Data processing method and system for screening cosmetic functional components

Abstract

The invention relates to the technical field of computational simulation and numerical analysis, in particular to a data processing method and system for screening cosmetic functional components. The method comprises the following steps: acquiring optical characteristic parameters of the sun-screening agent; carrying out light scattering analysis according to the optical characteristic parameters, and carrying out optical efficiency index calculation to obtain an optical efficiency index; obtaining a crystal structure of the sun-screening agent; extracting surface modification parameters of the crystal structure, and measuring and calculating the surface energy gradient to obtain a surface energy gradient map; carrying out active site space distribution calculation according to the surface energy gradient map to obtain an active site distribution characteristic map; and carrying out stability and activity balance relation quantification according to the active site distribution characteristic graph and the optical efficiency index to obtain a stability-activity balance factor. By constructing a three-layer progressive systematic data processing framework, the long-term efficacy and potential risk of the components can be predicted more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of computational simulation and numerical analysis, and particularly to a data processing method and system for screening cosmetic efficacy ingredients. Background Art

[0002] At present, the evaluation of sunscreens mainly relies on methods such as SPF and PA value determination, particle size characterization, crystal form analysis, and static penetration experiments, forming a relatively standardized but still limited evaluation system. The data processing method has developed from simple single-value recording to multi-parameter statistical analysis, but a systematic multi-dimensional data processing framework has not yet been formed.

[0003] Existing methods mainly rely on single indicators such as SPF value to evaluate the performance of sunscreens, and cannot describe the complex relationship between the microscopic physical properties and macroscopic protection efficacy of sunscreens; traditional analysis regards the sample as a homogeneous system and ignores the influence of internal component diversity on efficacy; single parameters are difficult to reveal the complete mechanism of action for efficacy, resulting in the development of sunscreens relying on the trial-and-error method, with low efficiency and insufficient scientificity.

[0004] Existing methods only conduct tests at fixed time points, lacking time dimension analysis and unable to reflect the dynamic changes of efficacy over time; there are significant differences between standard test conditions and actual use environments, ignoring key influencing factors such as temperature, humidity, and friction; the dynamic interaction process between the sunscreen and the skin and the formation process of the protective film are not investigated; static safety assessment cannot accurately predict the percutaneous absorption risk under actual use conditions, resulting in a deviation between the assessment result and the actual use situation.

[0005] In summary, the limitations of single indicator evaluation and static evaluation methods in the existing technology need to be solved urgently. Summary of the Invention

[0006] Based on this, it is necessary to provide a data processing method and system for screening cosmetic efficacy ingredients to solve at least one of the above technical problems.

[0007] To achieve the above object, a data processing method for screening cosmetic efficacy ingredients includes the following steps: Step S1: Obtain the optical characteristic parameters of the sunscreen; perform light scattering analysis based on the optical characteristic parameters and calculate the optical efficacy index to obtain the optical efficacy index; Step S2: Obtain the crystal structure of the sunscreen; extract the surface modification parameters of the crystal structure and measure the surface energy gradient to obtain the surface energy gradient map; perform calculation of the spatial distribution of active sites based on the surface energy gradient map to obtain the active site distribution characteristic map; perform quantification of the stability-activity balance relationship based on the active site distribution characteristic map and the optical efficacy index to obtain the stability-activity balance factor; Step S3: Establish a multi-level skin structure, perform dynamic simulation of interfacial contact according to the stability-activity balance factor, and obtain a dynamic characteristic map of interfacial contact; perform molecular-level adsorption analysis based on the dynamic characteristic map of interfacial contact to obtain molecular adsorption mechanism data; Step S4: Perform simulation of the protective film formation process based on the molecular adsorption mechanism data to obtain a dynamic map of film formation; use the dynamic map of film formation to track the percutaneous penetration path and perform safety assessment to obtain a percutaneous dynamic safety factor; use the percutaneous dynamic safety factor to perform safety screening of active ingredients to obtain active ingredient safety data.

[0008] The present invention combines multi-modal particle size analysis and light scattering theory to establish an Optical Efficiency Index (OEI), which can more comprehensively evaluate the optical performance of sunscreens, breaking through the limitations of traditional methods that rely solely on a single index (such as the SPF value). The OEI comprehensively considers factors such as particle size distribution, scattering efficiency, and wavelength dependence, and can more accurately predict the blocking ability of sunscreens against ultraviolet rays of different wavelengths, thereby guiding the optimization of sunscreen formulations and improving the sunscreen effect. By analyzing crystal structure, surface modification parameters, surface energy gradient, and active site distribution, a Stability-Activity Balance Factor (SABF) is established. This factor can quantify the balance relationship between the stability and activity of sunscreens, overcoming the limitations of traditional methods that only focus on a single crystal form or surface modification. Through the SABF, the performance of sunscreens can be more comprehensively evaluated, and the optimization of surface modification strategies can be guided, such as selecting appropriate modifiers and modification methods, so as to maintain the activity of sunscreens while improving their stability, ultimately enhancing the sunscreen effect and safety. By establishing a multi-level skin structure model and combining dynamic simulation of interfacial contact, molecular-level adsorption analysis, simulation of the protective film formation process, and tracking of the percutaneous penetration path and safety assessment, a percutaneous dynamic safety factor (TDSC) is finally obtained. The TDSC comprehensively considers the interaction between sunscreens and the skin, the formation process of the protective film, and the percutaneous penetration risk of sunscreens, and can more accurately predict the safety of sunscreens under actual use conditions, overcoming the limitations of traditional static assessment methods. Using the TDSC for safety screening of active ingredients can effectively exclude sunscreens with a high percutaneous penetration risk, thereby improving the safety of cosmetics and protecting the health of consumers. Therefore, the present invention provides a data processing method for screening active ingredients of cosmetics, which solves the problems of single index evaluation and limitations of static assessment methods in the prior art by constructing a three-layer progressive systematic data processing framework. It realizes a comprehensive assessment from micro to macro and from static to dynamic, thus more accurately predicting the long-term efficacy and potential risks of ingredients.

[0009] Preferably, the present invention further provides a data processing system for screening cosmetic efficacy ingredients, which is used to execute the data processing method for screening cosmetic efficacy ingredients as described above. The data processing system for screening cosmetic efficacy ingredients includes: A sunscreen efficacy analysis module, which is used to obtain the optical characteristic parameters of sunscreen agents; perform light scattering analysis based on the optical characteristic parameters, and calculate the optical efficacy index to obtain the optical efficacy index; A stability and activity analysis module, which is used to obtain the crystal structure of sunscreen agents; extract the surface modification parameters of the crystal structure, and measure the surface energy gradient to obtain a surface energy gradient map; perform calculation of the spatial distribution of active sites based on the surface energy gradient map to obtain an active site distribution characteristic map; quantify the stability-activity balance relationship based on the active site distribution characteristic map and the optical efficacy index to obtain a stability-activity balance factor; A contact dynamics simulation module, which is used to establish a multi-level skin structure, and perform interface contact dynamics simulation based on the stability-activity balance factor to obtain an interface contact dynamics characteristic map; perform molecular-level adsorption analysis based on the interface contact dynamics characteristic map to obtain molecular adsorption mechanism data; A percutaneous safety assessment module, which simulates the process of forming a protective film based on the molecular adsorption mechanism data to obtain a film-forming dynamics map; uses the film-forming dynamics map to track the percutaneous penetration path and perform safety assessment to obtain a percutaneous dynamic safety coefficient; uses the percutaneous dynamic safety coefficient to perform safety screening of efficacy ingredients to obtain efficacy ingredient safety data. Description of the Drawings

[0010] Figure 1 It is a schematic diagram of the step flow of a data processing method for screening cosmetic efficacy ingredients; Figure 2 It is a schematic diagram of the detailed implementation step flow of step S1 in the present invention.

[0011] The realization of the purpose, functional characteristics and advantages of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiments

[0012] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0014] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0015] In the embodiments of the present invention, with reference to Figure 1 as shown, it is a schematic flowchart of the steps of the data processing method for screening cosmetic efficacy ingredients of the present invention. In this example, the data processing method for screening cosmetic efficacy ingredients includes the following steps: Step S1: Obtain the optical characteristic parameters of the sunscreen agent; perform light scattering analysis based on the optical characteristic parameters, and calculate the optical efficacy index to obtain the optical efficacy index; In the embodiments of the present invention, first, basic parameters such as the particle size distribution, specific surface area, porosity, and true density of the sunscreen agent are measured by instruments such as a laser particle size analyzer, a specific surface area and pore size analyzer, and a true density meter, and its optical characteristic parameters such as reflectivity, transmittance, refractive index, and extinction coefficient in the ultraviolet band are measured by a UV-Vis-NIR spectrophotometer and an ellipsometer. Then, based on the Mie scattering theory, combined with the multi-peak characteristics of the particle size distribution and the optical characteristic parameters, a light scattering model is constructed to calculate the scattering and absorption efficiencies at different wavelengths. Finally, by comprehensively considering the particle size distribution index and the refractive index weighting function, a normalized optical efficacy index (OEI) is obtained through integral calculation, and this index quantitatively characterizes the ultraviolet blocking ability of the sunscreen agent.

[0016] Step S2: Obtain the crystal structure of the sunscreen agent; extract the surface modification parameters of the crystal structure, and measure the surface energy gradient to obtain a surface energy gradient map; perform calculation of the spatial distribution of active sites based on the surface energy gradient map to obtain an active site distribution characteristic map; quantitatively determine the stability-activity balance relationship based on the active site distribution characteristic map and the optical efficacy index to obtain a stability-activity balance factor; In the embodiments of the present invention, an X-ray diffractometer (XRD) is used to measure the crystal structure of the sunscreen, determine its crystal phase composition (such as anatase, rutile, etc.), lattice parameters, and crystallinity, and make a preliminary correlation with the OEI obtained in step S1. Then, by means of infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), Zeta potential analysis, etc., surface modification parameters such as the types of chemical modification groups, coverage density, and surface charge on the surface of the sunscreen are analyzed. Next, a contact angle measuring instrument is used to measure the contact angles of the sunscreen in different liquids, and combined with the crystal structure data, a surface energy gradient model is constructed to calculate the change in surface energy from the surface to the crystal interior. Furthermore, based on density functional theory (DFT) calculations, active sites (such as oxygen vacancies, coordinatively unsaturated Ti atoms) on different crystal planes are identified, and the accessibility of the active sites is evaluated in combination with the surface modification parameters. Finally, by comprehensively considering the crystal form stability, active site distribution, and OEI, a three-dimensional correlation model and an equilibrium factor calculation formula are constructed to obtain the stability-activity balance factor (SABF), which quantitatively characterizes the trade-off relationship between the light protection efficacy and long-term stability of the sunscreen.

[0017] Step S3: Establish a multi-level skin structure, and perform dynamic simulation of interfacial contact according to the stability-activity balance factor to obtain a dynamic interfacial contact characteristic diagram; perform molecular-level adsorption analysis based on the dynamic interfacial contact characteristic diagram to obtain molecular adsorption mechanism data; In the embodiments of the present invention, a multi-level skin structure model (stratum corneum, epidermis, dermis) is constructed, and in combination with the surface characteristic parameters (specific surface area, pore size distribution, surface charge, SABF, etc.) and mechanical parameters (Young's modulus, Poisson's ratio) of the sunscreen, the discrete element method (DEM) is used to simulate the initial contact process between the sunscreen particles and the skin surface to obtain the contact area and pressure distribution. Then, the lattice Boltzmann method (LBM) or molecular dynamics (MD) is used to simulate the spreading, aggregation, and dispersion behaviors of the sunscreen on the skin surface, and in combination with molecular docking and MM / PBSA calculations, the molecular adsorption mechanism between the sunscreen and the components of the skin stratum corneum (keratin, ceramide, cholesterol) is analyzed.

[0018] Based on the molecular adsorption mechanism data, simulate the process of forming a protective film to obtain a dynamic film-forming map; use the dynamic film-forming map to track the percutaneous penetration path and perform safety assessment to obtain the percutaneous dynamic safety coefficient; use the percutaneous dynamic safety coefficient to screen the safety of active ingredients to obtain the safety data of active ingredients; In the embodiments of the present invention, by constructing a transient diffusion equation and numerically solving it using the finite element method (FEM) or the finite volume method (FVM), the diffusion and adsorption processes of sunscreen agents on the skin surface are simulated, and then the formation process of the protective film is simulated to obtain the spatio-temporal distributions of parameters such as film thickness, coverage rate, and density. Furthermore, the percutaneous penetration path of the sunscreen agent is simulated by a multi-scale modeling method, and combined with the uniformity index of the protective film and the barrier function impact score, the transdermal dynamic safety coefficient (TDSC) is calculated. Based on the transdermal dynamic safety coefficient (TDSC) and combined with the toxicological data of the sunscreen agent, a complete set of safety screening processes for active ingredients is established. The toxicological data of the sunscreen agent and its various components are collected and sorted, including data such as acute toxicity, skin irritation, phototoxicity, sensitization, mutagenicity, and carcinogenicity. For each sunscreen agent component, its no observable adverse effect level (NOAEL) or lowest observable adverse effect level (LOAEL) is determined. Then, a safety threshold for TDSC is set. The setting of this threshold needs to comprehensively consider the expected usage mode of the sunscreen agent (such as whole-body use or local use), usage frequency, user population (such as adults or children), and the requirements of regulatory agencies. For example, a relatively high TDSC threshold (such as TDSC > 100) can be set for sunscreen products for whole-body use, and a relatively low TDSC threshold (such as TDSC > 10) can be set for sunscreen products for local use. For each sunscreen agent formula to be screened, its TDSC value is calculated according to step S3. The calculated TDSC value is compared with the set safety threshold. If the TDSC value is higher than the safety threshold, it is considered that the sunscreen agent formula has passed the preliminary safety screening; if the TDSC value is lower than the safety threshold, it is considered that the formula has potential safety risks and needs further evaluation or adjustment of the formula. For formulas with TDSC values close to the safety threshold, a more detailed risk assessment can be carried out. For example, the exposure dose of the sunscreen agent under actual use conditions can be considered, and the margin of safety (MoS) is calculated. MoS = NOAEL / SED, where SED is the systemic exposure dosage. SED can be estimated by calculating the maximum concentration (Cmax) and penetration depth of the sunscreen agent in the skin, combined with parameters such as skin surface area and body weight. If the MoS value is greater than 100, the sunscreen agent is generally considered safe. According to the TDSC value, toxicological data, and risk assessment results, the sunscreen agent formulas are sorted and screened. Formulas with high TDSC values, low toxicity, and large MoS values are selected as candidate formulas. The screening results (including TDSC values, toxicological data, MoS values, safety assessment conclusions, etc.) are sorted into active ingredient safety data, providing a scientific basis for the development and safety evaluation of sunscreen products.

[0019] As an example of the present invention, refer to Figure 2 shown, in this example, step S1 includes: Step S11: Measure the basic parameters of the original sunscreen sample; analyze the particle size distribution curve according to the basic parameters to obtain the particle size distribution parameters; In the embodiment of the present invention, a Mastersizer 3000 laser particle size analyzer is used to measure the basic parameters of the original sunscreen sample. Take 1.0 g of the sunscreen sample and disperse it in 50 mL of deionized water, and ultrasonically disperse it for 5 minutes. Use a wet injection system, set the obscuration between 5% and 10%, and set the circulation speed to 2500 rpm. Measure three times and take the average value, and record the particle size distribution data. At the same time, use an ASAP 2460 specific surface area and pore size analyzer to measure the specific surface area and pore size distribution of the sample by the nitrogen adsorption method at a temperature of 77 K. Take 0.5 g of the sample for vacuum degassing treatment for 12 hours, and set the degassing temperature to 150 °C. Use the static volumetric method to measure the adsorption-desorption isotherm to obtain the specific surface area and pore volume data. In addition, use a true density meter AccuPyc II 1340 to measure the true density of the sunscreen. Take 1 g of the sample for ten cycle tests and record its true density value. Based on the above test data, analyze the particle size distribution curve, and use OriginPro 2021 software to fit the particle size distribution data with a Gaussian function to obtain the particle size distribution parameters, including the median particle size D50, D10, D90, and the distribution width index SPAN = (D90 - D10) / D50.

[0020] Step S12: Identify and separate the multi-peak characteristics of the particle size distribution parameters to obtain the multi-peak characteristic quantification data; In the embodiment of the present invention, identify and separate the multi-peak characteristics of the particle size distribution parameters obtained in step S11. Use the Python 3.8 programming language combined with the signal.find_peaks function in the SciPy library to detect the peaks of the particle size distribution curve. Set the prominence (peak significance) parameter of the peak detection to 0.1, and the minimum value of the width (peak width) parameter to 0.05 μm to identify multiple peaks in the particle size distribution. For each identified peak, use the PeakAnalyzer module to fit it with a Gaussian function to separate each sub-peak. Calculate the peak area, peak height, center position (particle size corresponding to the peak), and full width at half maximum of each sub-peak. By calculating the percentage of the area of each sub-peak in the total peak area, obtain the relative content of each sub-peak, and generate the multi-peak characteristic quantification data, including the center particle size, full width at half maximum, and relative content of each sub-peak.

[0021] Step S13: Extract the optical characteristic parameters from the basic parameters; construct a light scattering analysis based on the optical characteristic parameters and the multi-peak characteristic quantification data to obtain a scattering efficiency mapping matrix; In the embodiment of the present invention, optical characteristic parameters are extracted from the basic parameters of the original sunscreen sample measured in step S11. A Lambda 950 ultraviolet-visible-near-infrared spectrophotometer equipped with an integrating sphere accessory is used to measure the reflectance and transmittance spectra of the sunscreen in the wavelength range of 290 nm - 400 nm. During the test, the sunscreen sample is evenly coated in a quartz cuvette, and the film thickness is controlled to be 20 μm. The scanning step size is set to 1 nm, and the scanning speed is 200 nm / min. According to the measured reflectance and transmittance data, the absorption coefficient (K) and scattering coefficient (S) of the sunscreen are calculated using the Kubelka-Munk theory. Further, a V-770 ellipsometer is used to measure the refractive index (n) and extinction coefficient (k) of the sunscreen in the wavelength range of 290 nm - 400 nm. The incident angle is set to 70°, and the scanning step size is 1 nm. Based on the ellipsometry measurement results, the functional relationships of the refractive index and extinction coefficient of the sunscreen varying with wavelength are obtained by fitting with the Cauchy model. The absorption coefficient, scattering coefficient, refractive index, and extinction coefficient are used as optical characteristic parameters for subsequent construction of light scattering analysis.

[0022] Step S14: Perform wavelength-dependent analysis based on the scattering efficiency mapping matrix and optical characteristic parameters to obtain wavelength-dependent response data; In the embodiment of the present invention, using the Mie scattering theory, the scattering efficiency factor Qsca and absorption efficiency factor Qabs of a single sunscreen particle in the wavelength range of 290 nm - 400 nm are calculated using the PyMieScatt library in Python 3.8. The input parameters include the multi-peak feature quantization data (center particle size and full width at half maximum of each sub-peak) obtained in step S12 and the optical characteristic parameters (functional relationships of refractive index and extinction coefficient varying with wavelength) obtained in step S13. During the calculation, the wavelength step size is set to 1 nm. For each wavelength, according to the relative content of each sub-peak, Qsca and Qabs are weighted and summed to obtain the overall scattering efficiency and absorption efficiency of the sunscreen at that wavelength. Wavelength-dependent scattering response curves and absorption response curves are constructed. The areas under the curves are calculated as indicators of the total scattering ability and total absorption ability respectively. The scattering response curves and absorption response curves are normalized to obtain wavelength-dependent response data.

[0023] Step S15: Calculate the optical efficiency index based on the wavelength-dependent response data and multi-peak feature quantization data to obtain the optical efficiency index; In the embodiments of the present invention, based on the multi-peak feature quantization data, the particle size distribution index (PDI) is calculated. The formula is used: PDI = (σ / d)^2, where σ is the standard deviation of the particle size distribution and d is the number-average particle size. Then, the refractive index weighting function (RWF) is constructed. The formula is used: RWF = ∫(n(λ) - 1)^2 × S(λ)dλ, where n(λ) is the function of the refractive index of the sunscreen agent changing with wavelength, S(λ) is the normalized scattering response curve, and the integration range is 290nm - 400nm. The product of PDI and RWF is calculated through numerical integration to obtain a comprehensive value. In order to eliminate the influence of dimensions and make the results comparable, the product is normalized. The formula is used: OEI = (PDI × RWF) / (PDI × RWF)max, where (PDI × RWF)max is the maximum value of (PDI × RWF) among all test samples. The finally obtained OEI is the optical efficiency index, and the numerical range is between 0 and 1.

[0024] Preferably, step S2 includes the following steps: Step S21: Measure the crystal structure of the original sample of the sunscreen agent and make a preliminary correlation with the optical efficiency index to obtain the crystal phase structure characteristic data; Step S22: Analyze the surface modification characteristics of the crystal phase structure characteristic data to obtain the surface modification parameters; Step S23: Calculate the surface energy gradient according to the surface modification parameters and the crystal phase structure characteristic data to obtain the surface energy gradient map; Step S24: Calculate the spatial distribution of active sites according to the surface modification parameters, the surface energy gradient map and the crystal phase structure characteristic data to obtain the active site distribution characteristic map; Step S25: Quantify the stability-activity balance relationship according to the active site distribution characteristic map to obtain the stability-activity balance factor.

[0025] In the embodiments of the present invention, a D8 Advance type X-ray diffractometer (XRD) is used to determine the crystal structure of the original sunscreen sample. Take 1.0 g of the sunscreen powder sample and grind it in an agate mortar until the particle size is less than 45 μm. Spread the ground sample evenly in the sample cell and compact it. Set the Cu target Kα radiation (λ = 0.15406 nm), tube voltage 40 kV, and tube current 40 mA. The scanning range of 2θ is 10° - 80°, the scanning step is 0.02°, and the scanning speed is 2° / min. Use Jade6.5 software to perform phase analysis on the obtained XRD pattern, and determine the crystal phases present in the sample (such as anatase TiO2, rutile TiO2, ZnO, etc.) according to the standard PDF cards. Adopt the Rietveld whole-profile fitting method to refine the lattice parameters (a, b, c), unit cell volume (V), and relative content (wt%) of each crystal phase. For each crystal phase, calculate its crystallinity Xc. Xc is defined as the percentage of the integrated area of the diffraction peaks of this crystal phase in the total integrated area of the diffraction peaks. Perform a preliminary correlation on the obtained crystal phase composition, lattice parameters, unit cell volume, crystallinity, and the optical efficiency index (OEI) calculated in step S15. For example, construct a relationship curve between OEI and the anatase / rutile ratio, analyze the influence of different crystal phase compositions on the optical efficiency, and generate crystal phase structure characteristic data. Use a Nicoletis50 type Fourier transform infrared spectrometer (FTIR) to analyze the chemical composition of the surface modification groups of the sunscreen. Take 0.1 g of the sunscreen sample and mix it evenly with dry KBr powder at a mass ratio of 1:100, grind it, and then press it into a tablet. Set the scanning range from 4000 cm-1 to 400 cm-1, resolution 4 cm-1, and scanning times 32 times. Perform baseline correction and normalization on the obtained infrared spectrum. Identify the characteristic absorption peaks and determine the types of surface modifiers (such as silanes, phosphates, polymers, etc.). Use a STA449F3 type synchronous thermal analyzer (TGA) to determine the coverage density of the surface modification layer. Take 10 mg of the sunscreen sample and heat it from room temperature to 800 °C at a heating rate of 10 °C / min in a nitrogen atmosphere. Record the curve of the sample mass change with temperature. Calculate the weight loss percentage within the decomposition temperature range of the modification layer, and combine the molecular weight of the modifier to calculate the surface coverage density, with the unit of mg / m2. Use a Zetasizer Nano ZS type laser particle size and Zeta potential analyzer to measure the surface charge. Take 0.01 g of the sample and disperse it in 10 mL of deionized water, adjust the pH value to 3 - 10, and ultrasonicate for 5 minutes. Measure the curve of the Zeta potential change with pH to determine the isoelectric point. Take the types of surface groups, coverage density, and Zeta potential as surface modification parameters. Use an OCA25 type contact angle measuring instrument and the sessile drop method to measure the contact angle of the sunscreen in different liquids. Select three test liquids: deionized water, diiodomethane, and ethylene glycol. Press the sunscreen powder into a disc with a diameter of 13 mm and a thickness of 1 mm.Place the wafer on the sample stage and use a microsyringe to drop 3 μL of the test liquid onto the surface of the wafer respectively. Use a CCD camera to capture the droplet profile, analyze the droplet shape through SCA20 software, and calculate the contact angle. Measure 5 different positions for each liquid and take the average value. According to the Young-Dupré equation and the Owens-Wendt-Rabel-Kaelble (OWRK) method, calculate the surface free energy of the sunscreen and its dispersion component and polar component. Based on the crystal phase structure characteristic data, use MaterialsStudio software to construct atomic models of different crystal planes (such as the anatase (101), (001) planes, and the rutile (110), (100) planes). Select the COMPASS force field using the Forcite module and perform molecular dynamics simulations. Calculate the surface energy of each crystal plane. Combine the surface modification parameters to construct a surface energy gradient model from the surface modification layer to the interior of the crystal. Assume that the surface energy decays linearly or exponentially along the direction perpendicular to the surface, plot the curve of surface energy versus depth, and generate a surface energy gradient map. According to the surface energy gradient map obtained in step S23, identify the potential active sites on different crystal planes (such as the (101) plane and (001) plane of anatase TiO2) in the crystal phase structure characteristic data, mainly including oxygen vacancies and coordinatively unsaturated Ti atoms (step S241). Use the CASTEP module of MaterialsStudio software to calculate the formation energy of oxygen vacancies on different crystal planes based on density functional theory (DFT). Set the exchange-correlation functional to GGA-PBE, the plane wave cutoff energy to 400 eV, and the Brillouin zone k-point mesh to 4×4×4. The position of the oxygen vacancy with a lower formation energy in the calculation results is the highly active oxygen vacancy. For coordinatively unsaturated Ti atoms, by analyzing the crystal structure, count the coordination numbers of Ti atoms on different crystal planes. Ti atoms with a coordination number less than 6 are potential active sites. Calculate the site density of these active sites and count the number of various active sites per unit area (step S242). Then, combine the surface modification parameters obtained in step S22 to evaluate the influence of the modifying group on the active sites (step S243). According to the type of modifying group determined by infrared spectroscopy analysis, judge the interaction mode between it and the active site (such as chemical bonding, electrostatic adsorption, etc.). Combine the Zeta potential data to analyze the shielding effect of the surface charge on the charged active sites (such as oxygen vacancies). Calculate the distance between the modifying group and the active site. If the distance is less than the chemical bond length or the van der Waals radius, it is considered that the active site is shielded. According to the shielding degree, calculate the active site shielding coefficient, that is, the ratio of the number of shielded active sites to the total number of active sites. Correct the active site density data according to the active site shielding coefficient, consider the steric hindrance effect of the modifying group, and calculate the accessibility of the active sites (step S244). For the active sites that are not completely shielded, calculate the ratio of their exposed area to the total area to obtain the active site accessibility data.Finally, combining the corrected active site density and accessibility data, as well as the crystal plane type, an active site distribution map is generated (step S245). On the crystal structure model, different types and active sites are represented by markers of different colors and sizes, obtaining an active site distribution characteristic map. A dynamic evaluation of crystal form stability is performed on the active site distribution characteristic map (step S251). Using the CASTEP module of Materials Studio software, the stability of the sunscreen crystal structure is simulated under different temperature (25°C, 35°C, 45°C) and humidity (50%RH, 70%RH, 90%RH) conditions. The change rate of lattice parameters with temperature and humidity is calculated. For crystal forms that may undergo phase transitions (such as anatase to rutile transformation), the phase transition activation energy is calculated. The simulation time is set to 1 ns and the time step is 1 fs. According to the simulation results, a crystal form stability index is calculated, and this index is negatively correlated with the degree of lattice distortion and the phase transition activation energy. Then, based on the crystal form stability index obtained in step S251, the active site distribution characteristic map obtained in step S24, and the optical efficiency index (OEI) obtained in step S15, an activity-stability coupling analysis is performed (step S252). A two-dimensional scatter plot of activity (characterized by active site density and accessibility) and stability (characterized by the crystal form stability index) is constructed. The relationship between the active site distribution and lattice distortion, phase transition activation energy is analyzed. Combining the OEI data, the activity and stability characteristics corresponding to high OEI values are analyzed. The Pearson correlation coefficient between activity and stability is calculated to judge the degree of linear correlation between the two. Based on the activity-stability correlation matrix obtained in step S252, the surface modification parameter set obtained in step S22, and the optical efficiency index (OEI) obtained in step S15, a three-dimensional correlation model is constructed (step S253). Taking the active site density, crystal form stability index, and OEI as the three coordinate axes, a three-dimensional space model is constructed. The sample data points with different surface modification parameters are projected into this three-dimensional space. Software such as OriginPro2021 is used for three-dimensional surface fitting to establish a quantitative relationship between activity, stability, and OEI. For example, a multiple linear regression model or a non-linear regression model (such as Gaussian process regression) can be used for fitting. By adjusting the model parameters, the fitting surface is made to approximate the actual data point distribution as much as possible. The goodness of fit (R2) and prediction accuracy of the model are evaluated. Finally, based on the three-dimensional synergy model obtained in step S253 and the activity-stability correlation matrix obtained in step S252, a stability-activity balance factor is calculated (step S254). Based on the three-dimensional model, a stability-activity balance factor (SABF) is defined. The design principle of SABF is: to maximize stability on the premise of ensuring a certain activity (such as OEI higher than the threshold); at the same time, consider the regulatory effect of surface modification on activity and stability.For example, the SABF can be calculated using the following formula: SABF = α × (StabilityIndex / StabilityIndex_max) + (1 - α) × (ActivityIndex / ActivityIndex_max) × ModificationFactor. Here, StabilityIndex is the crystal form stability index, and StabilityIndex_max is the maximum value among all samples; ActivityIndex is the comprehensive evaluation index of active sites (considering density and accessibility comprehensively), and ActivityIndex_max is the maximum value among all samples; α is the weight coefficient (0 < α < 1), which is used to adjust the relative importance between stability and activity and can be determined by the expert scoring method or the analytic hierarchy process; ModificationFactor is the surface modification contribution coefficient, which is determined according to surface modification parameters (such as modification layer thickness, coverage density, binding strength with active sites, etc.), and the quantitative relationship with surface modification parameters can be established by methods such as multiple linear regression. Calculate the SABF value of each sample, and sort and screen the sunscreen agents according to the SABF value.

[0026] Preferably, step S24 includes the following steps: Step S241: Identify the crystal plane active sites of the crystal phase structure characteristics data according to the surface energy gradient map; Step S242: Calculate the site density of the crystal plane active sites to obtain the active site density data; Step S243: Evaluate the influence of the modifying group according to the active site density data and surface modification parameters to obtain the active site shielding coefficient; Step S244: Analyze the accessibility of the active site density data according to the active site shielding coefficient to obtain the active site accessibility data; Step S245: Generate an active site distribution characteristic map according to the active site accessibility data and the crystal plane active sites.

[0027] In the embodiments of the present invention, the crystal structure is determined according to the crystal phase structure characteristic data, such as anatase TiO2. Then, a crystal structure model is constructed using the crystal modeling tool of the software. Next, the surface energy gradient map is mapped onto the crystal structure model. The crystal plane active sites are identified according to the surface energy gradient values. For example, a threshold is set, and the regions where the surface energy gradient values are higher than the threshold are considered active sites. The identified active sites are displayed on the crystal structure model with different colors or marks. For example, the active sites can be marked in red and the non-active sites in blue. The active site density data is calculated using MaterialsStudio software. The crystal plane to be analyzed is selected, such as the (101) crystal plane of anatase TiO2. Then, the area of this crystal plane is calculated. Next, the number of active sites on this crystal plane is counted. Finally, the number of active sites is divided by the crystal plane area to obtain the active site density data, with the unit of number / nm2. For example, if the area of the (101) crystal plane is 10 nm2 and there are 5 active sites on this crystal plane, the active site density is 0.5 number / nm2. The type and quantity of the modifying groups are determined according to the surface modification parameters. For example, the modifying group is a silane coupling agent and the quantity is 100 number / nm2. Then, the area covered by the modifying groups is calculated. Assuming that the area covered by each silane coupling agent molecule is 0.1 nm2, the area covered by 100 silane coupling agent molecules is 10 nm2. Next, the ratio of the active sites covered by the modifying groups, i.e., the active site shielding coefficient, is calculated. For example, if the crystal plane area is 100 nm2 and the active site density is 0.5 number / nm2, the number of active sites is 50. If the area covered by the modifying groups is 10 nm2, the number of active sites covered by the modifying groups is 10 nm2 × 0.5 number / nm2 = 5. Therefore, the active site shielding coefficient is 5 / 50 = 0.1. The accessibility data is defined as the active site density not shielded by the modifying groups, i.e., active site accessibility data = active site density data × (1 - active site shielding coefficient). For example, if the active site density data is 0.5 number / nm2 and the active site shielding coefficient is 0.1, the active site accessibility data is 0.5 number / nm2 × (1 - 0.1) = 0.45 number / nm2. The active site accessibility data is mapped onto the crystal structure model. Different colors or sizes are used to represent different active site accessibility data. For example, the active sites with high accessibility are represented in red and the active sites with low accessibility are represented in blue. An active site distribution characteristic map is generated, which intuitively shows the accessibility of the active sites on different crystal planes.

[0028] Preferably, step S25 includes the following steps: Step S251: Dynamically evaluate the crystal form stability of the active site distribution characteristic map to obtain a crystal form stability index; Step S252: Perform activity-stability coupling analysis based on the crystal form stability index, the active site distribution characteristic diagram, and the optical efficiency index to obtain an activity-stability correlation matrix; Step S253: Construct a three-dimensional correlation model based on the activity-stability correlation matrix, the surface modification parameter set, and the optical efficiency index to obtain a three-dimensional synergistic effect model; Step S254: Calculate the stability-activity balance factor based on the three-dimensional synergistic effect model and the activity-stability correlation matrix to obtain the stability-activity balance factor.

[0029] In the embodiment of the present invention, using the CASTEP module of the MaterialsStudio software, a supercell model of a sunscreen crystal containing active sites (oxygen vacancies, coordinatively unsaturated Ti atoms) is constructed. For anatase TiO2, the (101) and (001) crystal planes are selected to construct the supercell, and the supercell size is at least 2×2×2. According to the site type and concentration determined by the active site distribution characteristic diagram, corresponding numbers and positions of defects are introduced into the supercell. The exchange-correlation functional is set to GGA-PBE, the plane wave cut-off energy is 450 eV, and the Brillouin zone k-point grid density is 0.04 Å-1. The molecular dynamics (MD) simulation method is used to simulate the dynamic changes of the crystal structure under different temperature (298K, 308K, 318K) and humidity (relative humidity 50%, 70%, 90%) conditions. The Nose-Hoover thermostat and Andersen barostat are used to control the temperature and pressure. The simulation duration is 1 ns, and the time step is 1 fs. Record the changes of the lattice parameters (a, b, c) and the unit cell volume (V) with time during the simulation. Calculate the root mean square deviation (RMSD) of the lattice parameters and the volume expansion coefficient. For crystal forms that may undergo phase transitions (such as the transformation from anatase to rutile), by constructing the phase transition path, calculate the phase transition activation energy. The CompleteLST / QST method is used to search for the transition state, and frequency analysis is performed to verify the transition state. According to the Arrhenius equation, calculate the phase transition rate constant at different temperatures.

[0030] Based on the MD simulation results and phase transition calculation results, comprehensively evaluate the crystal form stability. Construct a crystal form stability index (StabilityIndex). StabilityIndex = 1 / (RMSD_avg × β × Ea), where RMSD_avg is the average root mean square deviation of lattice parameters, β is the volume expansion coefficient, and Ea is the phase transition activation energy (if there is a phase transition). The larger the StabilityIndex value, the higher the crystal form stability. The active site distribution characteristic map provides information on the type, density, and accessibility of active sites. The density and accessibility of active sites are weighted and summed to obtain a comprehensive activity index (ActivityIndex). For example, ActivityIndex = 0.6 × oxygen vacancy density × oxygen vacancy accessibility + 0.4 × coordinatively unsaturated Ti atom density × Ti atom accessibility. The weight coefficients are determined according to the relative contributions of different types of active sites to the sunscreen effect. Construct a three-dimensional scatter plot of activity (ActivityIndex), stability (StabilityIndex), and optical efficiency index (OEI). Use OriginPro 2021 software to plot the scatter plot and perform data visualization analysis. Calculate the Pearson correlation coefficient (r) between ActivityIndex, StabilityIndex, and OEI pairwise. The closer the absolute value of r is to 1, the stronger the linear correlation. A positive value indicates a positive correlation, and a negative value indicates a negative correlation. Construct an activity-stability correlation matrix R. R = [[1, r(A, S), r(A, O)], [r(S, A), 1, r(S, O)], [r(O, A), r(O, S), 1]], where r(A, S) represents the Pearson correlation coefficient between ActivityIndex and StabilityIndex, r(A, O) represents the Pearson correlation coefficient between ActivityIndex and OEI, and r(S, O) represents the Pearson correlation coefficient between StabilityIndex and OEI. This matrix reflects the mutual relationship between activity, stability, and optical efficiency. Select the comprehensive evaluation index of active sites (ActivityIndex), crystal form stability index (StabilityIndex), and optical efficiency index (OEI) as the three coordinate axes to construct a three-dimensional space. Project the sample data points with different surface modification parameters (modifier type, coverage density, Zeta potential) into this three-dimensional space. Use the Scikit-learn library in Python 3.8 to perform multiple regression analysis. Try various regression models, including multiple linear regression, polynomial regression, and support vector regression (SVR). Use ActivityIndex, StabilityIndex, and surface modification parameters as independent variables and OEI as the dependent variable to train the regression model.The cross-validation method is used to evaluate the prediction performance of the model, and the model with the highest R2 value and the smallest root mean square error (RMSE) is selected. For the SVR model, the grid search method is used to optimize the kernel function type (linear, polynomial, radial basis function) and hyperparameters (C, γ). Based on the optimal regression model, a functional relationship of OEI = f(ActivityIndex, StabilityIndex, ModificationParameters) is constructed, where ModificationParameters represents the surface modification parameters. This functional relationship is the three-dimensional synergistic model, which quantitatively describes the synergistic effect among activity, stability, surface modification, and optical efficiency. Based on the three-dimensional synergistic model, the SABF calculation formula is designed. Considering maximizing stability on the premise of ensuring a certain activity (OEI is higher than the set threshold OEI_threshold). At the same time, the regulatory effects of surface modification on activity and stability are introduced. SABF = [α×(StabilityIndex / StabilityIndex_max)+Q×(ActivityIndex / ActivityIndex_max)]×ModificationFactor, where Q = (1 - α) when OEI≥OEI_threshold; SABF = 0 when OEI < OEI_threshold. Among them, StabilityIndex is the crystal form stability index obtained in step S251, and StabilityIndex_max is the maximum value among all test samples; ActivityIndex is the comprehensive evaluation index of active sites calculated in step S252, and ActivityIndex_max is the maximum value among all test samples; α is the weight coefficient (0 < α < 1), which is used to adjust the relative importance between stability and activity and can be determined by the analytic hierarchy process (AHP); ModificationFactor is the surface modification contribution coefficient. According to the surface modification parameters (modifier type, coverage density, Zeta potential) obtained in step S22, a quantitative relationship model between ModificationFactor and surface modification parameters is established by methods such as multiple linear regression or neural network. For example, ModificationFactor = β1×modifier type+β2×coverage density+β3×Zeta potential+β0, where β0, β1, β2, β3 are regression coefficients. Substitute the values of each parameter into the SABF calculation formula to calculate the SABF value of each sample. The higher the SABF value, the better the balance between the activity and stability of the sunscreen agent.

[0031] Preferably, the interface contact dynamic simulation described in step S3 includes: Characterize the surface material of the original sample of the sunscreen according to the stability-activity balance factor to obtain surface characteristic parameters; Determine the contact mechanics parameters according to the surface characteristic parameters and the multi-level skin structure to obtain the contact mechanics parameters; Simulate the dynamics of the initial contact process according to the contact mechanics parameters to obtain the initial contact dynamics trajectory; Precisely calculate the contact area according to the initial contact dynamics trajectory to obtain the multi-scale contact area map; Construct the contact pressure field finely according to the multi-scale contact area map to obtain the interface pressure distribution contour map; Simulate and analyze the spreading dynamics according to the interface pressure distribution contour map to obtain the spreading dynamics characteristic data; Analyze the particle aggregation and dispersion mechanism according to the spreading dynamics characteristic data and the surface pressure distribution contour map to obtain the aggregation and dispersion distribution map; Construct a time evolution sequence for the aggregation and dispersion distribution map and the spreading dynamics characteristic data; Integrate the interface contact dynamic characteristics for the time evolution sequence and the aggregation and dispersion distribution map to obtain the interface contact dynamic characteristic map.

[0032] In the embodiments of the present invention, an atomic force microscope (AFM, Bruker Dimension Icon) is used to characterize the morphology of sunscreen particles. The tapping mode is selected, and a silicon probe (Tap300Al-G, spring constant 40 N / m, resonance frequency 300 kHz) is used. The scanning range is set to 1 µm × 1 µm, and the scanning rate is 1 Hz. The AFM images are analyzed to measure the average height, roughness (root mean square roughness Rq), and surface characteristic size of the particles. X-ray photoelectron spectroscopy (XPS, Thermo Scientific K-Alpha) is used to analyze the surface element composition and chemical state of the sunscreen. An Al Kα ray source (1486.6 eV) is used, the full spectrum scanning range is 0 - 1200 eV, the step size is 1 eV, and the pass energy is 50 eV; the narrow spectrum scans the high-resolution spectra of C1s, O1s, Ti2p, and other modifying elements (such as Si, P), the step size is 0.1 eV, and the pass energy is 20 eV. The XPS spectra are deconvoluted and fitted to quantitatively analyze the atomic percentages and chemical states of each element. Combining with the SABF value obtained in step S25, the relationship between SABF and surface roughness, element composition, and chemical state is analyzed. For example, the relationship curves of SABF with surface oxygen vacancy concentration and Ti3+ / Ti4+ ratio are constructed. The morphological parameters (average height, Rq, surface characteristic size) measured by AFM, the surface element composition and chemical state measured by XPS, and the specific surface area, pore size distribution, surface charge, etc. data obtained in the previous steps are integrated into a set of surface characteristic parameters. A multi-layer skin structure model is constructed. The thickness of the stratum corneum is set to 15 µm, divided into 15 layers, each layer 1 µm; the thickness of the epidermis is set to 80 µm; the thickness of the dermis is set to 2 mm. The finite element analysis software COMSOL Multiphysics 5.6 is used to construct the skin model. The stratum corneum is meshed using brick-shaped elements (Hexahedral element), and the epidermis and dermis are meshed using tetrahedral elements (Tetrahedral element). Mesh size: 0.5 µm for the stratum corneum, 5 µm for the epidermis, and 50 µm for the dermis. According to the literature data, the mechanical parameters of each layer of the skin are set. Stratum corneum: Young's modulus 10 MPa, Poisson's ratio 0.4; Epidermis: Young's modulus 0.1 MPa, Poisson's ratio 0.45; Dermis: Young's modulus 0.05 MPa, Poisson's ratio 0.45. Considering the viscoelastic properties of the skin, the Prony series is introduced to describe the stress relaxation behavior. According to the particle size distribution data measured by the laser particle size analyzer in step S11, a model of the sunscreen particle population that conforms to the actual distribution is generated. The particle shape is assumed to be spherical. The NanoScope Analysis software is used to analyze the AFM images to obtain the Young's modulus of the sunscreen particles. The indentation mode is used, and a diamond probe (DCP, tip curvature radius 20 nm) is selected.Indentation tests were performed on individual sunscreen agent particles with a maximum load of 500 nN and a loading / unloading rate of 50 nN / s. The load-displacement curve was analyzed according to the Oliver-Pharr method to calculate the Young's modulus of the particles. The Poisson's ratio was set to 0.3. Based on the Hertz contact theory and the JKR (Johnson-Kendall-Roberts) theory, the contact force between the particles and the skin was calculated. Van der Waals forces and electrostatic forces were considered. The Van der Waals force was calculated using the Hamaker constant, and the value of the Hamaker constant was 6×10-20 J. The electrostatic force was calculated using the double-layer model, and the surface charge density was calculated based on the Zeta potential. The friction coefficient was set to 0.4. The mechanical parameters of each skin layer, the Young's modulus of the sunscreen agent particles, the Poisson's ratio, the Hamaker constant, the surface charge density, and the friction coefficient were used as contact mechanical parameters. The discrete element method (DEM) was used to simulate the initial contact process between the sunscreen agent particle swarm and the skin surface. The LIGGGHTS open-source software was used for DEM simulation. Based on the particle size distribution data obtained in step S11 and the particle model generated in Example 2 of step S3 (interface contact dynamic simulation section), 10,000 sunscreen agent particles were randomly generated in the simulation area. The initial position of the particles was set at a height of 1 µm from the skin surface. The skin surface was set as a fixed boundary. The contact force model between particles and between particles and the skin used the Hertz-Mindlin no-slip model. The normal force was calculated according to the Hertz theory, and the tangential force was calculated according to the Mindlin theory, considering Coulomb friction. According to the contact mechanical parameters obtained in Example 2 of step S3 (interface contact dynamic simulation section), the simulation parameters were set. Young's modulus, Poisson's ratio, Hamaker constant, surface charge density, and friction coefficient. The time step was set to 1×10-9 s, and the total simulation duration was 1×10-6 s. During the simulation, the changes in the position, velocity, force, and torque of each particle over time were recorded. The coordinates and velocity data of all particles at each time step were output as the initial contact dynamics trajectory. The initial contact dynamics trajectory obtained in Example 3 of step S3 (interface contact dynamic simulation section) was analyzed to calculate the contact area between the particles and the skin surface. The Python 3.8 programming language was used, combined with the NumPy and SciPy libraries for data processing. The coordinates and radius data of all particles at each time step were read. For each particle, the distance between it and the skin surface was calculated. If the distance was less than the sum of the particle radius and the skin surface roughness (determined according to the AFM measurement results), it was considered that the particle was in contact with the skin surface.According to Hertz contact theory, calculate the contact radius \(a\) at each contact point: \(a = (3FR / 4E^*)^{1 / 3}\), where \(F\) is the normal contact force (obtained from the DEM simulation results), \(R\) is the particle radius, \(E\) is the effective elastic modulus, \(1 / E=(1 - ν_1^2) / E_1+(1 - ν_2^2) / E_2\), \(E_1\) and \(ν_1\) are the Young's modulus and Poisson's ratio of the particle, and \(E_2\) and \(ν_2\) are the Young's modulus and Poisson's ratio of the skin. Calculate the contact area at each contact point: \(A = \pi a^2\). Sum up the contact areas of all contact points to obtain the total contact area. Calculate the ratio of the contact area to the skin surface area to get the contact rate. To obtain a multi-scale contact area map, divide the skin surface into multiple grids (e.g., 100×100). Count the number of contact points and the total contact area within each grid. Generate a contact point density map and a contact area distribution map. The color shade indicates the magnitude of the contact point density or contact area. According to Hertz contact theory, calculate the contact pressure \(P\) at each contact point: \(P=(6FE^2 / \pi^3R^2)^{1 / 3}\), where \(F\) is the normal contact force (obtained from the DEM simulation results), \(R\) is the particle radius, and \(E\) is the effective elastic modulus. Divide the skin surface into the same grids as in Example 4 of Step S3. For each grid, calculate the average value of the contact pressures of all contact points within it as the average contact pressure of this grid. To consider the influence of skin surface roughness on the pressure distribution, introduce a correction factor. Calculate the correction factor according to the skin surface roughness \(R_q\) measured by AFM: \(C = 1 + k×R_q / a\), where \(a\) is the average contact radius and \(k\) is an empirical coefficient (e.g., take 0.5). Multiply the average contact pressure of each grid by the correction factor to obtain the corrected pressure value. Generate a pressure distribution contour map. The color shade indicates the pressure magnitude, with red representing the high-pressure area and blue representing the low-pressure area. Superimpose and display the contact point positions on the contour map for comparative analysis of the relationship between the pressure distribution and the contact point distribution. Calculate the maximum contact pressure and the average contact pressure. Construct a three-dimensional simulation region with dimensions of 10μm×10μm×5μm. Set the bottom boundary as the skin surface with micro-topography (constructed according to the AFM measurement results). The initial distribution of sunscreen particles is determined according to the simulation results of Example 3 of Step S3. Adopt the D3Q19 model and set the lattice spacing to 50nm. Set the kinematic viscosity of the fluid (simulating the sunscreen matrix) to \(1×10^{-5}m^2 / s\) and the density to 1000kg / m3. The interaction between particles and the fluid is treated using the immersed boundary method (IBM). The inter-particle interaction forces include van der Waals forces and electrostatic forces, which are calculated according to the parameters in Example 2 of Step S3. Consider the driving effect of surface tension on spreading. Calculate the interfacial tension coefficient \(\gamma\) between the sunscreen and the skin surface according to the contact angle measurement results in Step S23. Introduce a surface tension term in the LBM model. The total simulation duration is 1ms and the time step is 1ns. During the simulation process, record the velocity field and density field of the fluid and particles at each time step. Calculate the change in the spreading area of the sunscreen on the skin surface over time.The spreading area is defined as the skin surface area covered by the sunscreen agent. The spreading rate is calculated, which is the rate of change of the spreading area with time. Analyze the curves of the spreading area and spreading rate changing with time, and extract the characteristic parameters of spreading kinetics, including the maximum spreading area, the time required to reach the maximum spreading area, the average spreading rate, etc. Use the Python 3.8 programming language, combined with the NumPy, SciPy, and Matplotlib libraries for data analysis and visualization. Calculate the interparticle interaction energy. According to the DLVO theory, the interparticle interaction energy includes the van der Waals potential energy and the double-layer electrostatic potential energy. The van der Waals potential energy is calculated using the Hamaker constant, and the electrostatic potential energy is calculated using the Debye-Hückel approximation. Calculate the interaction energy according to the parameters (Hamaker constant, Zeta potential) in Example 2 of Step S3. Analyze the relationship between the interparticle interaction energy and the particle spacing. If the interaction energy is negative and has a large absolute value, it indicates that there is a strong attraction between the particles and aggregation is likely to occur; if the interaction energy is positive or negative but has a small absolute value, it indicates that there is a repulsive force or a weak attraction between the particles and they are easy to disperse. Divide the simulation area into multiple grids (e.g., 100×100). Count the number of particles in each grid. Calculate the particle concentration of each grid, which is the number of particles divided by the grid area. Generate a particle concentration distribution map. The color depth represents the particle concentration level, with red indicating the high-concentration area (aggregation area) and blue indicating the low-concentration area (dispersion area). Analyze the relationship between the particle concentration distribution and the interfacial pressure distribution. Calculate the Pearson correlation coefficient between the particle concentration and the interfacial pressure. If the correlation coefficient is positive, it indicates that the particles tend to aggregate in the high-pressure area; if the correlation coefficient is negative, it indicates that the particles tend to aggregate in the low-pressure area. Construct an aggregation index. For example, use the Morisita index: Iδ = q×Σni(ni - 1) / N(N - 1), where q is the number of grids, ni is the number of particles in the i-th grid, and N is the total number of particles. Iδ > 1 indicates an aggregated distribution, Iδ = 1 indicates a random distribution, and Iδ < 1 indicates a uniform distribution. Select multiple representative time points, such as 0.1 ms, 0.2 ms, 0.5 ms, 1 ms. For each time point, generate the corresponding aggregation-dispersion distribution map (particle concentration distribution map) and spreading state map (sunscreen agent coverage area). Calculate the key parameters at each time point, including: the total contact area (calculated according to Example 4 of Step S3), the contact rate, the average contact pressure (calculated according to Example 5 of Step S3), the spreading area, the spreading rate, the aggregation index (calculated according to Example 7 of Step S3). Arrange these parameters in chronological order to form a time series. Construct a time evolution model. For example, an exponential function or a logarithmic function can be used to fit the curve of the spreading area changing with time. Use an autoregressive model (AR) or a moving average model (MA) to describe the change of the aggregation index with time.Time series analysis and modeling are performed using the statsmodels library in Python 3.8. The goodness of fit and prediction accuracy of the model are evaluated. Visualization is performed using the Matplotlib and Mayavi libraries in Python 3.8. A three-dimensional graph is created, where the x-axis and y-axis represent the two-dimensional coordinates of the skin surface, and the z-axis represents time. At each time point, the corresponding aggregation-dispersion distribution map (particle concentration distribution map) is superimposed on the three-dimensional graph as a two-dimensional color map. The color intensity represents the particle concentration.

[0033] Preferably, the molecular-level adsorption and desorption described in step S3 includes: Performing molecular docking simulation calculations based on the interface contact dynamic characteristic map to obtain molecular docking conformation data; Performing binding energy calculations based on the molecular docking conformation data to obtain binding energy distribution data; Analyzing the components of the interaction force of the binding energy distribution data to obtain an interaction force component map; Performing environmental sensitivity dynamic simulation based on the interaction force component map and the binding energy distribution data to obtain environmental factor response data; Predicting the long-time scale adsorption behavior based on the environmental factor response data and the interaction force component map to obtain an adsorption stability time curve; Performing comprehensive integration of the molecular adsorption mechanism on the adsorption stability time curve, the environmental factor response data, and the interaction force component map to obtain molecular adsorption mechanism data.

[0034] In the embodiments of the present invention, one particle is selected from the aggregation region and one particle is selected from the dispersion region. According to the crystal structure determined by XRD analysis in step S21 and the active sites identified in step S24, an atomic-level model of the sunscreen particle surface is constructed. For example, for anatase TiO2, the (101) plane is selected as the main exposed plane, and a surface model containing oxygen vacancies and coordinatively unsaturated Ti atoms is constructed. The size of the surface model is at least 2 nm × 2 nm. According to the main components of the skin stratum corneum, molecular models of keratin, ceramide, and cholesterol are constructed. The molecular models are parameterized using the CHARMM36 force field. Molecular docking simulations are performed using AutoDockVina software. The sunscreen surface model is set as the receptor, and the keratin, ceramide, and cholesterol molecules are set as ligands respectively. The size of the grid box is set so that it can cover the entire sunscreen surface. The grid spacing is set to 0.375 Å. The exhaustiveness parameter is set to 20, and multiple docking calculations are performed. For each ligand-receptor combination, AutoDock Vina generates multiple docking conformations and sorts the conformations according to the scoring function. The 10 conformations with the lowest scoring function values are selected as candidate docking modes. PDB format files of each candidate docking mode are output, recording their three-dimensional coordinates and scoring function values, to obtain molecular docking conformation data. The molecular mechanics / Poisson-Boltzmann surface area (MM / PBSA) method is used to calculate the binding free energy. The g_mmpbsa tool is used for the calculation. For each candidate docking conformation, a 100-ps molecular dynamics (MD) simulation is performed. The GROMACS software is used for the MD simulation. The CHARMM36 force field and the TIP3P water model are used. The temperature is set to 310 K (simulating the skin surface temperature), and the pressure is set to 1 atm. Periodic boundary conditions are adopted. One frame is saved every 1 ps, and a total of 100 frames of trajectories are obtained. MM / PBSA calculations are performed on the trajectories obtained from the MD simulation. The individual components of the binding free energy are calculated, including van der Waals energy, electrostatic energy, polar solvation energy, and nonpolar solvation energy. The binding free energy ΔGbind = ΔEvdw + ΔEelec + ΔGpolar + ΔGnonpolar. The average binding free energy of each candidate docking conformation is obtained by averaging the binding free energies calculated for the 100 frames of trajectories. The candidate docking conformations are sorted according to the average binding free energy. The conformation with the lowest binding free energy is selected as the most favorable binding mode. Statistical analysis is performed on the binding free energies and their components of all candidate docking conformations to generate binding energy distribution data. For each candidate docking conformation, the contributions of van der Waals interactions, electrostatic interactions, hydrogen bond interactions, and hydrophobic interactions are calculated respectively. The van der Waals interaction energy is directly obtained from the MM / PBSA calculation results. The electrostatic interaction energy is also directly obtained from the MM / PBSA calculation results.To analyze the electrostatic interactions more precisely, the Coulomb interaction energy between the sunscreen surface and the components of the skin stratum corneum was calculated: E_coulomb = Σ(qi × qj) / (4πε0εrrij), where qi and qj are the charges of atom i and atom j respectively (determined according to the CHARMM36 force field), ε0 is the vacuum permittivity, εr is the relative permittivity (taken as 80), and rij is the distance between atom i and atom j. The VMD software was used to analyze hydrogen bonds. The geometric criteria for hydrogen bonds were set: the donor-acceptor distance is less than 3.5 Å, and the hydrogen-acceptor-acceptor front atom angle is greater than 120°. The number and average bond length of hydrogen bonds in each candidate docking conformation were counted. The contribution of hydrogen bonds to the binding free energy was calculated. It was assumed that the contribution of each hydrogen bond is -2 kcal / mol. The hydrophobic interaction was evaluated by calculating the change in the solvent-accessible surface area (SASA). The NACCESS software was used to calculate the SASA of the sunscreen surface and the components of the skin stratum corneum before and after docking. The hydrophobic interaction energy ΔGhydrophobic = γ × ΔSASA, where γ is the surface tension coefficient (taken as 0.03 kcal / (mol·Å2)), and ΔSASA is the change in SASA. The van der Waals interaction energy, electrostatic interaction energy, hydrogen bond interaction energy, and hydrophobic interaction energy were normalized to obtain the relative contributions of each interaction force component. A radar chart was plotted to show the relative magnitudes of each interaction force component, obtaining the interaction force component map. The docking conformation with the lowest binding free energy was selected as the initial conformation. The GROMACS software was used for molecular dynamics (MD) simulations. The simulation system included the sunscreen surface, the components of the skin stratum corneum (keratin, ceramide, or cholesterol), and water molecules. The temperatures were set to 298 K, 308 K, and 318 K respectively (simulating different environmental temperatures). The relative humidities were set to 50%, 70%, and 90% respectively (achieved by changing the number of water molecules). The pH values were set to 4.5, 5.5, and 6.5 respectively (simulating the pH value range of the skin surface, achieved by adjusting the protonation state of charged amino acid residues). For each environmental condition, a 1-ns MD simulation was performed. The CHARMM36 force field and the TIP3P water model were used. The Nose-Hoover thermostat and the Parrinello-Rahman barostat were used to control the temperature and pressure. One frame was saved every 1 ps, and a total of 1000 frames of trajectories were obtained. The trajectories obtained from the MD simulations were analyzed. The changes in parameters such as the binding free energy (using the MM / PBSA method), the number of hydrogen bonds, and the solvent-accessible surface area (SASA) over time were calculated. The average values and standard deviations of each parameter under different environmental conditions were calculated. The effects of temperature, humidity, and pH on the binding free energy, the number of hydrogen bonds, and SASA were analyzed. For example, a curve of the binding free energy versus temperature was constructed to analyze whether an increase in temperature would lead to weakened binding. A curve of the number of hydrogen bonds versus humidity was constructed to analyze whether an increase in humidity would affect the stability of the hydrogen bond network.The simulation results are sorted to obtain environmental factor response data. Since the time scale of molecular dynamics simulation is limited (usually in the nanosecond to microsecond range), it is impossible to directly simulate the adsorption process over several hours. The kinetic Monte Carlo (KMC) method is used for long-time scale simulation. Based on the binding free energy and the number of hydrogen bonds obtained from MD simulation, the rate constants of adsorption and desorption are calculated. It is assumed that the adsorption process is a first-order reaction, and the desorption process is also a first-order reaction. The adsorption rate constant ka = A × exp(-Ea / RT), where A is the pre-exponential factor, Ea is the adsorption activation energy (negatively correlated with the binding free energy), R is the gas constant, and T is the temperature. The desorption rate constant kd = B × exp(-Ed / RT), where B is the pre-exponential factor and Ed is the desorption activation energy (positively correlated with the binding free energy). The pre-exponential factors A and B can be estimated from the MD simulation results or set according to empirical values. Based on ka and kd, the probabilities of adsorption and desorption are calculated: pa = ka / (ka + kd), pd = kd / (ka + kd). The Gillespie algorithm is used for KMC simulation. The simulation duration is set to 8 hours. At each time step, an adsorption event or a desorption event is randomly determined according to pa and pd. The change in the number of skin cutin layer component molecules adsorbed on the sunscreen surface over time is recorded. The adsorption coverage is calculated, which is the number of adsorbed molecules divided by the maximum number of adsorbable sites on the sunscreen surface. A curve of the adsorption coverage versus time, i.e., the adsorption stability time curve, is plotted. Considering factors such as friction and sweat in the actual use scenario, interference events are introduced in the KMC simulation. For example, a friction event is simulated every 30 minutes, and a certain proportion of adsorbed molecules are randomly removed. A sweat secretion event is simulated every 1 hour to increase the number of water molecules in the system, and ka and kd are adjusted according to the environmental factor response data obtained in Example 4 of step S3. To consider the influence of different environmental conditions, the adsorption stability time curves under different temperature, humidity, and pH value conditions are simulated. The differences between the curves under different conditions are compared, and the influence of environmental factors on the long-time adsorption stability is analyzed. Using data visualization methods, the above data (adsorption stability time curve, environmental factor response data, and interaction force component maps) are visually presented in the form of charts. Combining quantitative data and qualitative descriptions forms complete molecular adsorption mechanism data. This data can comprehensively reflect the interaction characteristics between the sunscreen and skin cutin layer components, as well as the influence of environmental factors on adsorption stability.

[0035] Preferably, the simulation of the protective film formation process in step S4 includes: Construct a transient diffusion equation based on the molecular adsorption mechanism data and the interfacial contact dynamic characteristic diagram to obtain transient diffusion kinetic data; Construct an initial particle distribution state diagram of the sunscreen based on the transient diffusion kinetic data and the molecular adsorption mechanism data; Calculate the dynamic spreading trajectory using the initial particle distribution state diagram to obtain the spreading trajectory of the sunscreen agent; Simulate the interaction between particles based on the spreading trajectory of the sunscreen agent to obtain the dynamic data of particle aggregation; Analyze the microscopic reorganization process of the dynamic data of particle aggregation to obtain the molecular reorganization configuration spectrum; Calculate the evolution of the film layer structure based on the molecular reorganization configuration spectrum to obtain the timing data of film layer construction; Calculate the thickness spatio-temporal distribution of the timing data of film layer construction; Evaluate the coverage rate and density based on the thickness spatio-temporal distribution to obtain the film layer coverage characteristic diagram; Perform stability-activity mapping correlation based on the film layer coverage characteristic diagram to obtain the structure-efficacy correlation data; Generate a comprehensive dynamic map of the protective film by combining the structure-efficacy correlation, the film layer coverage characteristic diagram, and the thickness spatio-temporal distribution to obtain the dynamic map of film formation.

[0036] In the embodiments of the present invention, Fick's second law is used to describe the diffusion process: ∂c / ∂t = D∇²c, where c is the concentration of sunscreen particles, t is time, D is the diffusion coefficient, and ∇² is the Laplace operator. Considering the influence of adsorption on the diffusion process, an adsorption term is introduced into the equation: ∂c / ∂t = D∇²c - ka×c×(1 - θ) + kd×θ, where ka is the adsorption rate constant, kd is the desorption rate constant, and θ is the surface coverage (the ratio of the number of sunscreen particles adsorbed on the skin surface to the maximum number of particles that can be adsorbed). ka and kd are determined according to the values obtained from the KMC simulation in Example 5 of Step S3 (Molecular-level adsorption analysis part). The diffusion coefficient D is related to the particle size, temperature, and medium viscosity. D is calculated according to the Einstein-Stokes equation: D = kT / (6πηr), where k is the Boltzmann constant, T is the absolute temperature, η is the viscosity of the medium (simulated sunscreen matrix), and r is the hydrodynamic radius of the particle. The medium viscosity is determined according to the sunscreen formulation or measured by a rheometer. The transient diffusion equation is numerically solved using the finite difference method (FDM) or the finite element method (FEM). The FEM solution is performed using COMSOL Multiphysics software. A two-dimensional computational domain is constructed to simulate the skin surface. The size of the computational domain is determined according to the results of Example 9 of Step S3 (Interface contact dynamic simulation part). Boundary conditions are set. At the skin surface boundary, the adsorption and desorption fluxes are set. At the other boundaries of the computational domain, zero-flux boundary conditions are set. Initial conditions are set. At the initial moment, the concentration distribution of sunscreen particles is determined according to the results of Example 3 of Step S3 (Interface contact dynamic simulation part). The solution time is set. The solution time is set to 1 hour, and the time step is set to 1 minute. The transient diffusion equation is solved to obtain the variation of the concentration c of sunscreen particles with time and space, and the variation of the surface coverage θ with time, as transient diffusion kinetic data. The transient diffusion kinetic data provides the distribution of the concentration of sunscreen particles on the skin surface at the initial moment (t = 0). The Python 3.8 programming language, combined with the NumPy and Matplotlib libraries, is used for data processing and visualization. The concentration data at t = 0 is extracted from the transient diffusion kinetic data. The concentration data is converted into a particle number distribution. According to the sunscreen particle size distribution data measured by the laser particle size analyzer in Step S11, the concentration data is converted into the number of particles of different sizes. A graph of the initial distribution state of the particles is generated. A scatter plot is used to represent the particle distribution. The x-axis and y-axis represent the two-dimensional coordinates of the skin surface. Each point represents a sunscreen particle, the size of the point represents the relative size of the particle, and the color of the point represents the adsorption state (for example, red represents adsorbed, and blue represents unadsorbed). The adsorption state is determined according to the molecular adsorption mechanism data. If the binding free energy of the sunscreen particle at a certain position to the components of the skin stratum corneum is lower than the set threshold, the particle is considered adsorbed. The microscopic morphology of the skin surface (constructed according to the AFM measurement results) is superimposed and displayed in the graph.For example, skin texture and pores are represented by contour lines. The molecular dynamics (MD) simulation or coarse-grained molecular dynamics (CGMD) simulation method is adopted. Since the number of sunscreen particles is large and the simulation time is long, CGMD simulation is more appropriate. The LAMMPS software is used for CGMD simulation. Each sunscreen particle is represented as a coarse-grained bead. The interaction potential between the beads adopts the Lennard-Jones (LJ) potential: V(r)=4ε[(σ / r)12-(σ / r)6], where r is the bead spacing, ε is the potential well depth, and σ is the effective diameter. The values of ε and σ are determined according to the physicochemical properties of the sunscreen particles (such as Hamaker constant, surface tension). The skin surface is set as a fixed boundary, and its microscopic morphology is constructed according to the AFM measurement results. The interaction between the particles and the skin surface also adopts the LJ potential, and the parameters are determined according to the interfacial tension between the sunscreen and the skin. The simulation temperature is set to 310K (simulating the skin surface temperature). The NVT ensemble (constant number of particles, volume, and temperature) is adopted. The time step is set to 10fs, and the total simulation duration is 100ns. During the simulation, the change of the position of each coarse-grained bead (sunscreen particle) over time is recorded. One frame is saved every 1ps, and a total of 10,000 frames of trajectories are obtained. The trajectory data is visualized to obtain the dynamic spreading trajectory of the sunscreen particles. The VMD software is used for visualization. The movement paths of different particles are represented by trajectory lines of different colors. The molecular dynamics (MD) or coarse-grained molecular dynamics (CGMD) simulation method (the same as in Example 3 of Step S3) is adopted. The LAMMPS software is used for simulation. In the simulation system, in addition to considering the interaction between the sunscreen particles and the skin surface, the interaction between the particles also needs to be considered. The inter-particle interaction includes van der Waals force, electrostatic force, and hydrogen bond interaction (if there are groups on the sunscreen surface that can form hydrogen bonds). The van der Waals force is described by the LJ potential. The electrostatic force is described by the Coulomb interaction: V(r)=(qi×qj) / (4πε0εrr), where qi and qj are the charges of particles i and j respectively (calculated according to the Zeta potential), ε0 is the vacuum permittivity, εr is the relative permittivity (determined according to the dielectric constant of the sunscreen matrix), and r is the particle spacing. If there is hydrogen bond interaction, the Morse potential or a special potential function for hydrogen bonds is used to describe it. The simulation parameter settings are similar to those in Example 3 of Step S3. During the simulation, the change of the position, velocity, and force of each particle over time is recorded. One frame is saved every 1ps. Analyze the change of the particle spacing over time. Calculate the radial distribution function g(r): g(r)=(n(r) / ρ) / (4πr2dr), where n(r) is the number of particles in the range from distance r to r+dr from the reference particle, and ρ is the average particle number density of the system. g(r) reflects the distribution of the particle spacing. The appearance of a peak in g(r) indicates that the particles tend to aggregate at this distance.Calculate the mean squared displacement (MSD) of the particles: MSD(t) = <|r(t) - r(0)|²>, where r(t) is the position of the particle at time t, r(0) is the initial position of the particle, and < > represents the ensemble average. MSD reflects the average amplitude of particle motion. A linear increase in MSD with time indicates that the particles undergo free diffusion; a slowdown in the increase of MSD with time indicates that the particle motion is restricted and aggregation will occur. Visualize the simulation results. Use VMD software to plot the motion trajectories of the particles, and use different colors to represent different aggregation states (for example, red represents aggregates and blue represents dispersed particles). Generate dynamic data on particle aggregation, including the radial distribution function, mean squared displacement, and changes in aggregate size over time. Select representative aggregates from the dynamic data on particle aggregation. Selection criteria for aggregates: relatively large size (containing at least 10 particles) and long existence time (existing for at least 10 ns). For the selected aggregates, analyze the change in the coordination number of each particle over time. The coordination number is defined as the number of particles whose distance from the center of the target particle is less than 1.5 times its effective diameter. Calculate the average coordination number of the particles inside the aggregate. Analyze the orientation of the particles. For non-spherical particles (such as rod-shaped and sheet-shaped particles), calculate the orientation parameter of the particles. For example, for rod-shaped particles, the orientation parameter S can be defined as S = <(3cos²θ - 1) / 2>, where θ is the angle between the long axis of the particle and the reference direction (such as the normal direction of the skin surface), and < > represents the ensemble average. S = 1 indicates that the particles are completely aligned parallel to the reference direction, S = -0.5 indicates that the particles are completely perpendicular to the reference direction, and S = 0 indicates that the particles are randomly oriented. Analyze the relative positions of the particles inside the aggregate. Calculate the centroid coordinates of the particles. Construct a three-dimensional structural model of the aggregate. Use VMD software for visualization. Use different colors or shapes to represent particles with different coordination numbers and different orientations. Analyze the morphology of the aggregate. The aggregate can be spherical, ellipsoidal, chain-shaped, layered, etc. Calculate the shape factor of the aggregate. For example, for an ellipsoidal aggregate, calculate the ratio of its major axis to its minor axis. Analyze the porosity of the aggregate. Calculate the proportion of the volume of the voids inside the aggregate to the total volume. The Voronoi tessellation method can be used to calculate the porosity. Select multiple time points (for example, every 10 ns) to generate structural snapshots of the aggregate. Compare the structural snapshots at different time points to analyze the evolution process of the aggregate. For example, observe whether the shape of the aggregate changes, whether the coordination number and orientation of the particles change, and whether the porosity changes, etc. Statistically analyze the structural characteristics of the aggregate (coordination number, orientation parameter, shape factor, porosity, etc.) to generate a molecular reorganization configuration spectrum. The molecular reorganization configuration spectrum shows the microscopic arrangement and evolution law of the sunscreen particles inside the aggregate in the form of a graph. For example, a histogram can be used to represent the distribution of the coordination number, a rose diagram can be used to represent the distribution of the orientation parameter, and a curve diagram can be used to represent the changes in the shape factor and porosity over time.Coarse-grained molecular dynamics (CGMD) simulation or dissipative particle dynamics (DPD) simulation methods are adopted. Due to the need to simulate a large system and a long time, DPD simulation is more suitable. The DPD simulation is carried out using the LAMMPS software. Each sunscreen particle is represented as a dissipative particle. The interaction potential between particles includes conservative force, dissipative force, and random force. The conservative force is calculated according to the inter-particle interaction parameters determined in Example 4 of Step S3. The dissipative force and random force are determined according to the DPD theory and are used to simulate the viscosity and thermal fluctuations of the system. The skin surface is set as a fixed boundary, and its microscopic morphology is constructed according to the AFM measurement results. The simulation parameters are set similar to those in Example 3 and Example 4 of Step S3, but the simulation time is longer, such as 1 µs. During the simulation, the changes in the position and velocity of each particle over time are recorded. One frame is saved every 100 ps, and a total of 10,000 frames of trajectories are obtained. Analyze the formation process of the film layer. Observe whether the particles form a continuous film layer and whether the film layer covers the entire skin surface. Calculate the thickness of the film layer. The simulation area is divided into multiple thin layers along the direction perpendicular to the skin surface, and the density of sunscreen particles in each thin layer is calculated. The position corresponding to the thin layer with the maximum density is the upper surface of the film layer. The film layer thickness is defined as the distance between the upper surface and the skin surface. Calculate the porosity of the film layer. The Voronoi tessellation method is used to calculate the volume of the voids inside the film layer. The porosity is defined as the ratio of the void volume to the total volume of the film layer. Analyze the uniformity of the film layer. Calculate the fluctuations in the film layer thickness at different positions. The root mean square deviation (RMSD) can be used as an indicator of the film layer uniformity. Visualize the simulation results. The VMD software is used to draw the three-dimensional structure snapshots of the film layer, and different colors are used to represent regions with different thicknesses or densities. Generate the time-series data of film layer construction, including the changes in parameters such as film layer thickness, porosity, and uniformity over time. Extract the data of the film layer thickness change over time from the time-series data of film layer construction. Calculate the average film layer thickness. The simulation area (skin surface) is divided into multiple grids (e.g., 100×100). For each grid, calculate the average value of the film layer thickness within the grid. Generate the spatial distribution map of the film layer thickness. A two-dimensional color map is used to represent the thickness distribution, and the color depth represents the thickness magnitude. Superimpose and display the microscopic morphology of the skin surface (constructed according to the AFM measurement results) in the figure. Analyze the relationship between the film layer thickness distribution and the skin surface morphology. For example, observe whether the film layer tends to aggregate in the depressions of the skin texture. Calculate the standard deviation of the film layer thickness distribution as an indicator of the film layer uniformity. The larger the standard deviation, the more uneven the film layer thickness distribution. Select multiple representative time points (e.g., 10 ns, 100 ns, 1 µs). For each time point, generate the corresponding spatial distribution map of the film layer thickness. Compare the distribution maps at different time points and analyze the evolution process of the film layer thickness distribution over time. For example, observe whether the film layer becomes more uniform over time. Generate the spatio-temporal distribution map of the film layer thickness.Three-dimensional graphs can be used, where the x-axis and y-axis represent the two-dimensional coordinates of the skin surface, the z-axis represents time, and the color represents the film thickness. Evaluate the coverage rate and density of the sunscreen on the skin surface. Calculate the coverage rate. The coverage rate is defined as the ratio of the skin surface area covered by the sunscreen film layer to the total skin surface area. Set a thickness threshold (e.g., 10 nm). If the film thickness at a certain position is greater than the threshold, it is considered that this position is covered by the sunscreen. Count the number of covered grids and divide it by the total number of grids to obtain the coverage rate. Calculate the density. The density reflects the compactness of the film layer. Multiple methods can be used to calculate the density. One method is to calculate the volume fraction of sunscreen particles in the film layer. The volume fraction is defined as the ratio of the total volume of sunscreen particles to the total volume of the film layer (including particles and voids). Another method is to calculate the porosity of the film layer (calculated according to Example 6 of Step S3), and then subtract the porosity from 1 to obtain the density. Analyze the changes of the coverage rate and density over time. Plot the curves of the coverage rate and density changing over time. Generate a film layer coverage characteristic diagram. A two-dimensional color map can be used to represent the spatial distribution of the coverage rate and density. For example, use one color to represent the coverage rate and another color to represent the density, and the shade of the color represents the numerical value. Superimpose and display the microscopic topography of the skin surface in the figure. Analyze the relationship between the coverage rate, density and the skin surface topography. Obtain the stability-activity balance factor (SABF) from Step S25 and the optical efficiency index (OEI) data from Step S15. Use SABF and OEI as the performance indicators of the sunscreen. Use the coverage rate and density as the structural indicators of the film layer. Construct a structure-performance correlation model. Multiple methods can be used to establish the correlation model. A simple method is to calculate the Pearson correlation coefficient and analyze the linear correlation between the coverage rate, density and SABF, OEI. A more complex method is to use machine learning methods such as multiple linear regression, support vector regression (SVR) or neural network to establish a non-linear relationship model between the structural indicators and performance indicators. Use the cross-validation method to evaluate the prediction performance of the model. Select the model with the highest R2 value and the smallest root mean square error (RMSE). Generate structure-efficacy correlation data. This data quantitatively describes the relationship between the structural characteristics (coverage rate, density) of the sunscreen film layer and its stability and activity (SABF, OEI) in the form of charts or mathematical models. Construct a comprehensive film formation dynamic atlas. Use data visualization methods to display multi-dimensional data in an intuitive form.

[0037] Preferably, the transdermal penetration path tracking and safety assessment in Step S4 include: Conduct a quantitative evaluation of the protective film uniformity on the film formation dynamic atlas to obtain a protective film uniformity index; Evaluate the impact on the skin barrier function according to the protective film uniformity index and the film formation dynamic atlas to obtain a barrier function impact score; Percutaneous penetration path tracking is performed based on the barrier function influence score and transient diffusion kinetics data to obtain a percutaneous penetration risk map; Dynamic safety comprehensive calculation is performed based on the percutaneous penetration risk map, the protective film uniformity index, and the barrier function influence score to obtain a percutaneous dynamic safety factor.

[0038] In the embodiments of the present invention, multiple methods are used to calculate the uniformity index. 1. Coefficient of variation of thickness (CV): Calculate the standard deviation of the film layer thickness on the entire skin surface, and then divide it by the average film layer thickness to obtain CV. The smaller the CV value, the better the uniformity. 2. Roughness parameter (Rq): Consider the upper surface of the film layer as a two-dimensional surface and calculate its root mean square roughness Rq. The smaller the Rq value, the better the uniformity. 3. Uniformity index (UI): Divide the skin surface into multiple grids (e.g., 100×100). For each grid, calculate the average value of the film layer thickness therein. Count the number of grids (Nhigh) with a thickness greater than one standard deviation of the average thickness and the number of grids (Nlow) with a thickness less than one standard deviation of the average thickness. UI = 1 - (Nhigh + Nlow) / Ntotal, where Ntotal is the total number of grids. The closer the UI value is to 1, the better the uniformity. Considering the above multiple indicators comprehensively, a comprehensive protective film uniformity index (PMUI) is constructed. For example, the weighted average method can be used: PMUI = w1×(1 - CV / CVmax) + w2×(1 - Rq / Rqmax) + w3×UI, where w1, w2, and w3 are weight coefficients (which can be determined by the expert scoring method or the analytic hierarchy process), and CVmax and Rqmax are the maximum values in all samples. The larger the PMUI value, the better the uniformity of the protective film.

[0039] Evaluate the impact of sunscreen agents on skin barrier function. Consider the following aspects: 1. Interaction with stratum corneum lipids: Certain components in sunscreen agents (such as surfactants, organic solvents) interact with stratum corneum lipids, leading to disorder of the lipid bilayer structure, thereby reducing skin barrier function. According to the molecular adsorption mechanism data obtained in step S3 (molecular-level adsorption analysis section), analyze the binding free energy of sunscreen agent components with stratum corneum lipids (ceramides, cholesterol, fatty acids). The more negative the binding free energy, the stronger the interaction and the greater the potential impact on barrier function. 2. Impact on skin moisture content: An ideal sunscreen agent should have a certain degree of moisturizing property, or at least should not cause excessive loss of skin moisture. According to the membrane-forming dynamic map, analyze the porosity and density of the membrane layer. Excessive porosity or too low density will lead to accelerated water evaporation. 3. Impact on skin pH value: Maintaining a weakly acidic environment (pH 4.5 - 5.5) on the skin surface is crucial for maintaining barrier function. According to the environmental factor response data obtained in step S3 (molecular-level adsorption analysis section), analyze whether the sunscreen agent will significantly change the pH value of the skin surface. 4. Integrity of the protective film: A uniform and dense protective film can better block the penetration of external substances, thereby protecting skin barrier function. Take the protective film uniformity index (PMUI) as an important indicator. Considering the above factors comprehensively, construct a barrier function impact score (BFIS) system. The scoring method can be used to score each factor (for example, 1 - 5 points, 1 point indicates the least impact, and 5 points indicates the greatest impact), and then perform weighted averaging. For example: BFIS = 0.4 × lipid interaction score + 0.2 × moisture impact score + 0.1 × pH impact score + 0.3 × (1 - PMUI). The smaller the BFIS value, the smaller the impact of the sunscreen agent on skin barrier function.

[0040] Evaluate the impact of sunscreen agents on skin barrier function. Consider the following aspects: 1. Interaction with stratum corneum lipids: Certain components in sunscreen agents (such as surfactants, organic solvents) interact with stratum corneum lipids, leading to disorder of the lipid bilayer structure, thereby reducing skin barrier function. According to the molecular adsorption mechanism data obtained in step S3 (molecular-level adsorption analysis section), analyze the binding free energy of sunscreen agent components with stratum corneum lipids (ceramide, cholesterol, fatty acid). The more negative the binding free energy, the stronger the interaction and the greater the potential impact on barrier function. 2. Impact on skin moisture content: An ideal sunscreen agent should have a certain degree of moisturizing property, or at least should not cause excessive loss of skin moisture. According to the film-forming dynamic atlas, analyze the porosity and density of the film layer. Excessive porosity or too low density may lead to accelerated water evaporation. 3. Impact on skin pH value: Maintaining a weakly acidic environment (pH 4.5 - 5.5) on the skin surface is crucial for maintaining barrier function. According to the environmental factor response data obtained in step S3 (molecular-level adsorption analysis section), analyze whether the sunscreen agent will significantly change the pH value of the skin surface. 4. Integrity of the protective film: A uniform and dense protective film can better block the penetration of external substances, thereby protecting skin barrier function. Take the protective film uniformity index (PMUI) as an important indicator. Considering the above factors comprehensively, construct a barrier function impact score (BFIS) system. The scoring method can be used to score each factor (for example, 1 - 5 points, 1 point indicates the least impact, and 5 points indicates the greatest impact), and then perform weighted averaging. For example: BFIS = 0.4 × lipid interaction score + 0.2 × moisture impact score + 0.1 × pH impact score + 0.3 × (1 - PMUI). The smaller the BFIS value, the smaller the impact of the sunscreen agent on skin barrier function.

[0041] Track the penetration path of sunscreen active ingredients in the skin and evaluate the percutaneous penetration risk. Transient diffusion kinetic data describe the change of sunscreen particle concentration (c) over time and space. Since sunscreens usually exist in the form of nanoparticles, the possibility of direct penetration through the intact stratum corneum is relatively small. The following penetration pathways are mainly considered: 1. Through hair follicles and sebaceous glands: These skin appendages provide a way for sunscreens to bypass the stratum corneum. 2. Through intercellular spaces: Sunscreens may penetrate along the lipid bilayer gaps between stratum corneum cells. 3. Through damaged skin: If there are minor wounds or inflammation on the skin, sunscreens are more likely to penetrate. A multi-scale modeling method is used to simulate the percutaneous penetration process. First, a three-dimensional structural model of the skin is established, including the stratum corneum, epidermis, dermis, and hair follicles and sebaceous glands. The stratum corneum is modeled as a multi-layer structure, with each layer representing a stratum corneum cell layer. The intercellular spaces are filled with lipid bilayers. Simplified geometric models are used for hair follicles and sebaceous glands. Then, the transient diffusion equation is applied to this model. Since the diffusion coefficients are different in different regions, the diffusion equation needs to be modified. In the stratum corneum region, the diffusion coefficient is relatively low; in the hair follicle and sebaceous gland regions, the diffusion coefficient is relatively high; in the damaged skin region, the diffusion coefficient is the highest. The diffusion coefficients in different regions are adjusted according to the Barrier Function Impact Score (BFIS). The larger the BFIS value, the more severely the barrier function is damaged, and the corresponding diffusion coefficient is also larger. The modified transient diffusion equation is numerically solved using the Finite Element Method (FEM) or the Finite Volume Method (FVM). The FEM solution is carried out using the COMSOL Multiphysics software. Boundary conditions are set. On the skin surface, the change of sunscreen concentration over time is set (determined according to the film-forming dynamic atlas). At the bottom of the dermis layer, a zero-concentration boundary condition is set (assuming that the sunscreen that penetrates into the dermis layer is rapidly cleared by blood circulation). Initial conditions are set. At the initial moment, the sunscreen concentration is zero. Solve the transient diffusion equation to obtain the change of sunscreen concentration in the skin over time and space. Generate a percutaneous penetration risk map. A three-dimensional graph is used, where the x-axis and y-axis represent the two-dimensional coordinates on the skin surface, and the z-axis represents the skin depth. The sunscreen concentration is represented by color, with red indicating high-concentration areas and blue indicating low-concentration areas. The positions of hair follicles and sebaceous glands are superimposed and displayed in the graph. Analyze the main penetration paths of sunscreens. For example, observe whether the sunscreens are mainly concentrated around hair follicles and sebaceous glands. Calculate the cumulative amount of sunscreens at different skin depths (such as the stratum corneum, epidermis, dermis).

[0042] Comprehensively evaluate the percutaneous safety of sunscreen agents. Consider the following aspects: 1. Penetration depth and cumulative amount: The deeper the penetration depth of the sunscreen agent into the skin and the larger the cumulative amount, the higher the potential risk. According to the percutaneous penetration risk map, calculate the concentration and cumulative amount of the sunscreen agent reaching different skin layers (such as the epidermis and dermis). 2. Protective effect of the protective film: A uniform and dense protective film can effectively block the penetration of the sunscreen agent. Take the protective film uniformity index (PMUI) as an important indicator. 3. Impact on skin barrier function: The greater the impact of the sunscreen agent on the skin barrier function, the greater the possibility of its penetration. Take the barrier function impact score (BFIS) as an important indicator. 4. Toxicity of the sunscreen agent: The toxicity of different sunscreen agent components is different. According to the literature data or the results of toxicological experiments, determine the toxicity parameters of the sunscreen agent (such as NOAEL, the no-observed-adverse-effect level). Construct a calculation formula for the percutaneous dynamic safety coefficient (TDSC). For example: TDSC = (PMUI / BFIS) × (NOAEL / Cmax), where Cmax is the maximum concentration of the sunscreen agent in the skin (determined according to the percutaneous penetration risk map). The larger the TDSC value, the higher the percutaneous safety of the sunscreen agent.

[0043] Preferably, the present invention further provides a data processing system for screening cosmetic efficacy ingredients, which is used to execute the data processing method for screening cosmetic efficacy ingredients as described above. The data processing system for screening cosmetic efficacy ingredients includes: A sunscreen efficacy analysis module, which is used to obtain the optical characteristic parameters of the sunscreen agent; perform light scattering analysis according to the optical characteristic parameters, and calculate the optical efficacy index to obtain the optical efficacy index; A stability and activity analysis module, which is used to obtain the crystal structure of the sunscreen agent; extract the surface modification parameters of the crystal structure, and measure the surface energy gradient to obtain the surface energy gradient map; perform the calculation of the spatial distribution of active sites according to the surface energy gradient map to obtain the active site distribution characteristic map; quantify the stability-activity balance relationship according to the active site distribution characteristic map and the optical efficacy index to obtain the stability-activity balance factor; A contact dynamic simulation module, which is used to establish a multi-level skin structure, and perform an interface contact dynamic simulation according to the stability-activity balance factor to obtain the interface contact dynamic characteristic map; perform a molecular-level adsorption analysis according to the interface contact dynamic characteristic map to obtain the molecular adsorption mechanism data; A percutaneous safety assessment module, which simulates the protective film formation process according to the molecular adsorption mechanism data to obtain the film formation dynamic map; uses the film formation dynamic map to track the percutaneous penetration path and perform a safety assessment to obtain the percutaneous dynamic safety coefficient; uses the percutaneous dynamic safety coefficient to perform a safety screening of the efficacy ingredients to obtain the safety data of the efficacy ingredients.

[0044] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0045] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A data processing method for screening cosmetic efficacy ingredients, characterized in that, It includes the following steps: Step S1: Obtain the optical property parameters of the sunscreen agent; perform light scattering analysis based on the optical property parameters, and calculate the optical efficacy index to obtain the optical efficacy index; Step S2: Obtain the crystal structure of the sunscreen agent; extract the surface modification parameters of the crystal structure, and measure the surface energy gradient to obtain the surface energy gradient map; perform calculation of the spatial distribution of active sites based on the surface energy gradient map to obtain the active site distribution characteristic map; perform quantification of the stability-activity balance relationship based on the active site distribution characteristic map and the optical efficacy index to obtain the stability-activity balance factor; Step S3: Establish a multi-level skin structure, and perform dynamic simulation of interface contact based on the stability-activity balance factor to obtain the dynamic characteristic map of interface contact; perform molecular-level adsorption analysis based on the dynamic characteristic map of interface contact to obtain molecular adsorption mechanism data; Step S4: Perform simulation of the protective film formation process based on the molecular adsorption mechanism data to obtain the dynamic film formation map; use the dynamic film formation map to perform tracking and safety assessment of the percutaneous penetration path to obtain the percutaneous dynamic safety factor; use the percutaneous dynamic safety factor to perform safety screening of the active ingredients to obtain the safety data of the active ingredients.

2. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that Step S1 includes the following steps: Step S11: Measure the basic parameters of the original sample of the sunscreen agent; perform analysis of the particle size distribution curve based on the basic parameters to obtain the particle size distribution parameters; Step S12: Identify and separate the multi-peak characteristics of the particle size distribution parameters to obtain the quantified multi-peak characteristic data; Step S13: Extract the optical property parameters from the basic parameters; construct light scattering analysis based on the optical property parameters and the quantified multi-peak characteristic data to obtain the scattering efficacy mapping matrix; Step S14: Perform wavelength dependence analysis based on the scattering efficacy mapping matrix and the optical property parameters to obtain the wavelength-dependent response data; Step S15: Calculate the optical efficacy index based on the wavelength-dependent response data and the quantified multi-peak characteristic data to obtain the optical efficacy index.

3. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Measure the crystal structure of the original sample of the sunscreen agent and perform preliminary correlation with the optical efficacy index to obtain the crystal phase structure characteristic data; Step S22: Perform surface modification characteristic analysis on the crystal phase structure characteristic data to obtain the surface modification parameters; Step S23: Measure the surface energy gradient based on the surface modification parameters and the crystal phase structure characteristic data to obtain the surface energy gradient map; Step S24: Perform calculation of the spatial distribution of active sites based on the surface modification parameters, the surface energy gradient map and the crystal phase structure characteristic data to obtain the active site distribution characteristic map; Step S25: Perform quantification of the stability-activity balance relationship based on the active site distribution characteristic map to obtain the stability-activity balance factor.

4. The data processing method for screening cosmetic efficacy ingredients according to claim 1, wherein Step S24 includes the following steps: Step S241: Identify the crystal plane active sites of the crystal phase structure characteristic data based on the surface energy gradient map; Step S242: Calculate the site density of the crystal plane active sites to obtain the active site density data; Step S243: Evaluate the influence of the modifying group based on the active site density data and surface modification parameters to obtain the active site shielding coefficient; Step S244: Analyze the accessibility of the active site density data based on the active site shielding coefficient to obtain the active site accessibility data; Step S245: Generate an active site distribution characteristic map based on the active site accessibility data and the crystal plane active sites.

5. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that Step S25 includes the following steps: Step S251: Dynamically evaluate the crystal form stability of the active site distribution characteristic map to obtain the crystal form stability index; Step S252: Perform active-stability coupling analysis based on the crystal form stability index, the active site distribution characteristic map, and the optical efficiency index to obtain the active-stability correlation matrix; Step S253: Construct a three-dimensional correlation model based on the active-stability correlation matrix, the surface modification parameter set, and the optical efficiency index to obtain a three-dimensional synergistic effect model; Step S254: Calculate the stability-activity balance factor based on the three-dimensional synergistic effect model and the active-stability correlation matrix to obtain the stability-activity balance factor.

6. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that The interface contact dynamic simulation described in Step S3 includes: Characterize the surface material of the original sunscreen sample based on the stability-activity balance factor to obtain surface characteristic parameters; Determine the contact mechanics parameters based on the surface characteristic parameters and the multi-level skin structure to obtain the contact mechanics parameters; Perform dynamic simulation of the initial contact process based on the contact mechanics parameters to obtain the initial contact dynamics trajectory; Precisely calculate the contact area based on the initial contact dynamics trajectory to obtain a multi-scale contact area map; Construct a fine contact pressure field based on the multi-scale contact area map to obtain an interface pressure distribution cloud map; Perform spreading dynamics simulation and analysis based on the interface pressure distribution cloud map to obtain spreading dynamics characteristic data; Analyze the particle aggregation and dispersion mechanism based on the spreading dynamics characteristic data and the surface pressure distribution cloud map to obtain an aggregation and dispersion distribution map; Construct a time evolution sequence for the aggregation and dispersion distribution map and the spreading dynamics characteristic data; Integrate the interface contact dynamic characteristics of the time evolution sequence and the aggregation and dispersion distribution map to obtain an interface contact dynamic characteristic map.

7. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that The molecular-level adsorption analysis described in Step S3 includes: Perform molecular docking simulation calculations based on the interface contact dynamic characteristic map to obtain molecular docking conformation data; Calculate the binding energy based on the molecular docking conformation data to obtain the binding energy distribution data; Analyze the components of the interaction force for the binding energy distribution data to obtain an interaction force component map; Perform dynamic simulation of environmental sensitivity based on the interaction force component map and the binding energy distribution data to obtain environmental factor response data; Predict the long-time scale adsorption behavior based on the environmental factor response data and the interaction force component map to obtain an adsorption stability time curve; Comprehensively integrate the molecular adsorption mechanism for the adsorption stability time curve, the environmental factor response data, and the interaction force component map to obtain molecular adsorption mechanism data.

8. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that The simulation of the protective film formation process described in Step S4 includes: Construct the transient diffusion equation based on the molecular adsorption mechanism data and the interfacial contact dynamic characteristic diagram to obtain the transient diffusion kinetic data; Construct the initial particle distribution state diagram of the sunscreen based on the transient diffusion kinetic data and the molecular adsorption mechanism data; Use the initial particle distribution state diagram to calculate the dynamic spreading trajectory and obtain the spreading trajectory of the sunscreen; Simulate the interaction between particles based on the spreading trajectory of the sunscreen to obtain the dynamic data of particle aggregation; Analyze the microscopic reorganization process of the dynamic data of particle aggregation to obtain the molecular reorganization configuration spectrum; Calculate the evolution of the film layer structure based on the molecular reorganization configuration spectrum to obtain the time series data of film layer construction; Calculate the thickness spatio-temporal distribution of the time series data of film layer construction; Evaluate the coverage rate and density based on the thickness spatio-temporal distribution to obtain the film layer coverage characteristic diagram; Perform stability-activity mapping correlation based on the film layer coverage characteristic diagram to obtain the structure-efficacy correlation data; Generate a comprehensive dynamic map of the protective film by synthesizing the structure-efficacy correlation, the film layer coverage characteristic diagram and the thickness spatio-temporal distribution to obtain the dynamic map of film formation; 9. The data processing method for screening cosmetic efficacy ingredients according to claim 1, characterized in that The percutaneous penetration path tracking and safety assessment described in step S4 include: Quantitatively evaluate the uniformity of the protective film for the dynamic map of film formation to obtain the protective film uniformity index; Evaluate the impact on the skin barrier function based on the protective film uniformity index and the dynamic map of film formation to obtain the barrier function impact score; Track the percutaneous penetration path based on the barrier function impact score and the transient diffusion kinetic data to obtain the percutaneous penetration risk map; Perform a comprehensive dynamic safety calculation based on the percutaneous penetration risk map, the protective film uniformity index and the barrier function impact score to obtain the percutaneous dynamic safety coefficient; 10. A data processing system for screening cosmetic efficacy ingredients, characterized in that, A data processing method for screening cosmetic efficacy ingredients as claimed in claim 1, and the data processing system for screening cosmetic efficacy ingredients includes: A sunscreen efficacy analysis module for obtaining the optical characteristic parameters of the sunscreen; performing light scattering analysis based on the optical characteristic parameters and calculating the optical efficacy index to obtain the optical efficacy index; A stability and activity analysis module for obtaining the crystal structure of the sunscreen; extracting the surface modification parameters of the crystal structure and measuring the surface energy gradient to obtain the surface energy gradient map; calculating the spatial distribution of active sites based on the surface energy gradient map to obtain the active site distribution characteristic map; quantifying the stability-activity balance relationship based on the active site distribution characteristic map and the optical efficacy index to obtain the stability-activity balance factor; A contact dynamic simulation module for establishing a multi-level skin structure and performing an interfacial contact dynamic simulation based on the stability-activity balance factor to obtain the interfacial contact dynamic characteristic diagram; performing molecular-level adsorption analysis based on the interfacial contact dynamic characteristic diagram to obtain the molecular adsorption mechanism data; A percutaneous safety assessment module for simulating the formation process of the protective film based on the molecular adsorption mechanism data to obtain the dynamic map of film formation; using the dynamic map of film formation to perform percutaneous penetration path tracking and safety assessment to obtain the percutaneous dynamic safety coefficient; using the percutaneous dynamic safety coefficient to perform safety screening of efficacy ingredients to obtain the safety data of efficacy ingredients.

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