Method for measuring skin penetration efficiency of roxburgh rose based on artificial skin model

Abstract

The invention relates to the technical field of penetration efficiency testing, and particularly discloses a method for testing roxburgh rose skin penetration efficiency based on an artificial skin model.The method includes the steps that under the simulated actual use scene, the penetration process of the roxburgh rose on the artificial skin model is monitored, a penetration image set is obtained, and the process can highly restore the actual use situation; according to the method, a data basis fitting the reality is provided for subsequent analysis, research is closer to the actual effect of a product, then denoising processing is performed on a permeation image set, attribute parameters are collected and analyzed to quantify quality characteristics, whether the fluorescence labeling intensity is extracted or not is judged accordingly, high-quality images can be effectively screened out, noise interference is avoided, and the accuracy of fluorescence labeling is improved. And finally, according to the fluorescence labeling intensity, the penetration efficiency is matched, the attribute parameters of the penetration efficiency curve are updated and analyzed, the penetration efficiency curve is managed and visualized, and the change trend of the penetration efficiency along with time can be clearly presented by updating the curve.

Description

Technical Field

[0001] The invention relates to the technical field of penetration efficiency testing, in particular to a method for measuring the penetration efficiency of roxburghii skin based on an artificial skin model. Background Art

[0002] The current process for measuring the skin penetration efficiency of sea buckthorn extract usually uses animal experiments or in vitro skin tissue experiments. The animal experiment process is to first select a suitable animal model, shave, clean and other pre-treatments on the animal skin, and then evenly apply the sea buckthorn extract solution or preparation to the animal skin surface. At different set time points, the residual samples on the skin surface are collected in a specific way, and the skin tissue is extracted for component analysis to monitor the penetration of the sea buckthorn extract. The in vitro skin tissue experiment is to obtain in vitro skin tissue from humans or animals, and after pre-treatment such as cleaning and slicing, it is placed in a diffusion cell or other device, and the sea buckthorn extract is added. The receiving liquid is collected at the specified time point or the skin tissue is sliced ​​and analyzed to obtain penetration-related data.

[0003] For example, the invention patent with announcement number CN114034608B discloses a method for measuring the skin penetration efficiency of glycerol glucoside based on an artificial skin model. Because the artificial skin model has a structure and function similar to normal human skin and does not have species differences, the skin penetration efficiency of glycerol glucoside is tested based on the artificial skin model. The test results provide a more reliable and accurate reference for the practical application of glycerol glucoside. Replacing animal epidermis with an artificial skin model can also avoid the sacrifice of a large number of experimental animals and reduce experimental costs. In addition, this method can clearly observe the location of the skin layer reached by the penetration behavior of the glycerol glucoside solution by taking pictures under a fluorescence microscope. By detecting the penetration concentration of glycerol glucoside, the amount of glycerol glucoside solution that has penetrated the artificial skin model can be reflected. The higher the penetration concentration, the better the skin penetration efficiency.

[0004] For example, the invention patent with publication number CN117705674A announces a method for predicting the oil-water phase permeability curve of heavy oil hot water chemical composite synergistic enhancement, including: Step 1: Conducting core displacement experiments under different hot water temperatures, polymer viscosities, and interfacial tensions of oil displacement agents; Step 2: Establishing a numerical model for characterizing the flooding of the hot water chemical composite system, and performing automatic history fitting inversion of the phase permeabilities under different hot water temperatures, polymer viscosities, and interfacial tensions; Step 3: Constructing functional relationships between hot water temperature, polymer viscosity, interfacial tension, and phase permeability curve parameters respectively; Step 4: Performing multiple regression to determine the specific form of the phase permeability curve under the hot water chemical composite system; Step 5: Calculating the phase permeabilities under different hot water chemical composite systems.

[0005] However, in the process of implementing the embodiments of the present application, the present application found that the above-mentioned technology has at least the following technical problems: due to the complex and changeable experimental environment, the acquisition equipment is easily interfered by various external factors during operation, thereby introducing noise sources into the collected original data. The existence of these noise data makes the quality of the original data uneven, and cannot directly meet the strict requirements of subsequent analysis on data accuracy, completeness and consistency. If the original data containing a large amount of noise is directly analyzed, it will inevitably lead to deviations in the analysis results, and it will be impossible to truly and accurately reflect the penetration performance of the sea buckthorn extract in the artificial skin model, thereby affecting the scientific evaluation and reasonable application of the skin absorption performance of the sea buckthorn extract. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a method for measuring the skin penetration efficiency of roxburghii based on an artificial skin model, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for measuring the skin penetration efficiency of sea buckthorn (Roxburgh) based on an artificial skin model, comprising: step 1, monitoring the penetration process of sea buckthorn (Roxburgh) extract on the artificial skin model under a simulated actual usage scenario, thereby obtaining a penetration image set of the sea buckthorn (Roxburgh) extract; step 2, denoising the penetration image set of the sea buckthorn (Roxburgh) extract, and after the processing is completed, collecting and analyzing the attribute parameters of the penetration image set, thereby quantifying the quality characteristics of the penetration image set, and determining whether to extract the fluorescent labeling intensity of the sea buckthorn (Roxburgh) extract from the penetration image set based on the quality characteristics of the penetration image set; step 3, matching the penetration efficiency of the sea buckthorn (Roxburgh) extract based on the fluorescent labeling intensity of the sea buckthorn (Roxburgh) extract, thereby updating the sea buckthorn (Roxburgh) extract penetration efficiency curve, updating and analyzing the attribute parameters of the sea buckthorn (Roxburgh) extract penetration efficiency curve, thereby managing the sea buckthorn (Roxburgh) extract penetration efficiency curve, and visualizing the sea buckthorn (Roxburgh) extract penetration efficiency curve.

[0008] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0009] (1) The present invention provides a method for measuring the skin penetration efficiency of sea buckthorn extract based on an artificial skin model. When measuring the skin penetration efficiency of sea buckthorn extract, first, the penetration process on the artificial skin model is monitored under a simulated actual use scenario and a penetration image set is obtained. This process can highly restore the actual use situation, provide a data basis that fits the actual situation for subsequent analysis, and make the research closer to the actual effect of the product. Then, the penetration image set is denoised, and attribute parameters are collected and analyzed to quantify quality characteristics, and based on this, it is determined whether to extract the fluorescence labeling intensity. This step can effectively screen out high-quality images, avoid noise interference, ensure that the extracted fluorescence labeling intensity data is reliable, and provide strong support for accurately evaluating the penetration situation. Finally, the penetration efficiency is matched according to the fluorescence labeling intensity, and the attribute parameters of the penetration efficiency curve are updated and analyzed, managed and visualized. By updating the curve, the change trend of the penetration efficiency over time can be clearly presented. The visualization processing makes the data more intuitive and easy to understand, which is convenient for researchers to quickly grasp the penetration characteristics of sea buckthorn extract.

[0010] (2) The present invention quantifies the quality characteristics of the penetration image set with specific indicators, providing a reliable basis for subsequent operations. The advantage is that it can accurately locate image problems. For example, when the clarity is insufficient, targeted adjustments can be made to avoid blind operations. The quality index is used to determine whether to extract the fluorescent marker intensity. When the quality is insufficient, the image is sharpened. The benefits are significant: sharpening can enhance the edge details of the image, making the outline of the fluorescent marker of the sea buckthorn extract clearer, facilitating the accurate extraction of intensity data, and reducing errors caused by image blur. At the same time, it can also issue warnings for unusable image sets and terminate invalid analysis processes in a timely manner. Through these measures, the overall process can efficiently screen high-quality image data, ensure accurate extraction of fluorescent marker intensity, and then accurately match the penetration efficiency.

[0011] (3) When updating the penetration efficiency curve, the present invention combines the fluorescence labeling intensity and the timestamp to clearly present the variation trend of the penetration efficiency of the sea buckthorn extract over time, facilitates intuitive observation of the penetration process, analyzes the curve attribute parameters and quantifies the data quality index, accurately identifies problems such as abnormal values ​​and deviation duration in the curve, and provides a clear direction for subsequent optimization. When managing the curve, the validity of the parameters is determined based on the data quality index, invalid parameters can be eliminated, and data accuracy can be guaranteed. When determining whether to optimize the data acquisition process, the secondary optimization acquisition can significantly improve the quality of the penetration image set by adjusting the imaging gain and the number of image frames, thereby improving the accuracy of the fluorescence labeling intensity extraction. The four-level early warning mechanism can provide timely feedback on the data quality status, facilitate timely adjustment of the data acquisition strategy, and ensure the smooth progress of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0013] Figure 1 Schematic diagram of the method of the present invention.

[0014] Figure 2 This is a flow chart of the quality assessment of the penetration image set of the present invention.

[0015] Figure 3 This is a flow chart of quality assessment of optimized penetration image sets according to the present invention.

[0016] Figure 4 This is a flow chart of the quality assessment of the permeation efficiency curve data of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] Reference Figure 1 As shown, the present invention provides a method for measuring the skin penetration efficiency of the roxburgh pear based on an artificial skin model, comprising: step 1, monitoring the penetration process of the roxburgh pear extract on the artificial skin model under a simulation of an actual use scenario, thereby obtaining a penetration image set of the roxburgh pear extract; step 2, denoising the penetration image set of the roxburgh pear extract, and after the processing is completed, collecting and analyzing the attribute parameters of the penetration image set, thereby quantifying the quality characteristics of the penetration image set, and determining whether to extract the fluorescent labeling intensity of the roxburgh pear extract from the penetration image set according to the quality characteristics of the penetration image set; step 3, matching the penetration efficiency of the roxburgh pear extract according to the fluorescent labeling intensity of the roxburgh pear extract, thereby updating the roxburgh pear extract penetration efficiency curve, updating and analyzing the attribute parameters of the roxburgh pear extract penetration efficiency curve, thereby managing the roxburgh pear extract penetration efficiency curve, and visualizing the roxburgh pear extract penetration efficiency curve.

[0019] The present invention mainly uses innovative experimental design and analysis technology to accurately evaluate the skin permeability of the roxburghii extract. An artificial skin model with a multi-layer structure is used to simulate the physiological structure and function of human skin. The roxburghii extract is evenly applied to the surface of the artificial skin model. By controlling experimental conditions (such as temperature, humidity, time, etc.), actual usage scenarios are simulated to ensure the accuracy and reliability of the experimental results. A specially designed detection and analysis module is used to monitor the penetration process of the roxburghii extract in real time, calculate the penetration efficiency and cumulative penetration amount, and evaluate the skin absorption performance.

[0020] Fluorescence labeling intensity is the core indicator for evaluating the penetration efficiency of sea buckthorn extract: sea buckthorn extract needs to be pre-labeled with fluorescent groups. By photographing frozen sections of artificial skin models incubated at different time points, that is, a set of penetration images at different time points, the fluorescence labeling intensity is analyzed from the penetration image set using image processing software (such as ImageJ) (the net value is obtained after deducting the background). At the same time, a fluorescence intensity-concentration standard curve is constructed, and the fluorescence intensity of the culture medium is detected to analyze the penetration concentration, thereby achieving a multi-dimensional evaluation of the penetration efficiency of sea buckthorn extract.

[0021] The present invention describes a single-shot Rosa roxburghii extract penetration image acquisition and processing process: A set of penetration images is regularly acquired in a simulated scenario, and their quality characteristics are quantified after de-noising. The quality index determines whether to extract the fluorescence marker intensity. If the quality does not meet the standard, the acquisition parameters are optimized and the image is re-acquired until the quality meets the standard. Once the standard is met, the fluorescence intensity is extracted, the penetration efficiency is matched and the curve is updated. The curve quality is analyzed, and the data acquisition process is managed accordingly. Alerts are triggered as needed, and the acquisition process is repeated multiple times in this cycle.

[0022] In a specific embodiment, the present invention quantifies the quality characteristics of the penetration image set with specific indicators, providing a reliable basis for subsequent operations, with the advantage of being able to accurately locate image problems. For example, when the clarity is insufficient, targeted adjustments can be made to avoid blind operations, and the quality index is used to determine whether to extract the fluorescent marker intensity. When the quality is insufficient, the image is sharpened, which has significant benefits: sharpening can enhance the edge details of the image, making the outline of the fluorescent marker of the sea buckthorn extract clearer, facilitating the accurate extraction of intensity data, and reducing errors caused by image blur. At the same time, an early warning of unusable image sets can be issued, and invalid analysis processes can be terminated in time. Through these measures, the overall process efficiently screens high-quality image data, ensures accurate extraction of fluorescent marker intensity, and thus accurately matches penetration efficiency.

[0023] Specifically, the quality characteristics of the penetration image set are quantified. The specific quantification process is as follows: the attribute parameters of the penetration image set include the texture clarity, gradient variance and signal-to-noise intensity ratio of the penetration image set; texture clarity reflects the sharpness and recognizability of the distribution details of the prickly pear extract in all images in the penetration image set (such as the boundary of the penetration area and the concentration gradient). The Canny, Sobel and other edge detection algorithms are used to extract all the boundaries of the penetration area in the penetration image set, and the average gradient amplitude of the boundary pixels is calculated. Texture clarity = mean(boundary pixel) The gradient variance measures the degree of dispersion of the gradient amplitudes of all images in the penetration image set. The gradient amplitude of each pixel in the image is calculated using methods such as Sobel and Prewitt, and the gradient amplitude variance of all pixels is calculated to obtain the gradient variance. The signal-to-noise intensity ratio is the ratio of the penetration signal (distribution of the roxburghii extract) to the background noise (such as instrument noise and unevenness of the artificial skin model). The grayscale mean of the penetration area of ​​all images in the penetration image set is divided by the grayscale standard deviation of the background area, and the final result is recorded as the signal-to-noise intensity ratio.

[0024] Extract the texture definition, gradient variance and signal-to-noise intensity ratio from the database; define the texture definition, indicating the lower limit value of the texture definition; define the gradient variance, indicating the lower limit value of the gradient variance; define the signal-to-noise intensity ratio, indicating the lower limit value of the signal-to-noise intensity ratio.

[0025] The metric ratio values ​​are extracted from the database to quantify the influence of the relative ratio between texture clarity and defined texture clarity, the relative ratio between gradient variance and defined gradient variance, and the relative ratio between signal-to-noise intensity ratio and defined signal-to-noise intensity ratio on the quality index of the penetration image set. The influence degrees are summarized to obtain the quality index of the penetration image set.

[0026] The quality index of the penetration image set is used to digitally indicate the quality characteristics of the penetration image set. The specific expression is:

[0027]

[0028] Where QUF is the quality index of the penetration image set, CLA is the texture clarity of the penetration image set, DF_CLA is the bounded texture clarity, VAR is the gradient variance of the penetration image set, DF_VAR is the bounded gradient variance, RAT is the signal-to-noise intensity ratio of the penetration image set, DF_RAT is the bounded signal-to-noise intensity ratio, a is the texture clarity metric ratio value stored in the database, b is the gradient variance metric ratio value stored in the database, and c is the signal-to-noise intensity ratio metric ratio value stored in the database.

[0029] The texture clarity measurement ratio value indicates the degree of influence of the ratio between texture clarity and the defined texture clarity on the quality index of the penetration image set; the gradient variance measurement ratio value indicates the degree of influence of the ratio between gradient variance and the defined gradient variance on the quality index of the penetration image set; the signal-to-noise intensity ratio measurement ratio value indicates the degree of influence of the ratio between signal-to-noise intensity ratio and the defined signal-to-noise intensity ratio on the quality index of the penetration image set; the database stores several mapping relationships, such as parameter measurement ratio value mapping relationships. These mapping relationships are bound to image data and experimental metadata through structured tables (such as parameter mapping tables, weight configuration tables) or associated query statements (such as JOIN operations) to form a complete image quality assessment system. Therefore, the texture clarity measurement ratio value, gradient variance measurement ratio value and signal-to-noise intensity ratio measurement ratio value can be directly queried from the database. The value range of the texture clarity measurement ratio value, gradient variance measurement ratio value and signal-to-noise intensity ratio measurement ratio value is between 0 and 1.

[0030] A high signal-to-noise ratio is fundamental to improving texture clarity and gradient variance: When the signal-to-noise ratio is high, image noise is effectively suppressed, allowing the boundaries and details (such as concentration gradients) of the area where the sea buckthorn extract has penetrated to be more clearly presented, thereby directly enhancing texture clarity (i.e., increasing edge sharpness and increasing the gradient amplitude of boundary pixels). Simultaneously, a high signal-to-noise ratio also amplifies the contrast between the penetrated area and the background, causing the gradient amplitude to change dramatically at the boundary, which in turn significantly increases the gradient variance (i.e., increasing the discreteness of the gradient amplitude). Conversely, if the signal-to-noise ratio is low, noise blurs boundaries and obscures details, resulting in a decrease in both texture clarity and gradient variance. Furthermore, a positive feedback relationship exists between texture clarity and gradient variance: high texture clarity implies sharp boundaries and dramatic gradient amplitude changes, which in turn increase gradient variance. High gradient variance, on the other hand, reflects the significant difference between the penetrated area and the background, further verifying texture clarity. Ultimately, the three work together to influence the image quality index: a high signal-to-noise ratio lays the foundation for image quality by improving clarity and gradient variance; clarity and gradient variance directly reflect the usability of the image by quantifying edge sharpness and contrast changes; if any parameter is low (such as insufficient signal-to-noise ratio leading to noise interference), the other parameters will also deteriorate, thereby lowering the overall quality index.

[0031] Specifically, whether to extract the fluorescent labeling intensity of the sea buckthorn extract from the penetration image set is determined based on the quality characteristics of the penetration image set, specifically: whether to extract the fluorescent labeling intensity of the sea buckthorn extract from the penetration image set is determined based on the quality index of the penetration image set; if the quality index of the penetration image set is greater than or equal to the defined quality index stored in the database, it is determined that the fluorescent labeling intensity of the sea buckthorn extract is directly extracted from the penetration image set; the defined quality index represents the lower limit value of the quality index.

[0032] Extracting the fluorescence labeling intensity of the sea buckthorn extract from the penetration image set refers to normalizing the penetration image set (such as grayscale value standardization), eliminating uneven illumination or background noise interference, locating the fluorescence labeling area through edge detection or region segmentation algorithm (such as Otsu threshold method), ensuring that only the target area is analyzed, and summing the pixel grayscale values ​​of the fluorescence labeling area to obtain the fluorescence labeling intensity.

[0033] If the quality index of the penetration image set is less than the defined quality index, then based on the quality index of the penetration image set and the defined quality index, increasing the sharpening intensity of the penetration image set;

[0034] Based on the quality index of the penetration image set and the definition quality index, the sharpening intensity of the penetration image set is improved, which means subtracting the quality index of the penetration image set from the definition quality index, and marking the result as the quality index deviation value of the penetration image set. The sharpening intensity increase coefficient corresponding to the quality index deviation value of the penetration image set is queried from the database, and the sharpening intensity increase coefficient is multiplied by the sharpening intensity of the penetration image set. The result is the improvement result of the sharpening intensity of the penetration image set. The sharpening intensity increase coefficient represents a proportional value greater than 1, which represents the proportional value of increasing the sharpening intensity.

[0035] If the quality index of the penetration image set is lower than the critical value, there may be problems such as blurring, noise interference, or unclear edges, which makes it difficult to distinguish the fluorescent marker signal from the background noise, thereby affecting the accurate extraction of the fluorescent marker intensity. Adjusting the sharpening intensity can enhance the edge contrast and detail clarity of the image, making the fluorescent marker signal more prominent and facilitating subsequent quantitative analysis.

[0036] After the improvement is completed, the quality index of the penetration image set is updated and compared with the defined quality index again. If the quality index of the updated penetration image set is still less than the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract will not be extracted from the penetration image set, and the penetration image set is marked as an unusable penetration image set. At the same time, the penetration image set of the sea buckthorn extract is re-acquired. If the quality index of the updated penetration image set is greater than or equal to the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract will be extracted from the penetration image set.

[0037] This process ensures the data reliability of fluorescence labeling intensity analysis through dynamic quality verification and closed-loop optimization. Its core benefits are: filtering low-quality data, allowing only image sets that meet quality standards to enter the analysis, avoiding misjudgment of fluorescence intensity due to blur, noise or signal distortion (such as preventing background noise interference from inflating the permeability); improving resource efficiency, automatically marking unusable images and triggering re-collection, avoiding invalid analysis and waste of computing resources; ensuring the repeatability of results, unifying threshold comparison to eliminate human errors, ensuring that different batches of experiments follow consistent standards, and providing reliable data support for the penetration research of sea buckthorn extract.

[0038] Figure 2 This is the flow chart of the quality assessment of the penetration image set of the present invention. After simulating the actual scene to monitor the penetration process to obtain the initial penetration image set, the image set is first denoised, and its attribute parameters such as brightness and contrast are collected and analyzed. Then, the quality characteristics are quantified and the quality index is calculated; if the quality index is greater than or equal to the defined quality threshold, it is judged to be up to standard and the fluorescence marker intensity is directly extracted; if it does not meet the standard, that is, the quality index is less than the defined quality threshold, the image sharpening intensity is increased and the quality index is recalculated. If the updated quality index meets the standard, the fluorescence marker intensity is extracted, otherwise the image set is marked as unavailable and the optimization acquisition process is started.

[0039] Furthermore, the penetration image set of the sea buckthorn extract was re-obtained, and the specific analysis process was as follows: based on the quality index of the penetration image set and the defined quality index, the quality index deviation value of the penetration image set was obtained; the quality index deviation value refers to the result of subtracting the quality index of the penetration image set from the defined quality index.

[0040] Based on the quality index deviation value of the penetration image set, the image acquisition imaging gain increase coefficient is extracted from the database, and the acquisition process of the penetration image set of the sea buckthorn extract is optimized, so that the penetration image set of the sea buckthorn extract is re-obtained and marked as the optimized penetration image set; the database stores a mapping table of quality index deviation value-image acquisition imaging gain increase coefficient, so that the image acquisition imaging gain increase coefficient can be extracted from the database based on the quality index deviation value of the penetration image set through the mapping relationship, and the image acquisition imaging gain increase coefficient is multiplied by the current image acquisition imaging gain. The result is recorded as the optimized image acquisition imaging gain, and the image acquisition imaging gain increase coefficient is a proportional value greater than 1, indicating that the image acquisition imaging gain is increased by a proportional value.

[0041] The quality index of the optimized penetration image set is obtained and compared with the defined quality index; if the quality index of the optimized penetration image set is greater than the defined quality index, the fluorescence labeling intensity of the sea buckthorn extract is extracted from the optimized penetration image set; if the quality index of the optimized penetration image set is less than or equal to the defined quality index, the sharpening intensity of the optimized penetration image set is improved based on the quality index of the optimized penetration image set and the defined quality index. After the improvement is completed, the quality index of the optimized penetration image set is updated and compared with the defined quality index again. If the quality index of the optimized penetration image set is still less than the defined quality index, it is determined not to extract the fluorescence labeling intensity of the sea buckthorn extract from the optimized penetration image set. Marking intensity, and marking the optimized penetration image set as an unusable penetration image set, and at the same time issuing a first-level warning for the data acquisition process, if the quality index of the optimized penetration image set is greater than or equal to the defined quality index, then determining the fluorescent labeling intensity of the sea buckthorn extract extracted from the optimized penetration image set; refers to subtracting the quality index of the optimized penetration image set from the defined quality index, marking the result as the quality index deviation value of the optimized penetration image set, querying the sharpening intensity increase coefficient corresponding to the quality index deviation value of the optimized penetration image set from the database, multiplying the sharpening intensity increase coefficient by the sharpening intensity of the optimized penetration image set, and the result obtained is the improvement result of the sharpening intensity of the optimized penetration image set.

[0042] The triggering condition for the first-level warning is: after adjusting the imaging gain, the quality index of the optimized penetration image set is still less than or equal to the defined quality index, indicating that the image quality requirements cannot be met by simply optimizing the imaging gain. After increasing the sharpening intensity, the updated quality index of the optimized penetration image set is still less than the defined quality index, indicating that the image quality cannot meet the expected standards through the existing parameter adjustment (gain + sharpening). The first-level warning prompts that there may be systemic problems with the current image acquisition equipment or parameter settings (such as unstable light source, excessive sensor noise, lens contamination, etc.), resulting in image quality that cannot be improved through conventional optimization methods. If the image set is continued to be used to extract fluorescent labeling intensity, it may lead to error in the results or invalid analysis, and subsequent experimental steps need to be suspended.

[0043] This process improves data quality by dynamically optimizing image acquisition parameters, which has significant advantages. First, based on the quality index deviation value, the imaging gain increase coefficient is extracted from the database and the current gain is adjusted. This can specifically enhance the image signal strength and improve quality defects caused by insufficient exposure or noise interference. Secondly, if the quality is still not up to standard after optimization, the details are further optimized by increasing the sharpening intensity to ensure image clarity. This process maximizes the use of valid data and reduces invalid experiments through hierarchical optimization (gain adjustment → sharpening enhancement). At the same time, a first-level early warning mechanism is used to promptly identify systemic problems and avoid waste of resources.

[0044] The total data volume of the unavailable penetration image set is obtained and compared with the defined data volume stored in the database. If the total data volume of the unavailable penetration image set is greater than the defined data volume, a secondary warning is issued for the data acquisition process. If the total data volume of the unavailable penetration image set is less than or equal to the defined data volume, no secondary warning is issued for the data acquisition process. The above-mentioned defined data volume refers to the upper limit value of the total data volume of the unavailable penetration image set. The total data volume of the unavailable penetration image set can be obtained through file attribute statistics.

[0045] The second-level warning is triggered in the following circumstances: When the total data volume (such as file size) of the accumulated unusable penetration image set is greater than the defined data volume stored in the database, it indicates that image quality problems frequently occur during the experiment and there may be systemic risks. The second-level warning refers to marking the time when the second-level warning is triggered, the current unavailable data volume and the defined data volume in the experimental log, and notifying the experiment leader through a system pop-up window, email or SMS, prompting "The unavailable data volume exceeds the limit and the problem needs to be checked immediately."

[0046] Figure 3 This is the quality assessment flow chart of the optimized penetration image set of the present invention. After the optimized image set acquisition process, the quality index of the optimized penetration image set is calculated. If the quality index is greater than or equal to the defined quality threshold, it is judged to be up to standard and the fluorescent marker intensity is extracted; if the quality index is less than the defined quality threshold, the sharpening intensity is increased and the quality index is recalculated. If the optimized quality index after update is greater than or equal to the defined quality threshold, the fluorescent marker intensity is extracted; otherwise, the optimized image set is marked as unusable and a data anomaly warning is triggered, prompting the experimenter to check the experimental process or equipment status.

[0047] Step 3: According to the fluorescence labeling intensity of the sea buckthorn extract, the penetration efficiency of the sea buckthorn extract is matched, thereby updating the sea buckthorn extract penetration efficiency curve, updating and analyzing the attribute parameters of the sea buckthorn extract penetration efficiency curve, thereby managing the sea buckthorn extract penetration efficiency curve and visualizing the sea buckthorn extract penetration efficiency curve.

[0048] In a specific embodiment, when updating the penetration efficiency curve, the present invention combines the fluorescent labeling intensity and the timestamp to clearly present the changing trend of the penetration efficiency of the sea buckthorn extract over time, facilitates the intuitive observation of the penetration process, analyzes the curve attribute parameters and quantifies the data quality index, and can accurately identify problems such as outliers and deviation duration in the curve, providing a clear direction for subsequent optimization. When managing the curve, the validity of the parameters is determined based on the data quality index, and invalid parameters can be eliminated to ensure data accuracy. When determining whether to optimize the data acquisition process, the secondary optimization acquisition can significantly improve the quality of the penetration image set by adjusting the imaging gain and the number of image frames, thereby improving the accuracy of the fluorescent labeling intensity extraction. The four-level early warning mechanism can provide timely feedback on the data quality status, facilitate timely adjustment of the data acquisition strategy, and ensure the smooth progress of the test.

[0049] Specifically, the attribute parameters of the penetration efficiency curve of the sea buckthorn extract are analyzed, and the specific analysis process is as follows: the attribute parameters of the penetration efficiency curve of the sea buckthorn extract include the maximum data deviation time of the penetration efficiency curve of the sea buckthorn extract, the abnormal value ratio of the penetration efficiency curve of the sea buckthorn extract, and the curve smoothness of the penetration efficiency curve of the sea buckthorn extract; the maximum data deviation time refers to the longest time period during which the deviation between the penetration efficiency value at a certain moment and the overall trend (such as the fitting curve or the mean) exceeds the preset threshold in the penetration efficiency curve. The baseline trend line is calculated based on the penetration efficiency curve, and polynomial fitting, exponential fitting or sliding average methods can be used (such as 5-point sliding average of the curve to smooth the data); secondly, the penetration efficiency value at each time point is calculated relative to the baseline trend. Absolute deviation, and set the deviation threshold (such as 10% of the baseline value) according to experimental requirements; then, traverse the curve, identify the time periods in which the deviation exceeds the threshold in continuous time points, and record the duration of each time period; finally, select the time period with the longest duration from all deviation time periods, and the length of this time period is the maximum deviation duration of the data. This parameter can quantify the duration of local anomalies in the penetration process and is used to evaluate data stability and the degree of experimental interference. The outlier ratio refers to the proportion of data points judged as outliers in the penetration efficiency curve to the total data points, which can be obtained through analysis based on the quartile method. The curve smoothness reflects the degree of fluctuation of the penetration efficiency curve. The second-order derivative (or curvature) of the curve is calculated, and the mean of its absolute value can be used as a quantitative indicator of curve smoothness.

[0050] The maximum data deviation time, the outlier ratio and the reference curve smoothness are extracted from the database; the maximum data deviation time is defined, indicating the upper limit of the maximum data deviation time; the outlier ratio is defined, indicating the upper limit of the outlier ratio; the reference curve smoothness is the reference value of the curve smoothness.

[0051] The quality index of the penetration image set corresponding to the penetration efficiency is obtained and marked as the final image quality index.

[0052] The measurement ratio values ​​were extracted from the database to quantify the influence of the final image quality index, the relative ratio between the maximum deviation time of the data and the defined maximum deviation time of the data, the relative ratio between the outlier ratio and the defined outlier ratio, and the deviation value between the curve smoothness and the reference curve smoothness on the data quality index of the penetration efficiency curve of the sea buckthorn extract. The various influence degrees were aggregated to obtain the data quality index of the penetration efficiency curve of the sea buckthorn extract.

[0053] The data quality index of the penetration efficiency curve of the roxburghii extract is used to digitally indicate the data quality level of the penetration efficiency curve of the roxburghii extract. The specific expression is:

[0054]

[0055] QUC is the data quality index of the penetration efficiency curve of the sea buckthorn extract, QUF_NEW is the final image quality index, A is the final image quality index measurement ratio value stored in the database, TL is the data maximum deviation time of the penetration efficiency curve of the sea buckthorn extract, FD_TL is the defined data maximum deviation time, PRO is the outlier ratio of the penetration efficiency curve of the sea buckthorn extract, FD_PRO is the defined outlier ratio, SMO is the curve smoothness of the penetration efficiency curve of the sea buckthorn extract, CSMO is the reference curve smoothness, d is the data maximum deviation time ratio value stored in the database, f is the outlier ratio measurement ratio value stored in the database, and g is the curve smoothness measurement ratio value.

[0056] The final image quality index measurement ratio value is used to quantify the influence of the final image quality index on the data quality index of the penetration efficiency curve of the sea buckthorn extract; the data maximum deviation duration measurement ratio value is used to quantify the influence of the ratio between the data maximum deviation duration and the defined data maximum deviation duration on the data quality index of the penetration efficiency curve of the sea buckthorn extract; the outlier ratio measurement ratio value is used to quantify the influence of the ratio between the outlier ratio and the defined outlier ratio on the data quality index of the penetration efficiency curve of the sea buckthorn extract; the curve smoothness measurement ratio value, the deviation between the curve smoothness and the reference curve smoothness on the data quality index of the penetration efficiency curve of the sea buckthorn extract The degree of impact, several mapping relationships are stored in the database, such as parameter measurement ratio value mapping relationships. These mapping relationships are bound to image data and experimental metadata through structured tables (such as parameter mapping tables, weight configuration tables) or associated query statements (such as JOIN operations) to form a complete image quality assessment system. Therefore, the final image quality index measurement ratio value, the length ratio value at the maximum deviation of the data, the outlier ratio measurement ratio value, and the curve smoothness measurement ratio value can be directly queried from the database. The final image quality index measurement ratio value, the length ratio value at the maximum deviation of the data, the outlier ratio measurement ratio value, and the curve smoothness measurement ratio value all range from 0 to 1.

[0057] The duration of maximum data deviation and the proportion of outliers are key parameters reflecting the stability of the permeation experiment process. When there are persistent disturbances during the experiment (such as instrument fluctuations or operational errors), the permeation efficiency curve will deviate from the baseline trend for a longer period of time, thereby increasing the duration of the maximum data deviation. This disturbance also leads to more extreme values ​​(outliers), resulting in an increased proportion of outliers. Curve smoothness reflects the degree of fluctuation in the permeation efficiency curve. If the experimental process is unstable or there is noise interference, the curve will exhibit violent fluctuations, resulting in reduced smoothness. The final image quality index, a comprehensive evaluation metric for the permeation image set, is directly dependent on image clarity, noise level, and detail. High-quality permeation images can more accurately reflect the dynamic changes in permeation efficiency, thereby reducing curve deviations and outliers caused by image distortion or noise. Conversely, low-quality images can introduce artifacts or blurred boundaries, resulting in reduced curve smoothness, increased deviation duration, and a higher proportion of outliers. Ultimately, the duration of maximum data deviation, the proportion of outliers, and curve smoothness jointly determine the data quality index of the permeation efficiency curve by quantifying the stability of the experimental process and the reliability of the data.

[0058] Specifically, the penetration efficiency curve of the sea buckthorn extract is updated, and the specific updating process is: based on the fluorescent labeling intensity-penetration efficiency mapping table stored in the database, the fluorescent labeling intensity of the sea buckthorn extract is used as an automatic index label and input into the mapping table to obtain the penetration efficiency of the sea buckthorn extract, thereby completing the matching process of the penetration efficiency of the sea buckthorn extract; obtaining the timestamp corresponding to the fluorescent labeling intensity of the sea buckthorn extract, marking it as the extraction time point of the penetration efficiency of the sea buckthorn extract, adding the extraction time point of the penetration efficiency of the sea buckthorn extract and the penetration efficiency of the sea buckthorn extract to the sea buckthorn extract penetration efficiency curve, thereby updating the sea buckthorn extract penetration efficiency curve, the timestamp corresponding to the fluorescent labeling intensity of the sea buckthorn extract refers to the acquisition time point of the penetration image set corresponding to the penetration efficiency.

[0059] Furthermore, the penetration efficiency curve of the sea buckthorn extract is managed. The specific management process is: the data quality index of the penetration efficiency curve of the sea buckthorn extract is compared with the data quality threshold preset in the database. If the data quality index of the penetration efficiency curve of the sea buckthorn extract is greater than the data quality threshold, the extraction time point of the penetration efficiency of the sea buckthorn extract and the penetration efficiency of the sea buckthorn extract are marked as valid parameters, so as to continuously obtain the penetration image set of the sea buckthorn extract; the data quality threshold represents the lower limit value of the data quality index.

[0060] If the data quality index of the penetration efficiency curve of the roxburghii extract is less than or equal to the data quality threshold, the extraction time point of the penetration efficiency of the roxburghii extract and the penetration efficiency of the roxburghii extract are marked as abnormal parameters, and it is determined whether the data acquisition process is optimized.

[0061] Specifically, it is determined whether the data acquisition process is optimized. The specific determination process is: the final image quality index is differentially processed with the defined quality index, and the processing result is marked as the image quality margin; the image quality margin refers to the result of subtracting the defined quality index from the final image quality index.

[0062] The image quality margin is compared with the defined image quality margin preset in the database. If the image quality margin is greater than the defined image quality margin, the abnormal data is marked as an invalid parameter, the invalid parameter is removed from the sea buckthorn extract penetration efficiency curve, and it is determined that the data acquisition process is not optimized, and the acquisition process of the penetration image set of the sea buckthorn extract is optimized again; the defined image quality margin is a numerical value used to determine whether the fluorescent labeling intensity is increased.

[0063] If the image quality margin is less than or equal to the defined image quality margin, a fluorescent labeling intensity increase coefficient is matched from the database based on the image quality margin, thereby increasing the fluorescent labeling intensity of the sea buckthorn extract and updating the penetration efficiency of the sea buckthorn extract; an image quality margin-fluorescent labeling intensity increase coefficient mapping table is stored in the database, so that through the mapping relationship, based on the image quality margin, the fluorescent labeling intensity increase coefficient can be queried and matched from the database, and the fluorescent labeling intensity increase coefficient is multiplied by the fluorescent labeling intensity of the sea buckthorn extract, thereby completing the increase in the fluorescent labeling intensity of the sea buckthorn extract, and the fluorescent labeling intensity increase coefficient represents a proportional value greater than 1, indicating a proportional value for increasing the fluorescent labeling intensity.

[0064] The updated penetration efficiency of the roxburghii extract and the extraction time point of the penetration efficiency of the roxburghii extract are added to the roxburghii extract penetration efficiency curve, thereby updating the roxburghii extract penetration efficiency curve.

[0065] The data quality index of the updated penetration efficiency curve of the sea buckthorn extract is obtained and compared with the data quality threshold. If the data quality index of the updated penetration efficiency curve of the sea buckthorn extract is greater than the data quality threshold, the extraction time point of the penetration efficiency of the sea buckthorn extract and the updated penetration efficiency of the sea buckthorn extract are marked as valid parameters, thereby continuously obtaining the penetration image set of the sea buckthorn extract.

[0066] This judgment process significantly improves experimental data accuracy by dynamically adjusting the fluorescent labeling intensity. When image quality meets the standard but the margin is insufficient (≤ a threshold), the fluorescent labeling intensity amplification factor is automatically matched and the signal is enhanced to compensate for minor flaws in imaging details and ensure analytical sensitivity. When the image quality margin is sufficient (> the threshold), the enhancement step is skipped to avoid data distortion caused by excessive processing. This mechanism balances data quality and experimental efficiency, ensuring reliability while reducing redundant operations and optimizing resource utilization.

[0067] If the data quality index of the updated sea buckthorn extract penetration efficiency curve is still less than or equal to the data quality threshold, the extraction time point of the sea buckthorn extract penetration efficiency and the updated sea buckthorn extract penetration efficiency will be marked as invalid parameters, the invalid parameters will be removed from the sea buckthorn extract penetration efficiency curve, and a data abnormality warning will be issued, that is, a warning email will be sent to the experiment leader, including details of the invalid parameters and recommended operations (such as checking equipment and reviewing samples).

[0068] Specifically, the acquisition process of the penetration image set of the sea buckthorn extract is secondary optimized, and the specific analysis process is: obtaining the data quality index deviation value of the sea buckthorn extract penetration efficiency curve, and matching the image acquisition imaging gain quadratic increase coefficient from the database; the data quality index deviation value refers to the result of subtracting the data quality threshold from the data quality index, and the database stores a data quality index deviation value-image acquisition imaging gain quadratic increase coefficient mapping table. Therefore, through the mapping relationship, based on the data quality index deviation value of the sea buckthorn extract penetration efficiency curve, the image acquisition imaging gain quadratic increase coefficient can be matched from the database, and the image acquisition imaging gain quadratic increase coefficient is multiplied by the current image acquisition imaging gain. The result is recorded as the optimized image acquisition imaging gain, and the image acquisition imaging gain quadratic increase coefficient is a proportional value greater than 1, which also represents the proportional value for increasing the image acquisition imaging gain.

[0069] At the same time, based on the data quality index deviation value of the penetration efficiency curve of the sea buckthorn extract, the image acquisition frame number increase coefficient is matched from the database, so that the image acquisition frame number included in the penetration image set is increased and adjusted, thereby completing the secondary optimization of the acquisition process of the penetration image set of the sea buckthorn extract; the data quality index deviation value-image acquisition frame number increase coefficient mapping table is stored in the database, so that through the mapping relationship, based on the data quality index deviation value of the penetration efficiency curve of the sea buckthorn extract, the image acquisition frame number increase coefficient is matched from the database, the image acquisition frame number increase coefficient is multiplied by the current image acquisition frame number, and the result is recorded as the optimized image acquisition frame number, and the image acquisition frame number increase coefficient is a proportional value greater than 1, indicating that the proportional value of the image acquisition frame number is increased.

[0070] This secondary optimization significantly improves image quality and experimental reliability by dynamically adjusting imaging gain and acquisition frame rate. Increasing imaging gain compensates for low signal intensity, enhancing fluorescence detail and preventing data deviations caused by underexposure. Increasing acquisition frame rate reduces random noise by stacking multiple frames, improving the signal-to-noise ratio and being particularly effective for weak-signal samples. The coordinated optimization of these two factors can specifically address data quality index deviations, ensuring that the penetration efficiency curve accurately reflects the true penetration behavior of the sea buckthorn extract.

[0071] After the optimization is completed, the penetration image set of the sea buckthorn extract is re-acquired and marked as the secondary optimized penetration image set of the sea buckthorn extract, and the quality index of the secondary optimized penetration image set is obtained and compared with the defined quality index; if the quality index of the secondary optimized penetration image set is greater than or equal to the defined quality index, the fluorescent labeling intensity of the sea buckthorn extract is directly extracted from the secondary optimized penetration image set, thereby updating the sea buckthorn extract penetration efficiency curve again, and determining whether to issue a fourth-level warning for the data acquisition process; if the quality index of the secondary optimized penetration image set is less than the defined quality index, the secondary optimization process is canceled, and a third-level warning is issued for the data acquisition process. The core condition for triggering the third-level warning is: if the quality index of the penetration image set after the secondary optimization is still less than the defined quality index, that is, both optimizations fail, the system automatically restores the imaging gain and the number of acquired frames to the initial value, and records the time, reason and parameters before the cancellation operation, thereby issuing a third-level warning based on the time, reason and parameters before the cancellation operation.

[0072] Furthermore, it is determined whether to issue a fourth-level warning to the data acquisition process. The specific determination process is: obtain the data quality index of the updated sea buckthorn extract penetration efficiency curve and compare it with the data quality threshold. If the data quality index of the updated sea buckthorn extract penetration efficiency curve is greater than the data quality threshold, it is determined that no fourth-level warning will be issued to the data acquisition process, and the penetration image set of the sea buckthorn extract will continue to be acquired; if the data quality index of the updated sea buckthorn extract penetration efficiency curve is less than or equal to the data quality threshold, the secondary optimization process will be canceled, and it will be determined that a fourth-level warning will be issued to the data acquisition process. The triggering condition for the fourth-level warning is: the data quality index of the updated penetration efficiency curve is less than or equal to the data quality threshold (that is, all three optimization attempts failed). The fourth-level warning refers to a red warning window popping up on the experiment management interface, triggering the laboratory red warning light and buzzer.

[0073] In a specific embodiment, the present invention provides a method for measuring the skin penetration efficiency of sea buckthorn (Roxburgh) based on an artificial skin model. When measuring the skin penetration efficiency of sea buckthorn (Roxburgh) extract, the method first monitors its penetration process on the artificial skin model and obtains a penetration image set in a simulated actual usage scenario. This process can highly restore the actual usage situation, provide a data basis that fits the actual situation for subsequent analysis, and make the research closer to the actual effect of the product. Then, the penetration image set is denoised, and attribute parameters are collected and analyzed to quantify quality characteristics. Based on this, it is determined whether to extract the fluorescent marker intensity. This step can effectively screen out high-quality images, avoid noise interference, ensure that the extracted fluorescent marker intensity data is reliable, and provide strong support for accurately evaluating the penetration situation. Finally, the penetration efficiency is matched according to the fluorescent marker intensity, and the attribute parameters of the penetration efficiency curve are updated and analyzed, managed and visualized. By updating the curve, the trend of the change of penetration efficiency over time can be clearly presented. The visualization processing makes the data more intuitive and easy to understand, which facilitates researchers to quickly grasp the penetration characteristics of the sea buckthorn extract.

[0074] Figure 4 This is a flow chart for evaluating the data quality of the penetration efficiency curve of the present invention. After matching the penetration efficiency based on the fluorescent marker intensity and updating the curve, the curve attribute parameters are analyzed and the data quality index is calculated. If the data quality index is greater than the data quality threshold, it is marked as a valid parameter and the image set is continuously acquired. If the data quality index is less than or equal to the data quality threshold, it is marked as an abnormal parameter and a determination is made as to whether the data acquisition process should be optimized. If not, a data abnormality warning is issued. If optimized, a secondary acquisition is performed and the quality index is recalculated. If the updated data quality index is greater than the data quality threshold, it is marked as a valid parameter. Otherwise, the secondary optimization is canceled and a level 4 warning is triggered.

[0075] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A method for measuring the skin penetration efficiency of roxburghii based on an artificial skin model, characterized in that: include: Step 1: monitoring the penetration process of the roxburghii extract on the artificial skin model in a simulated actual usage scenario, thereby obtaining a set of penetration images of the roxburghii extract; Step 2: Denoising the infiltration image set of the roxburghii extract. After the processing is completed, collecting and analyzing the attribute parameters of the infiltration image set to quantify the quality characteristics of the infiltration image set, and determining whether to extract the fluorescent labeling intensity of the roxburghii extract from the infiltration image set based on the quality characteristics of the infiltration image set; Step 3: According to the fluorescence labeling intensity of the sea buckthorn extract, the penetration efficiency of the sea buckthorn extract is matched, thereby updating the sea buckthorn extract penetration efficiency curve, updating and analyzing the attribute parameters of the sea buckthorn extract penetration efficiency curve, thereby managing the sea buckthorn extract penetration efficiency curve and visualizing the sea buckthorn extract penetration efficiency curve.

2. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 1, wherein: The quality characteristics of the quantified penetration image set are specifically quantified as follows: Attribute parameters of the penetration image set, including texture clarity of the penetration image set, gradient variance of the penetration image set, and signal-to-noise intensity ratio of the penetration image set; Extracting defined texture clarity, defined gradient variance, and defined signal-to-noise intensity ratio from the database; Extracting metric ratio values ​​from the database to quantify the influence of the relative ratio between texture clarity and defined texture clarity, the relative ratio between gradient variance and defined gradient variance, and the relative ratio between signal-to-noise intensity ratio and defined signal-to-noise intensity ratio on the quality index of the penetration image set, and summarizing the influence degrees to obtain the quality index of the penetration image set; The quality index of the penetration image set is used to digitally indicate the quality characteristics of the penetration image set.

3. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 1, wherein: The step of determining whether to extract the fluorescent labeling intensity of the roxburghii extract from the penetration image set based on the quality characteristics of the penetration image set is specifically as follows: Determining whether to extract the fluorescence labeling intensity of the roxburghii extract from the penetration image set according to the quality index of the penetration image set; If the quality index of the penetration image set is greater than or equal to the defined quality index stored in the database, then determining the intensity of the fluorescent labeling of the roxburghii extract directly extracted from the penetration image set; If the quality index of the penetration image set is less than the defined quality index, then based on the quality index of the penetration image set and the defined quality index, increasing the sharpening intensity of the penetration image set; After the improvement is completed, the quality index of the penetration image set is updated and compared with the defined quality index again. If the quality index of the updated penetration image set is still less than the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract will not be extracted from the penetration image set, and the penetration image set is marked as an unusable penetration image set. At the same time, the penetration image set of the sea buckthorn extract is re-acquired. If the quality index of the updated penetration image set is greater than or equal to the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract will be extracted from the penetration image set.

4. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 3, wherein: The specific analysis process of reacquiring the penetration image set of the roxburghii extract is as follows: Obtaining a quality index deviation value of the penetration image set based on the quality index of the penetration image set and the defined quality index; Based on the quality index deviation value of the penetration image set, the image acquisition imaging gain increase coefficient is extracted from the database, and the acquisition process of the penetration image set of the roxburgh roxburgh extract is optimized, so as to re-acquire the penetration image set of the roxburgh roxburgh extract and mark it as the optimized penetration image set; Obtaining the quality index of the optimized penetration image set and comparing it with the defined quality index; If the quality index of the optimized penetration image set is greater than the defined quality index, the fluorescence labeling intensity of the roxburghii extract is extracted from the optimized penetration image set; If the quality index of the optimized penetration image set is less than or equal to the defined quality index, the sharpening intensity of the optimized penetration image set is improved based on the quality index of the optimized penetration image set and the defined quality index. After the improvement is completed, the quality index of the optimized penetration image set is updated and compared with the defined quality index again. If the quality index of the optimized penetration image set is still less than the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract is not extracted from the optimized penetration image set, and the optimized penetration image set is marked as an unusable penetration image set. At the same time, a first-level warning is issued for the data acquisition process. If the quality index of the penetration image set is greater than or equal to the defined quality index, it is determined that the fluorescent labeling intensity of the sea buckthorn extract is extracted from the optimized penetration image set. The total data volume of the unusable penetration image set is obtained and compared with the defined data volume stored in the database. If the total data volume of the unusable penetration image set is greater than the defined data volume, a secondary warning is issued for the data acquisition process. If the total data volume of the unusable penetration image set is less than or equal to the defined data volume, no secondary warning is issued for the data acquisition process.

5. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 1, wherein: The updating process of the penetration efficiency curve of the roxburghii extract is as follows: Based on a fluorescent labeling intensity-penetration efficiency mapping table stored in a database, the fluorescent labeling intensity of the roxburghii roxburghii extract is used as an automatic index label and input into the mapping table to obtain the penetration efficiency of the roxburghii roxburghii extract, thereby completing the matching process of the penetration efficiency of the roxburghii roxburghii extract; A timestamp corresponding to the fluorescent labeling intensity of the roxburghii extract is obtained, marked as the extraction time point of the penetration efficiency of the roxburghii extract, and the extraction time point of the penetration efficiency of the roxburghii extract and the penetration efficiency of the roxburghii extract are added to the roxburghii extract penetration efficiency curve, thereby updating the roxburghii extract penetration efficiency curve.

6. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 1, characterized in that: The property parameters of the analysis of the penetration efficiency curve of the roxburghii extract are analyzed in detail as follows: The attribute parameters of the penetration efficiency curve of the roxburgh pear extract include the maximum deviation duration of the data of the penetration efficiency curve of the roxburgh pear extract, the abnormal value ratio of the penetration efficiency curve of the roxburgh pear extract, and the curve smoothness of the penetration efficiency curve of the roxburgh pear extract; Extract the maximum deviation duration of the defined data, the proportion of defined outliers, and the smoothness of the reference curve from the database; Obtain the quality index of the penetration image set corresponding to the penetration efficiency, and mark it as the final image quality index; Extracting metric ratio values ​​from the database to quantify the influence of the final image quality index, the relative ratio between the maximum deviation duration of the data and the defined maximum deviation duration of the data, the relative ratio between the outlier ratio and the defined outlier ratio, and the deviation between the curve smoothness and the reference curve smoothness on the data quality index of the penetration efficiency curve of the sea buckthorn extract, and aggregating the various influence degrees to obtain the data quality index of the penetration efficiency curve of the sea buckthorn extract; The data quality index of the penetration efficiency curve of the roxburgh roxburgh extract is used to digitally indicate the data quality level of the penetration efficiency curve of the roxburgh roxburgh extract.

7. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 1, characterized in that: The specific management process of the penetration efficiency curve of the roxburghii extract is as follows: Comparing the data quality index of the penetration efficiency curve of the roxburgh pear extract with a data quality threshold preset in a database, if the data quality index of the penetration efficiency curve of the roxburgh pear extract is greater than the data quality threshold, marking the extraction time point of the penetration efficiency of the roxburgh pear extract and the penetration efficiency of the roxburgh pear extract as valid parameters, thereby continuously acquiring a penetration image set of the roxburgh pear extract; If the data quality index of the penetration efficiency curve of the roxburghii extract is less than or equal to the data quality threshold, the extraction time point of the penetration efficiency of the roxburghii extract and the penetration efficiency of the roxburghii extract are marked as abnormal parameters, and it is determined whether the data acquisition process is optimized.

8. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 7, characterized in that: The determination of whether to optimize the data acquisition process is as follows: Performing difference processing on the final image quality index and the defined quality index, and marking the processing result as the image quality margin; The image quality margin is compared with a defined image quality margin preset in a database; if the image quality margin is greater than the defined image quality margin, the abnormal data is marked as an invalid parameter, the invalid parameter is removed from the penetration efficiency curve of the roxburghii extract, and it is determined that the data acquisition process is not optimized, and the acquisition process of the penetration image set of the roxburghii extract is optimized again; If the image quality margin is less than or equal to the defined image quality margin, a fluorescent labeling intensity increase coefficient is matched from a database based on the image quality margin, thereby increasing the fluorescent labeling intensity of the roxburghii extract and updating the penetration efficiency of the roxburghii extract; Adding the updated penetration efficiency of the roxburghii roxburghii extract and the extraction time point of the penetration efficiency of the roxburghii roxburghii extract to the roxburghii roxburghii extract penetration efficiency curve, thereby updating the roxburghii roxburghii extract penetration efficiency curve; Obtaining a data quality index of the updated penetration efficiency curve of the roxburghii extract and comparing it with a data quality threshold; if the data quality index of the updated penetration efficiency curve of the roxburghii extract is greater than the data quality threshold, marking the extraction time point of the penetration efficiency of the roxburghii extract and the updated penetration efficiency of the roxburghii extract as valid parameters, thereby continuously acquiring a penetration image set of the roxburghii extract; If the data quality index of the updated sea buckthorn extract penetration efficiency curve is still less than or equal to the data quality threshold, the extraction time point of the penetration efficiency of the sea buckthorn extract and the updated penetration efficiency of the sea buckthorn extract are marked as invalid parameters, the invalid parameters are removed from the sea buckthorn extract penetration efficiency curve, and a data abnormality warning is issued.

9. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 8, characterized in that: The acquisition process of the penetration image set of the roxburghii extract is optimized twice, and the specific analysis process is as follows: Obtain the data quality index deviation value of the penetration efficiency curve of the roxburghii extract, and match the image acquisition imaging gain quadratic increase coefficient from the database; At the same time, based on the data quality index deviation value of the penetration efficiency curve of the roxburghii extract, the image acquisition frame number increase coefficient is matched from the database, so as to increase the number of image acquisition frames contained in the penetration image set, thereby completing the secondary optimization of the acquisition process of the penetration image set of the roxburghii extract; After the optimization is completed, the permeation image set of the roxburghii extract is re-obtained and marked as the secondary optimized permeation image set of the roxburghii extract, and the quality index of the secondary optimized permeation image set is obtained and compared with the defined quality index; If the quality index of the secondary optimized penetration image set is greater than or equal to the defined quality index, the fluorescence labeling intensity of the roxburghii extract is directly extracted from the secondary optimized penetration image set, thereby updating the roxburghii extract penetration efficiency curve again, and determining whether to issue a fourth-level warning for the data acquisition process; If the quality index of the secondary optimized penetration image set is less than the defined quality index, the secondary optimization process will be canceled and a third-level warning will be issued for the data acquisition process.

10. A method for measuring roxburghii skin penetration efficiency based on an artificial skin model according to claim 9, characterized in that: The specific determination process of whether to issue a level 4 warning for the data acquisition process is as follows: Obtaining a data quality index of the updated penetration efficiency curve of the roxburghii extract and comparing it with the data quality threshold; if the data quality index of the updated penetration efficiency curve of the roxburghii extract is greater than the data quality threshold, determining not to issue a level 4 warning for the data acquisition process and continuing to acquire a penetration image set of the roxburghii extract; If the data quality index of the updated sea buckthorn extract penetration efficiency curve is less than or equal to the data quality threshold, the secondary optimization process will be canceled and a fourth-level warning will be issued for the data acquisition process.

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