Method for monitoring and safety evaluation of allergen concentration in cosmetics

Abstract

The application relates to a method for monitoring and safety evaluation of allergen concentration in cosmetics, aiming at the problem that the sampling and trapping efficiency and monitoring accuracy of allergens are affected by the particle size distribution of atomized droplets in existing spray-type cosmetics, the application collects spray droplet samples and analyzes the particle size distribution characteristics, combines the surface enrichment concentration and the bulk concentration extracted from the sampling filter membrane, evaluates the filter membrane interception efficiency and the allergen escape rule, further dynamically adjusts the inhalation exposure safety threshold according to the nozzle aperture specification, comprehensively analyzes the matching relationship between the interception efficiency and the threshold, realizes the accurate evaluation of respiratory safety risk and the verification of sampling consistency, and effectively improves the accuracy of allergen concentration monitoring and the reliability of safety evaluation in cosmetics.

Description

Technical Field

[0001] This application relates to digital data processing, specifically to a method for monitoring and assessing the concentration of allergens in cosmetics. Background Technology

[0002] In the field of cosmetic safety assessment, the study of allergen concentration monitoring and respiratory exposure risk is of paramount importance. This field is directly related to the health and safety of consumers, especially in the use of spray cosmetics, where atomized droplets may enter the human body through the respiratory tract. If potential allergens are not effectively controlled, they may cause allergic reactions or even more serious health problems. Many existing schemes often ignore the dynamic changes of spray droplets in the air and fail to fully consider the impact of droplet size on the capture and measurement of sensitizing substances. In particular, when droplet characteristics change, the monitoring results are easily interfered with, resulting in unreliable assessment conclusions. Focusing on specific technical challenges, the particle size distribution of spray droplets has become the core factor affecting the accuracy of allergen monitoring. The size of the droplet not only determines the suspension time and distribution range of the spray in the air, but also directly affects the evaporation rate of the allergen in the droplet. Due to the relatively large surface area of ​​smaller droplets, the allergen is more likely to evaporate into the air quickly, resulting in the actual measured concentration being lower than the true value. While larger droplets evaporate more slowly, they may be difficult to capture effectively due to their faster settling speed, which also affects the accuracy of monitoring results. Therefore, accurately assessing the volatility and capture efficiency of sensitizing substances under different droplet size distribution conditions has become a key issue in cosmetic safety risk assessment. Summary of the Invention

[0003] To address the problems existing in the prior art, and considering the complex and contradictory impact of droplet size distribution in aerosolized cosmetics on allergen sampling and collection efficiency in allergen concentration monitoring and safety assessment in cosmetics, conventional judgment suggests that reducing the nozzle orifice diameter to produce a more uniform fine mist improves sampling reproducibility. However, when considering the differences in allergen volatility, the increased surface area of ​​the fine droplets accelerates the escape of volatile allergens, leading to a lower measured concentration. By deploying an aerosol sampling unit and a droplet size detection unit in the cosmetic aerosol monitoring system, data on median droplet size distribution, sampling filter membrane retention efficiency, and the ratio of allergen surface enrichment concentration to bulk concentration can be obtained. Furthermore, the inhalation exposure safety threshold can be dynamically adjusted based on changes in nozzle orifice diameter to identify the degree of volatile allergen escape and thus assess respiratory safety risks. The purpose of this application is to provide a method for monitoring the concentration of allergens in cosmetics and for safety assessment.

[0004] The method for monitoring and assessing the concentration of allergens in cosmetics as described in this application includes: S101 collects initial droplet samples during the atomization process of spray cosmetics, identifies droplet surface tension characteristics and atomization rate, extracts droplet size distribution characteristics, and deploys sampling filter membranes simultaneously. S102, extract the surface enrichment concentration and bulk concentration from the sampled filter membrane, evaluate the retention efficiency of the filter membrane during the collection process based on the droplet size distribution characteristics and droplet surface tension characteristics, and analyze the emission law of volatile allergens from fine droplets by the atomization rate. S103, assess the trend of concentration ratio change based on the emission pattern of volatile allergens from fine droplets, mark the high emission risk level based on the trend of concentration ratio change, and obtain the degree of allergen emission; S104. Obtain the current nozzle orifice specification configuration information, combine the allergen dispersion degree with the nozzle orifice specification configuration information, analyze the changes in spray flow control attributes, evaluate the correlation factor between orifice specification and dispersion degree, and adjust the inhalation exposure safety threshold standard according to the correlation factor. S105, by analyzing the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency, assess respiratory safety risks, retrospectively verify the fluctuation pattern of sampling reproducibility, analyze the sampling consistency of respiratory safety risks, and iteratively update particle size distribution characteristics.

[0005] Preferably, in step S101, the cosmetic spray is started by the atomizing device, and an initial droplet sample is collected within a range of 5 to 10 cm at the nozzle outlet. The surface tension characteristics and atomization rate of the droplets are identified by the surface tension meter, and the tension parameter value is obtained. Based on the tension parameter value, the droplet particle size distribution characteristics are extracted. If the distribution deviation exceeds a preset threshold, the atomization pressure is adjusted to optimize the distribution uniformity. Based on the distribution uniformity, a sampling filter membrane is deployed. The sampling filter membrane includes a polytetrafluoroethylene membrane with a pore size of approximately 0.2 micrometers to capture additional droplet samples and obtain the optimized atomization rate characteristics.

[0006] Preferably, in S102, the sampling filter membrane is treated by solvent extraction to obtain the surface enrichment concentration and the bulk concentration, and the ratio of the surface enrichment concentration to the bulk concentration is calculated. Combined with droplet size distribution data and surface tension parameters, the degree of influence on the filter membrane capture process is evaluated. If the impact exceeds the preset threshold, the collection performance is optimized by adjusting the filter membrane pore parameters to determine the retention efficiency. Furthermore, based on the retention efficiency, atomization rate measurement equipment is used to record the evaporation data of fine droplets, simulate the escape path of volatile allergens, and extract the distribution characteristics to obtain the escape law of allergens in fine droplets.

[0007] Preferably, in step S103, the initial parameters of the fine droplets are obtained based on the statistical analysis of their particle size distribution, the droplet evaporation rate is determined, and the concentration ratio of the allergen is calculated using the evaporation kinetics formula. The concentration ratio is the ratio of the initial concentration to the concentration after evaporation, and the trend of the concentration ratio is obtained. The influence of temperature gradient is analyzed by gradient vector calculation to determine the high risk level of allergens. If the risk level exceeds the preset threshold, the allergen warning area is marked and its allergen distribution is obtained. The influence of environmental factors such as humidity and wind speed on the distribution of the allergen in the area is integrated to quantify the degree of allergen ...

[0008] Preferably, in S104, the current nozzle orifice specification configuration information is obtained, including the orifice diameter and the number of orifices. The degree of allergen dispersion is measured by an air monitor, and the dispersion particle concentration value is obtained by combining the orifice data. The change trend of flow rate and pressure in the spray flow control attributes is analyzed based on the concentration value. The correlation between pore size and dispersion is assessed based on the changing trend, and statistical methods such as regression analysis are used to obtain quantitative factor indicators. The inhalation exposure safety threshold is adjusted according to the indicators: if the indicators show a high positive correlation, including small pore size leading to high dispersion, the threshold is lowered.

[0009] Preferably, in S105, the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency is first analyzed, and the degree of matching is quantified by comparing the overlap between the efficiency curve and the threshold curve. Based on the matching relationship data, further assessment of respiratory safety risks can be made. For example, the correlation between exposure dose and health effects can be calculated using the deposition fraction model to derive a risk index. The system can be retrospectively verified to verify the fluctuation pattern of sampling reproducibility. Historical sampling data can be analyzed to identify and quantify volatility. Based on this, the consistency of data from multiple sampling points is analyzed to determine the stability of the sampling method. If the sampling consistency meets the preset threshold, the iterative optimization stage is entered. Based on the risk assessment results and the consistency status, the particle size distribution characteristic model is updated, including optimizing the parameters of the log-normal distribution, so as to realize the dynamic and continuous assessment and optimization of respiratory safety risks.

[0010] The method for monitoring and assessing the concentration of allergens in cosmetics described in this application has the advantage that by collecting and analyzing the particle size distribution characteristics of spray droplets, and combining the surface and bulk concentrations extracted from the sampled filter membrane, the filter membrane retention efficiency and the allergen dispersion law can be accurately assessed. This method overcomes the sampling error and concentration distortion caused by the change in droplet particle size distribution in traditional methods, making the monitoring results of allergen concentration more accurate and fundamentally enhancing the reliability of the overall safety assessment conclusion. By linking the degree of allergen dispersion with the nozzle orifice size in real time and analyzing the quantitative relationship between the two, the inhalation exposure safety threshold can be dynamically adjusted according to the specific product atomization characteristics. This adjustment mechanism makes the safety standard no longer a fixed value, but can change with product design and usage conditions, achieving more precise and personalized risk management. By analyzing the matching relationship between safety thresholds and retention efficiency to assess respiratory risks, and retrospectively verifying the consistency of sampling data, the particle size distribution characteristic model is iteratively updated. This process constitutes a closed-loop system that can continuously improve the accuracy of monitoring models and the timeliness of risk assessment, providing dynamic and reliable decision support for cosmetic formulation improvement and product quality control. The method is applicable not only to different types of spray cosmetics, but its core process and evaluation model can also be extended to other fields that need to assess aerosol exposure risks, demonstrating strong technical versatility and application flexibility. Attached Figure Description

[0011] Figure 1 This application describes a procedure for monitoring and assessing the concentration of allergens in cosmetics. Figure 1 ; Figure 2 This application describes a procedure for monitoring and assessing the concentration of allergens in cosmetics. Figure 2 ; Figure 3 This application describes a procedure for monitoring and assessing the concentration of allergens in cosmetics. Figure 3 . Detailed Implementation

[0012] like Figures 1-3 As shown in this application, a method for monitoring and assessing the concentration of allergens in cosmetics is described.

[0013] like Figures 1-3 As shown, S101 collects initial droplet samples during the atomization process of spray cosmetics, identifies droplet surface tension characteristics and atomization rate, extracts droplet size distribution characteristics, and deploys sampling filter membranes.

[0014] Further, in step S101, the cosmetic spray is activated by the atomizing device to obtain an initial droplet sample from the atomization process and determine the sample collection location; For the initial droplet sample, a surface tension meter was used to identify the surface tension characteristics and atomization rate to obtain the tension parameter values; The droplet size distribution characteristics are extracted from the tension parameter values. If the distribution deviation exceeds a preset threshold, the atomization pressure is adjusted to determine the distribution uniformity. Based on the distribution uniformity, a sampling filter membrane is deployed to capture additional droplet samples and obtain optimized atomization rate characteristics.

[0015] For example, in one embodiment, sampling of the atomization process of spray cosmetics first involves simulating a real-world usage scenario using a high-speed spray device, spraying the cosmetic liquid at a specific pressure to form initial atomized droplets.

[0016] For example, a precision sampling probe is placed at the nozzle outlet at a distance of 5 to 10 centimeters to capture droplet samples in the early stages of atomization. This sampling method ensures that the sample reflects the true atomization state and avoids external interference.

[0017] Specifically, the sampling process includes preheating the spray device to a stable temperature, such as running it at room temperature for 30 seconds, then triggering the spray and collecting droplets immediately. This method enables accurate acquisition of the initial droplets, providing a basis for subsequent analysis. Furthermore, after collecting the sample, identifying the droplet surface tension characteristics and atomization rate becomes a key step. The droplet surface tension characteristics can be measured using the pendant drop method or the Wilhelmy plate method. The pendant drop method involves suspending the droplet on a needle and using an optical system to observe shape changes, thereby calculating the surface tension value. For example, in a laboratory setting, collected droplets are placed in a tensiometer, and the curvature and density data of the droplets are recorded to obtain the surface tension coefficient. The atomization rate is determined by calculating the number and volume of droplets generated per unit time. The specific process includes using a high-speed camera to capture atomization sequences and analyzing the droplet generation rate between frames. This identification process helps to assess the atomization uniformity of cosmetics and ensure the uniform distribution of the product on the skin.

[0018] Preferably, the droplet size distribution characteristics are extracted using a laser particle size analyzer. This instrument irradiates the droplet sample with a laser beam, measures the diffraction pattern, and thus generates a particle size distribution curve.

[0019] Specifically, after the sample is diluted and injected into the analyzer, the system automatically calculates the average particle size, standard deviation, and distribution width, for example: In one possible implementation, for cosmetic sprays of different viscosities, the particle size distribution may show a range from micrometers to tens of micrometers. This extraction can reveal the statistical characteristics of atomization quality. Through this feature extraction, the technical solution is applicable to the optimization of various spray cosmetic formulations.

[0020] It should be noted that the simultaneous deployment of the sampling filter membrane is to filter out potential impurities and ensure sample purity. The filter membrane is usually made of microporous material, such as polytetrafluoroethylene membrane, with a pore size controlled at around 0.2 micrometers. During the sampling process, the filter membrane is installed downstream of the sampling probe and is carried out synchronously with the droplet collection. For example, when a spray is generated, the droplets first pass through a filter membrane to remove solid particles before entering a collection container. This deployment method enhances the reliability of the sample and is suitable for cosmetic production scenarios with high purity requirements. In another embodiment, for oily spray cosmetics, the sampling process can be adjusted to a low-pressure atomization mode to reduce droplet evaporation. When identifying surface tension, a temperature compensation algorithm is combined, and a volumetric flow meter is introduced to assist in the measurement when calculating the atomization rate. Dynamic light scattering is used to extract particle size distribution, supplementing the accuracy of laser analysis. At the same time, the sampling filter membrane can be made of lipophilic material to adapt to oil-based liquids. This variant demonstrates the application of the technical solution in different types of cosmetics.

[0021] Specifically, for water-based spray cosmetics, initial droplet sample collection emphasizes humidity control to prevent rapid drying of the droplets. The surface tension feature identification process includes the following details: First, stable droplets are formed, and then the image data is analyzed using software to obtain the tension value; the atomization rate is calculated by counting the droplet generation frequency, for example, the number of droplets per second multiplied by the average volume; Particle size distribution extraction involves data fitting to form a normal distribution model, while the deployed filter membrane ensures a sterile environment. This integrated approach enables efficient atomization performance evaluation.

[0022] Understandably, the implementation of this technical solution can support quality control in cosmetic research and development. Through precise feature analysis, it can identify potential problems such as uneven atomization, thereby guiding formula adjustments.

[0023] In one implementation, for sunscreen spray products, the sampling and analysis process can be extended to outdoor simulated conditions to verify actual usage effects. Furthermore, in laboratory-scale production, the collection of initial droplet samples can be automated by using a robotic arm to operate the probe, integrating sensor networks to identify steps, outputting surface tension and velocity data in real time, extracting particle size distribution using a multi-channel analyzer, and providing detailed classification statistics. At the same time, the filter membrane deployment includes an online replacement mechanism to ensure continuous sampling. This automation improves efficiency and is suitable for batch cosmetic testing. For example, in the atomization analysis of perfume spray cosmetics, the influence of volatile components is emphasized, surface tension characteristics are captured by adjusting the measurement time to capture dynamic changes, atomization rate calculation takes into account evaporation rate correction, particle size distribution extraction reveals the proportion of small particle size to assess fragrance diffusion, and filter membranes filter volatile residues. This specific application demonstrates the flexibility of the solution.

[0024] In one possible implementation, the entire process, from sampling to feature extraction, forms a closed-loop feedback loop for iterative optimization of the spray device parameters. Through atomization rate analysis, the emission patterns of volatile allergens in fine droplets are derived, such as the inverse relationship between the emission amount and the droplet diameter. This pattern is then applied to adjust the atomization rate and nozzle design, ultimately achieving precise control over the atomization characteristics of cosmetics and reducing the risk of allergies.

[0025] like Figures 1-3 As shown in S102, the surface enrichment concentration and bulk concentration are extracted from the sampled filter membrane. The retention efficiency of the filter membrane during the collection process is evaluated based on the droplet size distribution characteristics and droplet surface tension characteristics. The dispersion law of volatile allergens in fine droplets is analyzed by the atomization rate.

[0026] Further, in step S102, the surface enrichment concentration and bulk concentration are obtained by sampling filter membrane dissolution treatment, and the ratio of the surface enrichment concentration to the bulk concentration is determined. Based on the ratio and droplet size distribution data, the droplet surface tension parameter is obtained, and the degree of influence of the droplet surface tension parameter on the filter membrane collection process is determined. If the degree of influence exceeds the preset threshold, the pore size and density are adjusted by using the filter membrane pore distribution data to optimize the collection parameters and obtain the retention efficiency status. To assess the retention efficiency, an atomization rate measurement device was used to record the evaporation data of fine droplets, and the escape path simulation of volatile allergens was determined. Distribution characteristics were extracted from the simulated escape path to obtain the escape pattern of volatile allergens from fine droplets.

[0027] For example, in one embodiment, the process of extracting the surface enrichment concentration and bulk concentration from the sampling filter membrane is first achieved by solvent extraction.

[0028] Specifically, the collected filter membrane is placed in an organic solvent, such as an ethanol solution, and soaked for a period of time to dissolve the cosmetic ingredients attached to the surface. The surface layer and bulk layer of the filter membrane are separated by centrifugation, and the concentration of cosmetic ingredients in each layer is measured. This extraction method is suitable for cosmetic applications, such as in the atomization scenario of spray cosmetics, for detecting droplet-attached ingredients. It should be noted that surface enrichment concentration refers to the amount of cosmetic ingredients accumulated on the outer layer of the filter membrane, while bulk concentration involves the distribution within the pores of the filter membrane. These concentration values ​​are quantified using a spectrometer to provide a data basis for subsequent evaluation. Based on the extracted concentration data, the retention efficiency of the filter membrane during the trapping process is further evaluated by combining the droplet size distribution characteristics. The droplet size distribution characteristics are statistical data obtained by laser particle size analyzer, representing the probability distribution of droplet size, such as average particle size and standard deviation. In the evaluation, these distribution characteristics are combined with droplet surface tension characteristics. Surface tension characteristics refer to the tension value of the droplet surface, which can be measured by the pendant drop method. The specific process includes first calculating the adhesion probability of the droplet on the filter membrane. Based on the particle size distribution, if the particle size is smaller than the filter membrane pore size threshold, the adhesion efficiency is reduced. By introducing the effect of surface tension, when the tension is high, droplets are less likely to deform and pass through the filter membrane, thereby improving the retention efficiency. Through this combination, the collection efficiency of the filter membrane for droplets of different sizes can be evaluated. For example, in atomized air sampling, the retention efficiency for droplets with a diameter of 1-10 micrometers can reach more than 80%. This evaluation method is commonly used in the field of environmental monitoring, such as for evaluating droplet collection in industrial emissions.

[0029] Preferably, when assessing the retention efficiency, a mathematical model can be introduced to quantify the process.

[0030] For example, the retention efficiency is calculated based on Stokes' law and a surface tension correction factor. First, the droplet size distribution is described by a Gaussian distribution model, with the average particle size μ and standard deviation σ as inputs, and the probability density function f(d) of the particle size d as output. The surface tension is then estimated using Young's equation to determine the contact angle θ between the droplet and the filter membrane, with the surface tension γ and interfacial tension as inputs, and θ as output, thus correcting the capture probability. The calculation process is as follows: the particle size distribution is obtained based on the Gaussian distribution, and the settling velocity of each particle size is calculated using Stokes' law. ρ is the density (kg / m³) 3 g is 9.8 m / s 2 η is the viscosity (Pa·s); combined with the θ correction probability, the overall efficiency value is obtained as E=1-exp(-kv), where k is an empirical constant.

[0031] In one embodiment, the calculation of the retention efficiency, based on Stokes' law and the surface tension correction factor, further includes the following formula: Where E represents the overall retention efficiency, d represents the droplet size, f(d) represents the particle size distribution probability density function, v(d) represents the Stokes settling velocity, k(d) represents the surface tension correction factor, and d min d max The minimum and maximum particle sizes are represented by , and dd represents the differential element of the integral variable (particle size). First, the droplet size distribution probability density function f(d) is obtained to describe the probability of droplets of different sizes d appearing. Then, the settling velocity v(d) of the corresponding droplet size is calculated according to Stokes' law. Simultaneously, a surface tension correction factor k(d) is introduced to quantify the influence of droplet surface tension characteristics on the adhesion or penetration process of the filter membrane, yielding the overall retention efficiency E, which is obtained by subtracting the result of an exponential function from 1. The exponent of this exponential function is calculated based on the smallest droplet size d. min up to the maximum particle size d max Within the range, the above three factors include the product of surface tension correction factor k(d), sedimentation velocity v(d), and particle size distribution probability density f(d), which is obtained by integration. This model uses integration to superimpose the contributions of the entire particle size spectrum to achieve a comprehensive quantitative evaluation of the filter membrane's retention performance.

[0032] Specifically, the process involves collecting droplet samples, measuring their particle size distribution curves, and then calculating the surface tension value for each particle size segment. For example, for water-based droplets, the tension is approximately 72 mN / m. Next, these values ​​are input into an efficiency model, which calculates the overall retention efficiency by integrating the product of the particle size distribution and the tension factor. For example, in an air purifier filter membrane test scenario, if the average droplet size is 5 micrometers and the surface tension is high, the efficiency assessment shows a 15% increase in capture rate. This detailed assessment ensures the accurate quantification of filter membrane performance and demonstrates the flexibility of the technical solution when applied in the same field, such as laboratory air sampling. In this way, the evaluation process not only covers static capture but also considers dynamic droplet behavior, thus providing a basis for optimizing filter membrane design.

[0033] In one possible implementation, when analyzing the dispersion pattern of volatile allergens in fine droplets through atomization rate, the atomization rate is first defined as the rate at which a droplet transforms into a gaseous state per unit time. This can be measured using the mass loss method. The specific process involves placing fine droplets in a controlled environment and monitoring their volume change rate. For example, the atomization rate of droplets with a diameter of less than 2 micrometers at room temperature is approximately 0.1 micrograms per second. Then, the dispersion of volatile allergens, such as fragrance components in cosmetics, in the droplets is analyzed. The dispersion pattern refers to the pattern in which allergens are released into the air as the droplets evaporate. By recording the concentration changes during the atomization process, a dispersion curve is plotted. For example, the dispersion is rapid in the initial stage and then tends to stabilize. This analysis is applicable to the field of cosmetic atomization and is used to assess the diffusion risk of allergens. Furthermore, the atomization rate analysis can be combined with the aforementioned concentration extraction results to enhance accuracy. For example, the bulk concentration extracted from the filter membrane can be used as an initial value to simulate the evaporation process of tiny droplets.

[0034] Specifically, in the experimental setup, droplets containing allergens were injected, and the temperature and humidity were controlled to observe the effect of atomization rate on dispersion. If the atomization rate increased, the dispersion amount increased accordingly, forming a linear relationship. This analysis revealed how tiny droplets release volatile allergens in the air. For example, in a humid environment, the dispersion pattern showed that the peak occurred in the early stage of atomization. This method is universal in environmental health monitoring scenarios and demonstrates the adaptability of the technical solution to different humidity conditions.

[0035] Understandably, when evaluating the retention efficiency during the filter membrane capture process, variations in various scenarios can be considered, such as: In one embodiment, for high humidity air sampling, the surface tension characteristics of droplets decrease due to increased moisture, resulting in a decrease in retention efficiency; by adjusting the particle size distribution model, the evaluation shows that the efficiency drops from 90% to 70%. This variant description supports the broad protection of the claims and is consistent within the same air monitoring field. Specifically, the process of analyzing the dispersion patterns of volatile allergens in fine droplets through atomization rate can be modularized into two parts: data acquisition and pattern derivation. The data acquisition module monitors droplet mass changes in real time using sensors and calculates the atomization rate. The pattern derivation module fits the dispersion curve based on time-series data, for example, using an exponential decay model R(t) = R0 × e (-kt) This method describes the slowing of allergen release rates over time, where R(t) is the release rate at time t, R0 is the initial release rate, and k is the decay constant. The input is time t and the corresponding release rate data sequence, and the output is the fitting parameters R0 and k. The fitting steps include preprocessing the data, log-linearizing, and then fitting k and R0 using the least squares method. The specific process includes initially setting up droplet samples, recording the amount of evaporation per minute, and then analyzing the slope of the curve to find that the emission peak occurs at the highest point of the atomization rate. This modular method is applied in indoor air quality assessment, ensuring the systematic nature of the analysis and achieving accurate prediction in allergen control scenarios. Through this analysis, quantitative management of allergen diffusion in the air can be achieved.

[0036] In one embodiment, extracting the variant of surface enrichment concentration versus bulk concentration from a sampling filter membrane includes using ultrasound-assisted extraction to improve efficiency.

[0037] Specifically, the filter membrane is placed in an ultrasonic bath, where vibration promotes the release of pollutants from the surface and bulk phase. The concentration is then measured by chromatography. This variant is suitable for rapid sampling scenarios, such as outdoor environmental monitoring, demonstrating the diversity of extraction methods. Furthermore, combined with the aforementioned evaluation, the overall technical solution shows versatility in the implementation of air sampling. For example, under laboratory conditions, the retention efficiency assessment based on particle size distribution and surface tension characteristics, combined with atomization rate analysis, can effectively reveal the control effect of the filter membrane on volatile allergens. The specific process includes first extracting the concentration, then evaluating the efficiency, and finally analyzing the dispersion, thus forming a complete chain. In practical applications, this approach helps to optimize the selection of filter membrane materials and achieve better capture performance.

[0038] Preferably, the influence of temperature variables can be introduced when analyzing the dispersion law of volatile allergens in fine droplets; For example, as the temperature rises, the atomization rate increases, and the dispersion pattern shows that the release of allergens doubles. This optional feature enhances the flexibility of the scheme and allows for application in the same field, such as greenhouse air monitoring.

[0039] like Figures 1-3 As shown in S103, the trend of concentration ratio change is assessed based on the emission law of volatile allergens from fine droplets, and the high emission risk level is marked according to the trend of concentration ratio change to obtain the degree of allergen emission.

[0040] Further, in step S103, the initial parameters of the fine droplets are obtained through particle size distribution statistics to determine the droplet evaporation rate; The allergen concentration ratio is calculated based on the volatilization rate and the evaporation kinetics formula, where the concentration ratio is the ratio of the initial concentration to the concentration after volatilization, and the trend of the concentration ratio change is obtained. The influence of temperature gradient is analyzed by gradient vector calculation based on the aforementioned trend, where the temperature gradient is the rate of temperature change per unit distance, to determine the level of high escaping risk. If the risk level exceeds the threshold, an escape warning area is marked to obtain the regional escape distribution; The extent of allergen dispersal is obtained by integrating environmental factors, including humidity and wind speed, from the dispersion distribution in the region.

[0041] In one embodiment, the concentration ratio of the allergen is calculated using evaporation kinetics formulas. This concentration ratio, which is the ratio of the initial concentration to the concentration after volatilization, specifically includes: Among them, C r C represents the concentration ratio. init This indicates the initial allergen concentration, in μg / m³. 3 C vol This indicates the concentration of allergens after volatilization, in μg / m³. 3 .

[0042] For example, in one embodiment, the allergen dispersal pattern is assessed by monitoring the evaporation process of tiny droplets in the air. Tiny droplets typically refer to liquid particles with a diameter of less than 10 micrometers. These particles carry allergens such as dust mite proteins or mold spores and release volatile compounds during the evaporation process. The dispersal pattern is based on the principle of evaporative diffusion, that is, the decrease in surface tension of the droplet causes the internal allergens to diffuse outward.

[0043] Specifically, droplet data from air samples are first collected, including the number of droplets and their initial concentration. Then, an evaporation model is applied to calculate the escape rate. This model considers environmental factors such as temperature and humidity. For example, at a room temperature of 20 degrees Celsius, the droplet evaporation time can be estimated using the evaporation rate formula, thus obtaining a preliminary description of the pattern of allergen escape from droplets. This method ensures the objective quantification of the escape process and supports subsequent evaluation. Furthermore, based on the above escape pattern, the trend of concentration ratio change is evaluated. The concentration ratio refers to the ratio of the current allergen concentration in the air to the initial concentration, and its change is calculated through continuous sampling data.

[0044] For example, in an indoor air monitoring scenario, a sensor collects concentration data every minute to form a time series. Then, the trend of the series is analyzed, such as by using a moving average method to smooth the data and identify rising or falling patterns. If the concentration ratio exceeds 1.5 times in a short period of time, it is marked as a potential trend. This assessment process emphasizes dynamic monitoring, avoids static judgment, and ensures accurate capture of trends.

[0045] Preferably, after assessing the trend, a high escape risk level is marked, which is divided into three levels: low, medium, and high, and is marked based on the slope threshold of the concentration ratio change. For example, if the slope of the trend is greater than 0.1 per minute, it is marked as high risk. This marking process is achieved through threshold comparison and is applicable to different types of allergens, such as pollen or chemical volatiles, in the same indoor environment, thereby providing a risk warning.

[0046] In one embodiment, if the slope of the trend is greater than 0.1 per minute, it is marked as high risk, specifically including: Where S represents the slope of the concentration ratio change, in min. -1 ΔC r Δt represents the change in concentration ratio, and Δt represents the change in time, in minutes.

[0047] In one embodiment, the trend of change is analyzed by calculating and analyzing the effect of the temperature gradient through gradient vector calculation, specifically including: Acquire temperature distribution data by measuring the temperature distribution of the sampling area using a temperature sensor to obtain the temperature field T(x,y,z); The temperature gradient is calculated as follows: The temperature gradient represents the rate of change of temperature per unit distance. Where ∇T is the temperature gradient vector; T represents the temperature value at each point (x, y, z) in the sampling area or monitoring space; This represents the change in temperature per unit distance traveled along the x-axis. This represents the temperature change per unit distance along the y-axis. This represents the temperature change per unit distance along the vertical z-axis. (x, y, z) represents the location of any point within the defined monitoring area; The influence of temperature gradient on concentration change is analyzed by performing correlation analysis between the concentration change rate S and the temperature gradient ∇T. If the temperature gradient is large, the concentration change rate S is usually also large. The influence weight of temperature gradient on concentration change is quantified by vector dot product or regression analysis.

[0048] In one embodiment, in the gradient vector calculation, the temperature gradient ∇T is defined as the vector of partial derivatives of the temperature field T(x,y,z) with respect to spatial coordinates, and its components are... , , These represent the rate of temperature change in three dimensions, in °C / m. This gradient is used to quantify the effect of spatial temperature difference on the evaporation rate of allergens: the larger the gradient, the faster the droplets evaporate and the allergens dissipate, and the higher the risk level of dissipation in the corresponding area.

[0049] In one possible implementation, the degree of allergen dispersal is obtained by combining the above steps. The degree of dispersal is calculated by integrating the risk level and the concentration ratio, for example, by weighting the high-risk level with trend data to form a quantitative score. It should be noted that this level indicates the proportion of allergens released from the droplets into the air, and can be used to guide air purification measures in practical applications.

[0050] Specifically, this paper further elaborates on the detailed process of the escape of volatile allergens from tiny droplets. The escape of volatile allergens originates from the phase change mechanism of droplets. When droplets are exposed to air, surface moisture evaporates, and the concentration gradient of internal allergens migrates outward. This law can be verified through simulation experiments: place droplet samples containing allergens in a controlled environment and monitor the concentration changes before and after evaporation. For example, under indoor conditions with 50% humidity, the amount of allergen released after droplet evaporation can reach 30% of the initial content. This depends on the diffusion coefficient and the ambient wind speed. By recording release data at multiple time points, a regular curve can be established to predict future release behavior. This detailed regularity assessment helps identify high-risk periods, such as seasonal pollen peaks, thereby enabling preventative interventions in air quality management. The technical effect of this process is to improve the accuracy of release monitoring and reduce the probability of allergic reactions.

[0051] For example, in another implementation, this method is applied to an office air monitoring scenario. First, droplet samples are collected near the desktop, the volatilization pattern is analyzed, and then the concentration ratio trend is assessed. If the trend shows a rapid increase, it is marked as high risk, and the degree of emission is calculated to be at a medium level.

[0052] Understandably, the assessment of concentration ratio change trends can be extended to various indoor scenarios, such as living rooms or bedrooms. By adjusting the sampling frequency, such as once per hour, it can be adapted to different space sizes, ensuring the universality of trend assessment.

[0053] In one embodiment, environmental variable corrections are introduced when marking high escape risk levels; For example, considering the impact of air velocity on dispersion, if the velocity is higher than 2 meters per second, the threshold is adjusted to reflect accelerated volatilization, thereby making the risk labeling more accurate. Furthermore, the calculation of the degree of allergen dispersion can be achieved through data fusion. Trend data and risk levels are input into the fusion model, and the degree value is output. This value guides the automatic adjustment of the ventilation system in practice.

[0054] Preferably, historical data is used to calibrate the model during the assessment process, such as reviewing concentration changes over the past week, to optimize the accuracy of trend predictions, thereby supporting long-term air quality control.

[0055] like Figures 1-3 As shown in step S104, obtain the current nozzle orifice specification configuration information, combine the allergen dispersion degree with the nozzle orifice specification configuration information, analyze the changes in spray flow control attributes, evaluate the correlation factor between orifice specification and dispersion degree, and adjust the inhalation exposure safety threshold standard according to the correlation factor.

[0056] Further, in step S104, the current nozzle orifice diameter specification configuration information is obtained, and the orifice diameter range is determined by querying the configuration information in the database; The concentration of allergens was obtained by measuring the degree of allergen dispersion using an air monitoring instrument and combining it with the pore diameter range. Based on the concentration value of the dispersed particles, the changes in the spray flow control attributes are analyzed, and data is collected using a flow sensor to determine the trend of attribute changes. To assess the correlation factors between pore size and dispersion degree based on the aforementioned property change trends, regression analysis was used to fit the factor correlation coefficients to obtain factor quantitative indicators. The inhalation exposure safety threshold standard is adjusted according to the factor quantification index. If the factor quantification index is higher than the preset threshold, the particle diffusion simulation calibration threshold is integrated to determine the adjusted standard.

[0057] In one embodiment, regression analysis is used to fit the correlation coefficients of the factors to obtain factor quantitative indicators, specifically including: Where R represents the correlation coefficient (association factor), x i This indicates aperture specification data, in μm, y i Data representing the degree of dispersion, in μg / m³ 3 , denoted as mean, and n represents the sample size.

[0058] For example, in one implementation, the process of obtaining the current nozzle orifice specification configuration information first involves reading the physical parameters of the nozzle through the device's sensors.

[0059] Specifically, the nozzle orifice specifications include the orifice diameter and the number of orifices, and this information is stored in the device's configuration database; For example, in agricultural spraying systems, the system automatically queries a database or uses optical sensors to measure orifice values ​​to ensure real-time data. This method of acquisition helps improve the accuracy of subsequent analysis because orifice specifications directly affect the spray diffusion pattern. Through this step, the system can obtain accurate configuration data, providing a basis for integrating other factors. When combining the degree of allergen dispersal with nozzle orifice specification configuration information, it is necessary to consider the diffusion level of allergens such as pollen or chemical particles in the air.

[0060] For example, the degree of allergen emission can be quantified using environmental monitoring instruments, such as using a particle counter to measure the particle concentration per unit volume. This degree is then combined with the orifice size to generate a comprehensive dataset. The process involves data matching algorithms, where nozzles with smaller orifice diameters may result in higher emission concentrations because the spray particles are finer and more easily dispersed. In industrial spraying scenarios, this combination helps identify potential risk points and ensures the logical consistency of data fusion. Furthermore, analyzing changes in spray flow control attributes requires monitoring fluctuations in flow rate under different conditions. Spray flow control attributes include flow rate and pressure, which adjust with orifice size variations, for example: In one possible implementation, the system uses flow sensors to record data in real time and calculate the rate of change. If the orifice size increases, the flow rate may increase, leading to more pronounced property changes. This analysis is achieved by comparing baseline values ​​with real-time values, aiming to reveal how properties respond to external factors such as wind speed or liquid viscosity. In the field of spray safety control, this change analysis helps predict exposure risks. Assessing the correlation factor between orifice size and dispersion is a core step, and its process needs to be explained in detail. The correlation factor refers to indicators that quantify the relationship between orifice size and dispersion, such as correlation coefficients or influence weights. First, historical data is collected, including dispersion measurements at various orifice sizes. Then, factors are calculated using statistical analysis methods, such as using Pearson correlation analysis to quantify linear relationships.

[0061] Specifically, if the dispersion level is high when the pore diameter is 0.5 mm and decreases when it is 1 mm, the factor may indicate a negative correlation. This assessment also involves multivariate regression to isolate interference from other influences such as temperature. In agricultural spraying, such as for pesticide spraying, the system iteratively calculates these factors to ensure that the assessment covers scenarios under different crop environments. This detailed process can reveal how pore size dominates dispersion dynamics, thereby providing a basis for safety optimization. In this way, the extraction of correlation factors not only improves the accuracy of analysis but also enables quantitative management of exposure risks in business operations.

[0062] It should be noted that the process of adjusting the inhalation exposure safety threshold standard based on the correlation factors after the assessment adopts a threshold optimization strategy.

[0063] Specifically, if the correlation factor shows a high positive correlation, i.e., the small aperture leads to high dissipation, the safety threshold is lowered to a more stringent level, such as from 10 micrograms per cubic meter to 5 micrograms per cubic meter. This adjustment is based on preset rules, such as a threshold function, where the factor value is multiplied by a weighting coefficient to generate the new standard.

[0064] In one embodiment, for medical spray devices, this adjustment ensures that the user's inhalation exposure does not exceed safety limits.

[0065] Preferably, in another implementation, the overall process can be extended to continuous monitoring scenarios; For example, the system integrates a feedback loop to acquire orifice information in real time and combine it with escaping data to dynamically analyze flow rate changes and correlation factors, and finally automatically adjust the threshold. This versatility demonstrates the flexible application of the technical solution in the field of spray control.

[0066] Understandably, the technical effect of this adjustment process is to improve the accuracy of exposure safety, such as reducing the threshold in a timely manner in high-emission environments and reducing potential health risks.

[0067] In one embodiment, for small nozzle applications with an orifice size of 0.3 mm, when the dispersion level is high, the correlation factor evaluation shows that the threshold needs to be adjusted by 20% to achieve targeted control. Furthermore, the system can record the adjustment history for subsequent optimization to ensure continuous improvement of the solution.

[0068] like Figures 1-3 As shown in S105, the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency is analyzed through the retention efficiency status analysis, the respiratory safety risk is assessed, the fluctuation pattern of sampling reproducibility is verified retrospectively, the sampling consistency of respiratory safety risk is analyzed, and the particle size distribution characteristics are iteratively updated.

[0069] Further, in step S105, the matching relationship data between the adjusted inhalation exposure safety threshold standard and the retention efficiency is obtained by analyzing the retention efficiency status. Based on the matching relationship data, assess respiratory safety risks and determine the assessment results. From the respiratory safety risk assessment results, backtest the sampling reproducibility fluctuation pattern. Obtain the sampling reproducibility fluctuation pattern, analyze the sampling consistency of respiratory safety risks, and determine the status of the sampling. If the sampling consistency meets the preset threshold, the particle size distribution characteristics are iteratively updated.

[0070] For example, in one implementation, the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency is analyzed by the retention efficiency status. First, it is necessary to understand the concept of retention efficiency. Retention efficiency refers to the ability of respiratory protective equipment to capture particulate matter in the air. It is usually quantified by the proportion of particles of different sizes blocked by the filter medium. In the field of air quality monitoring, such as in industrial workshop environments, the adjusted inhalation exposure safety threshold standard is a value optimized based on exposure limits such as PM2.5 concentration thresholds. It is used to define the level of particulate matter exposure that the human respiratory tract can tolerate. The matching relationship analysis process involves comparing the correspondence between the actual retention efficiency data and the threshold standard.

[0071] Specifically, after collecting efficiency data from the equipment, the degree of matching is assessed by calculating the overlap between the efficiency curve and the threshold curve. For example, if the efficiency is lower than the threshold requirement, it is marked as a mismatch area, thus providing a basis for subsequent risk assessment. This analysis ensures the effectiveness of protective equipment and the consistency with safety standards, and is applicable to various scenarios in the same field, such as laboratory air sampling. Furthermore, based on the above matching relationship, respiratory safety risks are assessed by integrating exposure data and physiological models. Respiratory safety risk assessment considers the probability of particulate matter deposition in the lungs. The process includes inputting the matched threshold data into the risk model and calculating the correlation between exposure dose and health effects, for example: In one possible implementation, a sedimentation fraction model is used to estimate the distribution of particulate matter in the respiratory tract, and a risk index is derived by combining it with individual exposure time. If the matching relationship shows high efficiency, the risk index is reduced; otherwise, an alarm should be issued. This assessment method is universal in indoor air monitoring scenarios in the field of environmental protection and can quantify the risk of different particulate concentrations. It should be noted that backtracking and verifying the sampling reproducibility fluctuation pattern is a key step in ensuring data reliability. The sampling reproducibility fluctuation pattern refers to the variation pattern of multiple sampling results. The backtracking and verification process involves reviewing historical sampling data and analyzing the causes of fluctuations, such as ambient temperature or equipment deviation.

[0072] In one embodiment, the mean and standard deviation of each of the past 10 sampling datasets are calculated, and then the fluctuation pattern is identified by trend line fitting. For example, if the fluctuation exceeds 5%, it is verified as unstable, and the sampling protocol is adjusted accordingly. This backtracking method is applied in the continuous sampling scenario of air pollution monitoring, which enhances the repeatability of the data. In another implementation, the sampling consistency analysis of respiratory safety risks is based on the verification of fluctuation patterns. The consistency analysis examines the similarity between sampled data. The process includes comparing the risk index distribution of multiple sampling points and calculating consistency coefficients such as correlation coefficients.

[0073] Specifically, for example, in industrial emissions monitoring, multiple particulate matter samples are collected and analyzed to match the results of risk assessment. If the consistency coefficient is higher than 0.8, the sampling is considered reliable. This analysis process reveals the stability of the sampling method and expands its application in occupational health assessment in the same field.

[0074] Preferably, the iterative update of particle size distribution characteristics is achieved through feedback from the aforementioned analysis results. The particle size distribution characteristics describe the statistical properties of particle size, and the iterative update process involves adjusting the parameters of the distribution model based on consistency, for example: In one embodiment, the initial distribution model uses a log-normal distribution, and the mean and variance are iteratively optimized by combining risk assessment data. The updated features more accurately reflect the actual exposure situation. This iterative method is universal in the dynamic monitoring scenario of air quality control and can improve the accuracy of the overall assessment. For example, in a workshop air monitoring embodiment in the field of environmental protection, the entire process starts with the analysis of interception efficiency, gradually assesses risks and updates characteristics, and achieves continuous optimization of respiratory safety. Through this modular approach, the technical solution covers both static and dynamic sampling scenarios in the same field, demonstrating flexibility. It is understandable that the above implementation method ensures the continuity from data matching to feature updating through logical flow, and can achieve the accuracy of risk assessment and the reliability of sampling data in practical applications.

[0075] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A method for monitoring and assessing the concentration of allergens in cosmetics, characterized in that, include: S101 collects initial droplet samples during the atomization process of spray cosmetics, identifies droplet surface tension characteristics and atomization rate, extracts droplet size distribution characteristics, and deploys sampling filter membranes simultaneously. S102, extract the surface enrichment concentration and bulk concentration from the sampled filter membrane, evaluate the retention efficiency of the filter membrane during the collection process based on the droplet size distribution characteristics and droplet surface tension characteristics, and analyze the emission law of volatile allergens in fine droplets through atomization rate. S103, assessing the trend of concentration ratio changes based on the emission patterns of volatile allergens from fine droplets, marking high emission risk levels based on the trend of concentration ratio changes, and obtaining the degree of allergen emission, also includes: The initial parameters of the droplets are obtained by statistically analyzing the particle size distribution of the fine droplets, the droplet evaporation rate is determined, and the concentration ratio of the allergen is calculated by combining the evaporation kinetic formula. The concentration ratio is the ratio of the initial concentration to the concentration after evaporation, which is used to obtain the trend of the concentration ratio. Based on the changing trend of the concentration ratio, the temperature gradient is calculated and analyzed by gradient vector to quantify the impact of spatial temperature difference on the volatilization rate of allergens and to determine the level of high escape risk. S104. Obtain the current nozzle orifice specification configuration information, combine the allergen dispersion degree with the nozzle orifice specification configuration information, analyze the changes in spray flow control attributes, evaluate the correlation factor between orifice specification and dispersion degree, and adjust the inhalation exposure safety threshold standard according to the correlation factor. S105, by analyzing the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency, assess respiratory safety risks, retrospectively verify the fluctuation pattern of sampling reproducibility, analyze the sampling consistency of respiratory safety risks, and iteratively update particle size distribution characteristics.

2. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 1, characterized in that, In step S101, cosmetic spraying is started by atomizing device, initial droplet samples are collected within a preset distance range at the nozzle outlet, and surface tension meter is used to identify the surface tension characteristics and atomization rate of the droplets to obtain tension parameter values. Based on the tension parameter values, the droplet size distribution characteristics were extracted; If the distribution deviation exceeds the preset threshold, the atomization pressure is adjusted to optimize the distribution uniformity, and the sampling filter membrane is deployed according to the distribution uniformity.

3. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 1, characterized in that, In step S102, the sampled filter membrane is treated by solvent extraction to obtain the surface enrichment concentration and the bulk concentration, and the ratio of the surface enrichment concentration to the bulk concentration is calculated. Combined with droplet size distribution data and surface tension parameters, the degree of influence on the filter membrane capture process is evaluated. If the impact exceeds the preset threshold, the collection performance is optimized by adjusting the filter membrane pore parameters to determine the retention efficiency.

4. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 1, characterized in that, In step S104, the current nozzle orifice specification configuration information is obtained, including orifice diameter and number of orifices. The degree of allergen emission is measured by an air monitor, and the concentration value of the emitted particles is obtained by combining the orifice data. Analyze the changing trends of flow rate and pressure in the spray flow control attributes based on the concentration of escaping particles; The correlation factors between pore size and dispersion level are assessed based on the changing trend, and regression analysis is used to obtain quantitative factor indicators. The inhalation exposure safety threshold is then adjusted according to the factor indicators.

5. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 1, characterized in that, In step S105, the matching relationship between the adjusted inhalation exposure safety threshold standard and the retention efficiency is analyzed, and the degree of matching is quantified by comparing the overlap between the efficiency curve and the threshold curve. Based on the quantitative matching degree, respiratory safety risks are assessed, and the system backtracks to verify the fluctuation pattern of sampling reproducibility. Historical sampling data is analyzed to identify and quantify the volatility. Further analysis of the consistency of data from multiple sampling points is used to determine the stability of the sampling method. If the sampling consistency meets the preset threshold, the iterative optimization stage begins.

6. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 2, characterized in that, The preset distance range is 5 to 10 centimeters.

7. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 2, characterized in that, The sampling filter membrane is a polytetrafluoroethylene membrane with a pore size of 0.2 micrometers.

8. The method for monitoring and assessing the concentration of allergens in cosmetics according to claim 3, characterized in that, When evaluating the retention efficiency during the filter membrane capture process, Stokes' law and surface tension correction factor are used for calculation.

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