Percutaneous barrier function evaluation method and system based on precise temperature and humidity control

Abstract

The invention discloses a percutaneous barrier function evaluation method and system based on precise temperature and humidity control. The method comprises the steps that S1, an individualized skin barrier baseline model containing daytime fluctuation characteristics and temperature and humidity sensitivity parameters and a baseline database are constructed; s2, executing an environment adaptability evaluation scheme through dynamic temperature and humidity spectrum scanning; s3, multi-dimensional skin physiological parameters are collected, a static parameter set and a dynamic parameter set are distinguished, and data standardization is achieved through dynamic baseline subtraction and cross-parameter interference compensation by adopting a physiological constraint type data correction algorithm based on a skin physiological relevance matrix; s4, integrating the standardized parameters to calculate a comprehensive barrier function index; and S5, predicting a barrier function trend. The system comprises a precise environment control module, a multi-parameter acquisition module, an intelligent analysis module, a data storage and interaction module and a central processing module, the accuracy and comprehensiveness of percutaneous barrier function evaluation can be improved, and technical support is provided for skin health management, skin disease diagnosis and treatment and the like.

Description

Technical Field

[0001] This invention relates to the field of transdermal barrier function assessment technology, and in particular to a transdermal barrier function assessment method based on precise temperature and humidity control. Background Technology

[0002] As the core line of defense for maintaining healthy skin, the skin barrier function is crucial for the diagnosis and treatment of skin diseases, the development of skin care products, and daily skin health management. Currently, traditional transdermal barrier function assessment techniques have several limitations: First, assessments are mostly based on parameter detection under a single steady-state environment, failing to consider the natural diurnal fluctuations of skin parameters and the impact of temperature and humidity changes on the assessment results. This makes the data susceptible to baseline interference and difficult to reflect the true barrier function status of an individual. Second, existing technologies mostly collect static parameters such as transdermal water loss rate, neglecting the importance of dynamic parameters such as the rate of parameter change and relaxation time during environmental changes, and failing to comprehensively capture the skin barrier's response characteristics and adaptability to environmental stimuli. Third, the data processing stage lacks targeted interference compensation mechanisms, failing to eliminate systematic errors caused by individual diurnal rhythms and environmental baselines, and failing to address the cross-interference problem between different skin physiological parameters (such as stratum corneum moisture content and transdermal water loss rate), resulting in low data accuracy and standardization. Fourth, traditional assessments can only output the current barrier function status and cannot predict future trends in barrier function, making it difficult to meet the needs of prospective interventions and long-term monitoring of skincare product efficacy in clinical practice. These shortcomings make traditional assessment methods insufficient in terms of accuracy, comprehensiveness, and practicality regarding transdermal barrier function. Summary of the Invention

[0003] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for evaluating transdermal barrier function based on precise temperature and humidity control, so as to solve the problems of insufficient accuracy, comprehensiveness and practicality of transdermal barrier function in the prior art.

[0004] To achieve the above and other related objectives, the present invention provides the following technical solution: A transdermal barrier function assessment method based on precise temperature and humidity control includes the following steps: S1, constructing an individualized skin barrier baseline model and establishing a baseline database containing diurnal fluctuation characteristics and temperature and humidity sensitivity parameters; S2, under precise temperature and humidity regulation conditions, implementing an environmental adaptability assessment scheme, and achieving multi-dimensional stimulation and assessment of skin barrier function through dynamic temperature and humidity spectrum scanning; S3, collecting and distinguishing static and dynamic parameters, and processing the collected data using a physiologically constrained data correction algorithm. This physiologically constrained data correction algorithm is based on the skin physiological correlation matrix and achieves data standardization through dynamic baseline subtraction and cross-parameter interference compensation; S4, fusing the standardized static and dynamic parameters to calculate a comprehensive barrier function index; S5, constructing a prediction model based on an LSTM neural network and attention mechanism, inputting historical parameter sequences and environmental factor data, and outputting a trend prediction of barrier function.

[0005] This invention constructs an individualized skin barrier baseline model. First, steady-state data of skin physiological parameters are collected under constant conditions. Combining diurnal fluctuation patterns and multi-gradient temperature and humidity stimulation responses, an individualized baseline database is established, incorporating diurnal rhythm characteristics and environmental sensitivity parameters. Subsequently, an environmental adaptability assessment scheme is implemented under precisely controlled temperature and humidity conditions. Through multi-modal dynamic stimulation, including gradual, cyclical, and extreme environmental exposures, the functional response of the skin barrier is stimulated, while simultaneously collecting static parameters such as transepidermal water loss rate and stratum corneum moisture content, as well as dynamic parameters such as rate of change and relaxation time. Using a physiologically constrained data correction algorithm based on the skin physiological correlation matrix, the influence of individual rhythms and environmental drift is first eliminated through dynamic baseline subtraction, and then cross-parameter interference compensation is achieved through matrix inverse operations, completing data standardization. Finally, the standardized static and dynamic parameters are weighted and fused into a comprehensive barrier function index. Combined with an LSTM neural network and attention mechanism, historical parameter sequences and environmental factors are time-series modeled to predict the trend of skin barrier function.

[0006] In one embodiment of the present invention, S1 includes: S11, selecting multiple standardized detection time periods and performing steady-state baseline measurements on the target skin area under a constant baseline environment; S12, based on the steady-state baseline measurement data, extracting the diurnal dynamic variation patterns of various skin physiological parameters, and establishing an individualized circadian rhythm model. in Indicates the first Skin parameters over time The predicted value, For time variables, For fluctuation range, This is the baseline value for the menstrual cycle. For phase shift, S13: Execute multiple sets of environmental stimulus response tests, collecting steady-state response data of skin parameters under multiple gradient distributions of temperature and humidity combinations; S14: Construct a complete baseline model. The diurnal rhythm characteristics are coupled and integrated with the environmental sensitivity response, and expressed as:

[0007] in For standard reference temperature, For standard reference humidity, The current ambient temperature. The current relative humidity. , , These are the temperature sensitivity coefficient, humidity sensitivity coefficient, and temperature-humidity interaction coefficient, respectively; S15, the parameters of the baseline model are iteratively optimized through repeated measurements to establish an individualized baseline database containing statistical confidence intervals.

[0008] In constructing a personalized skin barrier baseline model, this invention first selects multiple standardized detection time periods and performs steady-state baseline measurements on the target skin area under constant temperature and humidity conditions to obtain the baseline levels of various skin physiological parameters. Based on multi-time-point measurement data, the diurnal dynamic variation patterns of each parameter are extracted, and a sine function is used to model its diurnal rhythm characteristics. Parameters such as fluctuation amplitude, phase shift, and periodic baseline value are fitted to establish a personalized diurnal rhythm prediction model. Subsequently, environmental stimulus response tests are conducted under multiple gradient temperature and humidity combinations, and steady-state response values ​​of skin parameters are collected. The sensitivity to temperature, humidity, and their interaction is analyzed to obtain temperature sensitivity coefficient, humidity sensitivity coefficient, and temperature-humidity interaction coefficient. Finally, the diurnal rhythm model is coupled and integrated with the environmental response function to construct a complete baseline model containing both time and environmental variables. The model parameters are iteratively optimized through repeated measurements, and the confidence intervals of each predicted value are determined using statistical methods, forming a baseline database with individual and environmental adaptability.

[0009] In one embodiment of the present invention, the environmental adaptability assessment scheme includes: a) progressive environmental exposure, starting from baseline temperature and humidity, gradually changing temperature and humidity parameters according to a preset gradient, maintaining each gradient point until skin parameters reach a steady state, and collecting skin parameters at each steady-state point; b) dynamic cyclic exposure, periodically changing between baseline temperature and humidity and target temperature and humidity, and recording the complete response curve of skin parameters as temperature and humidity change cyclically; c) extreme environment tolerance test, rapidly adjusting temperature and humidity to a preset physiological tolerance threshold range, and monitoring the skin barrier's ability to maintain function and its recovery characteristics under extreme environments.

[0010] The environmental adaptability assessment scheme of this invention systematically stimulates the physiological response of the skin barrier under different environmental disturbances through three complementary exposure modes, achieving multi-dimensional functional assessment. First, a progressive environmental exposure is employed, starting from an individualized baseline temperature and humidity, and gradually adjusting environmental parameters according to a preset gradient (e.g., temperature ±1°C per step, humidity ±5% RH per step). Each gradient point continues until the rate of change of skin physiological parameters (e.g., transepidermal water loss rate, TEWL) is lower than a set threshold (e.g., change <5% within 30 consecutive seconds). Once steady state is achieved, data is collected to construct the steady-state response spectrum of the skin under the gradient environment. Second, dynamic cyclic exposure is implemented, periodically adjusting between the baseline and target temperature and humidity at a specific frequency (e.g., 0.2–0.5 cycles / min) (using triangular or square wave modes), while simultaneously collecting skin parameters at high frequency, recording the complete response curve of its dynamic fluctuations with the environment, and capturing dynamic characteristics such as hysteresis and relaxation time. Finally, an extreme environment tolerance test was performed, in which the temperature and humidity were rapidly switched to a preset physiological challenge range (e.g., high temperature and low humidity: 40°C / 10% RH or low temperature and dryness: 10°C / 20% RH) within a short period of time (≤2 minutes). The skin barrier function was continuously monitored, and the process of parameters returning to baseline was recorded during the recovery phase to assess its recovery rate and elasticity.

[0011] In one embodiment of the present invention, S3 includes: S31, during the execution of the environmental adaptability assessment scheme, simultaneously collecting raw data of multi-dimensional skin physiological parameters; classifying the collected parameters into a static parameter set and a dynamic parameter set; the static parameter set includes the baseline value of transdermal water loss rate, stratum corneum water content, and sebum content measured under a specific steady-state environment; the dynamic parameter set includes the parameter change rate, relaxation time to reach a new steady state, hysteresis loop area, and area under the recovery curve measured during environmental changes; S32, constructing... The skin physiological correlation matrix M, matrix elements Indicates the first Item parameter for the first The physiological impact weight of the parameter, and satisfying S33, the original parameter vector With baseline prediction parameter vector The difference operation is performed to eliminate the baseline effects of individual circadian rhythms and environmental temperature and humidity, separating the net response signal caused by external stimuli or changes in internal state. The difference operation is ,in This is the vector of raw skin physiological parameters collected. To correspond to the current time Ambient temperature and ambient humidity The baseline prediction vector; S34, the physiological constraint data correction algorithm is defined as follows: in, This is the output of the initially corrected parameter vector. It is the inverse of the skin physiological correlation matrix. This is the net response signal vector after dynamic baseline subtraction. To correct the residual vector, the values ​​must satisfy... S35, using the inverse matrix of the skin physiological correlation matrix For the net response signal A linear transformation is performed to eliminate mutual interference between different physiological parameters; S36, after the dynamic baseline subtraction and cross-parameter interference compensation processing, the standardized static parameter set and dynamic parameter set are output.

[0012] This invention simultaneously collects raw data of multi-dimensional skin physiological parameters during environmental adaptability assessment and divides them into static and dynamic parameter sets. Static parameters include transepidermal water loss rate, stratum corneum water content, and sebum content measured under steady-state conditions, reflecting the basic physiological state of the skin barrier. Dynamic parameters include the rate of parameter change, relaxation time to reach a new steady state, hysteresis loop area, and area under the recovery curve, characterizing the dynamic response of the skin to external environmental disturbances. To eliminate systematic shifts caused by individual diurnal rhythms and environmental temperature and humidity, dynamic baseline subtraction is first performed by subtracting the original parameter vectors. With baseline prediction parameter vector Perform the difference operation to obtain the net response signal. Subsequently, a skin physiological correlation matrix M is introduced to model the coupling relationships between parameters. This is achieved through calculation... And act on A linear transformation is performed to compensate for cross-parameter disturbances, and the final output satisfies the residual constraints. Preliminary corrected parameter vector Standardization was completed. A two-stage correction strategy—dynamic baseline subtraction followed by cross-parameter interference compensation—significantly improved the accuracy and comparability of skin physiological data. Dynamic baseline subtraction effectively eliminated spurious signals caused by fluctuations in measurement time and environmental temperature and humidity, separating the true net response components and providing clean input for subsequent decoupling analysis. Based on this, a linear transformation was performed using the inverse of the skin physiological correlation matrix M, which scientifically compensated for the mutual interference between different parameters, avoiding functional misjudgments caused by parameter coupling in traditional methods.

[0013] In one embodiment of the present invention, the comprehensive barrier function index The expression is calculated using the weighted fusion and standardized static and dynamic parameters: in, , These are the standardized static parameter vector and the dynamic parameter vector, respectively. , These represent the weight vectors corresponding to the static and dynamic parameters, respectively. for transpose, for Transpose of; It is a balancing coefficient used to adjust the effects of static and dynamic parameters on the overall barrier function index. The proportion of contribution.

[0014] The comprehensive barrier function index proposed in this invention This significantly improves the systematic nature and clinical applicability of skin barrier assessment. By integrating static and dynamic parameters, It not only reflects the skin's basic physiological state but also encompasses its response speed, stability, and recovery ability to external environmental disturbances, avoiding the one-sidedness of traditional single-indicator assessments. The weighting mechanism allows for flexible adjustment of the contribution of each parameter according to different application scenarios, enhancing the model's interpretability and personalized adaptability; the balance coefficient... The reasonable range of values ​​ensures a balance between static and dynamic information contributions, preventing any single parameter from dominating the evaluation results. This index possesses good comparability and trend-tracking capabilities, and can be used in multiple fields such as long-term skin health management, early warning of skin diseases, and cosmetic efficacy evaluation, providing a core quantitative tool for achieving accurate and objective transdermal barrier function assessment.

[0015] In another embodiment of the present invention, a transdermal barrier function assessment system based on precise temperature and humidity control is provided. The system includes: a precise environmental control module for constructing a closed local skin detection environment to achieve precise adjustment and dynamic control of the temperature and humidity of the assessment environment; a multi-parameter acquisition module for simultaneously acquiring skin physiological parameters during environmental adaptability assessment; an intelligent analysis module for performing data correction, comprehensive barrier function index calculation, and trend prediction; a data storage and interaction module for data storage, human-computer interaction, and report generation; and a central processing module, serving as the core control and data interaction hub of the system, for coordinating the work of each module.

[0016] The transdermal barrier function assessment system based on precise temperature and humidity control described in this invention achieves fully automated assessment of skin barrier function through the collaborative work of multiple modules. The central processing module, as the core of the system, uniformly schedules all functional units: First, the precise environmental control module constructs a closed local skin detection environment, dynamically regulating temperature and humidity according to a preset program through gradient, cyclical, or extreme changes to stimulate multi-dimensional responses of the skin barrier; simultaneously, the multi-parameter acquisition module synchronously collects physiological parameters such as transdermal water loss rate, stratum corneum water content, and sebum content, distinguishing between steady-state and dynamic data. The collected raw data is transmitted to the intelligent analysis module, which performs dynamic baseline subtraction and cross-parameter interference compensation based on an individualized baseline model and skin physiological correlation matrix to complete data standardization; subsequently, it integrates static and dynamic parameters to calculate a comprehensive barrier function index, and combines an LSTM neural network to model historical sequences and environmental factors, outputting a trend prediction of barrier function. All data and analysis results are stored, visualized, and reported by the data storage and interaction module, supporting human-computer interaction and remote access.

[0017] In one embodiment of the present invention, the precise environmental control module includes: a temperature and humidity adjustment unit, which uses a Peltier thermostat and an ultrasonic humidification + semiconductor dehumidification combination device, combined with an adaptive PID control algorithm, to achieve temperature and humidity adjustment; a gradient and cycle control unit, which can generate temperature gradients and humidity gradients according to preset parameters, and support periodic reciprocating changes between baseline temperature and humidity and target temperature and humidity, and perform progressive exposure, dynamic cyclic exposure and extreme environment tolerance tests; an environmental monitoring and feedback unit, which integrates a high-precision temperature and humidity sensor, collects environmental parameters in real time and feeds them back to the central processing module to form a closed-loop control; and a safety protection unit, which automatically triggers the temperature and humidity adjustment unit to perform rapid cooling and dehumidification when the skin surface temperature or humidity exceeds the threshold during the extreme environment tolerance test.

[0018] The precision environmental control module described in this invention achieves high-precision, dynamic control of the skin detection microenvironment through multi-unit collaboration. The temperature and humidity control unit utilizes a Peltier thermostat for rapid temperature adjustment, combined with ultrasonic humidification and semiconductor dehumidification devices, providing bidirectional humidity control capabilities. Coupled with an adaptive PID control algorithm, it dynamically adjusts control parameters based on real-time deviations, ensuring rapid temperature and humidity response without overshoot. The gradient and cycle control unit generates temperature and humidity gradients according to preset programs, supporting gradual, periodic, or step-like switching between the baseline and target environment, thereby executing various stimulation modes such as gradual exposure, dynamic cyclic exposure, and extreme environment tolerance testing. The environmental monitoring and feedback unit integrates a high-precision temperature and humidity sensor, collecting environmental data in real-time adjacent to the detection area and feeding the signal back to the central processing module, forming a closed-loop control circuit to ensure a high degree of consistency between setpoints and actual values. The safety protection unit continuously monitors the skin surface microenvironment; once the temperature or humidity exceeds a preset physiological safety threshold, it immediately triggers a protection mechanism, instructing the temperature and humidity control unit to rapidly cool and dehumidify, ensuring the safety of the detection process.

[0019] In one embodiment of the present invention, the multi-parameter acquisition module includes: a static parameter acquisition unit, which integrates a transdermal water loss rate sensor, a stratum corneum moisture content sensor, and a sebum content sensor to acquire a set of static parameters under steady-state conditions; and a dynamic parameter acquisition unit, which shares a sensor array with the static parameter acquisition unit and acquires the parameter change rate, relaxation time, hysteresis loop area, and recovery curve area during environmental changes by increasing the sampling frequency and combining it with timestamp synchronization technology.

[0020] The multi-parameter acquisition module described in this invention achieves integrated acquisition of static and dynamic skin physiological parameters through a shared sensor array and time-division collaborative control. The static parameter acquisition unit integrates sensors for transepidermal water loss rate (TEWL), stratum corneum moisture content, and sebum content. After the environment reaches steady state, it acquires data at a conventional sampling frequency to obtain a set of static parameters reflecting the basic functions of the skin barrier. The dynamic parameter acquisition unit shares the same sensor array as the static unit. However, when environmental temperature and humidity change, a high-frequency sampling mode is triggered by the central processing module, and combined with precise timestamp synchronization technology, it records the complete response curves of each parameter over time in real time. By performing differential calculations, threshold determination, and integral analysis on the data sequence during the dynamic response process, dynamic characteristic parameters such as the rate of parameter change, the time required to reach a new steady state (relaxation time), the area under the hysteresis loop under cyclic stimulation, and the area under the curve during the recovery phase are extracted, thereby comprehensively capturing the time-varying response behavior of the skin barrier.

[0021] In one embodiment of the present invention, the intelligent analysis module includes: a physiological constraint correction unit: storing a skin physiological correlation matrix M and an individualized baseline database, and performing matrix inverse operations. The dynamic baseline subtraction algorithm standardizes the collected data, outputting standardized static and dynamic parameter sets; the index calculation unit calculates the comprehensive barrier function index based on the standardized static and dynamic parameter sets and the weight vector. Trend prediction unit: Integrates an LSTM neural network and attention mechanism model, takes historical parameter sequences and environmental factor data as input, and outputs future... Predicted value.

[0022] To achieve the above technical solution, the intelligent analysis module of this invention uses a three-level progressive processing method to standardize, quantify, and predict the trends of skin physiological data. The physiological constraint correction unit first calls the pre-stored skin physiological correlation matrix M and the individualized baseline database to standardize the collected raw parameters: first, using a dynamic baseline subtraction algorithm, the measured values ​​are subtracted from the baseline values ​​predicted based on time, temperature, and humidity to eliminate the influence of diurnal rhythms and environmental drift, obtaining a net response signal; then, matrix inverse operations are used... The signal is linearly transformed to compensate for interference between different physiological parameters, outputting standardized static and dynamic parameter sets. Subsequently, the exponential calculation unit weights and fuses the standardized parameters with a preset weight vector, incorporating a balance coefficient. The Comprehensive Skin Barrier Function Index (CBFI) is calculated to achieve unified quantification of multi-dimensional indicators. Finally, the trend prediction unit integrates an LSTM neural network and an attention mechanism model. Taking historical parameter sequences and environmental factors as input, it automatically captures long-term temporal dependencies and focuses on key time points and sensitive parameters to output a prediction of future changes in skin barrier function.

[0023] As described above, the transdermal barrier function assessment method and system based on precise temperature and humidity control of the present invention has the following beneficial effects: Significantly improved assessment accuracy: By constructing an individualized baseline model that includes diurnal fluctuation characteristics and temperature and humidity sensitivity parameters, combined with a dynamic baseline subtraction algorithm, the interference of individual diurnal rhythms and environmental temperature and humidity baselines on the detection data is effectively eliminated. At the same time, cross-parameter interference compensation is achieved by relying on the skin physiological correlation matrix, which solves the problem of cross-influence between different skin physiological parameters, greatly reduces data errors, and ensures that the standardized parameters can truly reflect the functional state of the skin barrier. The detection accuracy is significantly improved compared with traditional technologies.

[0024] The assessment dimensions are more comprehensive: On the one hand, a dynamic temperature and humidity spectrum scanning scheme with progressive exposure, dynamic cyclic exposure and extreme environment tolerance test is introduced into the environmental adaptability assessment to stimulate the skin barrier response in multiple scenarios; on the other hand, static parameters such as the baseline value of transdermal water loss rate and dynamic parameters such as the rate of parameter change are collected simultaneously to comprehensively cover the basic state of the skin barrier and its environmental adaptability, making up for the limitations of traditional technologies that only focus on static parameters.

[0025] The functionality and practicality have been greatly enhanced: by calculating the comprehensive barrier function index through weighted fusion of standardized parameters, the abstract barrier function status is transformed into a quantitative indicator, which is convenient for intuitive judgment and horizontal comparison. At the same time, the prediction model built based on LSTM neural network and attention mechanism can use historical parameter sequence and environmental factor data to output barrier function trend prediction, providing forward-looking guidance for early intervention of skin diseases, long-term efficacy monitoring of skin care products and other scenarios, thus expanding the application scope of transdermal barrier function assessment technology. Attached Figure Description

[0026] Figure 1 The diagram shown is a flowchart of the method of the present invention. Detailed Implementation

[0027] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. It should be noted that, unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0028] Example 1

[0029] Please see Figure 1 This invention provides a transdermal barrier function assessment method based on precise temperature and humidity control, comprising the following steps: S1, constructing an individualized skin barrier baseline model and establishing a baseline database containing diurnal fluctuation characteristics and temperature and humidity sensitivity parameters; S2, under precise temperature and humidity regulation conditions, executing an environmental adaptability assessment scheme, and realizing multi-dimensional stimulation and assessment of skin barrier function through dynamic temperature and humidity spectrum scanning; S3, collecting and distinguishing static and dynamic parameters, and processing the collected data using a physiologically constrained data correction algorithm, which is based on the skin physiological correlation matrix and achieves data standardization through dynamic baseline subtraction and cross-parameter interference compensation; S4, fusing the standardized static and dynamic parameters to calculate a comprehensive barrier function index; S5, constructing a prediction model based on an LSTM neural network and attention mechanism, inputting historical parameter sequences and environmental factor data, and outputting a barrier function trend prediction.

[0030] This invention constructs an individualized skin barrier baseline model. First, steady-state data of skin physiological parameters are collected under constant conditions. Combining diurnal fluctuation patterns and multi-gradient temperature and humidity stimulation responses, an individualized baseline database is established, incorporating diurnal rhythm characteristics and environmental sensitivity parameters. Subsequently, an environmental adaptability assessment scheme is implemented under precisely controlled temperature and humidity conditions. Through multi-modal dynamic stimulation, including gradual, cyclical, and extreme environmental exposures, the functional response of the skin barrier is stimulated, while simultaneously collecting static parameters such as transepidermal water loss rate and stratum corneum moisture content, as well as dynamic parameters such as rate of change and relaxation time. Using a physiologically constrained data correction algorithm based on the skin physiological correlation matrix, the influence of individual rhythms and environmental drift is first eliminated through dynamic baseline subtraction, and then cross-parameter interference compensation is achieved through matrix inverse operations, completing data standardization. Finally, the standardized static and dynamic parameters are weighted and fused into a comprehensive barrier function index. Combined with an LSTM neural network and attention mechanism, historical parameter sequences and environmental factors are time-series modeled to predict the trend of skin barrier function.

[0031] S1 includes: S11, selecting multiple standardized detection time periods and performing steady-state baseline measurements on the target skin area under a constant baseline environment; S12, based on the steady-state baseline measurement data, extracting the diurnal dynamic variation patterns of various skin physiological parameters, and establishing an individualized circadian rhythm model. in Indicates the first Skin parameters over time The predicted value, For time variables, For fluctuation range, This is the baseline value for the menstrual cycle. For phase shift, S13: Execute multiple sets of environmental stimulus response tests, collecting steady-state response data of skin parameters under multiple gradient distributions of temperature and humidity combinations; S14: Construct a complete baseline model. The diurnal rhythm characteristics are coupled and integrated with the environmental sensitivity response, and expressed as:

[0032] in For standard reference temperature, For standard reference humidity, The current ambient temperature. The current relative humidity. , , These are the temperature sensitivity coefficient, humidity sensitivity coefficient, and temperature-humidity interaction coefficient, respectively; S15, the parameters of the baseline model are iteratively optimized through repeated measurements to establish an individualized baseline database containing statistical confidence intervals.

[0033] In constructing a personalized skin barrier baseline model, this invention first selects multiple standardized detection time periods and performs steady-state baseline measurements on the target skin area under constant temperature and humidity conditions to obtain the baseline levels of various skin physiological parameters. Based on multi-time-point measurement data, the diurnal dynamic variation patterns of each parameter are extracted, and a sine function is used to model its diurnal rhythm characteristics. Parameters such as fluctuation amplitude, phase shift, and periodic baseline value are fitted to establish a personalized diurnal rhythm prediction model. Subsequently, environmental stimulus response tests are conducted under multiple gradient temperature and humidity combinations, and steady-state response values ​​of skin parameters are collected. The sensitivity to temperature, humidity, and their interaction is analyzed to obtain temperature sensitivity coefficient, humidity sensitivity coefficient, and temperature-humidity interaction coefficient. Finally, the diurnal rhythm model is coupled and integrated with the environmental response function to construct a complete baseline model containing both time and environmental variables. The model parameters are iteratively optimized through repeated measurements, and the confidence intervals of each predicted value are determined using statistical methods, forming a baseline database with individual and environmental adaptability.

[0034] The environmental adaptability assessment scheme includes: a) progressive environmental exposure, starting from baseline temperature and humidity, gradually changing temperature and humidity parameters according to a preset gradient, maintaining each gradient point until skin parameters reach a steady state, and collecting skin parameters at each steady-state point; b) dynamic cyclic exposure, periodically changing between baseline temperature and humidity and target temperature and humidity, and recording the complete response curve of skin parameters as temperature and humidity change cyclically; c) extreme environment tolerance test, rapidly adjusting temperature and humidity to a preset physiological tolerance threshold range, and monitoring the skin barrier's ability to maintain function and its recovery characteristics under extreme environments.

[0035] The environmental adaptability assessment scheme of this invention systematically stimulates the physiological response of the skin barrier under different environmental disturbances through three complementary exposure modes, achieving multi-dimensional functional assessment. First, a progressive environmental exposure is employed, starting from an individualized baseline temperature and humidity, and gradually adjusting environmental parameters according to a preset gradient (e.g., temperature ±1°C per step, humidity ±5% RH per step). Each gradient point continues until the rate of change of skin physiological parameters (e.g., transepidermal water loss rate, TEWL) is lower than a set threshold (e.g., change <5% within 30 consecutive seconds). Once steady state is achieved, data is collected to construct the steady-state response spectrum of the skin under the gradient environment. Second, dynamic cyclic exposure is implemented, periodically adjusting between the baseline and target temperature and humidity at a specific frequency (e.g., 0.2–0.5 cycles / min) (using triangular or square wave modes), while simultaneously collecting skin parameters at high frequency, recording the complete response curve of its dynamic fluctuations with the environment, and capturing dynamic characteristics such as hysteresis and relaxation time. Finally, an extreme environment tolerance test was performed, in which the temperature and humidity were rapidly switched to a preset physiological challenge range (e.g., high temperature and low humidity: 40°C / 10% RH or low temperature and dryness: 10°C / 20% RH) within a short period of time (≤2 minutes). The skin barrier function was continuously monitored, and the process of parameters returning to baseline was recorded during the recovery phase to assess its recovery rate and elasticity.

[0036] S3 includes: S31, during the execution of the environmental adaptability assessment scheme, simultaneously collecting raw data of multi-dimensional skin physiological parameters; classifying the collected parameters into a static parameter set and a dynamic parameter set; the static parameter set includes the baseline value of transdermal water loss rate, stratum corneum water content, and sebum content measured under a specific steady-state environment; the dynamic parameter set includes the parameter change rate, relaxation time to reach a new steady state, hysteresis loop area, and area under the recovery curve measured during environmental changes; S32, constructing... The skin physiological correlation matrix M, matrix elements Indicates the first Item parameter for the first The physiological impact weight of the parameter, and satisfying S33, the original parameter vector With baseline prediction parameter vector The difference operation is performed to eliminate the baseline effects of individual circadian rhythms and environmental temperature and humidity, separating the net response signal caused by external stimuli or changes in internal state. The difference operation is ,in This is the vector of raw skin physiological parameters collected. To correspond to the current time Ambient temperature and ambient humidity The baseline prediction vector; S34, the physiological constraint data correction algorithm is defined as follows: in, This is the output of the initially corrected parameter vector. It is the inverse of the skin physiological correlation matrix. This is the net response signal vector after dynamic baseline subtraction. To correct the residual vector, the values ​​must satisfy... S35, using the inverse matrix of the skin physiological correlation matrix For the net response signal A linear transformation is performed to eliminate mutual interference between different physiological parameters; S36, after the dynamic baseline subtraction and cross-parameter interference compensation processing, the standardized static parameter set and dynamic parameter set are output.

[0037] This invention simultaneously collects raw data of multi-dimensional skin physiological parameters during environmental adaptability assessment and divides them into static and dynamic parameter sets. Static parameters include transepidermal water loss rate, stratum corneum water content, and sebum content measured under steady-state conditions, reflecting the basic physiological state of the skin barrier. Dynamic parameters include the rate of parameter change, relaxation time to reach a new steady state, hysteresis loop area, and area under the recovery curve, characterizing the dynamic response of the skin to external environmental disturbances. To eliminate systematic shifts caused by individual diurnal rhythms and environmental temperature and humidity, dynamic baseline subtraction is first performed by subtracting the original parameter vectors. With baseline prediction parameter vector Perform the difference operation to obtain the net response signal. Subsequently, a skin physiological correlation matrix M is introduced to model the coupling relationships between parameters. This is achieved through calculation... And act on A linear transformation is performed to compensate for cross-parameter disturbances, and the final output satisfies the residual constraints. Preliminary corrected parameter vector Standardization was completed. A two-stage correction strategy—dynamic baseline subtraction followed by cross-parameter interference compensation—significantly improved the accuracy and comparability of skin physiological data. Dynamic baseline subtraction effectively eliminated spurious signals caused by fluctuations in measurement time and environmental temperature and humidity, separating the true net response components and providing clean input for subsequent decoupling analysis. Based on this, a linear transformation was performed using the inverse of the skin physiological correlation matrix M, which scientifically compensated for the mutual interference between different parameters, avoiding functional misjudgments caused by parameter coupling in traditional methods.

[0038] The comprehensive barrier function index The expression is calculated using the weighted fusion and standardized static and dynamic parameters: in, , These are the standardized static parameter vector and the dynamic parameter vector, respectively. , These represent the weight vectors corresponding to the static and dynamic parameters, respectively. for transpose, for Transpose of; It is a balancing coefficient used to adjust the effects of static and dynamic parameters on the overall barrier function index. The proportion of contribution.

[0039] The comprehensive barrier function index proposed in this invention This significantly improves the systematic nature and practicality of skin barrier assessment. By integrating static and dynamic parameters, It not only reflects the skin's basic physiological state but also encompasses its response speed, stability, and recovery ability to external environmental disturbances, avoiding the one-sidedness of traditional single-indicator assessments. The weighting mechanism allows for flexible adjustment of the contribution of each parameter according to different application scenarios, enhancing the model's interpretability and personalized adaptability; the balance coefficient... The reasonable range of values ​​ensures a balance between static and dynamic information contributions, preventing any single parameter from dominating the evaluation results. This index possesses good comparability and trend-tracking capabilities, and can be used in multiple fields such as long-term skin health management, early warning of skin diseases, and cosmetic efficacy evaluation, providing a core quantitative tool for achieving accurate and objective transdermal barrier function assessment.

[0040] Example 2

[0041] A transdermal barrier function assessment system based on precise temperature and humidity control is provided for performing the method described in Example 1. The system includes: a precise environmental control module for constructing a closed local skin detection environment to achieve precise adjustment and dynamic control of the temperature and humidity of the assessment environment; a multi-parameter acquisition module for simultaneously acquiring skin physiological parameters during environmental adaptability assessment; an intelligent analysis module for performing data correction, comprehensive barrier function index calculation, and trend prediction; a data storage and interaction module for data storage, human-computer interaction, and report generation; and a central processing module, serving as the core control and data interaction hub of the system, for coordinating the work of each module.

[0042] The transdermal barrier function assessment system based on precise temperature and humidity control described in this invention achieves fully automated assessment of skin barrier function through the collaborative work of multiple modules. The central processing module, as the core of the system, uniformly schedules all functional units: First, the precise environmental control module constructs a closed local skin detection environment, dynamically regulating temperature and humidity according to a preset program through gradient, cyclical, or extreme changes to stimulate multi-dimensional responses of the skin barrier; simultaneously, the multi-parameter acquisition module synchronously collects physiological parameters such as transdermal water loss rate, stratum corneum water content, and sebum content, distinguishing between steady-state and dynamic data. The collected raw data is transmitted to the intelligent analysis module, which performs dynamic baseline subtraction and cross-parameter interference compensation based on an individualized baseline model and skin physiological correlation matrix to complete data standardization; subsequently, it integrates static and dynamic parameters to calculate a comprehensive barrier function index, and combines an LSTM neural network to model historical sequences and environmental factors, outputting a trend prediction of barrier function. All data and analysis results are stored, visualized, and reported by the data storage and interaction module, supporting human-computer interaction and remote access.

[0043] The precise environmental control module includes: a temperature and humidity regulation unit, which uses a Peltier thermostat and a combination of ultrasonic humidification and semiconductor dehumidification, along with an adaptive PID control algorithm, to achieve temperature and humidity regulation; a gradient and cycle control unit, which can generate temperature and humidity gradients according to preset parameters and supports periodic changes between baseline and target temperature and humidity, performing progressive exposure, dynamic cyclic exposure, and extreme environment tolerance tests; an environmental monitoring and feedback unit, which integrates high-precision temperature and humidity sensors to collect environmental parameters in real time and feed them back to the central processing module to form a closed-loop control; and a safety protection unit, which automatically triggers the temperature and humidity regulation unit to perform rapid cooling and dehumidification when the skin surface temperature or humidity exceeds the threshold during the extreme environment tolerance test.

[0044] The precision environmental control module described in this invention achieves high-precision, dynamic control of the skin detection microenvironment through multi-unit collaboration. The temperature and humidity control unit utilizes a Peltier thermostat for rapid temperature adjustment, combined with ultrasonic humidification and semiconductor dehumidification devices, providing bidirectional humidity control capabilities. Coupled with an adaptive PID control algorithm, it dynamically adjusts control parameters based on real-time deviations, ensuring rapid temperature and humidity response without overshoot. The gradient and cycle control unit generates temperature and humidity gradients according to preset programs, supporting gradual, periodic, or step-like switching between the baseline and target environment, thereby executing various stimulation modes such as gradual exposure, dynamic cyclic exposure, and extreme environment tolerance testing. The environmental monitoring and feedback unit integrates a high-precision temperature and humidity sensor, collecting environmental data in real-time adjacent to the detection area and feeding the signal back to the central processing module, forming a closed-loop control circuit to ensure a high degree of consistency between setpoints and actual values. The safety protection unit continuously monitors the skin surface microenvironment; once the temperature or humidity exceeds a preset physiological safety threshold, it immediately triggers a protection mechanism, instructing the temperature and humidity control unit to rapidly cool and dehumidify, ensuring the safety of the detection process.

[0045] The multi-parameter acquisition module includes: a static parameter acquisition unit, which integrates a transdermal water loss rate sensor, a stratum corneum moisture content sensor, and a sebum content sensor to acquire a set of static parameters under steady-state conditions; and a dynamic parameter acquisition unit, which shares a sensor array with the static parameter acquisition unit and acquires the parameter change rate, relaxation time, hysteresis loop area, and area under the recovery curve during environmental changes by increasing the sampling frequency and combining it with timestamp synchronization technology.

[0046] The multi-parameter acquisition module described in this invention achieves integrated acquisition of static and dynamic skin physiological parameters through a shared sensor array and time-division collaborative control. The static parameter acquisition unit integrates sensors for transepidermal water loss rate (TEWL), stratum corneum moisture content, and sebum content. After the environment reaches steady state, it acquires data at a conventional sampling frequency to obtain a set of static parameters reflecting the basic functions of the skin barrier. The dynamic parameter acquisition unit shares the same sensor array as the static unit. However, when environmental temperature and humidity change, a high-frequency sampling mode is triggered by the central processing module, and combined with precise timestamp synchronization technology, it records the complete response curves of each parameter over time in real time. By performing differential calculations, threshold determination, and integral analysis on the data sequence during the dynamic response process, dynamic characteristic parameters such as the rate of parameter change, the time required to reach a new steady state (relaxation time), the area under the hysteresis loop under cyclic stimulation, and the area under the curve during the recovery phase are extracted, thereby comprehensively capturing the time-varying response behavior of the skin barrier.

[0047] The intelligent analysis module includes: a physiological constraint correction unit: storing the skin physiological correlation matrix M and an individualized baseline database, and performing matrix inverse operations. The dynamic baseline subtraction algorithm standardizes the collected data, outputting standardized static and dynamic parameter sets; the index calculation unit calculates the comprehensive barrier function index based on the standardized static and dynamic parameter sets and the weight vector. Trend prediction unit: Integrates an LSTM neural network and attention mechanism model, takes historical parameter sequences and environmental factor data as input, and outputs future... Predicted value.

[0048] The intelligent analysis module described in this invention achieves standardized, quantitative evaluation, and trend prediction of skin physiological data through a three-level progressive processing. The physiological constraint correction unit first calls the pre-stored skin physiological correlation matrix M and the individualized baseline database to standardize the collected raw parameters: first, a dynamic baseline subtraction algorithm is used to subtract the measured values ​​from the baseline values ​​predicted based on time, temperature, and humidity, eliminating the influence of diurnal rhythms and environmental drift to obtain the net response signal; then, matrix inverse operations are used... The signal is linearly transformed to compensate for interference between different physiological parameters, outputting standardized static and dynamic parameter sets. Subsequently, the exponential calculation unit weights and fuses the standardized parameters with a preset weight vector, incorporating a balance coefficient. The Comprehensive Skin Barrier Function Index (CBFI) is calculated to achieve unified quantification of multi-dimensional indicators. Finally, the trend prediction unit integrates an LSTM neural network and an attention mechanism model. Taking historical parameter sequences and environmental factors as input, it automatically captures long-term temporal dependencies and focuses on key time points and sensitive parameters to output a prediction of future changes in skin barrier function.

[0049] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. All equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this invention should still be covered by the claims of this invention.

Claims

1. A method for assessing transdermal barrier function based on precise temperature and humidity control, characterized in that, Includes the following steps: S1. Construct an individualized skin barrier baseline model and establish a baseline database that includes diurnal fluctuation characteristics and temperature and humidity sensitivity parameters; S2, under precise temperature and humidity control, executes an environmental adaptability assessment program, and achieves multi-dimensional stimulation and assessment of skin barrier function through dynamic temperature and humidity spectrum scanning; S3. Collect and distinguish static parameters from dynamic parameters, and process the collected data using a physiologically constrained data correction algorithm. The physiologically constrained data correction algorithm is based on the skin physiological correlation matrix and achieves data standardization through dynamic baseline subtraction and cross-parameter interference compensation. S4, calculates the comprehensive barrier function index by integrating standardized static and dynamic parameters; S5 is a predictive model built on LSTM neural network and attention mechanism. It takes historical parameter sequences and environmental factor data as input and outputs a trend prediction of barrier function.

2. The transdermal barrier function assessment method based on precise temperature and humidity control according to claim 1, characterized in that, S1 includes: S11, Select multiple standardized detection time periods and perform steady-state baseline measurements on the target skin area under a constant baseline environment; S12, Based on the steady-state baseline measurement data, extract the diurnal dynamic variation patterns of various skin physiological parameters and establish an individualized circadian rhythm model: in Indicates the first Skin parameters over time The predicted value, For time variables, For fluctuation range, This is the baseline value for the menstrual cycle. For phase shift, S13 is the periodic average baseline level of this parameter; S13, perform multiple sets of environmental stimulus response tests, and collect steady-state response data of skin parameters under multiple gradient distribution temperature and humidity combinations; S14, Construct the complete baseline model The diurnal rhythm characteristics are coupled and integrated with the environmental sensitivity response, and expressed as: in For standard reference temperature, For standard reference humidity, The current ambient temperature. The current relative humidity. , , These are the temperature sensitivity coefficient, humidity sensitivity coefficient, and temperature-humidity interaction coefficient, respectively; S15, the parameters of the baseline model are iteratively optimized through repeated measurements to establish an individualized baseline database containing statistical confidence intervals.

3. The transdermal barrier function assessment method based on precise temperature and humidity control according to claim 2, characterized in that, The environmental adaptability assessment scheme includes: a) progressive environmental exposure, starting from baseline temperature and humidity, gradually changing temperature and humidity parameters according to a preset gradient, maintaining each gradient point until skin parameters reach a steady state, and collecting skin parameters at each steady-state point; b) dynamic cyclic exposure, periodically changing between baseline temperature and humidity and target temperature and humidity, and recording the complete response curve of skin parameters as temperature and humidity change cyclically; c) extreme environment tolerance test, rapidly adjusting temperature and humidity to a preset physiological tolerance threshold range, and monitoring the skin barrier's ability to maintain function and its recovery characteristics under extreme environments.

4. The transdermal barrier function assessment method based on precise temperature and humidity control according to claim 2, characterized in that, S3 includes: S31, during the execution of the environmental adaptability assessment scheme, raw data of multi-dimensional skin physiological parameters are collected simultaneously; the collected parameters are divided into a static parameter set and a dynamic parameter set; the static parameter set includes the baseline value of transdermal water loss rate, stratum corneum water content, and sebum content measured under a specific steady-state environment; the dynamic parameter set includes the parameter change rate, relaxation time to reach a new steady state, hysteresis loop area, and area under the recovery curve measured during environmental changes. S32, Construction The skin physiological correlation matrix M, matrix elements Indicates the first Item parameter for the first The physiological impact weight of the parameter, and satisfying ; S33, the original parameter vector With baseline prediction parameter vector The difference operation is performed to eliminate the baseline effects of individual circadian rhythms and environmental temperature and humidity, separating the net response signal caused by external stimuli or changes in internal state. The difference operation is ,in This is the vector of raw skin physiological parameters collected. To correspond to the current time Ambient temperature and ambient humidity The baseline prediction vector; S34, the physiological constraint-type data correction algorithm is defined as follows: in, This is the output of the initially corrected parameter vector. It is the inverse of the skin physiological correlation matrix. This is the net response signal vector after dynamic baseline subtraction. To correct the residual vector, the values ​​must satisfy... ; S35, using the inverse matrix of the skin physiological correlation matrix For the net response signal A linear transformation is performed to eliminate the mutual interference between different physiological parameters; S36. After the dynamic baseline subtraction and cross-parameter interference compensation processing, the standardized static parameter set and dynamic parameter set are output.

5. The transdermal barrier function assessment method based on precise temperature and humidity control according to claim 4, characterized in that, The comprehensive barrier function index The expression is calculated using the weighted fusion and standardized static and dynamic parameters: in, , These are the standardized static parameter vector and the dynamic parameter vector, respectively. , These represent the weight vectors corresponding to the static and dynamic parameters, respectively. for transpose, for transpose; It is a balancing coefficient used to adjust the effects of static and dynamic parameters on the overall barrier function index. The proportion of contribution.

6. A transdermal barrier function assessment system based on precise temperature and humidity control, characterized in that, The system is configured to perform the method as described in any one of claims 1-5, comprising: The precision environmental control module is used to construct a closed local skin detection environment, enabling precise adjustment and dynamic control of the temperature and humidity of the assessment environment; A multi-parameter acquisition module is used to simultaneously acquire skin physiological parameters during environmental adaptability assessment. Intelligent analysis module: used to perform data correction, calculate the comprehensive barrier function index, and predict trends; The data storage and interaction module is used for data storage, human-computer interaction, and report generation. The central processing module, as the core control and data interaction hub of the system, is used to coordinate the work of various modules.

7. The transdermal barrier function assessment system based on precise temperature and humidity control according to claim 6, characterized in that, The precise environmental control module includes: The temperature and humidity control unit uses a Peltier thermostat and an ultrasonic humidification + semiconductor dehumidification combination device, combined with an adaptive PID control algorithm, to achieve temperature and humidity regulation; Gradient and Cyclic Control Unit: It can generate temperature gradient and humidity gradient according to preset parameters, and supports periodic changes between baseline temperature and humidity and target temperature and humidity to perform progressive exposure, dynamic cyclic exposure and extreme environment tolerance tests. Environmental monitoring feedback unit: integrates high-precision temperature and humidity sensors to collect environmental parameters in real time and feed them back to the central processing module to form a closed-loop control; Safety protection unit: When the skin surface temperature or humidity exceeds the threshold during the extreme environment tolerance test, the temperature and humidity regulation unit is automatically triggered to quickly cool down and dehumidify.

8. The transdermal barrier function assessment system based on precise temperature and humidity control according to claim 7, characterized in that, The multi-parameter acquisition module includes: The static parameter acquisition unit integrates a transdermal moisture loss rate sensor, a stratum corneum moisture content sensor, and a sebum content sensor to acquire a set of static parameters under steady-state conditions. The dynamic parameter acquisition unit shares a sensor array with the static parameter acquisition unit. By increasing the sampling frequency and combining it with timestamp synchronization technology, it acquires the parameter change rate, relaxation time, hysteresis loop area, and area under the recovery curve during the environmental change process.

9. The transdermal barrier function assessment system based on precise temperature and humidity control according to claim 8, characterized in that, The intelligent analysis module includes: Physiological constraint correction unit: Stores the skin physiological correlation matrix M and the individualized baseline database, and performs matrix inverse operations. The dynamic baseline subtraction algorithm is used to standardize the collected data and output a standardized static parameter set and a dynamic parameter set. The index calculation unit calculates the comprehensive barrier function index based on the standardized static parameter set, dynamic parameter set, and weight vector. ; Trend prediction unit: Integrates LSTM neural network and attention mechanism model, takes historical parameter sequence and environmental factor data as input, and outputs the predicted value of future barrier function index.

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