Medical beauty equipment cloud management system and management method

Abstract

The invention relates to the technical field of equipment management, in particular to a medical beauty equipment cloud management system and management method. The method comprises the following steps: acquiring a medical beauty equipment number and medical beauty equipment parameters; carrying out cloud platform data uploading on the medical beauty equipment parameters based on the medical beauty equipment number, and generating medical beauty equipment monitoring data; consumable information digital registration is carried out on the medical beauty equipment monitoring data, and a beauty equipment consumable digital registry is generated; the method comprises the following steps: acquiring beauty equipment consumable data through a beauty equipment consumable digital registry to obtain beauty equipment consumable sensing data; consumable loss influence analysis is carried out on the beauty equipment consumable sensing data, and a consumable state monitoring data set is generated; and performing consumable service life prediction on the consumable state monitoring data set, thereby generating consumable service life early warning data. According to the invention, through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform, the accuracy and comprehensiveness of cloud management of the medical beauty equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to a medical beauty equipment cloud management system and management method. Background Art

[0002] Initially, the medical beauty industry mainly relied on traditional local equipment management systems, which were usually managed on a single device basis, with low efficiency in information flow and data sharing, and lack of real-time monitoring and remote operation functions. With the increase in information needs, the industry began to seek more efficient and intelligent equipment management solutions. After entering the 21st century, the maturity of cloud computing technology has promoted the transformation of medical equipment management systems. Cloud computing can provide powerful data storage, computing and analysis capabilities. At the same time, the introduction of Internet of Things (IoT) technology enables medical beauty equipment to transmit data in real time, conduct remote monitoring and fault diagnosis. Through the cloud platform, medical institutions can achieve centralized management and remote control of equipment, greatly improving the utilization rate and management efficiency of equipment. In recent years, with the development of artificial intelligence (AI) technology, medical beauty equipment cloud management systems have gradually integrated functions such as intelligent diagnosis, predictive maintenance and personalized recommendations, which can optimize the use of equipment based on data analysis, improve treatment effects and customer experience. However, in the current traditional medical beauty equipment management system, the monitoring of equipment and consumables is usually decentralized, lacking real-time and automation. At the same time, the management of equipment and consumables is mostly static and periodic inspection, which is difficult to adjust dynamically, resulting in low comprehensiveness and accuracy of management. Summary of the invention

[0003] Based on this, it is necessary to provide a medical beauty equipment cloud management system and management method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above purpose, a medical cosmetic equipment cloud management method is provided, the method comprising the following steps: Step S1: Obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; Step S2: collecting beauty equipment consumables data through a digital registration table of beauty equipment consumables to obtain beauty equipment consumables sensing data; performing consumables loss impact analysis on the beauty equipment consumables sensing data to generate a consumables status monitoring data set; performing consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; Step S3: Evaluate the equipment consumables demand for medical cosmetology equipment monitoring data to generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical cosmetology equipment monitoring data to generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using consumables life warning data to generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; Step S4: Perform equipment management on the consumables allocation adjustment plan data set and generate an equipment consumables management report to execute medical beauty equipment cloud management operations.

[0005] The present invention ensures the centralization and real-time monitoring of equipment data by uploading the medical beauty equipment number and equipment parameters to the cloud platform, and realizes the accurate recording and efficient management of equipment and consumables data by digitally registering consumables information, providing a basis for subsequent data analysis and decision-making. By collecting the sensor data of beauty equipment consumables and performing loss analysis, the use of consumables can be accurately evaluated, and then their lifespan can be predicted and early warning data can be generated. In this way, consumable problems can be identified in advance, unexpected consumable failures or shortages can be avoided, and the operating efficiency and customer experience of the equipment can be improved. By evaluating the consumable demand based on equipment monitoring data, the consumable demand of each device can be accurately predicted to avoid waste or shortage of consumables. At the same time, the dynamic adjustment of the consumable allocation plan can be flexibly adjusted according to the consumable life warning data, ensuring that the consumables are replaced in time when needed, and further improving the reliability and resource utilization of equipment operation. By executing the consumable allocation adjustment plan and generating an equipment consumables management report, not only the automation and traceability of equipment management are realized, but also detailed operation and monitoring data are provided for medical beauty institutions, ensuring the transparency and standardization of management, and facilitating the subsequent optimization of management strategies. Therefore, the present invention improves the accuracy and comprehensiveness of cloud management of medical cosmetic equipment through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform.

[0006] Preferably, step S1 comprises the following steps: Step S11: Obtain the medical beauty equipment number and medical beauty equipment parameters; Step S12: performing data preprocessing on the medical cosmetic equipment parameters to generate standard medical cosmetic equipment parameters, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: assigning equipment identification to standard medical beauty equipment parameters based on the medical beauty equipment number to obtain medical beauty equipment identification data; uploading medical beauty equipment parameters to the cloud platform through the medical beauty equipment identification data to generate medical beauty equipment monitoring data; Step S14: Digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables.

[0007] The present invention ensures the accuracy and consistency of data by preprocessing the parameters of medical beauty equipment, including data cleaning, denoising, missing value filling and standardization, which helps to reduce errors or deviations caused by data problems. By assigning equipment identification and uploading data, the parameters of each medical beauty device can be accurately identified and tracked, thereby improving the management and maintenance efficiency of the equipment and enhancing the safety of equipment use. The digital registration of consumable information makes the consumable management of beauty equipment more efficient and transparent, and can monitor the use of consumables in real time, providing data support for optimizing inventory and resource allocation. By uploading and managing the monitoring data of medical beauty equipment, the equipment status can be obtained in real time, providing reliable data support for equipment maintenance, fault prediction, use optimization, etc., thereby improving the quality and safety of medical beauty services. Through data uploading on the cloud platform, information such as operation, monitoring, and maintenance of medical beauty equipment can be shared, promoting collaborative work between different departments, providing managers with decision-making basis, and improving overall business operation efficiency.

[0008] Preferably, step S14 comprises the following steps: Step S141: collecting basic attributes of equipment consumables information to generate consumables basic information data; performing equipment association processing on the consumables basic information data to generate consumables equipment association data; Step S142: Performing authenticity verification processing on the consumables equipment association table to generate a consumables authenticity verification result; performing environmental parameter monitoring on the consumables basic information data based on the consumables authenticity verification result to generate a consumables environment monitoring data set; Step S143: Perform life cycle initialization processing on the consumables environment monitoring data set to generate consumables life cycle initialization data; perform digital integration processing on the consumables life cycle initialization data to generate a digital registration table for beauty equipment consumables.

[0009] The present invention ensures the comprehensiveness and accuracy of consumables information through basic attribute collection and equipment association processing. The generated consumables equipment association data will help to establish a clear and effective relationship between each consumable and its corresponding equipment, thereby improving the relevance and traceability of the data. Authenticity verification processing can ensure the accuracy of consumables information and avoid the existence of false or untrue data. Environmental parameter monitoring can further ensure the use of consumables in the actual environment, provide more accurate environmental adaptability analysis, and improve the reliability and credibility of the data. Through life cycle initialization and digital integration processing, full tracking and management of each consumable from the beginning of use to disposal are provided. This digital management can provide clear records for the use, maintenance cycle, life estimation, etc. of consumables, and effectively help optimize the consumables use strategy. Through the generated digital registration table of beauty equipment consumables, the use, storage environment and life cycle of consumables can be monitored in real time and comprehensively, thereby providing accurate data support for equipment management, procurement decisions, inventory control and consumables management, and improving the efficiency of resource use. Authentic verification and environmental monitoring help to confirm the compliance of consumables, ensure that they are used in an environment that meets safety standards, reduce safety risks caused by non-compliant consumables, and ensure high standards and high safety of medical beauty services.

[0010] Preferably, step S2 comprises the following steps: Step S21: embedding IoT sensors into beauty equipment consumables using sensors through a digital registration table of beauty equipment consumables, and collecting beauty equipment consumables data based on the embedded IoT sensors to obtain beauty equipment consumables sensing data; Step S22: performing consumable usage status sensing on the beauty equipment consumables sensing data to generate consumable usage status sensing data; performing environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environment monitoring data; performing consumables loss impact analysis based on the consumables usage status sensing data and the consumables environment monitoring data to generate consumables usage impact analysis data; Step S23: transmitting the consumables usage impact analysis data back to the cloud platform for upload synchronization processing to generate a consumables status monitoring data set; Step S24: Use a preset machine learning algorithm to construct a beauty equipment consumable life model for the consumable status monitoring data set to obtain a consumable life prediction model; import the consumable status monitoring data set into the beauty equipment consumable life model to predict the consumable life, thereby generating consumable life warning data.

[0011] The present invention can realize real-time data collection of consumables by embedding sensors into beauty equipment consumables. The application of this Internet of Things technology improves the efficiency and accuracy of data collection, makes the consumables management of beauty equipment more intelligent and automated, and avoids the tediousness and errors of manual data recording. The perception of the use status of consumables and the monitoring of environmental parameters provide comprehensive real-time information. By sensing the data, the use of each consumable can be accurately grasped, abnormal or improper use can be discovered in time, and the equipment can be ensured to operate in the best state; the monitoring of environmental parameters ensures that the impact of external factors on consumables is effectively controlled. The consumable loss impact analysis in step S22 helps to identify which factors have a significant impact on the consumption of consumables. This helps to optimize resource utilization and consumables management, reduce unnecessary losses, and improve the cost-effectiveness of beauty equipment. By transmitting data back to the cloud platform, real-time synchronization of consumable status monitoring data is achieved. This data synchronization not only helps to share data between different systems, but also enables enterprise managers to obtain the status data of each device in real time, so as to make more timely and effective decisions. By constructing a beauty equipment consumable life prediction model through a machine learning algorithm, the remaining service life of the consumables can be predicted based on real-time data. This prediction model provides managers with an early warning function, which can notify in advance when consumables are close to being scrapped, thereby avoiding equipment downtime due to consumable damage and improving equipment availability and production efficiency. By predicting the life of consumables in advance and issuing early warnings, the equipment failure rate can be reduced, the working stability of the equipment can be improved, and sudden repairs caused by consumable failures can be avoided, reducing maintenance costs and downtime.

[0012] Preferably, step S22 includes the following steps: Step S221: Performing basic analysis on the sensor data of the beauty equipment consumables to generate analysis results of the consumables sensor data, wherein the basic analysis includes the temperature, pressure, working time and coolant consumption when the equipment is in use; Step S222: extracting the key indicators of the consumables usage status from the consumables sensing data analysis results to obtain the key indicators of the consumables usage status, wherein the extracted key indicators of the consumables usage status include the accumulated working time of the probe and the remaining amount of coolant; marking the key indicators of the consumables usage status in real time to generate consumables usage status perception data; Step S223: Perform environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environmental monitoring data; extract abnormal environmental parameters from the consumables environmental monitoring data using a preset environmental threshold range to obtain consumables abnormal environmental data; calculate the rate of change of environmental abnormal parameters on the consumables abnormal environmental data to obtain real-time environmental risk trend data; Step S224: Perform a cross-impact analysis on the consumables usage status perception data and the real-time environmental risk trend data to generate consumables usage impact analysis data.

[0013] The present invention can capture key parameters such as the operating temperature, pressure, working time and coolant consumption of the equipment in real time through basic analysis of the consumable sensor data. This analytical processing can effectively evaluate the working state of the equipment, provide a more accurate basis for the use of consumables, and ensure that the equipment is in the best working state. By extracting key indicators of the consumable use status (such as the cumulative working time of the probe and the remaining amount of coolant), the use progress and status of the consumables can be accurately monitored. By real-time determination marking, it is timely discovered whether the consumables are within the normal use range, which helps to prevent excessive or improper use and extend the service life of the equipment and consumables. By monitoring environmental parameters, the potential impact of the external environment on the performance of consumables can be identified in time. Using the preset environmental threshold range, environmental anomalies can be automatically identified and relevant data can be extracted to provide environmental adaptability analysis for the equipment. By calculating the change rate of environmental abnormality parameters, real-time environmental risk trend data can be obtained, environmental risks can be warned in advance, and equipment failures or consumable losses caused by environmental problems can be reduced. By cross-analyzing the consumable use status perception data and the real-time environmental risk trend data, the mutual influence between the equipment operation status and environmental changes can be revealed. This comprehensive analysis provides managers with a comprehensive risk assessment, helping them make more accurate maintenance decisions and reduce the risk of equipment failure and loss. Through comprehensive monitoring of consumables and the environment, managers can promptly understand the actual usage of equipment and consumables and adjust maintenance plans based on real-time data. This data-based decision can improve equipment maintenance efficiency, reduce sudden failures and maintenance costs, and ensure that equipment operates in the best condition.

[0014] Preferably, performing cross-impact analysis on the consumables usage status perception data and the real-time environmental risk trend data includes: Match the consumables usage status perception data with the real-time environmental risk trend data to generate consumables status-environment matching data; Perform high temperature environment screening on the consumables status-environment matching data to generate high temperature environment screening data; perform probe performance rate analysis on the consumables status-environment matching data based on the high temperature environment screening data to generate probe high temperature impact data; Perform low-temperature environment screening on the consumables status-environment matching data to generate low-temperature environment screening data; perform probe start-up time analysis on the consumables status-environment matching data based on the low-temperature environment screening data to generate probe low-temperature impact data; The probe high temperature impact data and the probe low temperature impact data are cross-analyzed to generate consumables usage impact analysis data.

[0015] The present invention generates consumable status-environment matching data by matching the consumable usage status perception data with the real-time environmental risk trend data, which can provide a comprehensive understanding of environmental changes and equipment operation status. This matching analysis provides accurate data support for in-depth exploration of how environmental factors affect the usage status of equipment consumables and helps predict potential usage risks. In the step, the high temperature environment is screened and the probe performance rate analysis is performed, so that the performance changes of the equipment at high temperature are timely evaluated. The generated probe high temperature impact data helps to identify the negative impact of high temperature on probe performance, such as performance degradation, misoperation, etc., and provides a scientific basis for the long-term stability of the equipment. Through low temperature environment screening and probe startup time analysis, the potential impact of low temperature on equipment startup can be evaluated and probe low temperature impact data can be generated. This helps to understand the possibility of equipment startup delay in low temperature environment, and take appropriate measures in advance to ensure the reliability of the equipment at low temperature. Cross-analyzing the impact data in high and low temperature environments can fully reveal the comprehensive impact of different ambient temperatures on equipment and consumables. Through the coordinated analysis of these two extreme environmental factors, the long-term impact of the environment on the equipment can be more accurately predicted, and the risk of equipment failure caused by environmental fluctuations can be reduced. This cross-analysis provides the equipment maintenance team with a more detailed view of the impact of temperature environment, allowing maintenance plans to be adjusted according to different environmental conditions. Knowing in advance the impact of high and low temperatures on equipment performance can help take action before potential problems occur, reducing maintenance costs and improving equipment availability.

[0016] Preferably, step S3 comprises the following steps: Step S31: Performing equipment usage frequency analysis on the medical beauty equipment monitoring data to obtain consumables usage frequency data; performing equipment consumables demand assessment on the consumables usage status perception data through the consumables usage frequency data to generate equipment consumables demand assessment data; Step S32: performing compatibility check based on the equipment consumables demand assessment data to generate consumables compatibility data; performing validity period priority allocation analysis on the consumables status monitoring data set to generate consumables allocation priority data; combining the consumables compatibility data with the allocation priority data to construct a consumables allocation plan to generate a consumables allocation plan data set; Step S33: using the consumable life warning data to divide the consumable allocation scheme data set into consumable life modes, generating unexpired consumable data and expired consumable data; dynamically adjusting the consumable allocation scheme data set based on the unexpired consumable data, generating a consumable allocation adjustment scheme data set; Step S34: Prompt replacement of expired consumables data, thereby generating equipment consumables replacement record data.

[0017] The present invention can accurately evaluate the demand for consumables of each device by analyzing the frequency of use of medical beauty equipment monitoring data. This provides data support for subsequent consumables management, ensures that the supply of consumables matches the equipment usage requirements, and avoids over-purchasing or shortages. By checking the compatibility of consumables, it is ensured that the consumables allocated to the equipment match the compatibility requirements of the equipment, and effectively avoids incompatible consumables from affecting the performance of the equipment or causing loss. In addition, the validity period priority allocation analysis can ensure that the consumables that are about to expire are used first in the consumables allocation, reduce waste, and improve resource utilization efficiency. Combining the compatibility data of the consumables and the allocation priority data, step S32 generates a reasonable consumables allocation scheme data set. This intelligent allocation scheme can be optimized according to the compatibility and validity period of the consumables, thereby improving the overall consumables usage efficiency, reducing equipment downtime, and ensuring the continuous operation of the equipment. Dynamically adjusting the allocation scheme according to the consumables life warning data is helpful for effective resource allocation before the consumables expire. The reasonable use of unexpired consumables can extend the service life of the equipment, improve the operating efficiency of the equipment, and reduce unnecessary maintenance costs. The expired consumables replacement reminder function provided ensures that the consumables used by the equipment are replaced in time to avoid affecting equipment performance or causing failures due to expired consumables. This mechanism helps equipment managers to timely grasp the usage status of consumables and reduce equipment problems caused by aging or expiration of consumables. Through comprehensive analysis of factors such as consumables demand, compatibility, and validity period, accurate resource allocation can be achieved, waste can be reduced, and the economic benefits of consumables use and equipment operating efficiency can be improved. At the same time, dynamic consumables allocation adjustment can also ensure that the equipment can receive optimal support throughout its life cycle.

[0018] Preferably, the frequency analysis of the use of medical cosmetic equipment monitoring data includes: Extract the time domain change data of the medical beauty equipment monitoring data to obtain the time domain change data of the equipment; perform fast Fourier transform on the time domain change data of the equipment to generate the frequency domain change data of the equipment; Performing periodic component analysis on the equipment usage frequency domain change data to generate equipment usage frequency domain periodic data; performing equipment usage frequency pattern recognition on the equipment usage frequency domain change data according to the equipment usage frequency domain periodic data to generate equipment usage frequency pattern data; Based on the device usage frequency pattern data, the usage change fluctuation slope of the device usage frequency domain change data is calculated to obtain the device usage change frequency domain fluctuation slope data; the device usage frequency domain change data is segmented into the maximum slope spectrum waveform by the device usage change frequency domain fluctuation slope data to generate the device usage maximum change waveform; The extreme value extraction is performed on the waveform with the largest change in equipment usage to obtain the frequency data of consumables usage, where the extreme value extraction includes peak-valley data extraction and peak-bottom data extraction.

[0019] The present invention can more carefully understand the use status of the equipment in different time periods by extracting the time domain change data of the equipment. Combined with the fast Fourier transform (FFT), the time domain data can be converted into frequency domain data, which is helpful to reveal the frequency characteristics and change rules of the equipment. The periodic component analysis of the frequency domain change data of the equipment and the identification of its use frequency pattern can help identify the regular use cycle and irregular use of the equipment. This process helps to predict the use trend of the equipment and foresee potential abnormal use in advance, so as to optimize the equipment scheduling and use management. Based on the calculation of the frequency domain fluctuation slope, the fluctuation and change trend in the use of the equipment can be effectively detected. By segmenting the maximum slope spectrum waveform, the key change points of the equipment in the use process can be more accurately identified, and the abnormal or frequent fluctuations of the equipment operation can be discovered in time. By extracting extreme values ​​(peak and valley data extraction and peak and bottom data extraction), the peak and valley states of the equipment in different cycles can be accurately determined, and then the actual use frequency of the equipment and consumables can be analyzed. This precise frequency data provides strong support for consumables management, and can effectively predict the consumption speed and cycle of consumables to avoid waste or shortage of resources. Refined analysis of equipment usage frequency can help managers better understand the equipment usage load and consumables consumption in different time periods, so as to conduct more reasonable procurement and inventory management. By identifying the trend of equipment usage changes, the equipment maintenance cycle and consumables replacement frequency can be better adjusted to ensure efficient operation of the equipment. By analyzing frequency domain changes and fluctuation slopes, potential risks in the use of equipment can be effectively identified, such as frequent load fluctuations or extreme usage conditions, so as to warn of equipment failures in advance, take repair or maintenance measures in time, and extend the service life of the equipment.

[0020] Preferably, step S4 comprises the following steps: Step S41: performing equipment management execution on the consumables allocation adjustment plan data set to generate equipment consumables management execution data; Step S42: Visualize the equipment consumables management execution data and the equipment consumables expiration replacement record data, and generate an equipment consumables management report to perform medical beauty equipment cloud management operations.

[0021] The present invention ensures that the matching of equipment and consumables is optimized by executing the consumables allocation adjustment scheme data set. The process generates equipment consumables management execution data by comprehensively analyzing the use frequency of the equipment, the demand for consumables and the expiration time, which can help managers accurately grasp the consumables usage of each device and timely adjust and optimize the consumables allocation plan. Through this process, more efficient consumables management can be achieved to avoid resource waste or supply shortage. The generation of equipment consumables management execution data can not only improve the utilization rate of consumables, but also accurately control the consumables consumption at different use stages of the equipment. Through intelligent equipment management execution, the need for manual intervention is reduced, the flexibility and accuracy of consumables allocation are improved, and the equipment can be ensured to operate normally at all stages without affecting the use effect due to consumables shortage or excess. The generated equipment consumables expiration replacement record data helps to track the life cycle of consumables. By monitoring these data, early warning of the expiration of consumables can be given, and the replacement of consumables can be better arranged to avoid affecting the normal operation of the equipment due to the expiration of equipment consumables. This timely warning mechanism can greatly reduce the equipment failure rate and ensure that medical beauty equipment is always in the best condition during use. By visualizing the equipment and consumables management execution data and the equipment and consumables expiration replacement record data, equipment and consumables management reports are generated. These reports provide equipment managers with easy-to-understand data presentations, helping them quickly identify potential problems in equipment and consumables management. Through this visual approach, managers can understand the usage status of equipment and consumables at a glance, so as to make more accurate and timely management decisions. The generated equipment and consumables management reports can directly support the execution of medical beauty equipment cloud management operations. The data in the report can be synchronized to the cloud platform in real time to ensure data sharing and remote monitoring of each management link. With the support of the cloud platform, remote management of equipment and consumables can be achieved, reducing manual management costs and improving work efficiency.

[0022] In this specification, a medical beauty equipment cloud management system is provided, which is used to execute the above-mentioned medical beauty equipment cloud management method, and the medical beauty equipment cloud management system includes: The parameter acquisition module is used to obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; The equipment consumables prediction module is used to collect beauty equipment consumables data through the beauty equipment consumables digital registration table to obtain beauty equipment consumables sensor data; perform consumables loss impact analysis on the beauty equipment consumables sensor data to generate a consumables status monitoring data set; perform consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; The equipment consumables allocation module is used to evaluate the equipment consumables demand based on the medical beauty equipment monitoring data and generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical beauty equipment monitoring data, and generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using the consumables life warning data, and generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; The management execution module is used to perform equipment management execution on the consumables allocation adjustment plan data set, generate equipment consumables management reports, and execute medical beauty equipment cloud management operations.

[0023] The beneficial effect of the present invention is that by obtaining the medical beauty equipment number and equipment parameters and uploading them to the cloud platform, medical beauty equipment monitoring data is generated, which provides a reliable basis for subsequent equipment status monitoring. The data upload to the cloud platform can synchronize the equipment parameter information in real time, ensure that the status of the equipment during use is continuously traceable, and improve the intelligence and automation level of equipment management. The digital registration of consumables information in step S1 can comprehensively manage consumables digitally through beauty equipment monitoring data to form a digital registration table for beauty equipment consumables. This not only helps to track the use of each device consumable, but also provides reliable data support for subsequent consumable loss analysis, life prediction and management, and realizes the transparency, precision and standardization of consumable management. By collecting the sensor data of beauty equipment consumables and analyzing the impact of loss, a consumable status monitoring data set is generated, which helps to timely identify problems in the use of consumables. At the same time, the consumable life warning data generated based on the monitoring data can predict the life cycle of consumables in advance, help managers plan the replacement and procurement time of consumables, and avoid excessive use or shortage of consumables, thereby improving operational efficiency. By evaluating the consumables demand of the equipment monitoring data and generating a consumables allocation plan based on the evaluation, the timely supply of consumables required by the equipment at different stages of use can be ensured. Further dynamic adjustment plans optimize resource allocation to ensure that each device obtains the right amount of consumables. At the same time, reasonable allocation is made based on the life warning data of the consumables to avoid the waste of expired consumables. By executing the consumables allocation adjustment plan, an equipment consumables management report is generated, which provides a decision-making basis for cloud management operations. Managers can visually understand the use of equipment and consumables through reports and make timely adjustments and optimizations. In addition, the visual reports of equipment and consumables improve the efficiency of collaboration between teams, help managers identify potential problems in a timely manner, and reduce the risk of equipment failure. Therefore, the present invention improves the accuracy and comprehensiveness of cloud management of medical beauty equipment through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a cloud management method for 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 unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. 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 medical cosmetic equipment cloud management method, the method comprising the following steps: Step S1: Obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; Step S2: collecting beauty equipment consumables data through a digital registration table of beauty equipment consumables to obtain beauty equipment consumables sensing data; performing consumables loss impact analysis on the beauty equipment consumables sensing data to generate a consumables status monitoring data set; performing consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; Step S3: Evaluate the equipment consumables demand for medical cosmetology equipment monitoring data to generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical cosmetology equipment monitoring data to generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using consumables life warning data to generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; Step S4: Perform equipment management on the consumables allocation adjustment plan data set and generate an equipment consumables management report to execute medical beauty equipment cloud management operations.

[0029] The present invention ensures the centralization and real-time monitoring of equipment data by uploading the medical beauty equipment number and equipment parameters to the cloud platform, and realizes the accurate recording and efficient management of equipment and consumables data by digitally registering consumables information, providing a basis for subsequent data analysis and decision-making. By collecting the sensor data of beauty equipment consumables and performing loss analysis, the use of consumables can be accurately evaluated, and then their lifespan can be predicted and early warning data can be generated. In this way, consumable problems can be identified in advance, unexpected consumable failures or shortages can be avoided, and the operating efficiency and customer experience of the equipment can be improved. By evaluating the consumable demand based on equipment monitoring data, the consumable demand of each device can be accurately predicted to avoid waste or shortage of consumables. At the same time, the dynamic adjustment of the consumable allocation plan can be flexibly adjusted according to the consumable life warning data, ensuring that the consumables are replaced in time when needed, and further improving the reliability and resource utilization of equipment operation. By executing the consumable allocation adjustment plan and generating an equipment consumables management report, not only the automation and traceability of equipment management are realized, but also detailed operation and monitoring data are provided for medical beauty institutions, ensuring the transparency and standardization of management, and facilitating the subsequent optimization of management strategies. Therefore, the present invention improves the accuracy and comprehensiveness of cloud management of medical cosmetic equipment through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform.

[0030] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a medical beauty equipment cloud management method of the present invention. In this example, the medical beauty equipment cloud management method includes the following steps: Step S1: Obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; In an embodiment of the present invention, the unique number of each device is obtained by scanning the unique identifier (such as a QR code, RFID tag or device serial number) on the device. The relevant technical parameters of the device are collected, including but not limited to the device model, manufacturer, device function, usage time, maintenance record and other information. The device number and device parameter information are collected in real time through the Internet of Things (IoT) technology or the API interface provided by the device. The collected device number and device parameters are uploaded to the cloud platform through encrypted transmission. The cloud platform must have data storage, analysis and security management functions to ensure the integrity and privacy of the data. After the device data is uploaded, the cloud platform generates corresponding device monitoring data according to the device number, and records the device operation status, fault records, performance indicators and other information in real time. During the use of medical beauty equipment, the consumables information related to the equipment (such as product number, specification, usage quantity, consumption record, etc.) is collected. The collected consumables information is digitally registered by scanning a barcode or QR code and associated with the corresponding device number and medical beauty equipment parameters. A digital registration table for beauty equipment consumables is generated on the cloud platform, which contains detailed data such as consumables information, usage records, consumption, expiration date, etc. for subsequent management and traceability. The cloud platform will integrate equipment monitoring data and consumables registration to build a centralized management system that supports real-time query, statistics and analysis. The system automatically generates reminders and reports based on equipment usage, consumables consumption and equipment failure records to help managers optimize equipment maintenance and consumables management.

[0031] Step S2: collecting beauty equipment consumables data through a digital registration table of beauty equipment consumables to obtain beauty equipment consumables sensing data; performing consumables loss impact analysis on the beauty equipment consumables sensing data to generate a consumables status monitoring data set; performing consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; In an embodiment of the present invention, the digital registry of beauty equipment consumables contains detailed information of each consumable, such as model, specification, usage history, consumption, remaining amount, etc. First, the system reads the relevant consumable information from the registry to determine the consumable accessories of each device. Use smart sensors or built-in Internet of Things devices to monitor the usage of consumables in real time. These sensors can record data such as consumable consumption, equipment working hours, and working intensity at each use. For example, temperature sensors, pressure sensors, vibration sensors, etc. can provide real-time information about consumable performance and consumption. The system automatically synchronizes the consumable usage data to the cloud platform by scanning the barcode or QR code of each consumable. For example, when the consumable starts to be used, the system records information such as the current time, device number, consumable number and quantity. The real-time data (such as temperature, pressure, working time) obtained by the sensor is uploaded to the system, and combined with the data in the digital registry, accurate consumption data is provided for each use of the consumable. All collected data (including sensor data and scan data) are synchronized to the cloud platform in real time to form beauty equipment consumable sensor data. The collected sensor data contains noise or abnormal values. The system needs to clean the data and remove invalid or abnormal data (such as too high or too low temperature and pressure values). Common data preprocessing includes: removing noise through smoothing filtering, mean filtering and other technologies. Standardize the data so that different types of sensor data are in the same dimension. Analyze the factors that affect the loss of beauty equipment consumables, such as usage time, equipment load, work intensity, etc. The specific analysis steps are as follows: Build a consumable loss prediction model based on the type of equipment and the usage environment. For example, use the load, working time, temperature and humidity of the equipment, combined with the historical maintenance data of the equipment, to build a consumable loss model. Through the collected sensor data and combined with the loss model, the system calculates the actual loss degree of each consumable. For example, a consumable will wear out faster in a high temperature environment, and a consumable will wear out prematurely under a large load. Based on the results of the loss impact analysis, the system generates a consumable status monitoring data set. By analyzing a large amount of historical data (including equipment usage history, consumable replacement history, equipment maintenance history, etc.), the system can identify the usage patterns and loss patterns of different types of consumables. Based on historical data, machine learning models (such as regression models, random forests, neural networks, etc.) are trained to predict the life of each consumable. Commonly used models include: Linear regression: used to predict the remaining life of consumables based on the intensity and time of equipment use. Random forest: predicts the progress of consumable wear by analyzing multiple influencing factors. Support vector machine (SVM): predicts life based on equipment working mode and consumable consumption mode. Based on the current working status and historical usage data of the consumables, the system evaluates the remaining life of each consumable. Based on the wear model and sensor data, the expected life of each consumable (such as predicted remaining working time, expected number of consumptions) is calculated.The system will update the life prediction of each consumable in real time based on new usage data and sensor feedback, and generate corresponding early warnings.

[0032] Step S3: Evaluate the equipment consumables demand for medical cosmetology equipment monitoring data to generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical cosmetology equipment monitoring data to generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using consumables life warning data to generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; In the embodiment of the present invention, real-time data of medical cosmetic equipment is collected, including the equipment's operating time, workload, usage frequency, equipment status, etc. The consumable status monitoring data set obtained from the previous step includes the usage status, consumption, and remaining life of the consumables. A demand prediction model is constructed based on the equipment usage frequency, workload, historical consumption data, and consumable loss prediction. The model predicts the number of consumables required in the future based on factors such as the daily usage of the equipment and the consumable consumption pattern. Formula: Demand prediction = f (equipment usage frequency, consumable consumption history, workload, equipment maintenance cycle) The system estimates the amount of each type of consumables required for each device based on the equipment type and expected work intensity, and generates personalized consumable demand assessment data for each device. This data set will include the following: equipment number and consumable type, demand for each consumable, expected consumable consumption time (by day, month, etc.), and consumable consumption rate (consumption per unit time). The system integrates the above evaluation results to generate an equipment consumable demand assessment data set, providing accurate quantitative data on the consumables required for each device. Based on the equipment consumables demand assessment data, the system prioritizes the allocation of consumables according to the equipment priority (such as equipment usage frequency and importance). For example, consumables should be allocated first to equipment with high usage frequency. The system compares the real-time equipment monitoring data (such as the equipment's working status and current usage) with the demand assessment data and adjusts the consumables allocation strategy in real time. According to the equipment's consumption demand, available inventory, storage capacity, and preset allocation rules (such as the minimum unit of each consumption, the maximum inventory, etc.), the system automatically generates the optimal consumables allocation plan. The specific steps are as follows: According to the demand forecast data and the equipment priority, the consumables are automatically allocated to each device. The allocation plan should take into account the equipment's operating status to ensure that high-priority equipment has sufficient consumables. Based on the real-time inventory data, ensure that the consumables allocation does not exceed the current inventory, and generate the consumables allocation plan data set. Based on the consumables life prediction data set (such as remaining life, number of days of use, etc.), the system monitors the remaining life of the consumables in real time and determines whether it has reached the warning threshold. If a consumable is close to expiration or is consumed, the system will trigger a warning. When consumables are about to expire or need to be replenished urgently, the system will automatically adjust the allocation of consumables based on the current allocation plan, inventory status and device priority. The adjustment strategy is as follows: For consumables that are about to expire, the system will prioritize them for high-priority devices. If some consumables are out of stock, the system will start the procurement process and automatically replenish the inventory. Based on the consumable expiration warning data, the system can adjust the task allocation of the equipment to give priority to the consumables that are about to expire.

[0033] Step S4: Perform equipment management on the consumables allocation adjustment plan data set and generate an equipment consumables management report to execute medical beauty equipment cloud management operations.

[0034] In an embodiment of the present invention, the adjusted consumables allocation scheme data set is transmitted to each device management module through the medical beauty equipment cloud platform. During specific execution, the platform will automatically execute the consumables allocation task according to the device's needs, priorities and consumables allocation. The system will prioritize the allocation of consumables to the devices that are running or about to run according to the status information of each device (such as whether the device is running or in standby state) to ensure the normal operation of the device. The system dynamically adjusts the allocation strategy based on the real-time device status. For example, if a device fails or shuts down, the system will automatically adjust the consumables allocation to ensure that other devices can obtain the required consumables in a timely manner. When the device is started, stopped or an abnormality occurs, the platform will re-evaluate the consumables allocation and ensure timely adjustment according to the device needs. After each consumables allocation task is completed, the system will automatically generate execution feedback to record detailed information in the allocation process, such as the allocation quantity, device number, allocation time, execution status, etc. The system automatically generates an execution log to record the execution of each step, including the allocation plan, execution time, executor (or system), any abnormal situation or adjustment history. The execution log will be associated with other equipment maintenance records for later analysis and auditing. The system summarizes data on equipment consumables allocation, equipment usage status, inventory status, consumables life forecast, equipment failure and maintenance records, etc. It includes a comparative analysis of the actual consumables allocation quantity, equipment operating time and consumables consumption rate for each device. It includes a comparative analysis of the actual consumables allocation quantity, equipment operating time and consumables consumption rate for each device. It will show the equipment's operating efficiency, failure frequency, repair and maintenance records, and the impact of these factors on consumables consumption. The report will also include the inventory status of consumables, predict future inventory gaps based on the current consumption rate, and provide procurement recommendations.

[0035] Preferably, step S1 comprises the following steps: Step S11: Obtain the medical beauty equipment number and medical beauty equipment parameters; Step S12: performing data preprocessing on the medical cosmetic equipment parameters to generate standard medical cosmetic equipment parameters, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: assigning equipment identification to standard medical beauty equipment parameters based on the medical beauty equipment number to obtain medical beauty equipment identification data; uploading medical beauty equipment parameters to the cloud platform through the medical beauty equipment identification data to generate medical beauty equipment monitoring data; Step S14: Digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables.

[0036] In an embodiment of the present invention, by reading a unique device number from a medical cosmetic device, each device is assigned a unique ID when it is purchased and registered to ensure that it can be accurately tracked throughout the management system. Based on the device number, the system automatically identifies the device type, such as laser cosmetic devices, microneedle therapy devices, etc., to ensure the accuracy of data collection and management. Through an interface or sensor, relevant parameters such as device operating time, power, frequency, temperature, humidity, etc. are automatically collected from each device. The device parameters collected in real time will be stored in a local database and used as basic data for subsequent processing and analysis. Abnormal values ​​in device parameters are detected and processed, such as removing extreme data points caused by sensor failure or data transmission problems. Irrelevant or repeated device parameters are eliminated to ensure that the data only includes information related to device performance and operating status. Appropriate algorithms (such as filtering, smoothing algorithms, etc.) are used to remove noise from device collected data to ensure the accuracy of parameters. For example, random noise is removed by moving average method. If the device parameters come from multiple sensors, they will contain noise of different frequencies, and Fourier transform and other methods are used to perform signal frequency domain analysis and denoising. The location of missing data is identified by algorithms, and statistical methods (such as mean and median filling) are usually used to fill missing values ​​to ensure the integrity of subsequent data processing. For key equipment parameters, machine learning models (such as regression analysis or time series prediction) are used to fill and predict missing values. The equipment parameter data is converted into a unified standard format through linear transformation, such as normalization or standardization, so that different equipment or different equipment parameters have the same dimension. Ensure that all equipment parameters use a unified unit (such as Celsius for temperature and watts for power) for subsequent unified management and analysis. After cleaning, denoising, filling and standardization, standard medical beauty equipment parameters are generated, and these standardized data will be used as input for subsequent steps. The standardized medical beauty equipment parameters are identified and assigned according to the equipment number to ensure that the parameter data of each device has a unique identifier. For example, the equipment number and date / time stamp are used as part of the identification. The equipment identification information is stored in the database together with the equipment parameter data to form complete identification data for each device. The identification data usually includes information such as equipment ID, equipment model, equipment status, and equipment location. The equipment identification data will be integrated into the cloud platform as the basis for real-time monitoring of the equipment and subsequent data analysis. Through device identification, the system can accurately associate the real-time data of each device with the device itself, which is convenient for later tracking and maintenance. The device identification data and standardized device parameters are uploaded to the cloud platform via the Internet. The cloud platform provides storage, processing and visual analysis services, and all device data is updated synchronously on the platform in real time. The cloud platform generates monitoring data for medical beauty equipment based on the uploaded device identification data and standardized parameters. These monitoring data include equipment operating status, performance data, and historical data.The consumables information required for each device, such as consumable type, consumption, and usage cycle, is collected through the device monitoring data and device management system. The system automatically extracts the consumables information related to the device and associates it with the device identification data. Based on the collected consumables data, the system generates a digital registry of beauty device consumables. The registry content includes information such as device ID, consumable name, model, usage quantity, usage period, and usage frequency.

[0037] Preferably, step S14 comprises the following steps: Step S141: collecting basic attributes of equipment consumables information to generate consumables basic information data; performing equipment association processing on the consumables basic information data to generate consumables equipment association data; Step S142: Performing authenticity verification processing on the consumables equipment association table to generate a consumables authenticity verification result; performing environmental parameter monitoring on the consumables basic information data based on the consumables authenticity verification result to generate a consumables environment monitoring data set; Step S143: Perform life cycle initialization processing on the consumables environment monitoring data set to generate consumables life cycle initialization data; perform digital integration processing on the consumables life cycle initialization data to generate a digital registration table for beauty equipment consumables.

[0038] In the embodiment of the present invention, by configuring an RFID tag or a QR code for each consumable, the sensor periodically scans the consumable information. When the consumable is connected to the device, the sensor reads the data and uploads it to the central server through wireless communication (such as Wi-Fi, Bluetooth or Zigbee). The collected basic data is uploaded to the database through the API interface to generate a consumable basic information data table. The device and the consumable are associated by the device number to generate a "consumable device association data table". The authenticity of the consumable is ensured by the blockchain verification system. By scanning the QR code or RFID tag, the system can access the historical information of the consumables verified on the blockchain to ensure that its source is legal and prevent counterfeit products from entering the market. By installing environmental sensors (such as temperature and humidity sensors), the storage environment is monitored in real time and data is collected for processing. Scan the QR code or RFID tag of the consumable and query and verify the product source information through the blockchain. The decentralized nature of the blockchain ensures that the data cannot be tampered with and ensures the authenticity of the information. If the product fails to pass the verification, the system automatically blocks the use of the consumable and marks it as "counterfeit". Install the temperature and humidity sensor and integrate it with the system to monitor the temperature, humidity and other parameters of the consumable storage environment in real time. The collected environmental data is uploaded to the cloud platform through the Internet of Things gateway. The cloud platform associates environmental data with consumables information to generate a "consumables environmental monitoring data set". The monitoring data is analyzed in real time. If an abnormality is found (such as too high temperature or too low humidity), the system will trigger an alarm. The use environment and life cycle data of consumables are analyzed using big data technology, and a data model is established to predict the life of consumables. Based on historical data and environmental monitoring results, data analysis technology (such as machine learning regression model) is used to make a preliminary estimate of the life cycle of each consumable. The system divides the life cycle of each consumable into multiple stages (such as: initial stage, use period, expiration period, etc.), and sets corresponding warning thresholds for each stage. The basic information of consumables, equipment-related information, authenticity verification results, environmental monitoring data and life cycle information are integrated into the cloud platform database. Using the distributed processing capabilities of cloud computing, all relevant data are integrated and standardized to generate a unified "digital registry of beauty equipment consumables". The integrated data on the cloud platform can support multi-terminal access, and administrators can query or update detailed information of consumables through the network interface.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: embedding IoT sensors into beauty equipment consumables using sensors through a digital registration table of beauty equipment consumables, and collecting beauty equipment consumables data based on the embedded IoT sensors to obtain beauty equipment consumables sensing data; Step S22: performing consumable usage status sensing on the beauty equipment consumables sensing data to generate consumable usage status sensing data; performing environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environment monitoring data; performing consumables loss impact analysis based on the consumables usage status sensing data and the consumables environment monitoring data to generate consumables usage impact analysis data; Step S23: transmitting the consumables usage impact analysis data back to the cloud platform for upload synchronization processing to generate a consumables status monitoring data set; Step S24: Use a preset machine learning algorithm to construct a beauty equipment consumable life model for the consumable status monitoring data set to obtain a consumable life prediction model; import the consumable status monitoring data set into the beauty equipment consumable life model to predict the consumable life, thereby generating consumable life warning data.

[0040] In the embodiment of the present invention, the beauty equipment consumables are intelligently sensed and data collected through embedded sensors, RFID tags and other technologies. Internet of Things sensors (such as temperature and humidity sensors, pressure sensors, flow sensors, etc.) are embedded in the beauty equipment consumables, and these sensors can monitor the status of the consumables (such as temperature, humidity, flow, etc.) in real time. According to the needs of the beauty equipment, select the appropriate sensor type (for example, RFID tags are used for item identification, and temperature and humidity sensors are used for environmental monitoring). The consumable status data is collected regularly or in real time through the Internet of Things device, including the data obtained by the sensor (such as consumption, environmental changes, frequency of use, etc.). The data collected by the sensor is transmitted to the central processing platform through a wireless network for preliminary storage and processing. The status is evaluated based on the collected sensor data (such as consumption, frequency of use, pressure changes, etc.) to determine whether the consumables are in normal working condition or are about to fail. Use data analysis methods (such as threshold analysis) to detect abnormal values ​​fed back by the sensor and mark them as abnormal consumable status. At the same time, analyze the collected environmental data (such as temperature, humidity, pressure) to determine whether environmental factors have an impact on the consumables. Use a multi-parameter fusion analysis method to combine multiple environmental factors with the consumable status for evaluation to generate "consumable environmental monitoring data." Combined with usage status and environmental data, data mining technology is used to analyze the impact of loss and evaluate the impact of different environmental conditions or usage patterns on the life of consumables. Use regression models, decision trees and other methods in machine learning to quantify the loss of consumables and generate "consumable usage impact analysis data". The "consumable usage impact analysis data" is transmitted back to the cloud platform through the Internet of Things gateway or API interface. Data is transmitted through encryption to ensure data security. After receiving the data, the cloud platform automatically synchronizes and stores it. The database processes the returned data to generate a complete "consumable status monitoring data set", which includes the status of consumables, environmental monitoring data, usage impact analysis results, etc. Through historical data and current consumable status monitoring data, a machine learning algorithm is used to train the model to predict the remaining life of consumables. Select a suitable machine learning algorithm (such as random forest, support vector machine or deep neural network) to model the life according to the collected usage data and environmental data. The model is trained through the training set (historical usage data) to generate a "beauty equipment consumable life model". The real-time collected "consumable status monitoring data set" is input into the life prediction model for real-time life estimation. Based on the model prediction results, "consumable life warning data" is generated to warn of consumables that have reached their service life. The system automatically sends warning data to relevant personnel or management systems to notify in advance of the need for consumable replacement or repair.

[0041] Preferably, step S22 includes the following steps: Step S221: Performing basic analysis on the sensor data of the beauty equipment consumables to generate analysis results of the consumables sensor data, wherein the basic analysis includes the temperature, pressure, working time and coolant consumption when the equipment is in use; Step S222: extracting the key indicators of the consumables usage status from the consumables sensing data analysis results to obtain the key indicators of the consumables usage status, wherein the extracted key indicators of the consumables usage status include the accumulated working time of the probe and the remaining amount of coolant; marking the key indicators of the consumables usage status in real time to generate consumables usage status perception data; Step S223: Perform environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environmental monitoring data; extract abnormal environmental parameters from the consumables environmental monitoring data using a preset environmental threshold range to obtain consumables abnormal environmental data; calculate the rate of change of environmental abnormal parameters on the consumables abnormal environmental data to obtain real-time environmental risk trend data; Step S224: Perform a cross-impact analysis on the consumables usage status perception data and the real-time environmental risk trend data to generate consumables usage impact analysis data.

[0042] In the embodiment of the present invention, by equipping with sensing devices such as temperature sensors, pressure sensors, time recording modules, coolant flow meters, and connecting with the main control unit through an interface module (such as UART or I2C), real-time data collection is realized. During the operation of the equipment, the sensor collects consumables-related data at a fixed sampling frequency (such as once per second); the data is preliminarily verified by the main control unit and recorded in the buffer area to prevent collection interruption or data loss. The temperature data is extracted, the fluctuation range is detected, and if the temperature change exceeds ±5°C, the abnormal fluctuation point is recorded; the pressure data is analyzed in time series to generate a pressure change curve chart to identify the peaks and troughs; the equipment opening period is accumulated to generate the cumulative working time; the flow data is read from the flow meter and time integrated to calculate the total consumption. The parsed data is stored as a structured file (such as JSON or CSV) to form the consumables sensor data parsing result for use in subsequent steps. The working time statistics module is called to read the parsed cumulative time and compare the current time with the preset safety threshold (such as 200 hours). If the threshold is exceeded, it is marked as "high risk", otherwise it is marked as "normal". Based on the initial capacity of the coolant and the total consumption of the analysis, calculate the remaining amount: remaining amount = initial capacity - total consumption; when the remaining amount is lower than the threshold (such as 10%), trigger the "low coolant" alarm. Create real-time status update logic: run key indicator checks every 5 minutes; update the timeout and low remaining status to the consumables usage status perception data with a timestamp. Configure temperature and humidity sensors and vibration sensors and install them around the equipment to ensure that real-time changes in the environment can be captured. Set the monitoring cycle (such as once every 2 seconds), collect environmental parameters in real time and record them in the environmental monitoring module. Write an anomaly detection algorithm: if the temperature exceeds the range (such as 20-30℃), record the time point and value of the excess; when the vibration data fluctuation frequency exceeds the preset threshold (such as above 5Hz), trigger an alarm. Use the following formula to calculate the rate of change of abnormal parameters: change rate = (current value - previous value) ÷ time interval; compare the rate result with the safety rate threshold to generate real-time environmental risk trend data and mark the trend type (such as "risk increasing" or "risk stable"). Input the consumables usage status perception data and real-time environmental risk trend data into the analysis engine; if the ambient temperature rises and the coolant consumption speed increases, record the conclusion of "high temperature accelerates coolant consumption"; if the vibration frequency increases and the cumulative working time of the probe is close to the safety threshold, record the conclusion of "vibration accelerates probe loss". Construct a multivariate regression model: ; Where Y represents the life of consumables, Xn represents environmental parameters or usage status indicators, αn is the correlation coefficient, To eliminate errors, generate consumables usage impact analysis data, including: the impact of various factors on the life of consumables (such as the impact of temperature changes on life is 20%); provide operational suggestions (such as "it is recommended to lower the operating temperature of the equipment" or "the probe needs to be replaced").

[0043] Preferably, performing cross-impact analysis on the consumables usage status perception data and the real-time environmental risk trend data includes: Match the consumables usage status perception data with the real-time environmental risk trend data to generate consumables status-environment matching data; Perform high temperature environment screening on the consumables status-environment matching data to generate high temperature environment screening data; perform probe performance rate analysis on the consumables status-environment matching data based on the high temperature environment screening data to generate probe high temperature impact data; Perform low-temperature environment screening on the consumables status-environment matching data to generate low-temperature environment screening data; perform probe start-up time analysis on the consumables status-environment matching data based on the low-temperature environment screening data to generate probe low-temperature impact data; The probe high temperature impact data and the probe low temperature impact data are cross-analyzed to generate consumables usage impact analysis data.

[0044] In an embodiment of the present invention, by extracting key state indicators from the consumables usage state perception data, such as the accumulated working time of the probe, the remaining amount of coolant, etc.; extracting environmental parameter trends from the real-time environmental risk trend data, such as the temperature change rate, the vibration fluctuation frequency, etc. The two types of data are time-aligned according to the timestamp. Using the logical association between key state indicators and environmental parameters, matching data is generated: for example, the accumulated working time of the probe is associated with the high temperature fluctuation frequency, and the consumables state-environment matching data is output, including the time point, state value, environmental parameter value and association strength mark (such as "strong correlation" or "weak correlation") of each group of matching. Set a high temperature screening threshold (such as ≥35℃). Extract temperature parameters from the consumables state-environment matching data, filter data entries that meet the high temperature conditions, and generate high temperature environment screening data. Based on the performance change rate of the probe in a high temperature environment, the analysis formula is as follows: Rh=ΔP÷Δt; where Rh is the high temperature impact rate of the probe; ΔP is the change in probe performance (such as the accumulated loss of working time or coolant consumption); Δt is the time interval. Perform rate analysis on high temperature environment data by time period and verify it with environmental trend data, such as the correlation between probe performance degradation and high temperature fluctuations. Set a low temperature screening threshold (such as ≤10°C). Extract temperature parameters from consumable status-environment matching data, filter data entries that meet low temperature conditions, and generate low temperature environment screening data. Measure the startup time of the probe from standby state to stable working state in a low temperature environment. The analysis formula is as follows: ; Where: Ts is the probe startup time; Te is the time point when the probe reaches a stable working state; Ti is the time point when the probe starts to start. Compare the startup time in a low-temperature environment with the benchmark startup time in a standard environment, calculate the increment of the startup time and evaluate the impact of low temperature on the startup efficiency. Align the probe high temperature impact data with the probe low temperature impact data, and compare their impact levels, parameter change trends, etc. Construct a multidimensional matrix, and input the high temperature and low temperature impact data as dimensions respectively: high temperature dimension: performance rate change, high temperature level; low temperature dimension: startup time increment, low temperature level. Use multivariate regression or correlation analysis technology to evaluate the combined impact of the two on the overall performance of the probe, and output consumables usage impact analysis data.

[0045] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Performing equipment usage frequency analysis on the medical beauty equipment monitoring data to obtain consumables usage frequency data; performing equipment consumables demand assessment on the consumables usage status perception data through the consumables usage frequency data to generate equipment consumables demand assessment data; Step S32: performing compatibility check based on the equipment consumables demand assessment data to generate consumables compatibility data; performing validity period priority allocation analysis on the consumables status monitoring data set to generate consumables allocation priority data; combining the consumables compatibility data with the allocation priority data to construct a consumables allocation plan to generate a consumables allocation plan data set; Step S33: using the consumable life warning data to divide the consumable allocation scheme data set into consumable life modes, generating unexpired consumable data and expired consumable data; dynamically adjusting the consumable allocation scheme data set based on the unexpired consumable data, generating a consumable allocation adjustment scheme data set; Step S34: Prompt replacement of expired consumables data, thereby generating equipment consumables replacement record data.

[0046] In the embodiment of the present invention, real-time data is obtained from the equipment monitoring system, including equipment operation time (unit: hour), number of startups, operation mode records, etc. The data source includes the equipment built-in sensor log, the operation recording system and the cloud historical data. The equipment usage frequency is calculated according to the formula: F=(T usage time ÷ T total time) × W, where T usage time is the equipment operation time, T total time is the monitoring period, and W is the weight factor (considering the operation mode), and "equipment usage frequency data" is generated and stored in the form of a chart or table. Consumables data related to equipment frequency (such as consumable type, number of times used, and remaining amount) are extracted. A model based on time series prediction (such as LSTM or ARIMA) is used to predict the future consumption trend of consumables and output the consumable usage status perception data. Combined with the equipment usage frequency, consumable consumption trend and historical replacement records, an evaluation model is established: D=F×C+R, where D is the consumable demand, F is the equipment frequency, C is the consumable quantity for a single operation, and R is the safety stock replenishment quantity, and the equipment consumable demand evaluation data is generated as input for subsequent steps. Load the equipment consumable compatibility database to match equipment models and consumable specifications. Use a rule engine (such as Drools) to verify compatibility in real time and output consumable compatibility data. Calculate the remaining validity period of consumables according to the formula: Tremaining=Texpired-Tcurrent. Sort by priority according to the remaining time. Generate consumable allocation priority data according to the priority allocation rule (for example, the shortest validity period is preferred). Use integer linear programming (ILP) to optimize allocation, with the goal of minimizing consumable waste while meeting equipment requirements, and output consumable allocation plan data sets for implementation by the operation team. Obtain real-time consumable life warning data, combine the expiration threshold, divide consumables into "unexpired" and "expired", and generate unexpired consumable data and expired consumable data. Dynamically adjust the allocation plan based on the real-time operation status of the equipment (such as the demand for new equipment or the change in the remaining amount after replacing consumables). Update the consumable allocation plan data set and output the consumable allocation adjustment plan data set. Filter the consumable items that are about to expire by querying the expired consumable data. Configure reminder parameters (such as 7 days in advance). Automatically generate replacement reminder records, including equipment, consumable type and replacement date. The reminder data is associated with the actual replacement operation records and stored as the equipment consumables replacement record data. The consumables replacement trend is analyzed in combination with historical records to provide data support for procurement decisions and consumables management optimization.

[0047] Preferably, the frequency analysis of the use of medical cosmetic equipment monitoring data includes: Extract the time domain change data of the medical beauty equipment monitoring data to obtain the time domain change data of the equipment; perform fast Fourier transform on the time domain change data of the equipment to generate the frequency domain change data of the equipment; Performing periodic component analysis on the equipment usage frequency domain change data to generate equipment usage frequency domain periodic data; performing equipment usage frequency pattern recognition on the equipment usage frequency domain change data according to the equipment usage frequency domain periodic data to generate equipment usage frequency pattern data; Based on the device usage frequency pattern data, the usage change fluctuation slope of the device usage frequency domain change data is calculated to obtain the device usage change frequency domain fluctuation slope data; the device usage frequency domain change data is segmented into the maximum slope spectrum waveform by the device usage change frequency domain fluctuation slope data to generate the device usage maximum change waveform; The extreme value extraction is performed on the waveform with the largest change in equipment usage to obtain the frequency data of consumables usage, where the extreme value extraction includes peak-valley data extraction and peak-bottom data extraction.

[0048] In an embodiment of the present invention, real-time usage data of the device is obtained from the monitoring system of the medical cosmetic device, including the running time, the startup time, the usage mode, etc. Then, by extracting the time domain change data of the device usage, time series data reflecting the usage status of the device is generated. Then, the fast Fourier transform (FFT) is used to convert the time domain data of the device usage into frequency domain data for frequency domain analysis. Specifically, through FFT, the original time domain signal x(t)x(t)x(t) is converted into a frequency domain signal X(f), where f is the frequency variable. The obtained frequency domain change data is subjected to periodic component analysis to identify the main frequency component in the signal, and then the usage cycle information of the device is extracted. These periodic components can reflect the fluctuation of the usage frequency of the device in different time periods. According to this frequency information, the device usage frequency pattern recognition is performed, and the device usage frequency is divided into different modes by using algorithms such as cluster analysis to identify the change trend of the frequency mode. Then, by calculating the change fluctuation slope in the device usage frequency mode, the frequency change amplitude of the device usage is further analyzed. The calculation formula is: S slope = ΔA ÷ Δf; wherein ΔA is the amplitude change amount, and Δf is the frequency change amount. Based on the frequency change slope, perform maximum slope spectrum waveform segmentation, and use the segmentation point to identify the main waveform in the frequency domain signal. Perform extreme value extraction, mainly through the peak and valley extraction algorithm to identify the peak and valley values ​​in the spectrum, further extract the peak frequency and valley frequency of the equipment, and calculate the equipment usage frequency mode data. Convert these frequency domain features (including peak and valley data, frequency cycle, etc.) into consumables usage frequency data to provide a basis for the consumables demand assessment of the equipment.

[0049] Preferably, step S4 comprises the following steps: Step S41: performing equipment management execution on the consumables allocation adjustment plan data set to generate equipment consumables management execution data; Step S42: Visualize the equipment consumables management execution data and the equipment consumables expiration replacement record data, and generate an equipment consumables management report to perform medical beauty equipment cloud management operations.

[0050] In an embodiment of the present invention, medical cosmetic equipment is dispatched according to the consumables allocation adjustment plan to ensure that the equipment can replenish consumables in time during use and update consumables according to a predetermined plan. The current status of the equipment is tracked through the equipment monitoring system, including the equipment usage time, the amount of consumables used, the current remaining amount of consumables, etc. The consumables are dynamically dispatched and allocated according to the demand, usage frequency and inventory status of the equipment consumables to ensure the normal operation of each device. Key data in the execution process of equipment consumables management, such as consumables replenishment time, consumables usage, equipment status, etc., are recorded to generate equipment consumables management execution data. Through data visualization tools, the equipment consumables management execution data is integrated with the equipment consumables expiration replacement record data. Through visualization forms such as charts and dashboards, the current consumables usage of the equipment, expired consumables, consumables replacement time and other information are clearly displayed. The visualization content includes: Consumables usage: the consumables usage and remaining amount of different equipment are displayed through pie charts or bar charts. Consumables expiration: the expiration time and replacement records of various consumables are displayed through a timeline or bar chart to remind the consumables that are about to expire. Equipment consumables allocation and usage trends: Display equipment consumables demand and allocation dynamics through line charts or trend charts. Convert the generated visual report into an equipment consumables management report and provide it to relevant managers for decision-making and subsequent operations. Upload the equipment consumables management report to the cloud platform to perform medical beauty equipment cloud management operations and realize remote monitoring and management of multiple devices and across regions.

[0051] In this specification, a medical beauty equipment cloud management system is provided, which is used to execute the above-mentioned medical beauty equipment cloud management method, and the medical beauty equipment cloud management system includes: The parameter acquisition module is used to obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; The equipment consumables prediction module is used to collect beauty equipment consumables data through the beauty equipment consumables digital registration table to obtain beauty equipment consumables sensor data; perform consumables loss impact analysis on the beauty equipment consumables sensor data to generate a consumables status monitoring data set; perform consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; The equipment consumables allocation module is used to evaluate the equipment consumables demand based on the medical beauty equipment monitoring data and generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical beauty equipment monitoring data, and generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using the consumables life warning data, and generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; The management execution module is used to perform equipment management execution on the consumables allocation adjustment plan data set, generate equipment consumables management reports, and execute medical beauty equipment cloud management operations.

[0052] The beneficial effect of the present invention is that by obtaining the medical beauty equipment number and equipment parameters and uploading them to the cloud platform, medical beauty equipment monitoring data is generated, which provides a reliable basis for subsequent equipment status monitoring. The data upload to the cloud platform can synchronize the equipment parameter information in real time, ensure that the status of the equipment during use is continuously traceable, and improve the intelligence and automation level of equipment management. The digital registration of consumables information in step S1 can comprehensively manage consumables digitally through beauty equipment monitoring data to form a digital registration table for beauty equipment consumables. This not only helps to track the use of each device consumable, but also provides reliable data support for subsequent consumable loss analysis, life prediction and management, and realizes the transparency, precision and standardization of consumable management. By collecting the sensor data of beauty equipment consumables and analyzing the impact of loss, a consumable status monitoring data set is generated, which helps to timely identify problems in the use of consumables. At the same time, the consumable life warning data generated based on the monitoring data can predict the life cycle of consumables in advance, help managers plan the replacement and procurement time of consumables, and avoid excessive use or shortage of consumables, thereby improving operational efficiency. By evaluating the consumables demand of the equipment monitoring data and generating a consumables allocation plan based on the evaluation, the timely supply of consumables required by the equipment at different stages of use can be ensured. Further dynamic adjustment plans optimize resource allocation to ensure that each device obtains the right amount of consumables. At the same time, reasonable allocation is made based on the life warning data of the consumables to avoid the waste of expired consumables. By executing the consumables allocation adjustment plan, an equipment consumables management report is generated, which provides a decision-making basis for cloud management operations. Managers can visually understand the use of equipment and consumables through reports and make timely adjustments and optimizations. In addition, the visual reports of equipment and consumables improve the efficiency of collaboration between teams, help managers identify potential problems in a timely manner, and reduce the risk of equipment failure. Therefore, the present invention improves the accuracy and comprehensiveness of cloud management of medical beauty equipment through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform.

[0053] 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.

[0054] 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 cloud management method for medical beauty equipment, characterized in that: The following steps are involved: Step S1: Obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; Step S2: collecting beauty equipment consumables data through a digital registration table of beauty equipment consumables to obtain beauty equipment consumables sensing data; performing consumables loss impact analysis on the beauty equipment consumables sensing data to generate a consumables status monitoring data set; performing consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; Step S3: Evaluate the equipment consumables demand for medical cosmetology equipment monitoring data to generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical cosmetology equipment monitoring data to generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using consumables life warning data to generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; Step S4: Perform equipment management on the consumables allocation adjustment plan data set and generate an equipment consumables management report to perform cloud management operations for medical beauty equipment.

2. The medical cosmetic equipment cloud management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain the medical beauty equipment number and medical beauty equipment parameters; Step S12: performing data preprocessing on the medical cosmetic equipment parameters to generate standard medical cosmetic equipment parameters, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: assigning equipment identification to standard medical beauty equipment parameters based on the medical beauty equipment number to obtain medical beauty equipment identification data; uploading medical beauty equipment parameters to the cloud platform through the medical beauty equipment identification data to generate medical beauty equipment monitoring data; Step S14: Digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables.

3. The medical cosmetic equipment cloud management method according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: collecting basic attributes of equipment consumables information to generate consumables basic information data; performing equipment association processing on the consumables basic information data to generate consumables equipment association data; Step S142: Performing authenticity verification processing on the consumables equipment association table to generate a consumables authenticity verification result; performing environmental parameter monitoring on the consumables basic information data based on the consumables authenticity verification result to generate a consumables environment monitoring data set; Step S143: Perform life cycle initialization processing on the consumables environment monitoring data set to generate consumables life cycle initialization data; perform digital integration processing on the consumables life cycle initialization data to generate a digital registration table for beauty equipment consumables.

4. The medical cosmetic equipment cloud management method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: embedding IoT sensors into beauty equipment consumables using sensors through a digital registration table of beauty equipment consumables, and collecting beauty equipment consumables data based on the embedded IoT sensors to obtain beauty equipment consumables sensing data; Step S22: performing consumable usage status sensing on the beauty equipment consumables sensing data to generate consumable usage status sensing data; performing environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environment monitoring data; performing consumables loss impact analysis based on the consumables usage status sensing data and the consumables environment monitoring data to generate consumables usage impact analysis data; Step S23: transmitting the consumables usage impact analysis data back to the cloud platform for upload synchronization processing to generate a consumables status monitoring data set; Step S24: Use a preset machine learning algorithm to construct a beauty equipment consumable life model for the consumable status monitoring data set to obtain a consumable life prediction model; import the consumable status monitoring data set into the beauty equipment consumable life model to predict the consumable life, thereby generating consumable life warning data.

5. The medical cosmetic equipment cloud management method according to claim 4, characterized in that: Step S22 includes the following steps: Step S221: Performing basic analysis on the sensor data of the beauty equipment consumables to generate analysis results of the consumables sensor data, wherein the basic analysis includes the temperature, pressure, working time and coolant consumption when the equipment is in use; Step S222: extracting the key indicators of the consumables usage status from the consumables sensing data analysis results to obtain the key indicators of the consumables usage status, wherein the extracted key indicators of the consumables usage status include the accumulated working time of the probe and the remaining amount of coolant; marking the key indicators of the consumables usage status in real time to generate consumables usage status perception data; Step S223: Perform environmental parameter monitoring processing on the beauty equipment consumables sensing data to generate consumables environmental monitoring data; extract abnormal environmental parameters from the consumables environmental monitoring data using a preset environmental threshold range to obtain consumables abnormal environmental data; calculate the rate of change of environmental abnormal parameters on the consumables abnormal environmental data to obtain real-time environmental risk trend data; Step S224: Perform a cross-impact analysis on the consumables usage status perception data and the real-time environmental risk trend data to generate consumables usage impact analysis data.

6. The medical cosmetic equipment cloud management method according to claim 5, characterized in that: The cross-impact analysis of consumables usage status perception data and real-time environmental risk trend data includes: Match the consumables usage status perception data with the real-time environmental risk trend data to generate consumables status-environment matching data; Perform high temperature environment screening on the consumables status-environment matching data to generate high temperature environment screening data; perform probe performance rate analysis on the consumables status-environment matching data based on the high temperature environment screening data to generate probe high temperature impact data; Perform low-temperature environment screening on the consumables status-environment matching data to generate low-temperature environment screening data; perform probe start-up time analysis on the consumables status-environment matching data based on the low-temperature environment screening data to generate probe low-temperature impact data; The probe high temperature impact data and the probe low temperature impact data are cross-analyzed to generate consumables usage impact analysis data.

7. The medical cosmetic equipment cloud management method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Performing equipment usage frequency analysis on the medical beauty equipment monitoring data to obtain consumables usage frequency data; performing equipment consumables demand assessment on the consumables usage status perception data through the consumables usage frequency data to generate equipment consumables demand assessment data; Step S32: performing compatibility check based on the equipment consumables demand assessment data to generate consumables compatibility data; performing validity period priority allocation analysis on the consumables status monitoring data set to generate consumables allocation priority data; combining the consumables compatibility data with the allocation priority data to construct a consumables allocation plan to generate a consumables allocation plan data set; Step S33: using the consumable life warning data to divide the consumable allocation scheme data set into consumable life modes, generating unexpired consumable data and expired consumable data; dynamically adjusting the consumable allocation scheme data set based on the unexpired consumable data, generating a consumable allocation adjustment scheme data set; Step S34: Prompt replacement of expired consumables data, thereby generating equipment consumables replacement record data.

8. The medical cosmetic equipment cloud management method according to claim 7, characterized in that: The frequency analysis of medical beauty equipment monitoring data equipment usage includes: Extract the time domain change data of the medical beauty equipment monitoring data to obtain the time domain change data of the equipment; perform fast Fourier transform on the time domain change data of the equipment to generate the frequency domain change data of the equipment; Performing periodic component analysis on the equipment usage frequency domain change data to generate equipment usage frequency domain periodic data; performing equipment usage frequency pattern recognition on the equipment usage frequency domain change data according to the equipment usage frequency domain periodic data to generate equipment usage frequency pattern data; Based on the device usage frequency pattern data, the usage change fluctuation slope of the device usage frequency domain change data is calculated to obtain the device usage change frequency domain fluctuation slope data; the device usage frequency domain change data is segmented into the maximum slope spectrum waveform by the device usage change frequency domain fluctuation slope data to generate the device usage maximum change waveform; The extreme value extraction is performed on the waveform with the largest change in equipment usage to obtain the frequency data of consumables usage, where the extreme value extraction includes peak-valley data extraction and peak-bottom data extraction.

9. The medical cosmetic equipment cloud management method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing equipment management execution on the consumables allocation adjustment plan data set to generate equipment consumables management execution data; Step S42: Visualize the equipment consumables management execution data and the equipment consumables expiration replacement record data, and generate an equipment consumables management report to perform medical beauty equipment cloud management operations.

10. A cloud management system for medical cosmetic equipment, characterized in that: Used to execute the medical cosmetic equipment cloud management method according to claim 1, the medical cosmetic equipment cloud management system comprises: The parameter acquisition module is used to obtain the medical beauty equipment number and medical beauty equipment parameters; upload the medical beauty equipment parameters to the cloud platform based on the medical beauty equipment number to generate medical beauty equipment monitoring data; digitally register the consumables information of the medical beauty equipment monitoring data to generate a digital registration table for beauty equipment consumables; The equipment consumables prediction module is used to collect beauty equipment consumables data through the beauty equipment consumables digital registration table to obtain beauty equipment consumables sensor data; perform consumables loss impact analysis on the beauty equipment consumables sensor data to generate a consumables status monitoring data set; perform consumables life prediction on the consumables status monitoring data set to generate consumables life warning data; The equipment consumables allocation module is used to evaluate the equipment consumables demand based on the medical beauty equipment monitoring data and generate equipment consumables demand evaluation data; construct a consumables allocation plan based on the equipment consumables demand evaluation data and medical beauty equipment monitoring data, and generate a consumables allocation plan data set; dynamically adjust the consumables allocation plan data set using the consumables life warning data, and generate a consumables allocation adjustment plan data set and equipment consumables expiration replacement record data; The management execution module is used to perform equipment management execution on the consumables allocation adjustment plan data set, generate equipment consumables management reports, and execute medical beauty equipment cloud management operations.

Images