Operation parameter monitoring and early warning method and system for medical beauty equipment

Abstract

The invention relates to the technical field of operation monitoring, in particular to an operation parameter monitoring and early warning method and system for medical beauty equipment. The method comprises the following steps: acquiring medical beauty equipment data; performing equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; carrying out equipment monitoring parameter integration through a multi-source sensor based on the action range data of the medical beauty equipment, and generating multi-modal monitoring data of the standard medical beauty equipment; and performing long-term performance degradation trend monitoring on the multi-mode monitoring data of the standard medical beauty equipment to generate long-term performance degradation trend data of the key parts of the equipment. Through multi-modal data integration, long-term performance trend analysis, biomechanical modeling and intelligent early warning decision optimization, the reliability and the intelligent level of monitoring and early warning of the operating parameters of the medical beauty equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of operation monitoring technology, and in particular to an operation parameter monitoring and early warning method and system for medical cosmetic equipment. Background Art

[0002] Early medical beauty equipment monitoring technology mainly relied on simple mechanical or electrical parameter detection, which could not meet the needs of high-precision treatment. With the advancement of sensor technology and computer algorithms, real-time data acquisition and analysis functions have been gradually introduced to detect the power output, temperature change and energy distribution of the equipment. However, these technologies can only monitor the status of the equipment with a single parameter, lack comprehensive early warning capabilities, and are difficult to deal with complex equipment abnormalities. In recent years, the application of artificial intelligence, big data and Internet of Things technologies has provided a new development direction for the monitoring and early warning of the operating parameters of medical beauty equipment. Through the fusion analysis of multi-source data by intelligent algorithms and the establishment of equipment operating status models, equipment failure trend prediction and real-time abnormal alarm can be achieved. However, the monitoring parameters of medical beauty equipment are usually derived from a single sensor, which cannot fully reflect the comprehensive status of the equipment, and fail to fully consider the impact of the equipment on the biomechanical properties of the skin, which leads to low reliability and intelligence level of monitoring and early warning of the operating parameters of medical beauty equipment. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for monitoring and early warning the operating parameters of medical cosmetic equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above-mentioned purpose, a method for monitoring and early warning of operating parameters of medical cosmetic equipment is provided, the method comprising the following steps: Step S1: Acquire medical beauty equipment data; perform equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate standard medical beauty equipment multimodal monitoring data; Step S2: Perform long-term performance degradation trend monitoring on the multimodal monitoring data of standard medical cosmetic equipment to generate long-term performance degradation trend data of key components of the equipment; generate a first warning signal based on the long-term performance degradation trend data of key components of the equipment to obtain a first warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate a second warning signal based on the skin stress distribution data to obtain a second warning signal for operation monitoring; Step S3: Upload the first operation monitoring warning signal and the second operation monitoring warning signal to the cloud platform for monitoring warning trigger frequency analysis to generate equipment monitoring warning trigger frequency data; construct a trigger decision based on the equipment monitoring warning trigger frequency data to generate medical beauty equipment operation warning decision data; Step S4: Evaluate the accuracy of the warning decision data for the operation of the medical cosmetic equipment to generate operation monitoring warning accuracy evaluation data; adjust the warning signals of the operation monitoring first warning signal and the operation monitoring second warning signal based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

[0005] The present invention can accurately identify the working area of ​​the equipment by analyzing the scope of action of the medical cosmetic equipment data, thereby ensuring that the monitored data is related to the actual operating environment and treatment area of ​​the equipment, reducing errors, and providing an accurate monitoring data basis. By integrating data from multi-source sensors, the multimodal information of the equipment can be fully collected to enhance the accuracy and comprehensiveness of the data. Long-term performance degradation trend monitoring can timely discover the potential failure risks of key components of the equipment, generate early warning signals in advance, and help intervene before the problem occurs, thereby extending the service life of the equipment. Biomechanical modeling takes into account the skin's reaction to the equipment and generates a second early warning signal, which can reflect the actual impact of the equipment on the patient and ensure the safety and effectiveness of the treatment. Uploading the early warning signal to the cloud platform can realize remote monitoring and data storage, and provide real-time data support for the management and maintenance of the equipment. Trigger frequency analysis helps to identify the frequent occurrence of early warning signals, optimize the decision-making process, avoid excessive early warning or missed reports, and improve the intelligence level of the system. By evaluating the accuracy of the early warning, the reliability of the early warning system can be verified and improved to ensure the effectiveness and timeliness of the early warning signal. Adjusting the early warning signal according to the evaluation results can dynamically optimize the monitoring and early warning system, so that it can better adapt to the actual operating status of the equipment, reduce false alarms and missed alarms, and further improve the intelligence and accuracy of equipment management. Therefore, the present invention improves the reliability and intelligence level of medical beauty equipment operating parameter monitoring and early warning through multimodal data integration, long-term performance trend analysis, biomechanical modeling and intelligent early warning decision optimization.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire medical beauty equipment data; Step S12: extracting text features from the medical beauty equipment data to generate medical beauty equipment text feature data; performing text semantic analysis on the medical beauty equipment text feature data to generate medical beauty equipment text semantic analysis data; Step S13: performing a medical beauty equipment action range analysis on the medical beauty equipment text semantic analysis data to generate medical beauty equipment action range data; integrating equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate medical beauty equipment multimodal monitoring data; Step S14: perform data preprocessing on the multimodal monitoring data of medical cosmetic equipment to generate standard multimodal monitoring data of medical cosmetic equipment, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization.

[0007] Through steps S11 and S12, the present invention realizes the preliminary collection of medical beauty equipment data and in-depth mining at the text semantic level, and can accurately extract the core information and functional description of the equipment, providing basic data support for subsequent analysis. The scope analysis in step S13 combines multi-source sensor technology, which not only improves the analytical depth of the equipment function, but also integrates the multimodal data of equipment monitoring to ensure the comprehensiveness and consistency of the data. Through the multimodal data preprocessing of S14, the problems of noise and missing values ​​in the original data are solved, the quality of the data is ensured, and standardized multimodal monitoring data is generated, laying a reliable foundation for subsequent modeling and analysis. Through text feature extraction, semantic parsing and multimodal data processing, an intelligent process from data collection to in-depth analysis is realized, which significantly improves the accuracy and efficiency of equipment monitoring parameter integration. This step system has strong adaptability, can be adjusted for different types of medical beauty equipment, is suitable for a variety of data types and monitoring needs, and has high versatility.

[0008] Preferably, integrating equipment monitoring parameters through multi-source sensors based on the range data of medical cosmetic equipment includes: The skin condition sensor is used to collect the user's real-time physiological signals within the range of action of the medical beauty device, and obtain the user's real-time physiological signals; the user's real-time physiological signals are conditioned to generate the user's real-time physiological conditioning signals; Perform analog-to-digital conversion on the user's real-time physiological conditioning signal to generate the user's real-time physiological data; perform data change correlation analysis on the user's real-time physiological data and medical beauty equipment data to generate monitoring correlation change data; use environmental sensors to collect environmental parameters based on the monitoring correlation change data to obtain the medical beauty equipment's working environment data; The medical beauty equipment data, the user's real-time physiological data and the medical beauty equipment's working environment data are integrated and displayed to generate multimodal monitoring data of medical beauty equipment.

[0009] The present invention collects the user's physiological signals in real time through a skin state sensor, and conditions and converts them into analog-to-digital signals to generate high-precision real-time physiological data of the user. This process ensures the integrity and accuracy of the user's physiological signals and provides a reliable data basis for personalized medical cosmetic operations. The user's real-time physiological data and medical cosmetic equipment data are analyzed for changes in association, the specific impact of equipment operation on the user's physiological state is identified, and monitoring-related change data is generated. This analysis method can timely discover abnormalities or optimization points in the equipment operation process, and improve the accuracy and practicality of monitoring. Combined with the equipment action environment data (such as temperature, humidity, etc.) collected by the environmental sensor, the equipment parameters are dynamically adjusted to adapt to different operating environments. The device is able to perceive and respond to changes in the external environment, and the safety of operation and user experience are improved. The medical cosmetic equipment data, the user's real-time physiological data, and the action environment data are integrated for data display to generate multimodal monitoring data, which provides support for the comprehensive analysis of equipment status and user feedback. Multimodal data integration makes up for the shortcomings of single data source monitoring and provides a more comprehensive perspective for the equipment monitoring system. The monitoring and change association analysis of the user's real-time physiological signals support the dynamic adjustment of medical cosmetic equipment and improve the real-time response capability of the equipment.

[0010] Preferably, step S2 comprises the following steps: Step S21: extracting the operating parameters of key components of the equipment from the multimodal monitoring data of the standard medical cosmetic equipment to obtain the operating parameters of the key components of the equipment, wherein the key components of the equipment include a laser, a radio frequency electrode, and a cooling system; Step S22: monitoring the long-term performance degradation trend of the operating parameters of the key components of the equipment, generating long-term performance degradation trend data of the key components of the equipment; generating a first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtaining a first early warning signal of operation monitoring; Step S23: extracting skin contact optical perception data from the multimodal monitoring data of the standard medical cosmetic equipment to obtain skin contact optical perception data; Step S24: Perform biomechanical modeling on the skin contact optical perception data to generate skin stress distribution data; identify potential risks based on the skin stress distribution data to generate skin micro-injury prediction data; generate a second warning signal based on the skin micro-injury prediction data to obtain a second warning signal for operation monitoring.

[0011] The present invention extracts operating parameters for key components such as lasers, radio frequency electrodes and cooling systems, and provides accurate data support for the operating status of the equipment. It helps to identify potential problems of the equipment and ensure the efficient operation and stability of medical cosmetic equipment. The long-term performance degradation trend analysis of the operating parameters of key components is carried out to identify the trend of component aging or performance degradation, and provide a scientific basis for equipment maintenance and replacement. The generation of the first warning signal can provide timely warnings before the equipment components fail, reduce the equipment failure rate, and avoid potential risks to users. Through optical perception technology, the optical parameters during the contact between the user's skin and the equipment are extracted, the user's skin condition is monitored in real time, and the equipment monitoring accuracy and data richness are improved. By performing biomechanical modeling on the optical perception data, skin stress distribution data is generated to provide a scientific basis for identifying potential skin risks. Skin micro-injury prediction data provides support for risk control of medical cosmetic equipment operation and reduces potential damage to the user's skin. The second warning signal is generated based on the skin micro-injury prediction data to further improve the warning mechanism. The first warning signal focuses on the status of the equipment itself, and the second warning signal focuses on user safety, building a multi-level and comprehensive monitoring and warning system. Combined with dual monitoring of equipment and users, it can quickly identify abnormal equipment operation and changes in user skin condition, significantly improving operational safety. It can identify user skin damage caused by equipment in advance, optimize the operation process of medical beauty equipment, and improve user experience and trust.

[0012] Preferably, step S22 includes the following steps: Step S221: extracting equipment bearing operation parameters from equipment key component operation parameters to obtain equipment bearing operation data, wherein the equipment bearing operation data includes equipment bearing vibration data and equipment bearing temperature data; Step S222: performing envelope demodulation on the equipment bearing vibration data to generate equipment bearing vibration characteristic data; performing fluctuation extraction processing on the bearing temperature data to generate equipment bearing temperature characteristic data; performing time series correlation processing on the equipment bearing vibration characteristic data and the equipment bearing temperature characteristic data to generate coupling characteristic data; Step S223: Calculate the vibration-temperature coupling coefficient of the coupling characteristic data to generate coupling index data; perform state mapping on the coupling index data to generate equipment health assessment data; perform trend fitting processing on the equipment health assessment data to generate long-term performance degradation trend data of key components of the equipment; Step S224: Generate a first warning signal for the long-term performance degradation trend data of key components of the equipment based on a preset first signal frequency to obtain a first warning signal for operation monitoring.

[0013] The present invention can comprehensively evaluate the operating status of the equipment and identify potential fault sources in advance by performing multi-dimensional monitoring of the vibration and temperature of key components of the equipment (such as bearings). Combining multi-modal data such as vibration and temperature for time series correlation processing and coupling analysis, the accuracy of equipment fault prediction is greatly improved, especially in the identification of long-term performance degradation trends. Timely operation health feedback generated by equipment health assessment data can help the operation team to conduct a comprehensive and real-time evaluation of the equipment status and ensure the stable operation of the equipment. Based on the fitting analysis of the long-term performance degradation trend, the performance degradation trend of key components of the equipment can be accurately identified, which helps to plan maintenance or replacement components in advance and reduce maintenance costs. The first warning signal is generated by a preset frequency threshold, and equipment maintenance personnel can take measures quickly according to different warning signals to prevent the problem from further expanding. Through the fusion analysis of vibration data and temperature data, the perception of the equipment operating status is improved, which is particularly suitable for complex equipment environments and ensures the comprehensiveness and accuracy of equipment monitoring. Timely discovery and repair of potential equipment failures helps to maintain the stability and efficiency of the equipment, reduce equipment downtime, and improve the overall reliability of the equipment.

[0014] Preferably, step S24 includes the following steps: Step S241: calculating the perception area of ​​the skin contact optical perception data to obtain the skin contact perception area; reconstructing the three-dimensional geometry of the skin contact surface of the skin contact optical perception data based on the skin contact perception area to generate the three-dimensional geometry data of the skin contact surface; Step S242: performing skin layer stress analysis on the three-dimensional geometric data of the skin contact surface to generate skin stress distribution data, wherein the skin layer stress analysis includes skin surface stress analysis and skin deep layer stress analysis; performing stress extreme value screening on the skin stress distribution data to obtain skin stress distribution extreme value data; Step S243: performing potential damage identification on the preset skin damage stress threshold and the skin stress distribution extreme value data. When the skin stress distribution extreme value data is greater than the preset skin damage stress threshold, marking stress concentration points on the skin stress distribution data based on the skin stress distribution extreme value data to generate stress concentration points. Step S244: using the stress concentration point to screen the skin stress distribution extreme value data for the remaining extreme value points, to obtain the remaining skin stress distribution extreme value data; marking the remaining skin stress distribution extreme value data as skin stress distribution extreme value data and cyclically executing step S243 until the skin stress distribution extreme value data is less than or equal to the preset skin damage stress threshold, to generate a stress concentration area; Step S245: Identify potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area, and generate skin micro-injury prediction data; generate a second warning signal for the skin micro-injury prediction data based on the preset first signal frequency to obtain a second warning signal for operation monitoring.

[0015] The present invention can accurately obtain the skin contact surface morphology through three-dimensional geometric reconstruction of skin contact optical perception data, and provide high-precision three-dimensional data support for subsequent stress analysis and potential damage identification. Through stress analysis of the surface and deep layers of the skin, the stress distribution of the skin under the action of medical beauty equipment can be comprehensively evaluated, and stress concentration areas at different levels can be identified to ensure that skin health is fully concerned. Through the screening of extreme values ​​of skin stress distribution and the marking of stress concentration points, potential skin damage risks can be identified in advance, problems can be discovered in time, the occurrence of skin micro-damage can be avoided, and the cumulative effect of damage can be reduced. Based on the skin micro-damage prediction data, a second early warning signal can be generated, and a fault warning can be issued in time to avoid skin damage, enhance the user's sense of security and trust in the device. Through the identification of stress concentration areas, potential high-risk areas of the skin can be accurately located, and the operating parameters of the equipment can be optimized to ensure that excessive pressure is not applied in these areas, thereby protecting the skin from damage. By continuously looping the execution steps, it is ensured that the stress distribution extreme value data is less than or equal to the preset threshold before stopping, and the early warning strategy can be adaptively adjusted according to dynamic changes to achieve intelligent and refined risk identification and management. Through precise stress analysis and damage identification, the risk of skin damage caused by improper equipment operation is greatly reduced, and the safety and reliability of medical beauty equipment are improved.

[0016] Preferably, identifying potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area includes: The contact stress characteristics of the stress concentration area are extracted to obtain the contact stress characteristic data; the contact stress characteristic data is divided into data sets to generate a model training set and a model test set; the model training set is trained by a convolutional neural network algorithm to generate a skin micro-injury prediction model; The skin microinjury prediction pre-model is optimized and iterated using the model test set to generate a skin microinjury prediction model; the three-dimensional geometric data of the skin contact surface is imported into the skin microinjury prediction model to identify potential skin risks and generate skin microinjury prediction data.

[0017] The present invention can accurately obtain stress information of the contact area by extracting contact stress features in the stress concentration area, provide a high-quality data basis for subsequent micro-damage prediction, and improve the accuracy of risk assessment. The contact stress feature data is divided into data sets to generate model training sets and test sets, so as to ensure the diversity and representativeness of data in the training process and improve the generalization ability and prediction accuracy of the model. The convolutional neural network (CNN) algorithm is used to train the training set, and the powerful feature learning ability of deep learning can be used to extract potential micro-damage patterns from complex stress data and generate a skin micro-damage prediction pre-model. By optimizing and iterating the model using the test set, the skin micro-damage prediction model can be continuously adjusted and improved to ensure the high accuracy and reliability of the model in practical applications, and effectively avoid false positives and false negatives. The three-dimensional geometric data of the skin contact surface is input into the optimized skin micro-damage prediction model, so that the potential risks of the skin can be identified in real time and accurately, and timely risk prompts can be provided to users to prevent skin damage. Through the application of the skin micro-damage prediction model, the skin risk can be intelligently evaluated according to the specific situation of each user, and personalized care suggestions and treatment plans can be provided to improve user experience and safety. Through accurate micro-damage prediction, it is possible to timely identify damage to the skin caused by equipment operation, avoid excessive pressure or improper use of the equipment, and thus improve the safety of medical cosmetic equipment.

[0018] Preferably, step S3 comprises the following steps: Step S31: integrating the first operation monitoring warning signal and the second operation monitoring warning signal to generate a medical cosmetic equipment operation monitoring warning signal; Step S32: Upload the medical beauty equipment operation monitoring and warning signals to the cloud platform for monitoring and warning triggering, and generate equipment monitoring and warning triggering data; Step S33: Perform trigger timing frequency analysis on the equipment monitoring and early warning trigger data to generate equipment monitoring and early warning trigger frequency data; Step S34: construct a trigger decision for the equipment monitoring early warning trigger data according to the equipment monitoring early warning trigger frequency data, and generate medical cosmetic equipment operation early warning decision data.

[0019] The present invention can comprehensively consider the operating status of the equipment and the risk of skin micro-injury by integrating the first and second warning signals of operation monitoring, improve the accuracy and reliability of the warning signal, and avoid false alarms or missed alarms caused by a single signal. The operation monitoring warning signal of the medical beauty equipment is uploaded to the cloud platform, which can realize real-time remote monitoring and data processing of the equipment status. Through the powerful computing and storage capabilities of the cloud platform, the operating status of the equipment is ensured to be continuously and accurately monitored and analyzed. By triggering and monitoring the equipment monitoring warning signal, the equipment abnormality or potential risk can be discovered in real time, and immediate feedback can be provided. The trigger mechanism can enhance the intelligence and adaptive ability of the equipment operation. The timing frequency analysis of the equipment monitoring warning trigger data can reveal the potential failure mode of the equipment, help the maintenance team to discover the equipment abnormality earlier and take necessary repair measures, and effectively reduce the risk caused by equipment failure. Based on the equipment monitoring warning trigger frequency data, the generated trigger decision construction data provides accurate decision support, helping operators and managers to make more reasonable prevention and maintenance decisions, thereby reducing the probability of equipment failure and safety accidents. Through the trigger timing frequency analysis, the system can dynamically adjust the warning strategy according to the actual operation of the equipment, realize system self-optimization, and thus improve the intelligence level of equipment management.

[0020] Preferably, step S4 comprises the following steps: Step S41: collecting decision feedback data on the operation early warning decision data of the medical cosmetic equipment to obtain early warning decision feedback data; evaluating the accuracy of the early warning decision data on the operation early warning decision data of the medical cosmetic equipment according to the early warning decision feedback data to generate operation monitoring early warning accuracy evaluation data; Step S42: Adjust the warning signals of the first warning signal of operation monitoring and the second warning signal of operation monitoring based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

[0021] The present invention can realize dynamic optimization of the early warning system by collecting early warning decision feedback data and based on this, evaluating the accuracy of the operation early warning decision data of the medical beauty equipment, so as to ensure that the early warning system can be continuously improved, improve the accuracy of early warning, and reduce false alarms and missed reports. The collection and evaluation of early warning decision feedback data can help the equipment monitoring system better adapt to the changes in the actual operating environment, thereby enhancing the flexibility and pertinence of the system and improving the monitoring effect. Through the application of early warning accuracy evaluation data, the system can timely adjust the early warning strategy according to the actual feedback, thereby optimizing the early warning signal of the operation monitoring in real time, and improving the accuracy and response speed of equipment monitoring. Through the continuously optimized early warning mechanism, the medical beauty equipment can make a more timely response when potential risks occur, effectively prevent the occurrence of equipment failure or safety accidents, and thus ensure the safety of users and equipment. Based on the evaluation and optimization adjustment of feedback data, the system realizes a more intelligent early warning operation, which can not only automatically detect and predict equipment abnormalities, but also self-adjust according to the operating conditions, and gradually improve the overall intelligence level of the equipment. By adjusting the early warning signal based on the early warning accuracy evaluation data, the parameters of the early warning system can be optimized according to the actual operating conditions, so that each monitoring work of the equipment can be carried out in the best state, and the stability and efficiency of the equipment operation can be improved. Accurate adjustment of early warning signals can provide reliable data support for equipment operators and managers, helping them to diagnose problems and develop response strategies more accurately, thereby improving decision-making efficiency in equipment maintenance and troubleshooting.

[0022] In this specification, a medical cosmetic equipment operation parameter monitoring and early warning system is provided, which is used to execute the above-mentioned medical cosmetic equipment operation parameter monitoring and early warning method. The medical cosmetic equipment operation parameter monitoring and early warning system includes: The data fusion module is used to obtain medical beauty equipment data; analyze the equipment range of medical beauty equipment data to generate medical beauty equipment range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment range data to generate standard medical beauty equipment multimodal monitoring data; The monitoring and early warning module is used to monitor the long-term performance degradation trend of the multimodal monitoring data of standard medical cosmetic equipment, generate the long-term performance degradation trend data of the key components of the equipment; generate the first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtain the first early warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate the second early warning signal based on the skin stress distribution data to obtain the second early warning signal for operation monitoring; The early warning prediction module is used to upload the first early warning signal of operation monitoring and the second early warning signal of operation monitoring to the cloud platform for monitoring and early warning trigger frequency analysis, and generate equipment monitoring and early warning trigger frequency data; trigger decision construction is performed according to the equipment monitoring and early warning trigger frequency data, and medical beauty equipment operation early warning decision data is generated; The early warning optimization module is used to evaluate the early warning accuracy of the operation early warning decision data of the medical cosmetic equipment and generate operation monitoring early warning accuracy evaluation data; based on the operation monitoring early warning accuracy evaluation data, the early warning signal of the operation monitoring first early warning signal and the operation monitoring second early warning signal are adjusted to perform the operation parameter monitoring and early warning optimization operation of the medical cosmetic equipment.

[0023] The beneficial effect of the present invention is that through multi-source sensor integration and equipment range analysis, the data fusion module can comprehensively and accurately collect the operation data of the equipment, ensure that all important parameters of the equipment are monitored, and this multimodal data fusion provides a reliable basis for subsequent analysis, improves the accuracy and comprehensiveness of the data, and avoids the deviation caused by a single sensor data. Through long-term performance degradation trend monitoring and biomechanical modeling, the potential failure risk of the equipment can be predicted in advance, and a second warning signal is generated based on the skin stress distribution to ensure that the equipment operation matches the patient's skin reaction, minimize the risk in the operation, and the generated warning signal provides important warning information for equipment maintenance, effectively extending the service life of the equipment and ensuring the stability of the treatment effect. By uploading the warning signal to the cloud platform and performing trigger frequency analysis, the module can provide real-time monitoring of the equipment operation status, and optimize the warning system according to the trigger frequency, avoiding excessive warning and underreporting, and improving the intelligence level of the warning. In addition, the trigger decision construction can dynamically adjust the monitoring strategy of the equipment, ensure the flexibility and adaptability of the warning system, and improve the efficiency of equipment management. By evaluating and adjusting the accuracy of the warning, the accuracy and reliability of the warning system are further improved. Optimizing the early warning signal based on the evaluation results can dynamically adapt to the actual operating status of the equipment, reduce the possibility of false alarms and missed alarms, and ensure the operating safety of the equipment and the stability of the treatment effect. The early warning optimization operation enables the entire system to have the ability of continuous optimization and self-adjustment, enhancing the intelligence and efficiency of medical cosmetic equipment. Therefore, the present invention improves the reliability and intelligence level of medical cosmetic equipment operating parameter monitoring and early warning through multimodal data integration, long-term performance trend analysis, biomechanical modeling, and intelligent early warning decision optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a method for monitoring and early warning of operating parameters of medical cosmetic equipment; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, and the term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a method for monitoring and early warning of operating parameters of medical cosmetic equipment, the method comprising the following steps: Step S1: Acquire medical beauty equipment data; perform equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate standard medical beauty equipment multimodal monitoring data; Step S2: Perform long-term performance degradation trend monitoring on the multimodal monitoring data of standard medical cosmetic equipment to generate long-term performance degradation trend data of key components of the equipment; generate a first warning signal based on the long-term performance degradation trend data of key components of the equipment to obtain a first warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate a second warning signal based on the skin stress distribution data to obtain a second warning signal for operation monitoring; Step S3: Upload the first operation monitoring warning signal and the second operation monitoring warning signal to the cloud platform for monitoring warning trigger frequency analysis to generate equipment monitoring warning trigger frequency data; construct a trigger decision based on the equipment monitoring warning trigger frequency data to generate medical beauty equipment operation warning decision data; Step S4: Evaluate the accuracy of the warning decision data for the operation of the medical cosmetic equipment to generate operation monitoring warning accuracy evaluation data; adjust the warning signals of the operation monitoring first warning signal and the operation monitoring second warning signal based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

[0029] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of the steps of a method for monitoring and warning the operation parameters of a medical cosmetic device according to the present invention. In this example, the method for monitoring and warning the operation parameters of a medical cosmetic device comprises the following steps: Step S1: Acquire medical beauty equipment data; perform equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate standard medical beauty equipment multimodal monitoring data; In the embodiment of the present invention, the data of medical cosmetic equipment is collected through embedded sensors, equipment control systems and external monitoring systems. The main data include but are not limited to: Equipment operation data: including laser power, radio frequency current, temperature, pressure, vibration and other parameters. Equipment control data: operation instructions issued by the control system, equipment switch status, operation mode, etc. Equipment environment data: including temperature, humidity, electromagnetic field strength, etc. of the surrounding environment of the equipment. Data is collected in real time through sensor networks (such as temperature sensors, pressure sensors, optical sensors, etc.). The operating parameters, control commands and environmental change data of the equipment are digitally stored and uploaded to the cloud platform using a data recording module (such as a PLC system, an embedded microcontroller). The collected medical cosmetic equipment data is preliminarily cleaned to remove noise and outliers and unify the data format. Physical modeling is performed using the propagation characteristics of the signals (such as laser and radio frequency signals) emitted by the equipment, combined with the working parameters of the equipment (such as power and frequency), to deduce the spatial distribution of the equipment's range of action. The model parameters are adjusted using actual data fed back by the equipment sensor, such as laser irradiation points, radio frequency electrode temperature, etc., to ensure the accuracy of the model. Combine the equipment parameters and knowledge in the biomedical field to determine the scope of the equipment, identify the skin layer, depth and affected area. Specifically include: determine the contact area between the equipment and the surface of the skin. By calculating the energy conduction and signal attenuation of the equipment, infer the deep-level impact of the equipment, and output the data of the scope of action of the medical beauty equipment. Based on the data of the scope of action of the medical beauty equipment, more physiological parameters are collected through multi-source sensors to further enhance the monitoring ability of the equipment and generate multimodal monitoring data of standard medical beauty equipment. The multi-source sensors include skin temperature sensors, heart rate sensors, sweat gland sensors, etc., which are used to collect user physiological data in real time and provide physiological feedback of the equipment. Such as temperature and humidity sensors, radiation detection sensors, electromagnetic field sensors, etc., are used to monitor the operating environment of the equipment to ensure that it works under suitable environmental conditions. Including equipment vibration sensors, power sensors, current sensors, etc., used to monitor the operating status of the equipment to ensure the stability and safety of the equipment. The data from different sensors are fused. The time synchronization algorithm is used to ensure the time consistency of the data of each sensor, and the data interpolation and smoothing algorithms are used to optimize the data of different sensors so that they can be fused under the same standard. Using data integration methods, the range data of medical cosmetic equipment, user physiological data, environmental data and equipment status data are comprehensively processed to form a multi-dimensional monitoring data set.

[0030] Step S2: Perform long-term performance degradation trend monitoring on the multimodal monitoring data of standard medical cosmetic equipment to generate long-term performance degradation trend data of key components of the equipment; generate a first warning signal based on the long-term performance degradation trend data of key components of the equipment to obtain a first warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate a second warning signal based on the skin stress distribution data to obtain a second warning signal for operation monitoring; In an embodiment of the present invention, by collecting multimodal monitoring data of the device, including sensor data such as temperature, pressure, current, vibration, etc., the real-time operation data of each key component of the device (such as a motor, heater, sensor, etc.) is recorded. The collected data is denoised and standardized to eliminate interference from external environmental factors and ensure the accuracy of the data. The collected data is denoised and standardized to eliminate interference from external environmental factors and ensure the accuracy of the data. According to the analysis results, long-term performance degradation trend data of key components of the device are generated to reflect the operating status and degradation rate of each component. The performance degradation threshold of each key component is set according to the equipment design standard and historical operation data. When the performance degradation rate of a certain component is monitored to exceed the set threshold, a first warning signal is generated to indicate that the device has a risk of failure or requires maintenance. According to the failure risk of different components, different levels of warning signals (such as low, medium, and high levels) are generated. Finite element analysis (FEA) or other biomechanical modeling methods are used to construct a stress-strain model of the skin based on the operation mode and monitoring data of the device. Multimodal data such as pressure and temperature of the device during actual use are collected and input into the biomechanical model to simulate the stress distribution of the skin under the action of the device. Based on the simulation results, the stress distribution data of the skin is generated, reflecting the pressure distribution, stress changes and skin damage risks in different areas. The safety threshold of skin stress is set according to the physiological tolerance of the skin. When the stress applied by the device on the skin exceeds the safety threshold, a second warning signal is generated to indicate that the device causes skin damage or excessive stress. Depending on the severity of the stress distribution, different levels of warning signals are generated to help adjust the device parameters or stop using it in time.

[0031] Step S3: Upload the first operation monitoring warning signal and the second operation monitoring warning signal to the cloud platform for monitoring warning trigger frequency analysis to generate equipment monitoring warning trigger frequency data; construct a trigger decision based on the equipment monitoring warning trigger frequency data to generate medical beauty equipment operation warning decision data; In an embodiment of the present invention, the first warning signal (performance degradation warning signal of key components of the equipment) and the second warning signal (skin stress exceeding standard warning signal) are uploaded to the cloud platform through a secure data transmission protocol (such as HTTPS, MQTT, etc.). The uploaded data should include information such as the trigger time, signal type, and warning level of the warning signal. A special data storage structure (such as a database or data warehouse) is created in the cloud platform to store the uploaded warning signal data. Through data cleaning and preprocessing, invalid or duplicate data is eliminated to ensure the accuracy of the signal trigger data. Based on the powerful data processing capabilities of the cloud platform, a time series analysis of the signal trigger frequency is performed, and the trigger frequency of different types of warning signals (such as the number of triggers per day, week, or month) is counted. Combined with factors such as the use of the equipment and environmental conditions, periodicity and trend analysis are performed, the trigger frequency changes of each warning signal are calculated, and the equipment monitoring warning trigger frequency data is generated. The data will reflect the overall health of the equipment, especially potential problems in key components and skin stress. Use machine learning or statistical analysis methods, such as cluster analysis, anomaly detection, etc., to identify the normal warning trigger frequency pattern of the equipment under different operating conditions. Compare the monitoring and early warning trigger frequencies of different devices, establish the normal trigger mode and abnormal mode of the early warning signal, and identify the frequent trigger or abnormal trigger frequency. According to the monitoring and early warning trigger frequency data, formulate equipment operation early warning decision rules. For example, when the performance degradation warning frequency of a key component reaches a certain value, it triggers maintenance or shutdown operation; or when the warning trigger frequency of skin stress increases abnormally, it prompts that the equipment settings need to be adjusted or safety measures need to be taken. The early warning decision rules can also be adjusted according to the actual use environment, maintenance history and operating load of the equipment to ensure that the decision is dynamic and adaptive. Based on the historical records of trigger frequency data and continuously collected real-time data, use data mining or deep learning models to optimize the decision model to make the decision more accurate. By establishing a prediction model, early warning of equipment failures and risks is given to ensure the safe operation of the equipment. Based on the trigger frequency analysis and early warning decision rules, medical beauty equipment operation early warning decision data is generated. The data includes: whether the equipment needs to be shut down immediately, maintained or adjusted. For example, stop using, adjust equipment parameters or conduct professional testing. According to the frequency and severity of the early warning trigger, the timeliness of the early warning signal is determined (such as immediate, short-term or long-term tracking). The generated medical beauty equipment operation warning decision data is fed back to the equipment control system or operator, and corresponding operational measures are taken in a timely manner, such as stopping the equipment, adjusting parameters or performing repairs.

[0032] Step S4: Evaluate the accuracy of the warning decision data for the operation of the medical cosmetic equipment to generate operation monitoring warning accuracy evaluation data; adjust the warning signals of the operation monitoring first warning signal and the operation monitoring second warning signal based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

[0033] In an embodiment of the present invention, actual fault events and warning data occurring during the operation of medical cosmetic equipment are collected, including equipment operation logs, fault detection data, and warning trigger history data. The actual fault events are marked to determine which faults are triggered by warning signals and which are false alarms or missed alarms. Key indicators for warning accuracy evaluation, such as accuracy, recall, F1 score, etc., are defined: Accuracy: the proportion of warning signals correctly identified. Recall: the proportion of all actual fault events successfully identified by warning signals. F1 score: the harmonic mean of accuracy and recall, an indicator that comprehensively measures the accuracy of warnings. Based on machine learning or statistical analysis methods, a warning accuracy evaluation model is constructed. The warning signals in historical data are compared with actual fault events to calculate and evaluate the accuracy of the warning signals. A confusion matrix can be used to evaluate the classification effect of the warning to distinguish between correct warnings, false alarms, missed alarms, and correct no warnings. Based on the evaluation model, the warning accuracy data for each device and each warning signal type is calculated, and an evaluation report is generated. This data will reflect the reliability and effectiveness of the warning signal in the actual operation of the device. According to the evaluation results, analyze the accuracy of each warning signal and identify the patterns of false alarms and missed alarms. Optimize and adjust the warning signals with frequent false alarms and missed alarms. For signals with more false alarms, it is necessary to increase the severity of the trigger conditions to avoid premature or excessive triggering of warnings. For signals with more missed alarms, the trigger conditions can be adjusted to lower the trigger threshold to ensure that potential problems can be identified earlier. Based on the accuracy evaluation results, a dynamic threshold adjustment method is adopted. For example, according to the frequency of use of the equipment, the operating environment and historical fault data, the threshold of the warning trigger is adaptively adjusted to avoid false alarms or missed alarms caused by setting the threshold too high or too low. Optimize the trigger logic of the first warning signal and the second warning signal of the operation monitoring, and adjust the parameters in the signal detection algorithm based on the accuracy evaluation data.

[0034] Preferably, step S1 comprises the following steps: Step S11: Acquire medical beauty equipment data; Step S12: extracting text features from the medical beauty equipment data to generate medical beauty equipment text feature data; performing text semantic analysis on the medical beauty equipment text feature data to generate medical beauty equipment text semantic analysis data; Step S13: performing a medical beauty equipment action range analysis on the medical beauty equipment text semantic analysis data to generate medical beauty equipment action range data; integrating equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate medical beauty equipment multimodal monitoring data; Step S14: perform data preprocessing on the multimodal monitoring data of medical cosmetic equipment to generate standard multimodal monitoring data of medical cosmetic equipment, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization.

[0035] In the embodiment of the present invention, the basic information data of the equipment, including the equipment name, model, function description, etc., is obtained through the database interface provided by the equipment manufacturer. Combined with the actual usage data of the medical institution, the real-time equipment operation parameters, such as temperature, power, light wave frequency, etc., are obtained. Multi-source data acquisition technology is used to supplement and obtain other necessary data from equipment sensors, user operation logs and related literature. Natural language processing technology (such as TF-IDF or Word2Vec) is used to extract key feature words in the medical beauty equipment data, such as equipment function, operating environment, scope of application, etc., to generate a structured data table (medical beauty equipment text feature data) containing equipment feature information. The pre-trained language model (such as BERT or GPT) is used to perform semantic analysis on the equipment text feature data to understand its contextual association and potential meaning. The text semantic analysis data of the medical beauty equipment is output, including the semantic category of the equipment function, the description of the applicable population and other semantic information. Based on the text semantic analysis data of the medical beauty equipment, the classification algorithm (such as SVM or logistic regression) is used to analyze the scope of application of the equipment to generate the scope of action data of the medical beauty equipment. The scope of action is mapped to specific application scenarios, such as the type of cosmetic surgery, the scope of skin treatment, etc. Collect equipment operating status (such as energy output, working environment parameters) through multi-source sensors. Use data fusion technology (such as weighted average or Kalman filtering) to integrate monitoring parameters and generate multimodal monitoring data for medical cosmetic equipment. Remove redundant or abnormal data, such as repeated records or data points that are obviously beyond the normal range. Apply signal processing algorithms (such as wavelet denoising or low-pass filtering) to remove noise signals in multimodal data. Use interpolation, regression prediction or deep learning methods to fill missing values ​​to ensure data integrity. Normalize multimodal monitoring data (such as Z-Score or Min-Max normalization) to make it suitable for subsequent analysis and modeling needs.

[0036] Preferably, integrating equipment monitoring parameters through multi-source sensors based on the range data of medical cosmetic equipment includes: The skin condition sensor is used to collect the user's real-time physiological signals within the range of action of the medical beauty device, and obtain the user's real-time physiological signals; the user's real-time physiological signals are conditioned to generate the user's real-time physiological conditioning signals; Perform analog-to-digital conversion on the user's real-time physiological conditioning signal to generate the user's real-time physiological data; perform data change correlation analysis on the user's real-time physiological data and medical beauty equipment data to generate monitoring correlation change data; use environmental sensors to collect environmental parameters based on the monitoring correlation change data to obtain the medical beauty equipment's working environment data; The medical beauty equipment data, the user's real-time physiological data and the medical beauty equipment's working environment data are integrated and displayed to generate multimodal monitoring data of medical beauty equipment.

[0037] In an embodiment of the present invention, a skin condition sensor is arranged within the range of action of a medical cosmetic device to monitor the user's skin temperature, humidity, conductivity and other physiological signals in real time. The sensor is synchronized with the control system of the medical cosmetic device to ensure the real-time and accuracy of signal acquisition. The user's real-time physiological signals (such as skin impedance, body surface temperature rise, microcirculation changes, etc.) are obtained through the skin condition sensor. The collected signals are recorded in the real-time monitoring system. The collected real-time physiological signals of the user are amplified, filtered and offset adjusted by analog signal processing technology to remove interference components. A band-pass filter is applied to eliminate low-frequency drift and high-frequency noise to ensure the stability and effectiveness of the signal. The conditioned signal data with enhanced physiological relevance is output as input for subsequent signal processing. The user's real-time physiological conditioning signal is converted from an analog signal to a digital signal. A high-precision analog-to-digital converter (such as a 16-bit ADC) is used to ensure the fine resolution of the sampled signal. The digitized real-time physiological data of the user is output for subsequent analysis. A correlation analysis model is established based on the user's real-time physiological data and the medical cosmetic device data (such as energy output and contact pressure) to calculate the change relationship between the two. Use correlation analysis (such as Pearson correlation coefficient) or dynamic time warping (DTW) algorithm to quantify the correlation between changes in data. Output monitoring correlation change data that reflects the changing rules between the effect of the device and the user's physiological response. Arrange environmental sensors within the range of medical cosmetic equipment to monitor environmental parameters such as temperature, humidity, light intensity and air quality. Collect real-time environmental parameters and synchronize them with monitoring correlation change data. Output accurate data related to the equipment use environment. Use multimodal data fusion technology (such as principal component analysis or multi-kernel learning) to integrate and process medical cosmetic equipment data, user real-time physiological data and medical cosmetic equipment action environment data. Output integrated and standardized multimodal monitoring data, including equipment operation status, user physiological response and environmental status data. This data is used for equipment performance optimization, user experience evaluation and safety monitoring.

[0038] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: extracting the operating parameters of key components of the equipment from the multimodal monitoring data of the standard medical cosmetic equipment to obtain the operating parameters of the key components of the equipment, wherein the key components of the equipment include a laser, a radio frequency electrode, and a cooling system; Step S22: monitoring the long-term performance degradation trend of the operating parameters of the key components of the equipment, generating long-term performance degradation trend data of the key components of the equipment; generating a first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtaining a first early warning signal of operation monitoring; Step S23: extracting skin contact optical perception data from the multimodal monitoring data of the standard medical cosmetic equipment to obtain skin contact optical perception data; Step S24: Perform biomechanical modeling on the skin contact optical perception data to generate skin stress distribution data; identify potential risks based on the skin stress distribution data to generate skin micro-injury prediction data; generate a second warning signal based on the skin micro-injury prediction data to obtain a second warning signal for operation monitoring.

[0039] In an embodiment of the present invention, by identifying the key components of the equipment, including the laser, the RF electrode and the cooling system, the operating parameter collection range is determined in combination with the equipment structure design and multimodal monitoring data. Parameters including laser power, wavelength, pulse frequency and spot energy density are extracted, and real-time data is recorded by an optical sensor. Parameters such as the output current, voltage, energy density and electrode temperature of the RF electrode are obtained, and collected using electromagnetic sensors and temperature sensors. The circulating fluid flow rate, temperature gradient and cooling efficiency of the cooling system are recorded, and relevant data are measured using flow sensors and thermocouples. The above data are integrated to form the operating parameters of the key components of the equipment as the basic data for equipment performance analysis. Based on the operating parameters of the key components of the equipment, a time series analysis model (such as ARIMA, LSTM) is used to predict the long-term performance change trend and monitor the performance degradation. Trend modeling is performed on indicators such as the laser attenuation rate, the aging degree of the RF electrode and the decline in the efficiency of the cooling system. The performance degradation trend data of each key component is output to quantify its operating life and performance decay rate. An early warning threshold is set (such as a 20% drop in laser power and a cooling efficiency lower than 85%), and the first early warning signal is triggered when the degradation trend approaches or exceeds the threshold. Output the first warning signal of operation monitoring, indicating that the equipment needs maintenance or replacement of parts. Arrange optical sensors at the point of contact between the medical cosmetic device and the user's skin to capture the reflection spectrum, absorption rate and scattering characteristics of the contact point. Collect optical data of the skin contact area (such as skin color change, reflected light intensity), and extract key indicators in combination with the optical characteristic analysis algorithm. Output optical perception data related to skin contact as input for biomechanical modeling. Based on the optical perception data of skin contact, a skin stress distribution model is established in combination with the biomechanical properties of the skin structure (such as elastic modulus and shear force). Use finite element analysis (FEA) to simulate the stress distribution of the skin under the action of the device. Calculate and output stress distribution data of different areas of the skin to identify high stress areas. According to the skin stress distribution data, use the risk assessment algorithm to detect the risk area that causes skin micro-injury, generate skin micro-injury prediction data, and quantify the potential risk level. Set the threshold for micro-injury prediction (such as stress exceeds the set safety value). When the predicted data exceeds the threshold, the second warning signal is triggered. Output the second warning signal of operation monitoring, indicating that the equipment parameters need to be adjusted or the operation needs to be suspended.

[0040] Preferably, step S22 includes the following steps: Step S221: extracting equipment bearing operation parameters from equipment key component operation parameters to obtain equipment bearing operation data, wherein the equipment bearing operation data includes equipment bearing vibration data and equipment bearing temperature data; Step S222: performing envelope demodulation on the equipment bearing vibration data to generate equipment bearing vibration characteristic data; performing fluctuation extraction processing on the bearing temperature data to generate equipment bearing temperature characteristic data; performing time series correlation processing on the equipment bearing vibration characteristic data and the equipment bearing temperature characteristic data to generate coupling characteristic data; Step S223: Calculate the vibration-temperature coupling coefficient of the coupling characteristic data to generate coupling index data; perform state mapping on the coupling index data to generate equipment health assessment data; perform trend fitting processing on the equipment health assessment data to generate long-term performance degradation trend data of key components of the equipment; Step S224: Generate a first warning signal for the long-term performance degradation trend data of key components of the equipment based on a preset first signal frequency to obtain a first warning signal for operation monitoring.

[0041] In an embodiment of the present invention, the vibration signal of the bearing is collected by using an acceleration sensor and a vibration sensor, and the vibration amplitude, frequency, acceleration and other data are recorded to generate the vibration data of the equipment bearing. A temperature sensor is used to monitor the operating temperature of the bearing in real time, and the temperature change data is recorded to generate the temperature data of the equipment bearing. Including equipment bearing vibration data and equipment bearing temperature data, providing basic data for subsequent analysis. The equipment bearing vibration data is envelope demodulated, and the frequency characteristics are extracted by Hilbert transform or FFT (Fast Fourier Transform) to generate equipment bearing vibration characteristic data. The vibration characteristic data includes indicators such as envelope amplitude, frequency peak and spectrum energy distribution. Fluctuation extraction is performed on the equipment bearing temperature data, and the characteristics such as temperature fluctuation amplitude and temperature rise rate are analyzed to generate equipment bearing temperature characteristic data. Based on a time series model (such as the DTW dynamic time warping algorithm), the equipment bearing vibration characteristic data and the equipment bearing temperature characteristic data are correlated and analyzed to quantify the relationship between vibration and temperature and generate coupling characteristic data. Combined with the coupling characteristic data, the coupling coefficient between vibration and temperature is calculated. The formula is as follows: ;in is the coupling coefficient, and They are vibration characteristic data and temperature characteristic data respectively. is the covariance, and The standard deviations of vibration and temperature are respectively. Output coupling index data to reflect the degree of interaction between vibration and temperature. Map the coupling index data to the equipment health status model (such as healthy, sub-healthy, fault precursor, etc.) to generate equipment health assessment data. The health assessment data can intuitively reflect the equipment status through the health index (such as 0-1 range). Perform trend fitting analysis on the equipment health assessment data, use regression models (such as linear regression, LSTM) to predict the long-term performance degradation trend of key components of the equipment, and generate long-term performance degradation trend data. Set the first signal frequency threshold for early warning, and define the critical value of vibration frequency or temperature change according to the equipment operation standard and actual needs. Compare the long-term performance degradation trend data of key components of the equipment with the first signal frequency threshold. When the trend data exceeds the threshold, a first early warning signal is generated to indicate an abnormality or potential fault. The signal can be displayed in real time through the equipment monitoring system or transmitted to maintenance personnel through the communication module so that corresponding maintenance measures can be taken.

[0042] Preferably, step S24 includes the following steps: Step S241: calculating the perception area of ​​the skin contact optical perception data to obtain the skin contact perception area; reconstructing the three-dimensional geometry of the skin contact surface of the skin contact optical perception data based on the skin contact perception area to generate the three-dimensional geometry data of the skin contact surface; Step S242: performing skin layer stress analysis on the three-dimensional geometric data of the skin contact surface to generate skin stress distribution data, wherein the skin layer stress analysis includes skin surface stress analysis and skin deep layer stress analysis; performing stress extreme value screening on the skin stress distribution data to obtain skin stress distribution extreme value data; Step S243: performing potential damage identification on the preset skin damage stress threshold and the skin stress distribution extreme value data. When the skin stress distribution extreme value data is greater than the preset skin damage stress threshold, marking stress concentration points on the skin stress distribution data based on the skin stress distribution extreme value data to generate stress concentration points. Step S244: using the stress concentration point to screen the skin stress distribution extreme value data for the remaining extreme value points, to obtain the remaining skin stress distribution extreme value data; marking the remaining skin stress distribution extreme value data as skin stress distribution extreme value data and cyclically executing step S243 until the skin stress distribution extreme value data is less than or equal to the preset skin damage stress threshold, to generate a stress concentration area; Step S245: Identify potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area, and generate skin micro-injury prediction data; generate a second warning signal for the skin micro-injury prediction data based on the preset first signal frequency to obtain a second warning signal for operation monitoring.

[0043] In an embodiment of the present invention, the contact area is identified by using the skin contact optical perception data, and the skin contact perception area is calculated. The size of the contact perception area is calculated and quantified into area data by using the integral method or image pixel statistics. Based on the skin contact perception area, combined with the optical depth information, a reconstruction algorithm (such as a light projection method or a point cloud fitting algorithm) is used to perform three-dimensional geometric reconstruction on the contact area to generate three-dimensional geometric data of the skin contact surface, which describes the geometric characteristics of the skin contact surface, including shape, curvature and contact depth. The three-dimensional geometric data of the skin contact surface is layered to distinguish the surface and deep structures of the skin. The finite element analysis method (FEA) is used to simulate the mechanical response of the surface and deep layers of the skin and calculate the stress distribution. The skin surface stress data and the skin deep stress data are output and integrated to generate skin stress distribution data. The skin stress distribution data is analyzed to screen out the stress extreme points in the area and generate skin stress distribution extreme value data. The stress extreme value includes the local maximum value and the minimum value, which characterizes the stress concentration on the skin. The skin stress distribution extreme value data is compared with the preset skin damage stress threshold. If the stress extreme value data is greater than the threshold, it is marked as a potential damage area and a stress concentration point is generated. Based on the stress concentration point, the stress distribution data is located to generate the spatial coordinates of the stress concentration point. Using the stress concentration point, the remaining extreme value points are screened out from the skin stress distribution extreme value data to generate the remaining skin stress distribution extreme value data. Repeat step S243 for the remaining extreme value data until the skin stress distribution extreme value data is less than or equal to the preset skin damage stress threshold. Mark and aggregate stress concentration points to form a stress concentration area, which reflects the potential high-risk damage area of ​​the skin. Based on the stress concentration area, the three-dimensional geometric data of the skin contact surface is analyzed to identify the damage area, generate skin micro-damage prediction data, and describe the potential skin damage type, location and severity. Using the preset first signal frequency threshold, the skin micro-damage prediction data is converted into a second warning signal. This signal reminds the equipment operator to take protective measures or adjust equipment parameters to avoid further development of skin damage.

[0044] Preferably, identifying potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area includes: The contact stress characteristics of the stress concentration area are extracted to obtain the contact stress characteristic data; the contact stress characteristic data is divided into data sets to generate a model training set and a model test set; the model training set is trained by a convolutional neural network algorithm to generate a skin micro-injury prediction model; The skin microinjury prediction pre-model is optimized and iterated using the model test set to generate a skin microinjury prediction model; the three-dimensional geometric data of the skin contact surface is imported into the skin microinjury prediction model to identify potential skin risks and generate skin microinjury prediction data.

[0045] In an embodiment of the present invention, stress distribution characteristics of the contact area are extracted according to the stress concentration area, including stress peak value, stress gradient, stress concentration range and directionality. Key features are extracted using algorithms such as principal component analysis (PCA) or statistical methods to generate contact stress feature data. Feature data are saved in the form of vectors or tensors as basic data for subsequent model training. The contact stress feature data is divided into a model training set and a model test set (such as an 8:2 ratio) in a certain ratio. It is ensured that the training set and the test set contain representative samples of various types of stress feature data to improve the generalization ability of the model. The training set and the test set are normalized to eliminate the dimensionality effect. Data enhancement: The training set is randomly rotated, scaled or noise is added to generate more samples to improve the robustness of the model. A convolutional neural network (CNN) is used as the basic model structure, and its characteristics include local feature extraction and spatial information capture capabilities. Input layer: The input is contact stress feature data. Convolution layer: Multi-layer convolution kernels extract the spatial pattern of contact stress features. Pooling layer: Downsampling reduces the data dimension while retaining key features. Fully connected layer: maps the extracted features to the prediction results of potential damage. Output layer: outputs the predicted probability of skin micro-damage risk. Using the model training set, use an optimization algorithm (such as Adam optimizer) to adjust the weight parameters. The loss function uses cross entropy loss or mean square error (MSE), which is selected according to the damage prediction task goal. Set the number of training rounds (epochs) and batch size, and train until the model converges. Input the model test set into the skin micro-damage prediction pre-model to evaluate the model's prediction accuracy, recall rate, and F1 score. Analyze the error distribution in the prediction results and identify deficiencies. Use error samples to optimize the model iteratively, including adjusting the network structure (such as adding convolutional layers or the number of neurons), changing the learning rate, or applying regularization (such as L2 regularization) to reduce overfitting. Continue to optimize until the model performance reaches the expected level. Output the optimized skin micro-damage prediction model, which has high prediction accuracy and generalization ability. Input the three-dimensional geometric data of the skin contact surface into the optimized skin micro-damage prediction model. The model automatically analyzes the input data and identifies the potential damage risk of the contact area, including the damage type, range and severity. The risk identification results are output in graphical or numerical form. The output of skin micro-injury prediction data includes the predicted micro-injury location, probability value and risk level. This data can be used as a basis for further equipment parameter adjustment or early warning generation.

[0046] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: integrating the first operation monitoring warning signal and the second operation monitoring warning signal to generate a medical cosmetic equipment operation monitoring warning signal; Step S32: Upload the medical beauty equipment operation monitoring and warning signals to the cloud platform for monitoring and warning triggering, and generate equipment monitoring and warning triggering data; Step S33: Perform trigger timing frequency analysis on the equipment monitoring and early warning trigger data to generate equipment monitoring and early warning trigger frequency data; Step S34: construct a trigger decision for the equipment monitoring early warning trigger data according to the equipment monitoring early warning trigger frequency data, and generate medical cosmetic equipment operation early warning decision data.

[0047] In an embodiment of the present invention, by receiving the first warning signal of operation monitoring (generated based on the long-term performance degradation trend data of the key components of the equipment). Receive the second warning signal of operation monitoring (generated based on the skin micro-injury prediction data). The two warning signals are time-stamped and synchronized to ensure the consistency of signal timing. The two signals are integrated using a signal integration algorithm (such as weighted fusion or signal priority judgment) to generate a comprehensive medical beauty equipment operation monitoring warning signal. The result after output signal integration includes the trigger probability, severity level and trigger time of each signal. The medical beauty equipment operation monitoring warning signal is uploaded to the cloud platform through an encrypted transmission protocol (such as HTTPS or MQTT) to ensure the security and reliability of data transmission. In the cloud platform, a distributed database is used to store the signal and record its trigger time, signal level and device identification and other meta information. The cloud platform monitors the warning signal in real time through the built-in monitoring rules. Once the warning condition is triggered (such as the signal level exceeds the set threshold), the corresponding warning processing program is started to generate equipment monitoring warning trigger data. The equipment monitoring warning trigger data is obtained from the cloud platform, including the trigger time series and trigger times of each warning signal. Based on the trigger data, the frequency distribution of the trigger signal is calculated using a time series analysis algorithm (such as fast Fourier transform FFT) to identify the dominant frequency and periodic characteristics of the signal trigger. The signal frequency data is statistically analyzed to generate trigger frequency data, such as average frequency, maximum frequency, and frequency fluctuation range. During the frequency analysis process, the abnormal frequency threshold is used to detect the frequency abnormal signal and mark the abnormal signal trigger event. The device monitoring warning trigger frequency data, including frequency characteristics and abnormal signal marks, is received. The frequency data is analyzed based on the preset trigger decision rules (such as threshold judgment rules or expert system rules). Trigger decision rule example: If the frequency of a signal is higher than the set threshold and the duration exceeds a specific time, it is marked as a high-risk event. According to the trend of signal frequency changes, the future equipment operation status is predicted and the warning level is adjusted. According to the trigger decision rules, the medical beauty equipment operation warning decision data is generated, which includes the following information: warning level (such as low, medium, high), warning type (such as equipment failure, skin risk), and response suggestions (such as adjusting equipment parameters, suspending operation). The medical beauty equipment operation warning decision data is uploaded to the cloud platform and pushed to the relevant management system or operation terminal for equipment adjustment and user notification.

[0048] Preferably, step S4 comprises the following steps: Step S41: collecting decision feedback data on the operation early warning decision data of the medical cosmetic equipment to obtain early warning decision feedback data; evaluating the accuracy of the early warning decision data on the operation early warning decision data of the medical cosmetic equipment according to the early warning decision feedback data to generate operation monitoring early warning accuracy evaluation data; Step S42: Adjust the warning signals of the first warning signal of operation monitoring and the second warning signal of operation monitoring based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

[0049] In an embodiment of the present invention, decision-making execution feedback data is obtained from medical cosmetic equipment users, operation and maintenance teams or automated monitoring systems, and the data includes but is not limited to: changes in equipment operation status (such as changes in vibration values ​​and temperature values), skin contact status records (such as micro-damage detection results), and user responses to equipment operations. Embedded sensors and data recording modules are used to automatically collect equipment operation-related data. Operation feedback, such as problem feedback or satisfaction scores, is collected through the user interface. The collected data is uploaded to the cloud platform for unified management and storage. Based on the decision feedback data, the accuracy of the operation monitoring early warning signal is evaluated, and the key indicators include: the ratio of the number of correct early warnings to the total number of early warnings, the proportion of situations where the early warning is not triggered but the risk actually occurs, and the proportion of situations where the early warning is triggered but the risk actually does not occur. The operation monitoring early warning signal (the first early warning signal and the second early warning signal) is compared with the actual situation of the feedback data to generate a classification evaluation matrix (such as TP, FP, FN, TN) of the early warning signal. Comprehensive indicators, such as F1 scores and AUC values, are calculated by statistical methods to generate operation monitoring early warning accuracy evaluation data. The operation monitoring early warning accuracy evaluation data is used to optimize the generation logic of the first early warning signal for operation monitoring and the second early warning signal for operation monitoring. In the case of a high false alarm rate, increase the threshold for triggering the warning signal and reduce unnecessary warning signals. In the case of a high false alarm rate, lower the threshold for triggering the warning signal and increase sensitivity. According to the historical accuracy evaluation data of the warning signal, adjust the weight coefficient during signal fusion and optimize the signal integration result. Introduce machine learning algorithms (such as adaptive threshold algorithms or decision tree models) to dynamically adjust the signal generation parameters according to the newly collected feedback data. Reintegrate the adjusted first warning signal and the second warning signal to generate a medical beauty equipment operation monitoring warning signal. Based on the optimized warning signal, re-trigger the monitoring and adjustment of the equipment operation parameters. Automatically adjust the operating parameters of key components of the equipment (such as laser power, cooling system temperature). Optimize skin contact conditions (such as applied pressure and duration) based on the adjusted second warning signal. Incorporate the optimized signal and its corresponding warning decision execution feedback data into the next round of accuracy evaluation to form a closed-loop optimization mechanism.

[0050] In this specification, a medical cosmetic equipment operation parameter monitoring and early warning system is provided, which is used to execute the above-mentioned medical cosmetic equipment operation parameter monitoring and early warning method. The medical cosmetic equipment operation parameter monitoring and early warning system includes: The data fusion module is used to obtain medical beauty equipment data; analyze the equipment range of medical beauty equipment data to generate medical beauty equipment range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment range data to generate standard medical beauty equipment multimodal monitoring data; The monitoring and early warning module is used to monitor the long-term performance degradation trend of the multimodal monitoring data of standard medical cosmetic equipment, generate the long-term performance degradation trend data of the key components of the equipment; generate the first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtain the first early warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate the second early warning signal based on the skin stress distribution data to obtain the second early warning signal for operation monitoring; The early warning prediction module is used to upload the first early warning signal of operation monitoring and the second early warning signal of operation monitoring to the cloud platform for monitoring and early warning trigger frequency analysis, and generate equipment monitoring and early warning trigger frequency data; trigger decision construction is performed according to the equipment monitoring and early warning trigger frequency data, and medical beauty equipment operation early warning decision data is generated; The early warning optimization module is used to evaluate the early warning accuracy of the operation early warning decision data of the medical cosmetic equipment and generate operation monitoring early warning accuracy evaluation data; based on the operation monitoring early warning accuracy evaluation data, the early warning signal of the operation monitoring first early warning signal and the operation monitoring second early warning signal are adjusted to perform the operation parameter monitoring and early warning optimization operation of the medical cosmetic equipment.

[0051] The beneficial effect of the present invention is that through multi-source sensor integration and equipment range analysis, the data fusion module can comprehensively and accurately collect the operation data of the equipment, ensure that all important parameters of the equipment are monitored, and this multimodal data fusion provides a reliable basis for subsequent analysis, improves the accuracy and comprehensiveness of the data, and avoids the deviation caused by a single sensor data. Through long-term performance degradation trend monitoring and biomechanical modeling, the potential failure risk of the equipment can be predicted in advance, and a second warning signal is generated based on the skin stress distribution to ensure that the equipment operation matches the patient's skin reaction, minimize the risk in the operation, and the generated warning signal provides important warning information for equipment maintenance, effectively extending the service life of the equipment and ensuring the stability of the treatment effect. By uploading the warning signal to the cloud platform and performing trigger frequency analysis, the module can provide real-time monitoring of the equipment operation status, and optimize the warning system according to the trigger frequency, avoiding excessive warning and underreporting, and improving the intelligence level of the warning. In addition, the trigger decision construction can dynamically adjust the monitoring strategy of the equipment, ensure the flexibility and adaptability of the warning system, and improve the efficiency of equipment management. By evaluating and adjusting the accuracy of the warning, the accuracy and reliability of the warning system are further improved. Optimizing the early warning signal based on the evaluation results can dynamically adapt to the actual operating status of the equipment, reduce the possibility of false alarms and missed alarms, and ensure the operating safety of the equipment and the stability of the treatment effect. The early warning optimization operation enables the entire system to have the ability of continuous optimization and self-adjustment, enhancing the intelligence and efficiency of medical cosmetic equipment. Therefore, the present invention improves the reliability and intelligence level of medical cosmetic equipment operating parameter monitoring and early warning through multimodal data integration, long-term performance trend analysis, biomechanical modeling, and intelligent early warning decision optimization.

[0052] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0053] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for monitoring and early warning of operating parameters of medical cosmetic equipment, characterized in that: The following steps are involved: Step S1: Acquire medical beauty equipment data; perform equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate standard medical beauty equipment multimodal monitoring data; Step S2: Perform long-term performance degradation trend monitoring on the multimodal monitoring data of the standard medical cosmetic equipment to generate long-term performance degradation trend data of the key components of the equipment; generate a first warning signal based on the long-term performance degradation trend data of the key components of the equipment to obtain a first warning signal for operation monitoring; Perform biomechanical modeling on multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; Generate a second warning signal according to the skin stress distribution data to obtain a second warning signal for operation monitoring; Step S3: Upload the first operation monitoring warning signal and the second operation monitoring warning signal to the cloud platform for monitoring and warning trigger frequency analysis to generate equipment monitoring and warning trigger frequency data; Trigger decision building is performed based on the equipment monitoring warning trigger frequency data to generate medical beauty equipment operation warning decision data; Step S4: Evaluate the accuracy of early warning decision data for operation of medical cosmetic equipment to generate operation monitoring early warning accuracy evaluation data; Based on the operation monitoring and early warning accuracy evaluation data, the early warning signal adjustment is performed on the operation monitoring first early warning signal and the operation monitoring second early warning signal to perform the operation parameter monitoring and early warning optimization operation of the medical cosmetic equipment.

2. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire medical beauty equipment data; Step S12: extracting text features from the medical beauty equipment data to generate medical beauty equipment text feature data; performing text semantic analysis on the medical beauty equipment text feature data to generate medical beauty equipment text semantic analysis data; Step S13: performing a medical beauty equipment action range analysis on the medical beauty equipment text semantic analysis data to generate medical beauty equipment action range data; integrating equipment monitoring parameters through multi-source sensors based on the medical beauty equipment action range data to generate medical beauty equipment multimodal monitoring data; Step S14: perform data preprocessing on the multimodal monitoring data of medical cosmetic equipment to generate standard multimodal monitoring data of medical cosmetic equipment, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization.

3. The operating parameter monitoring and early warning method of medical cosmetic equipment according to claim 2, characterized in that: Based on the range data of medical cosmetic equipment, equipment monitoring parameter integration through multi-source sensors includes: Using the skin condition sensor to collect the user's real-time physiological signals within the range of action of the medical beauty device, and obtain the user's real-time physiological signals; performing signal conditioning on the user's real-time physiological signals, and generating the user's real-time physiological conditioning signals; Perform analog-to-digital conversion on the user's real-time physiological conditioning signal to generate the user's real-time physiological data; perform data change correlation analysis on the user's real-time physiological data and medical beauty equipment data to generate monitoring correlation change data; use environmental sensors to collect environmental parameters based on the monitoring correlation change data to obtain the medical beauty equipment's working environment data; The medical beauty equipment data, the user's real-time physiological data and the medical beauty equipment's working environment data are integrated and displayed to generate multimodal monitoring data of medical beauty equipment.

4. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting the operating parameters of key components of the equipment from the multimodal monitoring data of the standard medical cosmetic equipment to obtain the operating parameters of the key components of the equipment, wherein the key components of the equipment include a laser, a radio frequency electrode, and a cooling system; Step S22: monitoring the long-term performance degradation trend of the operating parameters of the key components of the equipment, generating long-term performance degradation trend data of the key components of the equipment; generating a first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtaining a first early warning signal of operation monitoring; Step S23: extracting skin contact optical perception data from the multimodal monitoring data of the standard medical cosmetic equipment to obtain skin contact optical perception data; Step S24: Perform biomechanical modeling on the skin contact optical perception data to generate skin stress distribution data; identify potential risks based on the skin stress distribution data to generate skin micro-injury prediction data; generate a second warning signal based on the skin micro-injury prediction data to obtain a second warning signal for operation monitoring.

5. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: extracting equipment bearing operation parameters from equipment key component operation parameters to obtain equipment bearing operation data, wherein the equipment bearing operation data includes equipment bearing vibration data and equipment bearing temperature data; Step S222: performing envelope demodulation on the equipment bearing vibration data to generate equipment bearing vibration characteristic data; performing fluctuation extraction processing on the bearing temperature data to generate equipment bearing temperature characteristic data; performing time series correlation processing on the equipment bearing vibration characteristic data and the equipment bearing temperature characteristic data to generate coupling characteristic data; Step S223: Calculate the vibration-temperature coupling coefficient of the coupling characteristic data to generate coupling index data; perform state mapping on the coupling index data to generate equipment health assessment data; perform trend fitting processing on the equipment health assessment data to generate long-term performance degradation trend data of key components of the equipment; Step S224: Generate a first warning signal for the long-term performance degradation trend data of key components of the equipment based on a preset first signal frequency to obtain a first warning signal for operation monitoring.

6. The operating parameter monitoring and early warning method of medical cosmetic equipment according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: calculating the perception area of ​​the skin contact optical perception data to obtain the skin contact perception area; reconstructing the three-dimensional geometry of the skin contact surface of the skin contact optical perception data based on the skin contact perception area to generate the three-dimensional geometry data of the skin contact surface; Step S242: performing skin layer stress analysis on the three-dimensional geometric data of the skin contact surface to generate skin stress distribution data, wherein the skin layer stress analysis includes skin surface stress analysis and skin deep layer stress analysis; performing stress extreme value screening on the skin stress distribution data to obtain skin stress distribution extreme value data; Step S243: performing potential damage identification on the preset skin damage stress threshold and the skin stress distribution extreme value data. When the skin stress distribution extreme value data is greater than the preset skin damage stress threshold, marking stress concentration points on the skin stress distribution data based on the skin stress distribution extreme value data to generate stress concentration points. Step S244: using the stress concentration point to screen the skin stress distribution extreme value data for the remaining extreme value points, to obtain the remaining skin stress distribution extreme value data; marking the remaining skin stress distribution extreme value data as skin stress distribution extreme value data and cyclically executing step S243 until the skin stress distribution extreme value data is less than or equal to the preset skin damage stress threshold, to generate a stress concentration area; Step S245: Identify potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area, and generate skin micro-injury prediction data; generate a second warning signal for the skin micro-injury prediction data based on the preset first signal frequency to obtain a second warning signal for operation monitoring.

7. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 1, characterized in that: Identification of potential skin risks based on the three-dimensional geometric data of the skin contact surface according to the stress concentration area includes: The contact stress characteristics of the stress concentration area are extracted to obtain the contact stress characteristic data; the contact stress characteristic data is divided into data sets to generate a model training set and a model test set; the model training set is trained by a convolutional neural network algorithm to generate a skin micro-injury prediction model; The skin microinjury prediction pre-model is optimized and iterated using the model test set to generate a skin microinjury prediction model; the three-dimensional geometric data of the skin contact surface is imported into the skin microinjury prediction model to identify potential skin risks and generate skin microinjury prediction data.

8. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: integrating the first operation monitoring warning signal and the second operation monitoring warning signal to generate a medical cosmetic equipment operation monitoring warning signal; Step S32: Upload the medical beauty equipment operation monitoring and warning signals to the cloud platform for monitoring and warning triggering, and generate equipment monitoring and warning triggering data; Step S33: Perform trigger timing frequency analysis on the equipment monitoring and early warning trigger data to generate equipment monitoring and early warning trigger frequency data; Step S34: construct a trigger decision for the equipment monitoring early warning trigger data according to the equipment monitoring early warning trigger frequency data, and generate medical cosmetic equipment operation early warning decision data.

9. The method for monitoring and early warning of operating parameters of medical cosmetic equipment according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: collecting decision feedback data on the operation early warning decision data of the medical cosmetic equipment to obtain early warning decision feedback data; evaluating the accuracy of the early warning decision data on the operation early warning decision data of the medical cosmetic equipment according to the early warning decision feedback data to generate operation monitoring early warning accuracy evaluation data; Step S42: Adjust the warning signals of the first warning signal of operation monitoring and the second warning signal of operation monitoring based on the operation monitoring warning accuracy evaluation data to perform the operation parameter monitoring and warning optimization operation of the medical cosmetic equipment.

10. A monitoring and early warning system for operating parameters of medical cosmetic equipment, characterized in that: Used to execute the operation parameter monitoring and early warning method of medical cosmetic equipment according to claim 1, the operation parameter monitoring and early warning system of the medical cosmetic equipment comprises: The data fusion module is used to obtain medical beauty equipment data; analyze the equipment range of medical beauty equipment data to generate medical beauty equipment range data; integrate equipment monitoring parameters through multi-source sensors based on the medical beauty equipment range data to generate standard medical beauty equipment multimodal monitoring data; The monitoring and early warning module is used to monitor the long-term performance degradation trend of the multimodal monitoring data of standard medical cosmetic equipment, generate the long-term performance degradation trend data of the key components of the equipment; generate the first early warning signal based on the long-term performance degradation trend data of the key components of the equipment, and obtain the first early warning signal for operation monitoring; perform biomechanical modeling on the multimodal monitoring data of standard medical cosmetic equipment to generate skin stress distribution data; generate the second early warning signal based on the skin stress distribution data to obtain the second early warning signal for operation monitoring; The early warning prediction module is used to upload the first early warning signal of operation monitoring and the second early warning signal of operation monitoring to the cloud platform for monitoring and early warning trigger frequency analysis, and generate equipment monitoring and early warning trigger frequency data; trigger decision construction is performed according to the equipment monitoring and early warning trigger frequency data, and medical beauty equipment operation early warning decision data is generated; The early warning optimization module is used to evaluate the early warning accuracy of the operation early warning decision data of the medical cosmetic equipment and generate operation monitoring early warning accuracy evaluation data; based on the operation monitoring early warning accuracy evaluation data, the early warning signal of the operation monitoring first early warning signal and the operation monitoring second early warning signal are adjusted to perform the operation parameter monitoring and early warning optimization operation of the medical cosmetic equipment.

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