written into the business database, and the snapshot module periodically (e.g., at the end of each day) generates and archives data snapshots. Through snapshots, the system obtains a stable historical data view, providing a foundation for subsequent analysis.

[0044] Step S302: Data Integration and Analysis. The manager's control center calls the data service layer to obtain the latest business data and historical snapshot data, and integrates and analyzes the data in the AI ​​capability layer. For example, the control center triggers the analysis and inference engine to calculate the deviation between the day's performance and the target, calls the predictive model to predict the customer flow trend for the next week, and calls the content generation engine to draft marketing plans, etc. If more advanced intelligent support is needed during the analysis process, the control center can call external AI services to participate in the analysis and decision-making through the MCP multi-agent collaboration module, such as requesting a large language model to interpret the operation report and suggest improvement measures.

[0045] Step S303: Decision Making and Instruction Generation. Based on the analysis results of step S302, the management control center uses an autonomous decision-making engine to formulate optimized operational decision-making plans. This decision-making process comprehensively considers global business objectives and the status of each module, learns the optimal strategy from massive historical data through reinforcement learning algorithms, and can adaptively adjust decisions according to real-time environmental changes. Once the strategy is determined, the control center generates specific instructions and task lists and issues them to the relevant capability center modules. For example, the control center may issue instructions to the marketing module to adjust advertising placement plans, to the operations module to modify staff schedules, and to the consulting module to formulate new customer follow-up plans, etc.

[0046] Step S304: Task Execution and Collaborative Processing. After receiving instructions from the central control unit, each capability center module executes the corresponding task according to the instructions. During execution, if cross-module collaboration is involved (e.g., a new marketing campaign requires both the marketing module to publish content and the operations module to adjust on-site manpower), the relevant modules will coordinate and execute synchronously through the central control unit to ensure proper cooperation. For specific tasks requiring AI assistance, modules can utilize AI capability layer tools again, such as having the content generation engine polish tweets or the intent recognition engine parse customer replies. When necessary, the central control unit can also schedule multiple AI agents through the MCP interface to jointly complete complex tasks. For example, when developing personalized plans for VIP customers, the central control unit simultaneously calls on aesthetic design AI and dialogue AI to collaborate with the consultation module to generate customized plans that meet customer preferences.

[0047] Steps S305-S306: Result Monitoring and Feedback Learning. After a task is completed, the system monitors the results and collects feedback data. For example, the operations module summarizes new performance data, the consultation module records customer responses and satisfaction scores, and the marketing module tracks campaign conversion effects. This feedback data is aggregated through the data service layer to the manager's control center, triggering a data feedback learning mechanism. The control center compares the actual results with the expected goals and adjusts the parameters of the internal decision-making model through reinforcement learning or meta-learning algorithms, enabling the AI ​​autonomous decision-making engine to output better strategies in similar situations in the future. For example, if a marketing strategy is found to be ineffective, the system will reduce the rating bias for similar strategies in the model; if follow-up communication with a certain type of customer improves satisfaction, the system will strengthen the corresponding model node. Through daily feedback iteration, the system's intelligence level continuously improves, truly achieving self-evolution and continuous optimization.

[0048] Through the cyclical execution of steps S301-S306 described above, the medical aesthetics super manager system of the present invention can operate autonomously around the clock, dynamically adapt to changes in the business environment, and achieve intelligent operation management with a closed-loop process. For example, when entering peak season or encountering unexpected market situations, the system can automatically detect trends and quickly adjust various operational strategies; similarly, for individual customers, the system can continuously fine-tune service plans based on their latest feedback to provide them with the best experience.

[0049] It should be noted that the control method of this invention can be implemented as a software algorithm process and deployed on the computing platform of the manager's control center. The specific execution actions of each capability center module can be integrated with existing business systems through APIs, RPA (Robotic Process Automation), etc. For example, the instructions of the marketing module can call third-party marketing tool APIs to execute campaigns, and the instructions of the consultation module can send messages to customer service personnel through the WeChat interface. These implementation details are all within the protection scope of this invention.

[0050] The above-described specific embodiments of the present invention illustrate the architecture and workflow of the medical aesthetics super manager system. Based on the ideas of the present invention, those skilled in the art can add or remove functions or make equivalent technical substitutions for some modules, such as using different machine learning models or adding new capability center modules (such as financial analysis modules, training guidance modules, etc.) to adapt to specific needs.

[0051] As can be seen from the above embodiments of the present invention, the medical aesthetics super manager system based on intelligent organizational architecture provided by the present invention has the following characteristics: AI-driven centralized command and dispatch: The system centers on a manager's control hub, enabling AI to command and dispatch the entire business. This control hub issues instructions and strategies to various capability modules, transforming operational decisions from relying on human experience to being uniformly formulated by AI, significantly improving decision-making efficiency and scientific rigor. For example, when customer traffic changes or market trends fluctuate, the control hub can adjust operational strategies and resource allocation in real time, achieving rapid cross-departmental response.

[0052] Modular Capability Center Deployment: The system adopts a modular design similar to an organizational structure, dividing complex business processes into several capability center modules. Each module has clear responsibilities and is loosely coupled and integrated. This modular deployment allows for flexible expansion of system functionality: when new business capabilities need to be added (such as smart hardware integration, emotional interaction, etc.), corresponding modules can be added without affecting the overall architecture. Each module is developed and deployed independently, facilitating upgrades and maintenance, and interacts with the control center through a unified data interface, ensuring system consistency and scalability.

[0053] Snapshot and Feedback Iteration Mechanism: The system incorporates a snapshot system and a data feedback learning mechanism to achieve data-driven continuous intelligent optimization. The snapshot system ensures that operational data is recorded regularly, eliminating the impact of real-time data fluctuations on analysis and providing reliable data for AI decision-making. The data feedback mechanism allows the system to obtain results and feedback (such as changes in customer satisfaction and fluctuations in performance indicators) after executing decisions, and uses this feedback data to train and adjust AI model parameters, thereby "improving the intelligence level of the 'manager' every day." This closed-loop iteration makes the system increasingly intelligent and adaptable to the dynamically changing business environment.

[0054] The MCP (Multi-Agent Collaboration Protocol) enables the system to interact with multiple agents, overcoming the limitations of a single AI engine and forming collaborative intelligence. The MCP interface allows the central control unit to invoke various internal and external AI services (including natural language processing, large-scale model inference, and specialized algorithm modules) to collaboratively complete complex tasks, essentially introducing virtual "employees" or "advisors" for the system's use. For example, when facing complex business decisions, the central control unit can simultaneously invoke predictive models and conversational AI for deduction and argumentation; in customer service scenarios, the central control unit can link with a digital human interface to respond to customer inquiries in real time. Through agent collaboration, the system exhibits human-like teamwork intelligence, significantly improving decision-making quality and service levels.

[0055] Those skilled in the art will understand that, based on the above-mentioned technical solutions and features, the present invention has the following objective technical effects: Improving operational decision-making efficiency and accuracy: Thanks to unified decision-making via an AI central hub and multi-agent collaboration, this system can make faster and more accurate decisions than humans based on massive amounts of data. For example, setting business goals, adjusting marketing strategies, and optimizing customer service can all be done automatically, reducing blind spots in human decision-making and optimizing overall profitability.

[0056] Reduced labor costs and enhanced execution: The system handles a large number of daily operational analysis and decision-making tasks (such as report analysis, marketing content generation, and customer follow-up reminders), essentially functioning as a virtual operations team working 24 / 7. This reduces human involvement and improves operational consistency and sustainability. Simultaneously, AI decisions are directly transmitted to the execution layer through the capability center module, avoiding intermediate transmission losses and ensuring rapid decision implementation.

[0057] Enhance customer experience and satisfaction: Leveraging multi-touchpoint interaction and intelligent customer management, this system can provide personalized and timely services to each customer. For example, AI automatically recommends customized cosmetic medical plans based on customer data, and a digital assistant provides continuous follow-up after the procedure, answering questions promptly and increasing customer trust and satisfaction.

[0058] Building core competitiveness driven by data and algorithms: Through snapshot accumulation and continuous learning, the system has formed a high-value business data closed loop and algorithm assets.

[0059] In addition to the above, it should be noted that the terms "one embodiment," "another embodiment," and "embodiment" used in this specification refer to specific features, structures, or characteristics described in connection with that embodiment, which are included in at least one embodiment described in the general description of this application. The appearance of the same expression in multiple places in the specification does not necessarily refer to the same embodiment. Furthermore, when a specific feature, structure, or characteristic is described in connection with any embodiment, the intention is to suggest that implementing such a feature, structure, or characteristic in conjunction with other embodiments also falls within the scope of this invention.

[0060] Although the invention has been described herein with reference to several illustrative embodiments, it should be understood that many other modifications and implementations can be devised by those skilled in the art, which will fall within the scope and spirit of the principles disclosed herein. More specifically, various variations and modifications can be made to the components and / or layout of the subject matter arrangement within the scope of the disclosure, drawings, and claims. Besides variations and modifications to the components and / or layout, other uses will be apparent to those skilled in the art.

Claims

1. A medical beauty management system based on a smart organization structure, characterized by: It includes a manager control center, which is connected to the operation center module, consultation center module, marketing center module, data center module and product center module respectively; The operation center module, consultation center module, marketing center module, data center module, and product center module all access the data service layer through the AI ​​capability layer; The data service layer connects to the data storage layer; The data service layer is used to obtain real-time data from the medical aesthetics business system, preprocess the real-time data, and then store it in the data storage layer; at the same time, it periodically generates snapshot data of the real-time data and stores it in the data storage layer. The manager's control center is used to call the AI ​​capability layer to integrate and analyze the snapshot data and real-time data in the data storage layer, and then use the AI ​​autonomous decision-making model to make business optimization decisions based on the analysis results. The central management unit is also used to convert the business optimization decisions into specific task instructions, and distribute them to the operation center module, consultation center module, marketing center module, data center module and product center module respectively. The operation center module, consultation center module, marketing center module, data center module and product center module are used to execute the tasks in the task instructions, and after the task is completed, transmit the feedback data to the feedback learning unit. The manager control center is also used to update and train the AI ​​autonomous decision-making model using the data in the feedback learning unit. 2.The smart organization structure-based medical beauty management system according to claim 1, characterized in that: The operation center module, consultation center module, marketing center module, data center module, and product center module are used to call one or more intelligent agents through the MCP protocol interface to collaboratively complete the tasks in the task instructions when needed; the MCP protocol interface is used to interact and collaborate with one or more external intelligent agents in accordance with the MCP protocol. 3.The smart organization structure-based medical aesthetic management system according to claim 1 or 2, characterized in that: The system also includes an interactive touchpoint layer, which is connected to the operation center module, consultation center module, marketing center module, data center module and product center module respectively. The interactive touchpoint layer has multiple data interfaces, which are used to provide interactive interfaces with the AI ​​capability layer, AI autonomous decision-making model and external intelligent agents to each user through multiple data interfaces. 4.The smart organization structure-based medical aesthetic management system according to claim 3, characterized in that: The data interface can be any one or more of the following: Web interface interface, mobile application interface, WeChat Work interface, and smart hardware device interface.

5. The medical aesthetics management system based on intelligent organizational architecture according to claim 1, characterized in that: The operation center module, consultation center module, marketing center module, data center module, and product center module execute the tasks in the task instructions, which are to integrate and analyze the snapshot data in the data storage layer through the AI ​​capability layer, generate corresponding feedback data, and transmit it to the feedback learning unit.

6. A control method for the system according to any one of claims 1 to 5, characterized in that... The method includes the following steps: The data service layer obtains real-time data from the medical aesthetics business system, preprocesses the real-time data, and then stores it in the data storage layer; at the same time, it periodically generates snapshot data from the real-time data and also stores it in the data storage layer. The manager's control center calls on the AI ​​capability layer to integrate and analyze snapshot data and real-time data in the data storage layer, and then uses the AI ​​autonomous decision-making model to make business optimization decisions based on the analysis results. The central manager translates business optimization decisions into specific task instructions, which are then distributed to the Operations Center, Consulting Center, Marketing Center, Data Center, and Product Center modules respectively. The operation center module, consultation center module, marketing center module, data center module, and product center module are used to execute the tasks in the task instructions, and after the task is completed, transmit the feedback data to the feedback learning unit. The manager control center is also used to update and train the AI ​​autonomous decision-making model using the data in the feedback learning unit.

7. The control method according to claim 6, characterized in that: The method further includes the following steps: the operation center module, consultation center module, marketing center module, data center module, and product center module are used to call one or more intelligent agents through the MCP protocol interface to collaboratively complete the tasks in the task instructions when needed.

8. The control method according to claim 6, characterized in that: The method further includes the following steps: the interaction touchpoint layer provides each user with an interaction interface with the AI ​​capability layer, the AI ​​autonomous decision-making model and the external intelligent agent through multiple data interfaces.

9. The control method according to claim 6, characterized in that: The operation center module, consultation center module, marketing center module, data center module, and product center module execute the tasks in the task instructions, which are to integrate and analyze the snapshot data in the data storage layer through the AI ​​capability layer, generate corresponding feedback data, and transmit it to the feedback learning unit.

10. The control method according to claim 6, characterized in that: The method is continuously cyclical during system operation.

Images