Personalized comfort scheme generation method based on radio frequency beauty

Abstract

The invention relates to a personalized comfortable scheme generation method based on radio frequency beauty, and aims to solve the problem that in the prior art, an individual neglects anesthetic metabolism difference and dynamic adjustment intellectualization is insufficient, and the method comprises the following steps: collecting real-time anesthesia reaction data of a target object under the action of test radio frequency energy, analyzing the characteristics of the individual anesthesia absorbability, and generating a personalized comfortable scheme. The features are matched with a pre-established radio frequency beauty scheme strategy library to obtain an adaptive energy application strategy, an initial radio frequency parameter set is determined in combination with skin type parameters, dynamic feedback parameters directly associated with comfort are monitored in real time in the treatment process, and based on the change trend and the accumulated energy dosage, the energy application strategy is determined; and dynamically calculating a parameter adjustment amount through the intelligent adjustment rule set, and generating a real-time adjustment instruction to update subsequent energy output. According to the method, deep personalized customization and dynamic optimization of the radio frequency beauty scheme are realized, and the comfort and safety of the treatment process are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of medical information processing technology, and in particular to a method for generating personalized comfort solutions based on radiofrequency cosmetic procedures. Background Technology

[0002] Radiofrequency (RF) aesthetic techniques, as an important non-invasive method for skin rejuvenation and treatment, have been widely used in clinical practice. The principle is to apply radiofrequency energy at a specific frequency to skin tissue, generating a controllable thermal effect to stimulate collagen regeneration and remodeling, thereby achieving skin tightening, wrinkle reduction, and improved skin texture. However, during treatment, the intensity, duration, and mode of energy application must be matched to individual skin characteristics and tolerance. Inappropriate energy parameters can easily lead to patient pain, burns, or poor therapeutic effects. Therefore, achieving personalized and comfortable treatment is key to enhancing the clinical application value of this technology.

[0003] Currently, conventional personalized treatment plans largely rely on the operator's experience or static skin type classification, lacking real-time quantitative assessment and feedback adjustment of individual physiological responses during dynamic treatment. Although existing technologies have attempted to introduce real-time monitoring parameters such as skin temperature and impedance, these parameters are usually viewed independently and fail to be systematically correlated with the core prerequisite of an individual's metabolic absorption characteristics of pre-operative anesthetics. Individual differences in the absorption rate and stability of anesthetics directly affect their pain threshold and thermal perception to radiofrequency energy stimulation, representing a potentially crucial factor in determining treatment comfort and safety. Ignoring this factor limits the accuracy and adaptability of existing dynamic adjustment strategies.

[0004] Furthermore, existing radiofrequency energy adjustment strategies often rely on preset fixed rules or simple threshold judgments, lacking a closed-loop system capable of integrating multi-source dynamic feedback signals and making intelligent decisions based on historical effective case experience. This leads to lag and one-sidedness in protocol adjustments, making it difficult to maintain optimal comfort levels while ensuring therapeutic efficacy. Therefore, there is an urgent need for a radiofrequency cosmetic protocol generation method that can deeply integrate individual preoperative anesthesia response characteristics, real-time multidimensional physiological feedback, and intelligent strategy matching to achieve truly personalized and comfortable treatment. Summary of the Invention

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] A method for generating personalized comfort solutions based on radiofrequency aesthetics, comprising:

[0007] S1, receives initial anesthesia protocol data for the target cosmetic patient and real-time anesthesia response data obtained after implementing predetermined test radiofrequency energy under the initial anesthesia protocol;

[0008] S2, Based on the real-time anesthesia response data, determine the individual anesthesia absorption characteristics of the target cosmetic subject corresponding to the test radiofrequency energy effect, including absorption rate characteristics and absorption stability characteristics;

[0009] S3, Match the individual anesthesia absorption characteristics with the pre-established radiofrequency cosmetic treatment strategy library to obtain the mapping relationship between different anesthesia absorption characteristic ranges and corresponding radiofrequency energy application strategies;

[0010] S4. Based on the matched radio frequency energy application strategy and combined with the basic parameters of the target cosmetic object's skin type, determine the initial radio frequency parameter set for this radio frequency cosmetic operation, including the initial energy value, initial frequency, and initial single-point action duration.

[0011] S5, after starting the radio frequency energy output according to the initial radio frequency parameter set, monitor and obtain dynamic feedback parameters that are directly related to the comfort of the beauty object in real time;

[0012] S6. Based on the real-time change trend of the dynamic feedback parameters and combined with the currently output cumulative radio frequency energy dose, calculate the dynamic adjustment amount of the initial radio frequency parameter set based on the adjustment rule set.

[0013] S7. Generate a real-time adjustment command for the currently executed radio frequency energy output based on the dynamic adjustment amount, and update the subsequent radio frequency energy output based on the real-time adjustment command to generate and implement a personalized and comfortable radio frequency beauty plan adapted to the individual anesthesia absorption capacity of the target beauty object.

[0014] Furthermore, S2 also includes:

[0015] Extract the first physiological parameter value at a first predetermined time point after the start of the test radio frequency energy application from the physiological parameter sequence, and the second physiological parameter value at a second predetermined time point after the start of the test radio frequency energy application;

[0016] Calculate the rate of change of the ratio of the second physiological parameter value to the first physiological parameter value, and map the rate of change of this ratio to a first quantitative index as a numerical representation of the absorption rate characteristic;

[0017] The fluctuation range of the physiological parameter sequence between the first predetermined time point and the second predetermined time point is analyzed, the standard deviation of the physiological parameter values ​​within this time period is calculated, and the standard deviation is mapped to a second quantitative index as a numerical representation of the absorption stability feature.

[0018] The first quantitative index and the second quantitative index are combined into a feature vector, which serves as the individual anesthesia absorption capacity feature.

[0019] Further, S4 includes:

[0020] Obtain the basic parameters of the skin type of the target beauty object, which at least identify the preset type category to which the skin belongs;

[0021] Based on the skin type category, query the pre-stored skin type-radio frequency basic parameter lookup table to obtain the reference energy value, reference frequency range, and reference duration baseline corresponding to that skin type category;

[0022] Using the basic mode defined by the matched radio frequency energy application strategy as constraints, the reference energy value, reference frequency range, and reference duration baseline are corrected, wherein the correction includes:

[0023] If the basic mode specified by the radio frequency energy application strategy is a progressive enhancement mode, then the reference energy value is multiplied by a first coefficient to obtain the initial energy value, and the reference action duration baseline is divided into multiple incremental action periods;

[0024] The corrected initial energy value, initial frequency, and initial single-point duration are bound together to form the initial radio frequency parameter set.

[0025] Further, S6 includes:

[0026] Continuously collect real-time skin surface temperature, real-time skin impedance value, and real-time pain feedback level from users, and generate temperature data sequence, impedance data sequence, and pain level sequence respectively;

[0027] For a temperature data sequence, calculate its average rate of warming within the most recent time window, and compare the average rate of warming with a first temperature threshold associated with the absorption stability feature in the individual anesthesia absorption characteristics.

[0028] If the average heating rate exceeds the first temperature threshold, a first adjustment amount is generated to reduce the radio frequency energy output;

[0029] If the average heating rate is lower than a second temperature threshold, a second adjustment is generated to increase the radio frequency energy output;

[0030] For the impedance data sequence, its instantaneous drop rate is monitored. When the instantaneous drop rate exceeds a preset impedance change threshold related to the absorption rate characteristic in the individual anesthesia absorption capacity characteristics, a third adjustment amount is generated for instantaneously pausing the radio frequency energy output or significantly reducing the energy value.

[0031] For the pain level sequence, when the user's real-time pain feedback level is detected to exceed the preset level for a predetermined number of consecutive times, a fourth adjustment amount is generated to switch the radio frequency energy output mode or introduce a forced interval.

[0032] The system continuously accumulates the energy values ​​output from the start of radio frequency energy output to the current moment to obtain the current cumulative dose of radio frequency energy output; it also queries a predefined cumulative dose-adjustment attenuation coefficient table to obtain the adjustment intensity attenuation coefficient corresponding to different cumulative dose intervals.

[0033] The first, second, third, or fourth adjustment amount calculated based on the temperature data sequence, impedance data sequence, and pain level sequence is multiplied by the adjustment intensity attenuation coefficient corresponding to the current moment to obtain the final dynamic adjustment amount.

[0034] Furthermore, the steps for obtaining the user's real-time pain feedback level include:

[0035] During the radio frequency energy output process, the user is provided with multiple discrete levels of pain feedback input options through a human-computer interaction interface connected to the computing device;

[0036] When a sudden change is detected in the real-time skin surface temperature or the real-time skin impedance value, a prompt is triggered to prompt the user to provide pain feedback.

[0037] The system receives the pain level selected by the user through the human-computer interaction interface and uses it as the user's real-time pain feedback level at the current moment.

[0038] Furthermore, the radiofrequency beauty solution strategy library is constructed as follows:

[0039] Collect historical radiofrequency cosmetic case data, obtain anesthesia response data of historical subjects, the final radiofrequency energy application strategy that was adopted and marked as comfortable and effective, and the basic skin information of the historical subjects;

[0040] Historical individual anesthesia absorption characteristics were extracted from the anesthesia response data of each historical case;

[0041] Based on historical case data, cluster analysis was used to divide the historical individual anesthesia absorption characteristics into several characteristic intervals.

[0042] For each characteristic interval, the distribution of radio frequency energy application strategies marked as comfortable and effective in historical cases within that interval is statistically analyzed. The radio frequency energy application strategy with the highest frequency or the best overall evaluation is mapped to that characteristic interval and stored in the radio frequency beauty solution strategy library.

[0043] Further, S7 includes:

[0044] Parse the real-time adjustment command to obtain the target radio frequency parameters that need to be adjusted and their corresponding dynamic adjustment amounts;

[0045] Calculate the adjusted target radio frequency parameter value based on the dynamic adjustment amount;

[0046] Without interrupting the current RF energy output cycle or waiting for the current output cycle to end, the corresponding parameters of the RF energy output device are configured to the adjusted target RF parameter values, and the RF energy output of the next cycle continues or begins according to the new parameter values;

[0047] Record the time points of parameter adjustments, the content of the adjustments, and the dynamic feedback parameter status during the adjustments to form a complete adjustment log for this beauty plan.

[0048] Furthermore, before generating and implementing the personalized comfort radio frequency beauty solution, the method also includes a step of adaptively configuring the radio frequency energy output device, specifically including:

[0049] Obtain the initial set of radio frequency parameters determined for the target beauty object and the matched radio frequency energy application strategy;

[0050] Configure the operating frequency range of the radio frequency generator based on the initial frequency in the initial radio frequency parameter set;

[0051] Configure the waveform modulation mode of the radio frequency signal output according to the basic mode specified by the radio frequency energy application strategy;

[0052] Based on the treatment area and contour information of the target cosmetic object, and combined with the initial single-point action duration in the initial radiofrequency parameter set, the optimal radiofrequency electrode movement path and residence time distribution are calculated, and the path and distribution information are sent to the radiofrequency treatment device guidance module that is communicatively connected to the computing device.

[0053] Furthermore, the method also includes the steps of evaluating and storing the treatment plan after the radiofrequency cosmetic procedure is completed:

[0054] After the radiofrequency beauty procedure is completed, collect and record all dynamic feedback parameter data, parameter adjustment logs, and the final endpoint values ​​of skin physiological indicators.

[0055] Based on the collected data, a comprehensive evaluation report of this personalized comfort radiofrequency beauty solution is generated. The report includes at least a solution implementation stability score, a comfort deviation score, and a predicted effectiveness index.

[0056] All input parameters, process data, adjustment records, final parameter set, and comprehensive evaluation report of the personalized comfort radiofrequency beauty solution generated this time will be linked and stored to form new case data.

[0057] After obtaining subsequent efficacy feedback from the target cosmetic patient, the efficacy feedback information is associated with the new case data, and the comfort and effectiveness markers of the solutions in the case data are updated or confirmed based on the efficacy feedback information for subsequent optimization of the strategy library.

[0058] Furthermore, the physiological parameters reflecting the absorption status of the anesthetic agent in the real-time anesthesia response data are transcutaneous electrical impedance or local skin microcirculation blood flow velocity, and the test radiofrequency energy is a pre-stimulation radiofrequency pulse sequence with energy lower than that of conventional treatment.

[0059] This invention relates to a method for generating personalized comfort plans based on radiofrequency aesthetics. Addressing the shortcomings of existing technologies that neglect individual differences in anesthetic metabolism and lack of intelligent dynamic adjustment, this method collects real-time anesthetic response data of the target subject under test radiofrequency energy, analyzes their individual anesthetic absorption characteristics, matches these characteristics with a pre-established radiofrequency aesthetics plan strategy library to obtain an appropriate energy application strategy, and determines the initial radiofrequency parameter set by combining skin type parameters. During treatment, dynamic feedback parameters directly related to comfort are monitored in real time. Based on their changing trends and cumulative energy dose, the parameter adjustment amount is dynamically calculated through an intelligent adjustment rule set, and real-time adjustment instructions are generated to update subsequent energy output. This invention achieves deep personalization and dynamic optimization of radiofrequency aesthetics plans, aiming to significantly improve the comfort and safety of the treatment process. Attached Figure Description

[0060] Figure 1 A flowchart illustrating a method for generating a personalized comfort solution based on radiofrequency aesthetics, as claimed in an embodiment of the present invention.

[0061] Figure 2 A second workflow diagram of a personalized comfort solution generation method based on radiofrequency aesthetics, as claimed in an embodiment of the present invention;

[0062] Figure 3 A third workflow diagram of a personalized comfort solution generation method based on radiofrequency aesthetics, as claimed in an embodiment of the present invention;

[0063] Figure 4 The fourth flowchart is a method for generating a personalized comfort solution based on radiofrequency beauty, as claimed in an embodiment of the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of those features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications in the embodiments of this application, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationships and movements between components in a specific orientation as shown in the accompanying drawings. If the specific orientation changes, the directional indications will change accordingly. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0066] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] According to a first embodiment of the present invention, the present invention claims protection for a method for generating a personalized comfort plan based on radiofrequency aesthetics, referring to... Figure 1 ,include:

[0068] S1, receives initial anesthesia protocol data for the target cosmetic patient and real-time anesthesia response data obtained after implementing predetermined test radiofrequency energy under the initial anesthesia protocol;

[0069] S2, based on real-time anesthesia response data, determines the individual anesthesia absorption characteristics of the target cosmetic subject corresponding to the tested radiofrequency energy effects, including absorption rate characteristics and absorption stability characteristics;

[0070] S3, Match individual anesthesia absorption characteristics with a pre-established radiofrequency cosmetic treatment strategy library to obtain the mapping relationship between different anesthesia absorption characteristic ranges and corresponding radiofrequency energy application strategies;

[0071] S4. Based on the matched radio frequency energy application strategy and combined with the basic parameters of the target cosmetic subject's skin type, determine the initial radio frequency parameter set for this radio frequency cosmetic operation, including the initial energy value, initial frequency, and initial single-point treatment duration.

[0072] S5, after starting the radio frequency energy output according to the initial radio frequency parameter set, monitors and obtains dynamic feedback parameters that are directly related to the comfort of the beauty object in real time;

[0073] S6, based on the real-time change trend of dynamic feedback parameters, combined with the currently output cumulative radio frequency energy dose, calculates the dynamic adjustment amount of the initial radio frequency parameter set based on the adjustment rule set;

[0074] S7 generates a real-time adjustment command for the currently executing radio frequency energy output based on the dynamic adjustment amount, and updates the subsequent radio frequency energy output based on the real-time adjustment command, generating and implementing a personalized and comfortable radio frequency beauty plan adapted to the individual anesthesia absorption capacity of the target beauty subject.

[0075] In this embodiment, the computing device receives initial anesthesia protocol data for the target cosmetic patient about to undergo radiofrequency cosmetic surgery through its data input interface. The initial anesthesia protocol data includes at least the type, concentration, and initial dose information of the anesthetic agent to be administered. Simultaneously, it receives real-time anesthesia response data acquired during a predetermined test radiofrequency energy application to a specific skin area of ​​the target cosmetic patient after the implementation of the initial anesthesia protocol. The test radiofrequency energy application is output by a controllable radiofrequency device with preset fixed values. The real-time anesthesia response data includes physiological parameter measurements collected at multiple predetermined, non-uniform sampling time points after the start of the test radiofrequency energy application, such as 30 seconds, 90 seconds, and 150 seconds after the start of the application. These measurements are arranged in chronological order to form a physiological parameter sequence. The physiological parameters are indicators that can directly or indirectly reflect the absorption and diffusion status of the anesthetic agent in the subcutaneous tissue, specifically the transcutaneous electrical impedance value or the local skin microcirculation blood flow velocity value measured by Doppler flowmeter.

[0076] The processor of the computing device analyzes the physiological parameter sequence. First, it locates and extracts the first physiological parameter value corresponding to the first preset characteristic sampling time point, such as 30 seconds after the start of the test, and the second physiological parameter value corresponding to the second preset characteristic sampling time point, such as 150 seconds after the start of the test. Next, it calculates the degree of change of the second physiological parameter value relative to the first physiological parameter value. This degree of change is quantified by calculating the ratio or percentage difference, and this quantification result is marked as the first feature value. This first feature value characterizes the rate of anesthetic absorption and is called the absorption rate feature value. Then, it analyzes the fluctuation of the physiological parameter sequence across all data points between the first and second preset characteristic sampling time points. The fluctuation amplitude is quantified by calculating the standard deviation or mean absolute deviation of these data points, and this quantification result is marked as the second feature value. This second feature value characterizes the smoothness of the anesthetic absorption process and is called the absorption stability feature value. Finally, the first and second feature values ​​are combined into a two-dimensional vector, which is defined as the individual anesthetic absorption characteristic of the target cosmetic subject.

[0077] The computing device accesses a pre-generated and stored radiofrequency (RF) aesthetic protocol strategy library in its memory. This strategy library is a data lookup table or set of rules, internally divided into multiple entries. Each entry defines the association between a range of individual anesthetic absorbance characteristics and a recommended RF energy application strategy. The RF energy application strategy is a predefined procedural rule guiding how to apply RF energy. Its core content includes at least a basic mode definition for RF energy output, which is a mode label selected from progressive enhancement mode, rapid stabilization mode, and intermittent pulse mode. The computing device compares the obtained individual anesthetic absorbance characteristics with the characteristic value ranges of each entry in the strategy library, identifies the range into which the value falls, and retrieves the RF energy application strategy associated with that range, thus completing the matching process.

[0078] The computing device acquires basic parameters of the target cosmetic patient's skin type, which are determined and stored through pre-assessment, such as a classification identifier ranging from Type I (very sensitive) to Type V (very insensitive). Based on this skin type classification identifier, the computing device queries another pre-stored, static parameter lookup table to obtain a set of reference radiofrequency parameters corresponding to that skin type, including a reference energy value unit: joules, a reference frequency range unit: megahertz, and a reference single-point treatment duration baseline unit: seconds. Then, it applies the parameters according to the basic mode specified by the radiofrequency energy application strategy matched in step S103. As a constraint framework, the retrieved set of reference RF parameters is adaptively modified. For example, if the base mode is a progressive enhancement mode, the energy value is discounted and the total duration is divided into increasing sub-periods. If it is a fast stabilization mode, the reference value is directly adopted. If it is an intermittent pulse mode, the duration of action is redistributed according to the pulse rules. After the modification is completed, the final determined energy value, frequency value, and single-point action duration in the intermittent pulse mode are selected from the reference range according to the rules and encapsulated to form the initial RF parameter set for this RF beauty operation.

[0079] After the radio frequency energy output device is started according to the initial radio frequency parameter set and the formal radio frequency beauty operation is started on the target beauty object, the computing device continuously and in real time monitors and acquires dynamic feedback parameters that are directly related to the comfort perceived by the beauty object during the operation through its connected sensor array and human-computer interaction module.

[0080] These parameters include at least: real-time skin surface temperature measured by a contact temperature sensor in degrees Celsius, real-time skin impedance measured by the built-in circuitry of the radio frequency device in ohms, and real-time subjective pain feedback levels, such as 1-10, which are actively input by the user via a slider or level button on the touchscreen interface.

[0081] The processor of the computing device analyzes the changing trend of at least one dynamic feedback parameter in real time. This analysis includes: calculating the short-term rise or fall rate of real-time skin surface temperature data; monitoring instantaneous drops in real-time skin impedance values; and counting the number of times the user's pain feedback level continuously exceeds a certain threshold. Simultaneously, the processor continuously accumulates the energy value output by the radio frequency device from the start of the operation to the current moment, obtaining the current cumulative dose of radio frequency energy output. The computing device internally stores a predefined set of adjustment rules, which associates different dynamic feedback parameter change patterns with specific radio frequency parameter adjustment actions, such as reducing energy by X%, pausing output for Y seconds, or adjusting the frequency to the lower limit of the Z range. Furthermore, the judgment thresholds in these rules, such as the threshold for excessively fast changes, are related to the personal anesthesia absorption characteristics obtained in step S102, such as absorption stability characteristics. Based on the currently monitored dynamic feedback parameter change patterns, the processor queries the adjustment rule set to determine a preliminary adjustment direction and magnitude. Then, based on the current cumulative dose of radio frequency energy, it queries another attenuation coefficient table related to the personal anesthesia absorption characteristics to obtain an adjustment intensity attenuation coefficient. Multiply the initial adjustment range by the attenuation coefficient to calculate the final dynamic adjustment amount used for actual control commands.

[0082] The computing device generates a specific, formatted real-time adjustment command based on the calculated dynamic adjustment amount. This command is sent to the controller of the radio frequency energy output device via the device control bus. The controller of the radio frequency energy output device parses the command and adjusts its internal operating parameters immediately or at the end of the current output cycle, thereby changing the characteristics of the subsequently output radio frequency energy. This monitoring-calculation-adjustment process is continuously cyclical throughout the entire radio frequency aesthetic procedure, dynamically shaping the entire radio frequency energy output curve, thereby generating and implementing a personalized and comfortable radio frequency aesthetic plan that adapts to the individual anesthesia absorption characteristics of the target aesthetic subject from beginning to end.

[0083] Furthermore, referring to Figure 2 S2 also includes:

[0084] Extract the first physiological parameter value at the first predetermined time point after the start of the test radio frequency energy application from the physiological parameter sequence, and the second physiological parameter value at the second predetermined time point after the start of the test radio frequency energy application;

[0085] Calculate the rate of change of the ratio of the second physiological parameter value to the first physiological parameter value, and map this rate of change to a first quantitative index as a numerical representation of the absorption rate characteristic;

[0086] The fluctuation range of the physiological parameter sequence between the first predetermined time point and the second predetermined time point is analyzed, the standard deviation of the physiological parameter values ​​within this time period is calculated, and the standard deviation is mapped to a second quantitative index as a numerical representation of the absorption stability characteristics.

[0087] The first and second quantitative indicators are combined into a feature vector, which serves as a characteristic of individual anesthesia absorption capacity.

[0088] In this embodiment, the processor reads a sequence of physiological parameters and their corresponding timestamps. Based on features preset in the system configuration, it calculates time point parameters, precisely locates the data entry corresponding to a first predetermined time point in the sequence, and reads the physiological parameter measurement value from that entry, recording it as the first physiological parameter value, denoted as V1. Similarly, it locates the data entry corresponding to a second predetermined time point, denoted as T2, for example, 150 seconds after the start of the test, reads its measurement value, and records it as the second physiological parameter value, denoted as V2. The selection of T1 and T2 aims to capture the early state of anesthetic absorption and the relatively stable state, respectively.

[0089] The processor executes a subroutine to calculate the absorption rate characteristic. This subroutine first calculates the absolute change in V2 relative to V1, ΔV = V2 - V1, and then calculates the percentage of this change relative to V1, P = (ΔV / V1) * 100%. Alternatively, in another implementation, the ratio of V2 to V1, R = V2 / V1, is directly calculated. The calculated percentage P or ratio R is used as a raw rate index. To eliminate dimensions and facilitate subsequent matching, the processor transforms this raw rate index into a standardized numerical range using a linear or piecewise linear mapping function. The transformed value is formally defined as the first quantization index, denoted as Q_rate. The magnitude of Q_rate directly reflects the absorption rate; a larger value indicates a more significant change in physiological parameters during the time interval T1 to T2, i.e., a faster absorption rate.

[0090] The processor executes a subroutine to calculate the absorption stability characteristic. This subroutine first extracts all physiological parameter measurements between T1 and T2 (inclusive of times T1 and T2) from the physiological parameter sequence, forming a subsequence. Next, it calculates the arithmetic mean (Mean) of all data points in this subsequence. Then, it calculates the square of the difference between each data point and this mean, averages these squares, and finally takes the square root to obtain the standard deviation (σ) of the subsequence. The standard deviation (σ) quantifies the dispersion of the data around the mean, i.e., the amplitude of fluctuation. Similarly, for standardization, the processor transforms the calculated standard deviation (σ) to another standardized numerical range using another predefined mapping function. The transformed value is formally defined as the second quantization index, denoted as Q_stability. The magnitude of Q_stability directly reflects the volatility of the absorption process; a larger value indicates more drastic fluctuations in physiological parameters during the time interval T1 to T2, i.e., a more unstable absorption process.

[0091] The processor creates a two-dimensional data structure, using the first quantification metric, Q_rate, as the value of the first dimension and the second quantification metric, Q_stability, as the value of the second dimension. This two-dimensional data structure, namely the ordered pairs Q_rate and Q_stability, is output and stored as the individual anesthetic absorption characteristics of the target cosmetic subject. This feature vector fully characterizes the dynamic characteristics of the subject's anesthetic absorption during the testing phase, providing a precise and quantitative basis for subsequent strategy selection.

[0092] Furthermore, referring to Figure 3 S4 includes:

[0093] Obtain the basic parameters of the target beauty object's skin type, which at least identify the preset type category to which the skin belongs;

[0094] Based on the skin type category, consult the pre-stored skin type-radio frequency basic parameter reference table to obtain the reference energy value, reference frequency range, and reference treatment duration baseline corresponding to that skin type category;

[0095] The reference energy value, reference frequency range, and reference duration baseline are corrected based on the basic mode specified by the matched radio frequency energy application strategy.

[0096] The revisions include:

[0097] If the base mode specified by the radio frequency energy application strategy is the progressive enhancement mode, then the reference energy value is multiplied by a first coefficient to obtain the initial energy value, and the reference action duration baseline is divided into multiple incremental action periods;

[0098] The corrected initial energy value, initial frequency, and initial single-point duration are bound together to form an initial radio frequency parameter set.

[0099] In this embodiment, the computing device retrieves the profile record corresponding to the unique identifier, such as an ID number, of the target cosmetic patient from its stored customer profile database. Skin type information is then read from preset fields in this profile record. This information is stored as basic skin type parameters in a categorized manner, for example, using one of the Roman numerals I to VI of the Fitzpatrick classification, or using other defined classification labels such as a combination of labels like sensitive skin, tolerant skin, oily skin, and dry skin. This step ensures that subsequent parameter settings take into account the skin's basic tolerance and reaction characteristics.

[0100] The computing device accesses a static lookup table in its internal storage, namely the skin type-RF baseline parameter lookup table. This table uses skin type classification as the index key. The processor uses the acquired skin type classification as the query key to perform an exact match lookup in this table; the query result returns three baseline reference parameters associated with that skin type: the first is the reference energy value E_ref, a suggested single-point transmit energy value, typically in joules; the second is the reference frequency range F_min, F_max, an upper and lower limit of a frequency in megahertz, defining the safe and effective range of RF frequencies applicable to that skin type; and the third is the reference single-point application duration baseline T_ref, a suggested reference duration for continuously applying RF energy to a single skin region, in seconds.

[0101] The processor reads the successfully matched radio frequency energy application strategy and extracts its core basic mode attributes. Based on the specific value of this basic mode, it selects and executes one of three preset correction logic paths:

[0102] Path A corresponds to the progressive enhancement mode: This mode is suitable for objects with average or poor absorption stability, aiming to allow the skin to gradually adapt. The processor first multiplies E_ref by a preset first coefficient less than 1, such as 0.7, and the result is used as the initial energy value E_init. Next, the processor uses T_ref as the total planned duration and divides it into multiple consecutive durations according to a preset time-segmentation rule, for example, three segments with a duration ratio of 1:2:3. The RF energy output within each duration will be continuous, but the energy level between different durations can be planned to increase, which falls under the category of subsequent dynamic adjustment. The initial parameter set mainly determines the duration structure; therefore, the initial single-point duration is set to the duration of the first duration, T_init_seg1. For frequency, an intermediate or lower value is selected from the range of F_min and F_max as the initial frequency F_init.

[0103] Path B corresponds to the rapid stabilization mode: This mode is suitable for subjects with good absorption stability and high tolerability, aiming to quickly achieve a stable treatment state; the processor directly assigns E_ref to the initial energy value E_init. The processor calculates and determines a frequency value as the initial frequency F_init from the range of F_min and F_max, according to a rule for selecting the median value, such as taking the arithmetic mean. The processor directly assigns T_ref to the initial single-point treatment duration T_init. Parameter settings in this path are direct and aggressive.

[0104] Path C corresponds to the intermittent pulse mode: This mode is suitable for subjects with particularly fast absorption rates or those sensitive to pain, reducing heat accumulation and discomfort through intermittent output. The processor uses E_ref as a base, possibly multiplied by a coefficient close to 1 or directly as the initial energy value E_init. The processor selects a value from the range F_min and F_max as the initial frequency F_init. The key correction lies in the handling of duration. The processor treats T_ref as a total time window. Based on a preset pulse duty cycle rule, for example, a duty cycle of 50% (half output time and half intermittent time), and a preset pulse period length, the total window T_ref is divided into multiple sequences of alternating energy output periods and intermittent periods. The initial single-point duration T_init in the initial RF parameter set is explicitly defined here as the duration of each energy output period, not the total duration. For example, if the period is 4 seconds and the duty cycle is 50%, then T_init = 2 seconds.

[0105] After correcting E_ref, (F_min, F_max), and T_ref according to the selected path, and calculating the determined E_init, F_init, and T_init, the processor creates a new data structure. This data structure contains three well-defined fields: Field 1: Energy value, with a value of E_init; Field 2: Frequency, with a value of F_init; Field 3: Single-point duration, with a value of T_init. This data structure is named the Initial RF Parameter Set and output to the system's parameter buffer, ready for configuring the RF equipment and initiating the formal RF cosmetic procedure.

[0106] Further, S6 includes:

[0107] Continuously collect real-time skin surface temperature, real-time skin impedance value, and real-time pain feedback level from users, and generate temperature data sequence, impedance data sequence, and pain level sequence respectively;

[0108] For a temperature data sequence, calculate its average rate of warming within the most recent time window and compare the average rate of warming with a first temperature threshold associated with the absorption stability feature in the individual anesthesia absorption characteristics.

[0109] If the average heating rate exceeds the first temperature threshold, a first adjustment amount is generated to reduce the RF energy output;

[0110] If the average heating rate is below a second temperature threshold, a second adjustment is generated to increase the RF energy output;

[0111] For impedance data sequences, monitor their instantaneous drop rate. When the instantaneous drop rate exceeds a preset impedance change threshold related to the absorption rate characteristic in the individual's anesthesia absorption capacity, generate a third adjustment amount for instantaneously pausing radiofrequency energy output or significantly reducing the energy value.

[0112] For the pain level sequence, when the user's real-time pain feedback level is detected to exceed the preset level for a predetermined number of consecutive times, a fourth adjustment amount is generated to switch the radio frequency energy output mode or introduce a forced interval.

[0113] The system continuously accumulates the energy values ​​output from the start of radio frequency energy output to the current moment to obtain the current cumulative dose of radio frequency energy output; it also queries a predefined cumulative dose-adjustment attenuation coefficient table to obtain the adjustment intensity attenuation coefficient corresponding to different cumulative dose intervals.

[0114] The first, second, third, or fourth adjustment amount, calculated based on the temperature data sequence, impedance data sequence, and pain level sequence, is multiplied by the adjustment intensity attenuation coefficient corresponding to the current moment to obtain the final dynamic adjustment amount.

[0115] In this embodiment, the computing device continuously reads data from the temperature sensor, impedance measurement circuit, and human-computer interaction interface at a fixed sampling frequency, such as 10Hz, through its data acquisition module. After being timestamped, this data is sent to three independent data buffers: the first buffer stores real-time skin surface temperature data in chronological order, forming a temperature data sequence; the second buffer stores real-time skin impedance value data in chronological order, forming an impedance data sequence; and the third buffer stores real-time pain feedback level data of the user in event order whenever the user submits feedback, forming a pain level sequence. Each sequence maintains a sliding window of a preset length for recent trend analysis.

[0116] The processor executes an analysis subroutine for the temperature data sequence. This subroutine extracts all temperature data points within the most recent N seconds, for example 5 seconds, from the sequence, and uses the linear regression method to calculate the slope of the straight line fitted by these points. This slope is the average heating rate, denoted as Rate_temp. The processor calls a temperature threshold query function, which takes the absorption stability eigenvalue Q_stability in the individual anesthesia absorption characteristics determined in step S102 as the input parameter. Since the object with poor absorption stability may have relatively weak skin thermoregulation ability, this function outputs a relatively low warning threshold. Specifically, this function returns two thresholds: a higher first temperature threshold Th_high_temp and a lower second temperature threshold Th_low_temp. Subsequently, a comparison and judgment are made: if Rate_temp > Th_high_temp, it is determined that the temperature rise is too fast, there is a risk of burns or an enhanced sense of discomfort, and the processor generates a first adjustment amount object. This object contains the following information: the adjustment target parameter is the energy value, the adjustment operation is to decrease, and the adjustment amplitude is a preset value A1 proportional to the degree by which Rate_temp exceeds Th_high_temp. If Rate_temp < Th_low_temp, it is determined that the temperature rise is too slow, which may affect the therapeutic effect, and the processor generates a second adjustment amount object. Its adjustment target parameter is also the energy value, but the adjustment operation is to increase, and the amplitude is a preset value A2. If Rate_temp is between the two, no temperature-based adjustment amount is generated.

[0117] The processor executes another analysis subroutine for the impedance data sequence. This subroutine calculates the instantaneous change rate of the impedance value in real time, that is, the difference between the current sampling value and the previous sampling value divided by the sampling time interval. The processor calls an impedance change threshold query function, which takes the absorption rate eigenvalue Q_rate in the individual anesthesia absorption characteristics as the input parameter. For an object with a fast absorption rate, its impedance may change more violently under the action of the anesthetic, so this function outputs a relatively high impedance change threshold Th_imp_change. When it is monitored that the absolute value of the negative instantaneous decrease rate of the impedance exceeds Th_imp_change, it is determined that an instantaneous impedance drop has occurred. This usually means that there is a drastic change in the electrical properties of the skin tissue, which may be related to local overheating or a sudden change in tissue state. At this time, the processor immediately generates a third adjustment amount object. The information contained in this object is: the adjustment target parameter is the energy output state, the adjustment operation is to instantaneously pause or suddenly reduce the energy value to a safe level, and the duration of the adjustment is a preset fixed duration of Y seconds, or until the impedance value returns to stability.

[0118] The processor executes an analysis subroutine for the pain level sequence. This subroutine maintains a counter to record the number of consecutive feedbacks exceeding a preset pain level threshold, for example, level ≥ 7. Whenever a new pain level is received, the processor determines: if the new level is ≥ the threshold, the counter is incremented by 1; if the new level is < the threshold, the counter is reset to zero. When the counter reaches a preset consecutive count threshold, for example, 3 consecutive times, the processor determines that the user is in a state of persistent high pain. The processor then generates a fourth adjustment object. This object's adjustment strategy is more macroscopic: it may include switching the current energy output mode to a milder mode, such as switching from a fast, steady mode to an intermittent pulse mode, or immediately inserting a forced interval into the current output, the duration of which is longer than a regular interval.

[0119] While performing the above analysis in parallel, the processor continuously obtains the energy values ​​of the actual output energy pulses from the RF device controller and sums them to obtain the cumulative RF energy dose from the start of the operation to the current moment, denoted as D_acc. The processor accesses a cumulative dose-adjustment attenuation coefficient table. This table is a two-dimensional table, where the row index is the dose range of the cumulative dose D_acc, for example, 0-100J, 100-200J, ..., and the column index is related to the individual's anesthetic absorption characteristics, especially a rough classification of absorption stability. For the characteristic classification of poor absorption stability, within the same cumulative dose range, the corresponding adjustment intensity attenuation coefficient K_att value is smaller, and K_att decreases faster as the dose range increases. This design means that for unstable skin, when the cumulative dose is high in the later stages of treatment, the parameter adjustment amplitude made by the system to the feedback will be attenuated to a greater extent to avoid over-adjustment causing new instability. Based on the current D_acc and the individual's anesthetic absorption characteristics, the processor looks up the current K_att, a number between 0 and 1.

[0120] Within any given calculation cycle, the processor may obtain zero or one or more adjustment quantity objects from steps S1062, S1063, and S1064. The processor employs a priority logic: safety-related adjustments, such as the third adjustment quantity based on impedance drop, have the highest priority; followed by discomfort-relieving adjustments, such as the fourth adjustment quantity based on pain; and finally, comfort-optimizing adjustments, such as the first or second adjustment quantity based on temperature. In each cycle, the processor selects the highest-priority valid adjustment quantity object as the adjustment quantity to be executed. Then, the processor performs a composite calculation: multiplying the adjustment magnitude recorded in the adjustment quantity object, such as A1, A2, or a specific energy value or time value, with the retrieved current adjustment intensity attenuation coefficient K_att. The result is the final dynamic adjustment quantity. For example, if the adjustment quantity to be executed is a 15% reduction in energy, and the current K_att = 0.8, then the final dynamic adjustment quantity is a 12% reduction in energy. This final dynamic adjustment quantity is then passed to step S107 to generate control instructions.

[0121] Furthermore, the steps for obtaining the user's real-time pain feedback level include:

[0122] During the radio frequency energy output process, the user is provided with multiple discrete levels of pain feedback input options through a human-computer interaction interface connected to a computing device;

[0123] When a sudden change in real-time skin surface temperature or real-time skin impedance value is detected, a prompt is triggered for the user to provide pain feedback.

[0124] It receives the pain level selected by the user through the human-computer interaction interface and uses it as the user's real-time pain feedback level at the current moment.

[0125] In this embodiment, a dedicated real-time pain feedback panel is pre-loaded and displayed on the human-computer interaction screen of the computing device. This panel includes a clear visual analog scale, such as a horizontal scale line marked with levels 0 (no pain) and 10 (severe pain), and a slider that the user can drag. Alternatively, the panel may display a set of evenly spaced buttons labeled with numbers such as 1 to 10. The panel also provides clear text prompts to guide the user on how to respond when experiencing changes in pain.

[0126] Feedback triggering does not depend on a fixed time interval, but is driven by the system's status monitoring module. There are two specific triggering conditions: The first is time-cycle triggering, where the system sets a relatively long base feedback cycle, for example, every 60 seconds, periodically displaying a prompt in a prominent position on the screen, asking the user to confirm or update the current pain level. The second is event triggering, where, in step S1062 or S1063, the processor detects that the average heating rate Rate_temp exceeds a minor warning threshold, which is lower than the major threshold for generating the adjustment amount, or detects that the absolute value of the instantaneous rate of change of impedance exceeds a minor warning threshold. In this case, the system immediately interrupts the current display, forcibly pops up the real-time pain feedback panel, and provides an audible or vibration prompt, requesting the user to provide immediate pain feedback.

[0127] Users submit their subjective pain level by moving a slider to a specific mark or clicking a number button. The input processing module of the computing device captures this interaction and reads the corresponding level value. The processor then adds a precise timestamp to this feedback data and appends it as a new data point to the end of the pain level sequence. Simultaneously, the system closes the feedback panel and resumes normal operation. This combination of active and passive triggering ensures that the pain feedback data closely reflects the actual physical changes in the skin, providing high-quality input for subsequent analysis.

[0128] Furthermore, referring to Figure 4 The construction method of the radiofrequency beauty solution strategy library is as follows:

[0129] Collect historical radiofrequency cosmetic case data, obtain anesthesia response data of historical subjects, the final radiofrequency energy application strategy that was adopted and marked as comfortable and effective, and the basic skin information of the historical subjects;

[0130] Historical individual anesthesia absorption characteristics were extracted from the anesthesia response data of each historical case;

[0131] Based on historical case data, cluster analysis was used to divide the historical individual anesthesia absorption characteristics into several characteristic intervals.

[0132] For each characteristic interval, the distribution of radio frequency energy application strategies marked as comfortable and effective in historical cases within that interval is statistically analyzed. The radio frequency energy application strategy with the highest frequency or the best overall evaluation is mapped to that characteristic interval and stored in the radio frequency beauty solution strategy library.

[0133] In this embodiment, historical data after desensitization is systematically collected from multiple past, completed radiofrequency cosmetic cases. The data package for each case must contain three complete parts: the first part is historical anesthesia response data, which is the sequence of physiological parameters recorded when the historical subject was subjected to radiofrequency energy application similar to that in step S101 before the treatment of the historical subject; the second part is historical effective strategies, which is a complete description of the actual radiofrequency energy application strategies that were ultimately marked as comfortable and effective by clinical experts throughout the treatment process of the case, including its basic mode and key parameter range; the third part is basic information of the historical subject, including at least the skin type classification of the historical subject.

[0134] For each historical case collected, the historical individual anesthesia absorption capacity characteristics of that historical subject are extracted and calculated from its historical anesthesia response data, i.e., the physiological parameter sequence, which is a two-dimensional feature vector H_rate and H_stability.

[0135] Historical anesthesia absorption feature vectors extracted from all historical cases are compiled into a multidimensional dataset. Unsupervised clustering analysis methods, such as K-means clustering or DBSCAN clustering, are then used to automatically analyze this dataset. The goal of this analysis is to divide the entire feature space into K non-overlapping feature intervals based on the distribution density and distance of the feature vectors in two-dimensional space. Each feature interval corresponds to a cluster, which can be mathematically described by the coordinates of its cluster center and the radius or boundary range of the cluster. These intervals constitute the classification basis of the strategy library.

[0136] For each feature interval (cluster) defined in step Lib3, the following steps are performed: First, identify all historical cases where anesthesia absorption characteristics fall within that interval. Then, examine the historically effective strategies corresponding to these cases, statistically analyzing the frequency of various basic patterns such as gradual enhancement and rapid stabilization. The most frequently occurring basic pattern is identified as the recommended radiofrequency energy application strategy associated with that feature interval. If the frequencies are similar, an additional evaluation dimension is introduced, such as the average comfort score of cases under that pattern, selecting the optimal pattern based on comprehensive evaluation. Finally, a record is created in the strategy library, explicitly establishing a one-to-one mapping between the mathematical description of this feature interval and the recommended radiofrequency energy application strategy, and stored in the library. This construction process ensures that the recommendations in the strategy library are based on real and effective historical experience data.

[0137] Further, S7 includes:

[0138] Parse the real-time adjustment command to obtain the target RF parameters that need to be adjusted and their corresponding dynamic adjustment values;

[0139] Calculate the adjusted target RF parameter values ​​based on the dynamic adjustment amount;

[0140] Without interrupting the current RF energy output cycle or waiting for the current output cycle to end, the corresponding parameters of the RF energy output device are configured to the adjusted target RF parameter values, and the RF energy output of the next cycle continues or begins based on the new parameter values;

[0141] Record the time points of parameter adjustments, the content of the adjustments, and the dynamic feedback parameter status during the adjustments to form a complete adjustment log for this beauty plan.

[0142] In this embodiment, the real-time adjustment command generated by the instruction generation module of the computing device is a structured data packet. Upon receiving this data packet, the microcontroller built into the RF energy output device initiates a parsing routine. The parsing routine extracts key fields from the data packet, primarily including: target parameter identifier, adjustment operation type, and adjustment amount value.

[0143] The microcontroller calculates the new parameter value after adjustment based on the parsed adjustment operation type and adjustment amount, combined with the current operating value of the target parameter. For example, if the target parameter is output power, the current value is 50 watts, the adjustment operation is a reduction, and the adjustment amount is 10%, then the new parameter value is calculated as 50 * (1 - 0.1) = 45 watts. The calculation process follows the arithmetic logic preset in the firmware.

[0144] After calculating the new parameter values, the microcontroller will not immediately interrupt any ongoing RF energy output cycles to avoid current surges or waveform distortion. It first determines the current output status: if the device is in the interval between two output pulses, it immediately writes the new parameter values ​​into the corresponding hardware control registers, such as the DAC register to set the voltage to control power, the PLL register to set the frequency, and the timer reload value to set the duration. If the device is in an output pulse period, the microcontroller will wait for the current pulse cycle to end naturally and trigger the interrupt flag by the hardware timer. In the interrupt service routine at the end of the current pulse cycle, the microcontroller quickly writes the new parameter values ​​into the relevant registers. Subsequently, the device starts the energy output of the next pulse or the next working cycle based on the updated register parameters.

[0145] After successfully updating the parameters and starting the new output, the microcontroller or the host computing device communicating with it records this adjustment event in an operation log. The log entry includes at least: timestamp, a snapshot of the dynamic feedback parameters at the time of adjustment trigger, such as the temperature and impedance value at that time, the name of the parameter being adjusted, the value before adjustment, the value after adjustment, and the adjustment instruction ID on which it is based. These log entries are arranged in chronological order, and finally form a complete adjustment log of this beauty solution for post-event analysis and solution review.

[0146] Furthermore, before generating and implementing a personalized, comfortable radiofrequency beauty solution, the method also includes a step of adaptively configuring the radiofrequency energy output device, specifically including:

[0147] Obtain the initial set of radio frequency parameters determined for the target cosmetic object and the matched radio frequency energy application strategy;

[0148] Configure the operating frequency range of the RF generator based on the initial frequency in the initial RF parameter set;

[0149] Configure the waveform modulation mode of the RF signal output according to the basic mode specified by the RF energy application strategy;

[0150] Based on the treatment area and contour information of the target cosmetic patient, and combined with the initial single-point action duration in the initial radiofrequency parameter set, the optimal radiofrequency electrode movement path and residence time distribution are calculated, and this path and distribution information is sent to the radiofrequency treatment device guidance module that is connected to the computing device.

[0151] In this embodiment, the computing device sends a series of configuration commands to the core component of the RF energy output device—the RF generator—via the device control bus. First, based on the initial frequency F_init determined from the initial RF parameter set, the RF generator's frequency synthesizer is commanded to lock at this specific frequency value, F_init, with a small frequency tolerance range set. Second, according to the basic mode specified in the matched RF energy application strategy, the waveform modulation mode of the RF signal is configured. Specifically, if the basic mode is a progressive enhancement mode or a fast stabilization mode, it is configured as a continuous wave mode, i.e., outputting an unmodulated continuous sine wave; if the basic mode is an intermittent pulse mode, it is configured as a pulse wave mode, and further, based on the pulse period and duty cycle determined in the previous step, the period and duty cycle parameters of the pulse width modulator are set.

[0152] The computing device acquires digital information about the treatment area of ​​the target cosmetic patient. This information is obtained through pre-treatment photographic modeling or manual delineation, and includes at least the area and boundary contour of the treatment area. The device processor runs a rule-based path planning algorithm, which is not trained. This algorithm takes the treatment area area, contour, and initial single-point treatment duration T_init from the initial radiofrequency parameter set as its core inputs. Its planning logic is as follows: to uniformly cover the treatment area within a predetermined total time, the treatment area needs to be gridded, and the sequence of path points that the radiofrequency treatment handle containing the electrodes needs to move along, as well as the dwell time required at each path point (i.e., each single point), needs to be calculated. T_init serves as the baseline value for the dwell time at each point, and the algorithm will fine-tune it according to the grid point density and regional curvature, such as slightly shorter times for edge areas. Finally, the optimal radiofrequency electrode movement path and the dwell time distribution table for each point are generated. The computing device sends this path and dwell time schedule to a radio frequency treatment device guidance module that is separate from or integrated into the device via a communication interface such as Bluetooth or Wi-Fi. The guidance module can be an automated arm with a motor drive or a smart handle with screen prompts and haptic feedback to guide the operator to perform the treatment according to the plan.

[0153] Based on the individual characteristics of the patient, the computing device provides hardware selection suggestions to the operator based on the absorption stability characteristic value Q_stability obtained from the individual's anesthesia absorption characteristics. Specifically, if Q_stability is below a preset stability threshold, indicating that the patient's skin is sensitive to energy distribution uniformity, the system suggests via the user interface the selection of multi-polar radiofrequency electrodes with multi-point uniform discharge characteristics, such as quadrupole or hexapole radiofrequency heads. These electrodes can emit energy simultaneously from multiple directions, forming a more uniform and superficial thermal field in the tissue, reducing the risk of hot spots, and are suitable for unstable skin. Conversely, for patients with high stability, the system suggests using traditional monopolar or bipolar electrodes. Furthermore, the computing device configures the initial state of the electrodes according to the matched radiofrequency energy application strategy. For example, in progressive enhancement mode, the system controls the thermoelectric cooler (TEC) integrated with the electrode to pre-cool the initial temperature of the electrode-skin contact surface to a relatively low value, such as 20°C, to allow for subsequent progressive heating. In rapid stabilization mode, the contact surface temperature may be preheated to near skin temperature, such as 32°C, to quickly enter an effective treatment state. These configuration instructions are sent to the radiofrequency treatment handpiece via the control bus and are executed before treatment begins.

[0154] Furthermore, the method also includes steps for protocol evaluation and storage after the radiofrequency cosmetic procedure is completed:

[0155] After the radiofrequency beauty procedure is completed, collect and record all dynamic feedback parameter data, parameter adjustment logs, and the final endpoint values ​​of skin physiological indicators.

[0156] Based on the collected data, a comprehensive evaluation report of this personalized comfort radiofrequency beauty solution is generated. The report includes at least the solution implementation stability score, comfort deviation score, and estimated effectiveness index.

[0157] All input parameters, process data, adjustment records, final parameter set, and comprehensive evaluation report of the personalized comfort radiofrequency beauty solution generated this time will be linked and stored to form new case data;

[0158] After obtaining follow-up treatment feedback from the target cosmetic patients, the treatment feedback information is linked to new case data, and the comfort and effectiveness markers of the treatments in the case data are updated or confirmed based on the treatment feedback information, so as to be used for subsequent optimization of the strategy library.

[0159] In this embodiment, the processor of the computing device accesses all temporary data storage areas created during the operation and systematically collects and merges them. The collected data includes: raw sampled data from all dynamic feedback parameters throughout the timeline; all recorded parameter adjustment log entries; and endpoint values ​​of skin physiological indicators obtained from the last sensor measurement at the end of the operation, serving as the basis for predicting therapeutic efficacy, such as skin temperature, erythema index, or skin elastography measurements immediately after the operation. The processor integrates this time-series data, event log data, and endpoint snapshot data into a unified dataset for this operation.

[0160] The processor runs an evaluation report generation module that takes the dataset from this operation as input and automatically calculates and generates a structured, personalized, comprehensive evaluation report of the comfort radiofrequency beauty solution. This report includes several quantitative scoring items: a) Solution execution stability score: calculated based on the adjustment frequency and amplitude of radiofrequency energy output parameters throughout the operation; fewer adjustments and smaller amplitudes result in a higher score; b) Comfort deviation score: comprehensively assessed based on the average and maximum values ​​of the user's pain feedback level sequence, as well as the number of times the pain level exceeded the moderate pain threshold; c) Estimated effectiveness index: based on the percentage improvement of endpoint skin physiological indicators relative to the pre-operation baseline, combined with the cumulative total energy dose, an estimated effectiveness score is calculated using an empirical formula. The report also includes summaries of key events, such as maximum temperature and maximum impedance change.

[0161] The processor creates a new, complete radiofrequency cosmetic procedure case record. This record is a composite data structure that links and stores all the core information of this operation: input information includes initial anesthesia protocol data, individual anesthesia absorption characteristics, skin type, process information, initial radiofrequency parameter set, matched strategy identifier, complete dataset of this operation, and result information, including a comprehensive evaluation report. This new case record is assigned a unique case ID and stored in the historical case database.

[0162] The system provides an interface that allows users to input feedback on the treatment's effectiveness from the target patient several days or weeks later. This feedback can be subjective satisfaction ratings or objective retested skin indicators. When the operator inputs this feedback through the system interface, the system associates it with the corresponding radiofrequency aesthetic treatment case record stored in step S1093 based on the case ID. Simultaneously, based on the feedback, the system automatically or with operator confirmation marks the case record as comfortable and effective. This newly marked comfortable and effective case serves as a valuable data source for future periodic optimization, supplementation, or validation of the radiofrequency aesthetic treatment strategy library; however, updating the library itself is an independent, non-real-time management process.

[0163] Furthermore, the physiological parameters reflecting the absorption status of anesthetic agents in the real-time anesthesia response data are transcutaneous impedance or local skin microcirculation blood flow velocity, and the tested radiofrequency energy is a pre-stimulation radiofrequency pulse sequence with energy lower than that of conventional treatment.

[0164] In this embodiment, the physiological parameter reflecting the absorption status of the anesthetic is preferably transcutaneous electrical impedance tomography (TEPH). It is measured using a separate, low-power AC impedance measurement probe, either adjacent to or alternately across the same skin area where the test radiofrequency energy is applied. A decrease in impedance is typically associated with increased tissue fluid and changes in ion concentration, indirectly reflecting the penetration and distribution of the anesthetic. The test radiofrequency energy is specifically defined as a series of pre-stimulation radiofrequency pulses output by a radiofrequency energy output device. The energy value of this pulse sequence is fixed at 20% to 40% of the conventional treatment energy value, with a fixed pulse frequency, fixed pulse width, and a fixed total duration, for example, 180 seconds. Its purpose is not to produce a therapeutic effect, but rather to provide a standardized, low-intensity thermal and electrical stimulation background to elicit and observe the dynamic response of the anesthetic within the skin tissue, thereby obtaining a discriminative sequence of physiological parameters for feature extraction without substantially affecting the skin or interfering with subsequent formal treatment.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0167] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for generating personalized comfort solutions based on radiofrequency aesthetics, characterized in that, include: S1, receives initial anesthesia protocol data for the target cosmetic patient and real-time anesthesia response data obtained after implementing predetermined test radiofrequency energy under the initial anesthesia protocol; S2, Based on the real-time anesthesia response data, determine the individual anesthesia absorption characteristics of the target cosmetic subject corresponding to the test radiofrequency energy effect, including absorption rate characteristics and absorption stability characteristics; S3, Match the individual anesthesia absorption characteristics with the pre-established radiofrequency cosmetic treatment strategy library to obtain the mapping relationship between different anesthesia absorption characteristic ranges and corresponding radiofrequency energy application strategies; S4. Based on the matched radio frequency energy application strategy and combined with the basic parameters of the target cosmetic object's skin type, determine the initial radio frequency parameter set for this radio frequency cosmetic operation, including the initial energy value, initial frequency, and initial single-point action duration. S5, after starting the radio frequency energy output according to the initial radio frequency parameter set, monitor and obtain dynamic feedback parameters that are directly related to the comfort of the beauty object in real time; S6. Based on the real-time change trend of the dynamic feedback parameters and combined with the currently output cumulative radio frequency energy dose, calculate the dynamic adjustment amount of the initial radio frequency parameter set based on the adjustment rule set. S7. Generate a real-time adjustment command for the currently executed radio frequency energy output based on the dynamic adjustment amount, and update the subsequent radio frequency energy output based on the real-time adjustment command to generate and implement a personalized and comfortable radio frequency beauty plan adapted to the individual anesthesia absorption capacity of the target beauty object.

2. The method according to claim 1, characterized in that, The S2 further includes: Extract the first physiological parameter value at a first predetermined time point after the start of the test radio frequency energy application from the physiological parameter sequence, and the second physiological parameter value at a second predetermined time point after the start of the test radio frequency energy application; Calculate the rate of change of the ratio of the second physiological parameter value to the first physiological parameter value, and map the rate of change of this ratio to a first quantitative index as a numerical representation of the absorption rate characteristic; The fluctuation range of the physiological parameter sequence between the first predetermined time point and the second predetermined time point is analyzed, the standard deviation of the physiological parameter values ​​within this time period is calculated, and the standard deviation is mapped to a second quantitative index as a numerical representation of the absorption stability feature. The first quantitative index and the second quantitative index are combined into a feature vector, which serves as the individual anesthesia absorption capacity feature.

3. The method according to claim 1, characterized in that, The S4 includes: Obtain the basic parameters of the skin type of the target beauty object, which at least identify the preset type category to which the skin belongs; Based on the skin type category, query the pre-stored skin type-radio frequency basic parameter lookup table to obtain the reference energy value, reference frequency range, and reference duration baseline corresponding to that skin type category; Using the basic mode defined by the matched radio frequency energy application strategy as constraints, the reference energy value, reference frequency range, and reference duration baseline are corrected, wherein the correction includes: If the basic mode specified by the radio frequency energy application strategy is a progressive enhancement mode, then the reference energy value is multiplied by a first coefficient to obtain the initial energy value, and the reference action duration baseline is divided into multiple incremental action periods; The corrected initial energy value, initial frequency, and initial single-point duration are bound together to form the initial radio frequency parameter set.

4. The method according to claim 1, characterized in that, The S6 includes: Continuously collect real-time skin surface temperature, real-time skin impedance value, and real-time pain feedback level from users, and generate temperature data sequence, impedance data sequence, and pain level sequence respectively; For a temperature data sequence, calculate its average rate of warming within the most recent time window, and compare the average rate of warming with a first temperature threshold associated with the absorption stability feature in the individual anesthesia absorption characteristics. If the average heating rate exceeds the first temperature threshold, a first adjustment amount is generated to reduce the radio frequency energy output; If the average heating rate is lower than a second temperature threshold, a second adjustment is generated to increase the radio frequency energy output; For the impedance data sequence, its instantaneous drop rate is monitored. When the instantaneous drop rate exceeds a preset impedance change threshold related to the absorption rate characteristic in the individual anesthesia absorption capacity characteristics, a third adjustment amount is generated for instantaneously pausing the radio frequency energy output or significantly reducing the energy value. For the pain level sequence, when the user's real-time pain feedback level is detected to exceed the preset level for a predetermined number of consecutive times, a fourth adjustment amount is generated to switch the radio frequency energy output mode or introduce a forced interval. The system continuously accumulates the energy values ​​output from the start of radio frequency energy output to the current moment to obtain the current cumulative dose of radio frequency energy output; it also queries a predefined cumulative dose-adjustment attenuation coefficient table to obtain the adjustment intensity attenuation coefficient corresponding to different cumulative dose intervals. The first, second, third, or fourth adjustment amount calculated based on the temperature data sequence, impedance data sequence, and pain level sequence is multiplied by the adjustment intensity attenuation coefficient corresponding to the current moment to obtain the final dynamic adjustment amount.

5. The method according to claim 4, characterized in that, The steps for obtaining the user's real-time pain feedback level include: During the radio frequency energy output process, the user is provided with multiple discrete levels of pain feedback input options through a human-computer interaction interface connected to the computing device; When a sudden change is detected in the real-time skin surface temperature or the real-time skin impedance value, a prompt is triggered to prompt the user to provide pain feedback. The system receives the pain level selected by the user through the human-computer interaction interface and uses it as the user's real-time pain feedback level at the current moment.

6. The method according to claim 1, characterized in that, The radio frequency beauty solution strategy library is constructed as follows: Collect historical radiofrequency cosmetic case data, obtain anesthesia response data of historical subjects, the final radiofrequency energy application strategy that was adopted and marked as comfortable and effective, and the basic skin information of the historical subjects; Historical individual anesthesia absorption characteristics were extracted from the anesthesia response data of each historical case; Based on historical case data, cluster analysis was used to divide the historical individual anesthesia absorption characteristics into several characteristic intervals. For each characteristic interval, the distribution of radio frequency energy application strategies marked as comfortable and effective in historical cases within that interval is statistically analyzed. The radio frequency energy application strategy with the highest frequency or the best overall evaluation is mapped to that characteristic interval and stored in the radio frequency beauty solution strategy library.

7. The method according to claim 1, characterized in that, The S7 includes: Parse the real-time adjustment command to obtain the target radio frequency parameters that need to be adjusted and their corresponding dynamic adjustment amounts; Calculate the adjusted target radio frequency parameter value based on the dynamic adjustment amount; Without interrupting the current RF energy output cycle or waiting for the current output cycle to end, the corresponding parameters of the RF energy output device are configured to the adjusted target RF parameter values, and the RF energy output of the next cycle continues or begins according to the new parameter values; Record the time points of parameter adjustments, the content of the adjustments, and the dynamic feedback parameter status during the adjustments to form a complete adjustment log for this beauty plan.

8. The method according to claim 1, characterized in that, Before generating and implementing the personalized comfort radio frequency beauty solution, the method further includes a step of adaptively configuring the radio frequency energy output device, specifically including: Obtain the initial set of radio frequency parameters determined for the target beauty object and the matched radio frequency energy application strategy; Configure the operating frequency range of the radio frequency generator based on the initial frequency in the initial radio frequency parameter set; Configure the waveform modulation mode of the radio frequency signal output according to the basic mode specified by the radio frequency energy application strategy; Based on the treatment area and contour information of the target cosmetic object, and combined with the initial single-point action duration in the initial radiofrequency parameter set, the optimal radiofrequency electrode movement path and residence time distribution are calculated, and the path and distribution information are sent to the radiofrequency treatment device guidance module that is communicatively connected to the computing device.

9. The method according to claim 1, characterized in that, The method also includes steps for protocol evaluation and storage after the radiofrequency cosmetic procedure is completed: After the radiofrequency beauty procedure is completed, collect and record all dynamic feedback parameter data, parameter adjustment logs, and the final skin physiological endpoint values. Based on the collected data, a comprehensive evaluation report of this personalized comfort radiofrequency beauty solution is generated. The report includes at least a solution implementation stability score, a comfort deviation score, and a predicted effectiveness index. All input parameters, process data, adjustment records, final parameter set, and comprehensive evaluation report of the personalized comfort radiofrequency beauty solution generated this time will be linked and stored to form new case data. After obtaining subsequent efficacy feedback from the target cosmetic patient, the efficacy feedback information is associated with the new case data, and the comfort and effectiveness markers of the solutions in the case data are updated or confirmed based on the efficacy feedback information for subsequent optimization of the strategy library.

10. The method according to claim 1, characterized in that, The physiological parameters reflecting the absorption status of the anesthetic agent in the real-time anesthesia response data are transcutaneous electrical impedance or local skin microcirculation blood flow velocity, and the test radiofrequency energy is a pre-stimulation radiofrequency pulse sequence with energy lower than that of conventional treatment.

Images