Cosmetic adverse reaction analysis feedback method and system based on active monitoring

Abstract

The invention discloses a cosmetic adverse reaction analysis feedback method and system based on active monitoring, and relates to the technical field of cosmetic information management. According to the cosmetic adverse reaction analysis and feedback method based on active monitoring, use monitoring feedback information of different age groups of set cosmetics is obtained, prediction analysis is carried out, and a skin state feedback data set and a use behavior evaluation feedback data set of each age group are obtained; analyzing to obtain a bad skin feedback index and a bad use feedback index of each age group; the method comprises the following steps of: comparing a preset comprehensive bad feedback index threshold value with a preset comprehensive bad feedback index threshold value to generate a feedback suggestion and marking the feedback suggestion, simultaneously obtaining a cosmetic ingredient feedback safety index, and analyzing by combining the indexes to obtain a comprehensive bad feedback index of each age group. Therefore, instant feedback of users of different age groups in the cosmetic using process is effectively captured, targeted analysis is provided, and then the adverse reaction of the cosmetics is accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of cosmetics information management, and in particular to a cosmetics adverse reaction analysis feedback method and system based on active monitoring. Background Art

[0002] As consumers pay more and more attention to the quality and effects of cosmetics, the safety and applicability of cosmetics have become core issues in production and marketing. Consumers' skin reactions and usage behaviors when using cosmetics are often not monitored in a timely and comprehensive manner, resulting in a lag in feedback information, which easily makes cosmetics manufacturers face greater challenges in product optimization and risk management. In order to improve the timeliness and accuracy of cosmetics safety monitoring, more and more companies and institutions have begun to pay attention to collecting user feedback information in real time through active monitoring and data analysis, and then dynamically evaluate the use effect and safety of cosmetics, so as to effectively monitor consumers' adverse skin reactions and behaviors when using cosmetics.

[0003] The prior art, such as a method and system for analyzing and feeding back adverse reactions of cosmetics disclosed in the patent application with the announcement number CN118427574A, relates to the field of cosmetics information management technology. The method includes: collecting historical report data of adverse reactions caused by users using cosmetics, counting key indicators of adverse reactions caused by user information and cosmetics information, preprocessing the historical report data, building a probability model of adverse reactions of cosmetics according to key indicators, inputting the preprocessed historical report data into the probability model, simulating the probability model using the Monte Carlo simulation method, and then feeding back the risk simulation assessment results to the user end according to the risk level in combination with the expert survey method. The method can solve the problem of increased difficulty in developing a cosmetics risk supervision system and relatively reduced supervision efficiency due to the complex sources of cosmetics risk monitoring information, the lack of regulatory information analysis tools, and the lack of smooth information storage and communication.

[0004] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems: the existing technology does not fully take into account the dynamic and real-time user feedback information, especially the different reactions that user groups of different age groups are prone to when using cosmetics, which can easily lead to insufficient accuracy and specificity in adverse reaction analysis, making it difficult to fully reflect the actual safety of cosmetics in different groups, and thus making it difficult to obtain accurate feedback and suggestions. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and system for analyzing and feedback on adverse reactions of cosmetics based on active monitoring, which solves the problem that the prior art does not fully consider dynamic user feedback when analyzing adverse reactions of users of different age groups when using cosmetics, which easily leads to a lack of accuracy and pertinence in adverse reaction analysis.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring, comprising the following steps: obtaining usage monitoring feedback information of several age groups of set cosmetics, and inputting it into a pre-trained multimodal recognition model for predictive analysis to obtain a skin state feedback data set and a usage behavior evaluation feedback data set for each age group of the set cosmetics; performing data analysis on the skin state feedback data set and the usage behavior evaluation feedback data set for each age group of the set cosmetics, respectively, to obtain a skin adverse feedback index and a usage adverse feedback index for each age group of the set cosmetics; simultaneously obtaining an ingredient feedback safety index of the set cosmetics, and performing a comprehensive analysis in combination with the skin adverse feedback index and the usage adverse feedback index for each age group, to obtain a comprehensive adverse feedback index for each age group of the set cosmetics; performing a judgment analysis on the comprehensive adverse feedback index for each age group of the set cosmetics and a preset comprehensive adverse feedback index threshold set, wherein the comprehensive adverse feedback index threshold set includes a first comprehensive adverse feedback index threshold, a second comprehensive adverse feedback index threshold, and a third comprehensive adverse feedback index threshold, and marking is performed based on the judgment analysis results, and corresponding feedback suggestions are generated at the same time.

[0007] Furthermore, the multimodal recognition model is specifically a visual-text joint model, which includes a visual encoder layer, a text encoder layer, a joint embedding layer, and a regression output layer, and the specific steps of obtaining a skin condition feedback data set and a usage behavior evaluation feedback data set for each age group of set cosmetics are as follows: in the visual encoder layer of the visual-text joint model, image feature extraction is performed on the usage monitoring feedback information of each age group of set cosmetics to obtain a high-dimensional image feature vector for each age group of set cosmetics; in the text encoder layer of the visual-text joint model, text feature extraction is performed on the usage monitoring feedback information of each age group of set cosmetics to obtain a text feature vector for each age group of set cosmetics; in the joint embedding layer of the visual-text joint model, The high-dimensional image feature vector and text feature vector of each age group of the set cosmetics are fused to obtain the joint feature vector of each age group of the set cosmetics; in the regression output layer of the visual-text joint model, the joint feature vector of each age group of the set cosmetics is regressed and predicted to obtain the skin state feedback data set and the usage behavior evaluation feedback data set of each age group of the set cosmetics; the skin state feedback data set includes the skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value; the usage behavior evaluation feedback data set includes the usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index.

[0008] Furthermore, the specific formula for calculating the comprehensive adverse feedback index of each age group of the set cosmetics is as follows: ; in, To set the cosmetic The comprehensive negative feedback index of each age group, , The first Skin adverse feedback index and usage adverse feedback index for each age group, To set the safety index of cosmetic ingredients, , , , The following are the skin adjustment coefficient, usage adjustment coefficient, ingredient adjustment coefficient, and adverse interaction coefficient stored in the database. 1, 2, 3, ..., , The number of age groups.

[0009] Furthermore, the specific steps of obtaining the adverse skin feedback index of each age group of the set cosmetics are as follows: reading the skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value in the skin state feedback data set of each age group of the set cosmetics; and obtaining the skin oil secretion level reference value, skin keratin thickness reference value, skin surface moisture content reference value, and environmental factors of each age group of the set cosmetics; comprehensively analyzing the skin oil secretion level reference value, skin keratin thickness reference value, skin surface moisture content reference value, skin oil secretion level value, erythema index, skin keratin thickness value, and skin surface moisture content value of each age group of the set cosmetics to obtain the skin damage index of each age group of the set cosmetics; and comprehensively analyzing the skin temperature gradient value, melanin index, skin conductivity value, and environmental factors of each age group of the set cosmetics to obtain the skin sensitivity index of each age group of the set cosmetics, and comprehensively analyzing in combination with the skin damage index to obtain the adverse skin feedback index of each age group of the set cosmetics.

[0010] Furthermore, the specific formulas for calculating the skin damage index, skin sensitivity index, and skin adverse feedback index for each age group of the cosmetics are as follows: ; in, To set the cosmetic Skin damage index for each age group, , , , , , , The first Skin oil secretion level value for each age group, skin oil secretion level reference value, erythema index, skin keratin thickness value, skin keratin thickness reference value, skin surface moisture content value, skin surface moisture content reference value, , , , They are the oil secretion adjustment coefficient, erythema adjustment coefficient, skin keratin adjustment coefficient, and moisture content adjustment coefficient stored in the database. To set the cosmetic Skin sensitivity index for each age group, , , , The first Skin temperature gradient values, melanin index, skin conductivity values, and environmental factors for each age group. , , , , They are the sensitive interaction coefficient, temperature gradient adjustment coefficient, melanin adjustment coefficient, skin conductivity value, conductivity adjustment coefficient, and environment adjustment coefficient stored in the database. To set the cosmetic Skin adverse feedback index for each age group, , , , They are the damage coefficient, damage adjustment coefficient, sensitivity coefficient, and sensitivity adjustment coefficient stored in the database. , is a natural constant, 1, 2, 3, ..., , The number of age groups.

[0011] Furthermore, the specific steps for obtaining the adverse feedback index of the set cosmetics for each age group are as follows: read the usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index in the usage behavior evaluation feedback data set of the set cosmetics for each age group; and conduct a comprehensive analysis of the usage frequency value, usage dosage value, duration value, and product mixing index of the set cosmetics for each age group to obtain the usage risk index of the set cosmetics for each age group; conduct a comprehensive analysis of the comfort index, stickiness index, skin gloss index, and stinging index of the set cosmetics for each age group to obtain the sensory evaluation index of the set cosmetics for each age group, and conduct a comprehensive analysis in combination with the usage risk index to obtain the adverse feedback index of the set cosmetics for each age group.

[0012] Furthermore, the specific formula for calculating the sensory evaluation index and adverse feedback index of cosmetics for each age group is as follows: ; in, To set the cosmetic Sensory evaluation index of each age group, , , , The first The comfort index, skin gloss index, stickiness index, and tingling index of each age group. , , They are the positive adjustment coefficient, negative adjustment coefficient, and difference adjustment coefficient stored in the database, To set the cosmetic The negative feedback index for each age group, To set the cosmetic The use risk index for each age group, , , , They are the behavior adjustment coefficient, sensory evaluation adjustment coefficient, behavior and sensory evaluation difference adjustment coefficient, and behavior and sensory evaluation interaction adjustment coefficient stored in the database. 1, 2, 3, ..., , The number of age groups.

[0013] Furthermore, the specific steps for obtaining the ingredient feedback safety index of the set cosmetics are as follows: obtaining the concentration value, concentration reference value, irritation score value, allergenicity score value, and acnegenicity score value of each ingredient of the set cosmetics; and conducting a comprehensive analysis of the concentration value, concentration reference value, irritation score value, allergenicity score value, and acnegenicity score value of each ingredient of the set cosmetics to obtain the ingredient feedback safety index of the set cosmetics.

[0014] Furthermore, the specific steps of generating corresponding feedback suggestions based on the judgment and analysis results are as follows: if the comprehensive adverse feedback index of the cosmetics for each age group is lower than or equal to the preset first comprehensive adverse feedback index threshold, it is marked as no adverse reaction, and a first feedback suggestion is generated; if the comprehensive adverse feedback index of the cosmetics for each age group is higher than the preset first comprehensive adverse feedback index threshold and lower than or equal to the preset second comprehensive adverse feedback index threshold, it is marked as a mild adverse reaction, and a second feedback suggestion is generated; if the comprehensive adverse feedback index of the cosmetics for each age group is higher than the preset second comprehensive adverse feedback index threshold and lower than or equal to the preset third comprehensive adverse feedback index threshold, it is marked as a moderate adverse reaction, and a third feedback suggestion is generated; if the comprehensive adverse feedback index of the cosmetics for each age group is higher than the preset third comprehensive adverse feedback index threshold, it is marked as a severe adverse reaction, and a fourth feedback suggestion is generated.

[0015] The cosmetic adverse reaction analysis and feedback system based on active monitoring includes: a data acquisition module, which is used to acquire the usage monitoring feedback information of several age groups of the set cosmetics, and input it into a pre-trained multimodal recognition model for prediction analysis, so as to obtain the skin state feedback data set and the usage behavior evaluation feedback data set of each age group of the set cosmetics; a data analysis module, which is used to respectively perform data analysis on the skin state feedback data set and the usage behavior evaluation feedback data set of each age group of the set cosmetics, so as to obtain the skin adverse feedback index and the usage adverse feedback index of each age group of the set cosmetics; a comprehensive analysis module, which is used to simultaneously acquire the ingredient feedback safety index of the set cosmetics, and perform comprehensive analysis in combination with the skin adverse feedback index and the usage adverse feedback index of each age group, so as to obtain the comprehensive adverse feedback index of each age group of the set cosmetics; and a judgment feedback module, which is used to perform judgment analysis on the comprehensive adverse feedback index of each age group of the set cosmetics and a preset comprehensive adverse feedback index threshold set, wherein the comprehensive adverse feedback index threshold set includes a first comprehensive adverse feedback index threshold, a second comprehensive adverse feedback index threshold, and a third comprehensive adverse feedback index threshold, and is marked based on the judgment analysis result, and corresponding feedback suggestions are generated at the same time.

[0016] The present invention has the following beneficial effects: (1) This method for analyzing and feedbacking adverse reactions to cosmetics based on active monitoring obtains usage monitoring feedback information of set cosmetics for different age groups and inputs it into a pre-trained multimodal recognition model for predictive analysis, thereby updating the skin condition and usage evaluation behavior data of each age group in real time, thereby effectively capturing the immediate feedback of users of different age groups during the use of cosmetics, and then providing targeted analysis, thereby accurately evaluating the adverse reactions of cosmetics and improving the safety and effectiveness of product use.

[0017] (2) This method of analyzing and feedback on adverse reactions to cosmetics based on active monitoring, through comprehensive analysis of the skin adverse feedback index, usage adverse feedback index and ingredient feedback safety index, obtains a comprehensive adverse feedback index for each age group, thereby enabling an in-depth understanding of the performance of cosmetics in different user groups and timely detection of existing adverse reactions, and then adjusting product formulas or providing personalized usage recommendations accordingly, thereby reducing the occurrence of adverse reactions and improving the market acceptance of products.

[0018] (3) This method of analyzing and feedback on adverse reactions to cosmetics based on active monitoring compares the comprehensive adverse feedback index of each age group with the preset comprehensive adverse feedback index threshold, so as to timely detect the adverse reactions of cosmetics in specific groups and generate personalized feedback suggestions, thereby effectively avoiding large-scale adverse reactions and providing real-time guidance for product optimization, thereby ensuring that consumers of different age groups have a safer and more effective use experience.

[0019] (4) The cosmetics adverse reaction analysis and feedback system based on active monitoring obtains usage monitoring feedback information from users of different age groups in real time and performs intelligent analysis through a pre-trained multimodal recognition model. This ensures that the usage feedback of cosmetics is not limited to regular surveys or consumer complaints, but can obtain users' immediate responses at any time, thereby ensuring the accuracy and timeliness of feedback, and then quickly adjusting product formulas or optimizing usage suggestions, and ensuring that users reduce the occurrence of adverse reactions during use, thereby improving the market competitiveness of the product.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of the method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring.

[0022] Figure 2 The present invention is a flowchart of the specific steps for obtaining the adverse feedback index of the use of set cosmetics for each age group in the cosmetics adverse reaction analysis and feedback method based on active monitoring.

[0023] Figure 3 This is a block diagram of the cosmetic adverse reaction analysis and feedback system based on active monitoring of the present invention. DETAILED DESCRIPTION

[0024] See also Figure 1, the embodiment of the present invention provides a technical solution: a method for analyzing and feedbacking adverse reactions of cosmetics based on active monitoring, comprising the following steps: obtaining usage monitoring feedback information of several (usage) age groups of set cosmetics (it should be noted here that the usage monitoring feedback text information is user feedback actively collected by relevant personnel or institutions, including feedback text information and feedback image information, and the data source includes but is not limited to text information, inspection information, image information, etc. actively reported or monitored by consumers or medical professionals when using cosmetics in real time), and inputting it into a pre-trained multimodal recognition model for predictive analysis to obtain a skin state feedback data set and a usage behavior evaluation feedback data set for each age group of the set cosmetics; performing data analysis on the skin state feedback data set and the usage behavior evaluation feedback data set for each age group of the set cosmetics, respectively, to obtain skin adverse reactions of each age group of the set cosmetics. feedback index, adverse feedback index for use; at the same time, obtain the ingredient feedback safety index of the set cosmetics, and conduct a comprehensive analysis in combination with the adverse skin feedback index and the adverse use feedback index of each age group to obtain the comprehensive adverse feedback index of each age group of the set cosmetics; conduct a judgment analysis on the comprehensive adverse feedback index of each age group of the set cosmetics and the preset comprehensive adverse feedback index threshold set, the comprehensive adverse feedback index threshold set includes the first comprehensive adverse feedback index threshold (the lowest adverse feedback threshold, used to identify whether there is an adverse reaction in the use of cosmetics), the second comprehensive adverse feedback index threshold (the threshold of medium adverse feedback, used to identify moderate adverse reactions in the use of cosmetics), and the third comprehensive adverse feedback index threshold (the highest adverse feedback threshold, used to judge whether the cosmetics have serious adverse reactions), and mark them based on the judgment and analysis results, and generate corresponding feedback suggestions.

[0025] The specific formula for calculating the comprehensive adverse feedback index for each age group of a given cosmetic is as follows: ; in, To set the cosmetic The comprehensive negative feedback index of each age group, To set the cosmetic Skin adverse feedback index for each age group, is the skin adjustment coefficient stored in the database, To set the cosmetic The negative feedback index for each age group, is the usage adjustment factor stored in the database, To set the safety index of cosmetic ingredients, is the component adjustment coefficient stored in the database, is the bad interaction coefficient stored in the database, 1, 2, 3, ..., , The number of age groups.

[0026] It should be explained that the formula This item is used to adjust the cumulative effect of skin adverse feedback index, usage adverse feedback index and ingredient feedback safety index to avoid the comprehensive adverse feedback index being too high or too low.

[0027] , , , It can be obtained through the following steps: using historical data, combined with the skin adverse feedback index, usage adverse feedback index, and ingredient feedback safety index, statistical regression analysis is performed to quantify the specific impact of each factor on the comprehensive adverse feedback index, thereby fitting the initial weight value; secondly, using the sensitivity analysis method, adjust the value range of each coefficient, observe its impact on the comprehensive adverse feedback index, ensure the stability and rationality of the model, and based on the set cosmetic characteristics and actual conditions, correct and optimize the preliminary fitting coefficients to ultimately determine the coefficient value applicable to specific cosmetics.

[0028] Specifically, the multimodal recognition model is a visual-text joint model (i.e., Vision-Language Models). The visual-text joint model includes a visual encoder layer, a text encoder layer, a joint embedding layer, and a regression output layer. The specific steps of obtaining a skin state feedback dataset and a usage behavior evaluation feedback dataset for each age group of set cosmetics are as follows: In the visual encoder layer of the visual-text joint model, image feature extraction processing is performed on the usage monitoring feedback information of each age group of set cosmetics (i.e., feedback image information in the usage monitoring feedback information) (i.e., low-level to high-level features, such as edges, textures, and shapes, are extracted through convolution and pooling operations of the convolution layer, activation functions such as ReLU are used to increase nonlinear capabilities, and then methods such as maximum pooling are used to reduce the dimension of the feature map, reduce computational complexity, and retain significant features. The features after convolution and pooling are then flattened and mapped to a high-dimensional feature space through a fully connected layer to learn overall information. Finally, a feature vector of a fixed dimension is output through the fully connected layer to represent the high-dimensional features of the image, such as skin color, texture, erythema, etc.), to obtain a high-dimensional image feature vector for each age group of set cosmetics; In the text encoder layer of the visual-text joint model, the set The usage monitoring feedback information of each age group of cosmetics (i.e., the feedback text information in the usage monitoring feedback information) is subjected to text feature extraction processing (i.e., each word or subword in the text is converted into a high-dimensional vector, such as using Word2Vec, GloVe, based on Transformer: using the self-attention mechanism to learn the vocabulary relationship in the text, and obtaining the global representation of the text by averaging, weighting or concatenating word vectors, and using the vector corresponding to the CLS tag output by models such as BERT to represent the text features, and finally using pooling operations (maximum pooling / average pooling, etc.) to extract a vector representation of a fixed length), to obtain the text feature vector of each age group of the set cosmetics; in the joint embedding layer of the visual text joint model, the high-dimensional image feature vector and text feature vector of each age group of the set cosmetics are fused (i.e., the image feature vector and the text feature vector are concatenated in a certain dimension to form a larger vector, and the image and text features are weighted and summed to obtain a comprehensive feature vector, and the self-attention mechanism is used to combine the image and text features, the importance of each feature is calculated and weightedly fused to obtain the final joint feature vector), to obtain the joint feature vector of each age group of the set cosmetics;In the regression output layer of the visual text joint model, the joint feature vector of each age group of the set cosmetics is subjected to regression prediction processing (i.e., the joint feature vector is input into multiple fully connected layers, multiple fully connected layers are used, each task corresponds to an output branch, skin physiological state: such as erythema, skin oil secretion level, etc., output continuous values, usage behavior: such as frequency of use, duration, etc., output continuous values, establish an independent output layer for each task, each output layer is composed of several neurons, and the output dimension is determined according to the prediction indicator, such as skin physiological state: output numerical values ​​related to skin state, For example, the skin oil secretion level value, the joint feature vector will contain the skin surface oil features from the image, such as glossiness, oiliness, etc., and user feedback in the text, such as "greasy face", "face feels dry", etc. The image features will extract the oil secretion of the skin surface, such as the oiliness and glossiness on the skin, and the text features will extract emotions and descriptions, such as "greasy" and "dry" which reflect the skin oil situation. These image and text features are combined, and after regression processing by the fully connected layer, a continuous value is output to represent the skin oil secretion level, and the output is: Skin oil secretion level value , is a range value, such as 0 to 1, indicating the oiliness of the skin; Usage evaluation behavior: outputs values ​​related to usage behavior, such as comfort index. Image features are related to the reaction of the skin surface, such as oiliness, redness, etc. The text describes the user's feelings when using it, such as "feeling light" and "no burden after use". Image features assist in prediction through the surface reaction of the skin, such as dryness and greasiness, and text features are weighted to evaluate comfort through sentiment analysis, such as "non-irritating" and "very comfortable". This information is used through regression output to obtain a continuous comfort score and output The comfort index is a range value, such as 0 to 1, which indicates the comfort of the user when using cosmetics). The skin state feedback data set and the usage behavior evaluation feedback data set for each age group of the set cosmetics are obtained; the skin state feedback data set includes the skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value; the usage behavior evaluation feedback data set includes the usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index. ;

[0029] Among them, the skin oil secretion level value is the amount of sebum secreted on the skin surface, which is used to reflect the oily condition of the skin. Too high or too low oil secretion levels can easily lead to skin barrier damage.

[0030] The erythema index is a measure of the degree of erythema on the skin surface. It is used to reflect the damage or allergic reaction of the skin after external stimulation. A higher erythema index means that the skin has a stronger irritation reaction to cosmetics and there is a certain risk of skin damage.

[0031] The stratum corneum thickness value refers to the thickness of the protective barrier layer on the skin surface. It is used to reflect the barrier function of the skin. A stratum corneum that is too thin or too thick will affect the health of the skin.

[0032] The skin surface moisture content value is the moisture content on the skin surface, which is used to reflect the skin's hydration and moisturizing ability. Low moisture content often indicates that the skin lacks effective protection and nourishment and is susceptible to damage. Excessive moisture content in the skin can easily lead to insufficient oil secretion of the skin, thereby causing damage to the barrier function. Once the barrier is damaged, the skin is easily invaded by harmful external substances.

[0033] The skin temperature gradient value is the distribution of skin surface temperature. It is used to reflect that when the temperature gradient is small, the skin surface is relatively stable, the blood circulation is good, the skin may be more tolerant to external stimuli, and have fewer sensitive reactions.

[0034] The melanin index is a measure of the distribution of melanin in the skin. It reflects the degree of skin pigmentation and affects the skin's color and response to ultraviolet rays. People with a low melanin index have more sensitive skin and are easily damaged by ultraviolet rays.

[0035] The skin conductivity value is the skin's ability to conduct electric current. It is used to reflect that when the conductivity is high, the skin surface is moist and easily has better hydration. Good hydration helps maintain the skin's barrier function, thereby reducing skin sensitivity.

[0036] The frequency of use value refers to the frequency of daily use of cosmetics. It is used to reflect that frequent use will increase the long-term impact of the product on the skin, such as cumulative effects, which may easily lead to allergies or other adverse skin reactions.

[0037] The use dose value is the amount of product applied per use and is intended to reflect that higher doses of cosmetics are likely to cause greater skin risks.

[0038] The duration value is the duration from each use of the product to makeup removal. It is used to reflect that the product ingredients will act more deeply into the skin, affecting the skin barrier and function.

[0039] The product mixing index refers to the degree of use of multiple cosmetics in daily skin care. It means that users use multiple skin care products at the same time. It is used to reflect that when mixing multiple products, it is easy to cause interactions between ingredients and increase the risk of skin discomfort or allergies.

[0040] The comfort index is the user's sense of comfort when using cosmetics. A low comfort index indicates discomfort, excessive oiliness, stickiness, etc. when using the cosmetics, which directly affects the natural feeling of the skin and easily leads to allergic or uncomfortable feedback.

[0041] The stickiness index represents the degree of stickiness on the skin after the user applies cosmetics.

[0042] The skin glossiness index measures the intensity of the gloss on the skin surface.

[0043] The stinging index represents the stinging sensation when the user applies cosmetics.

[0044] Moreover, the parameters in the skin condition feedback dataset and the usage behavior evaluation feedback dataset are unitless parameters, and their value ranges are both between 0 and 1.

[0045] The visual encoder layer includes: a convolutional layer, an activation function, a pooling layer, a fully connected layer, and an output layer.

[0046] The convolutional layer is used to extract local features of an image, such as edges, textures, shapes, etc.

[0047] The activation function is used to introduce non-linearity so that the model can handle more complex features. The commonly used ReLU (Rectified Linear Unit) activation function is used, which increases non-linearity by setting negative values to 0.

[0048] The pooling layer is used to reduce the spatial dimension of the feature map, reduce computational complexity, and retain important features at the same time.

[0049] The fully connected layer is used to map the local features extracted by the convolutional layer and the pooling layer to a global feature space.

[0050] The output layer is used to output a high-dimensional feature vector at the end of the visual encoder layer as the final representation of the image.

[0051] The text encoder layer includes: a text preprocessing layer, a word embedding layer, a recurrent network layer, a Transformer layer, a self-attention layer, a text pooling layer, a text fully connected layer, and a text output layer.

[0052] The text preprocessing layer is used to clean and standardize the input text to prepare data for subsequent feature extraction. The specific steps are: tokenization, splitting the text into words or subwords and removing stop words, such as deleting common words with no practical meaning (such as "de", "shi", etc.), then lowercase conversion, converting all letters to lowercase to reduce unnecessary repetition, and finally stemming or lemmatization, restoring the words to their basic forms.

[0053] The word embedding layer is used to map each word or subword to a high-dimensional vector so that it can better represent the semantic information of the word.

[0054] The recurrent network layer is used to capture the temporal and dependency relationships in the text and is particularly suitable for processing long texts.

[0055] The Transformer layer is used to capture the relationship between words based on the self-attention mechanism, which is particularly suitable for capturing long-distance dependencies in text.

[0056] The self-attention layer is used to calculate the relationship between words in a sentence, helping the model better understand the dependencies between words in the text.

[0057] The text pooling layer is used to extract fixed-length vector representations from word vector sequences to reduce the amount of model computation.

[0058] The text fully connected layer is used to map the features extracted from the text to the final feature space.

[0059] The text output layer is used to finally generate a vector representing the text, which is usually used for subsequent tasks such as sentiment analysis, text classification, or regression tasks.

[0060] The joint embedding layer is used to fuse the feature vectors from the visual encoder and the text encoder to form a joint feature representation that can contain both image and text information.

[0061] The regression output layer includes a regression fully connected layer, a linear activation function, and output layer neurons.

[0062] The regression fully connected layer is used to map the joint feature vector or the features processed by the previous layer to the target space of the regression task, that is, to linearly combine the joint feature vector with the weight matrix, and then perform a nonlinear transformation through the activation function to output the feature vector or numerical value required for the regression task.

[0063] The linear activation function is used to transform the output of the network from the feature space to the numerical value required for the regression task.

[0064] The output layer neurons are used to correspond the number of neurons in the output layer to the target dimension of the regression task. For example, predicting each value of skin physiological state (such as erythema, allergic reaction, etc.) may require a separate output neuron. When there are multiple regression tasks (such as predicting skin physiological state and usage behavior at the same time), each task corresponds to a neuron.

[0065] The specific process of pre-training is as follows: Obtain several sets of feedback information (including images and corresponding text descriptions, such as image annotations, labels, user comments, etc.) and divide them into training sets and validation sets.

[0066] Training based on the training set: Number of training cycles: Set the number of training cycles (for example, train 50 or 100 times).

[0067] Forward propagation: Input the training set, extract features through the visual encoder and text encoder respectively, then fuse the features through the joint embedding layer, and finally input them into the regression layer for prediction.

[0068] Calculate loss: Calculate the loss function (such as mean squared error loss, contrastive loss, etc.) based on the model output and the true label.

[0069] Backpropagation: Calculate the gradient through the backpropagation algorithm and update the model parameters. Use optimization algorithms such as Adam optimizer or SGD to update the weights.

[0070] Optimizer selection: Commonly used optimizers include Adam, RMSProp, etc., which can effectively optimize multi-task regression models.

[0071] Learning rate adjustment: Use learning rate decay or scheduling strategies to avoid overfitting or non-convergence of training due to too fast training.

[0072] Batch size tuning: Choose an appropriate batch size based on hardware limitations and the size of the dataset. Typically, larger batch sizes speed up training but may cause out-of-memory usage.

[0073] Validation set: Regular evaluation is performed on the validation set to monitor the performance of the model in predicting skin physiological state and usage behavior.

[0074] Overfitting monitoring: Monitor the changes in training error and validation error to prevent the model from overfitting. You can use the Early Stopping strategy to stop training when the validation error no longer decreases.

[0075] Regression task evaluation: Use indicators such as R2 value, mean square error (MSE), and mean absolute error (MAE) to evaluate the model's prediction performance on skin physiological state and usage behavior.

[0076] Test set evaluation: Use the test set to perform a final evaluation of the model to verify the generalization ability of the model.

[0077] Task-specific fine-tuning: Fine-tune for specific tasks (such as skin physiological state prediction, usage behavior prediction) according to different application scenarios or task requirements.

[0078] Transfer learning: If there is a pre-trained model in a related field, you can use transfer learning to fine-tune the model on the target task dataset.

[0079] In this embodiment, the joint modeling of visual and text information can more comprehensively capture the effects of cosmetics on the skin and user feedback. The visual information provides the specific manifestations of the skin (such as oil secretion, erythema, etc.), while the text information provides the user's subjective feelings and descriptions (such as "greasy", "comfortable", etc.). The fusion of these two types of information helps the model to understand the skin condition and usage behavior more accurately, thereby improving the accuracy of adverse reaction analysis. Secondly, through multi-task learning, the model can not only predict the physiological state of the skin (such as oil secretion, erythema, etc.), but also evaluate the user's usage behavior (such as comfort, frequency, etc.), so that it can optimize multiple goals at the same time, thereby improving the overall efficiency and applicability of the model. At the same time, through the automated analysis of computer vision and natural language processing, human intervention can be reduced, thereby improving the efficiency of the entire evaluation process, so that manufacturers can get feedback on skin status and usage behavior in a short time, so as to make adjustments quickly and improve the use effect of cosmetics and user experience.

[0080] Specifically, the specific steps for obtaining the adverse skin feedback index for each age group of the set cosmetics are as follows: read the skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value in the skin state feedback data set for each age group of the set cosmetics; and obtain the skin oil secretion level reference value, skin keratin thickness reference value, skin surface moisture content reference value, and environmental factors for each age group of the set cosmetics; comprehensively analyze the skin oil secretion level reference value, skin keratin thickness reference value, skin surface moisture content reference value, skin oil secretion level value, erythema index, skin keratin thickness value, and skin surface moisture content value for each age group of the set cosmetics to obtain the skin damage index for each age group of the set cosmetics; and comprehensively analyze the skin temperature gradient value, melanin index, skin conductivity value, and environmental factors for each age group of the set cosmetics to obtain the skin sensitivity index for each age group of the set cosmetics, and conduct a comprehensive analysis in combination with the skin damage index to obtain the adverse skin feedback index for each age group of the set cosmetics.

[0081] Among them, the reference value of skin oil secretion level, the reference value of skin keratin thickness, and the reference value of skin surface moisture content can all be obtained through the skin database, and then standardized respectively.

[0082] The specific steps for obtaining environmental factors are as follows: The humidity value, air quality value and corresponding humidity reference value and air quality reference value of each person in each age group are obtained, and a ratio analysis is performed. A weighted processing is performed based on the ratio analysis results, and a mean analysis is performed based on the weighted processing results to obtain the environmental factors for each age group.

[0083] The humidity value and the air quality value can be obtained from the weather station monitoring report stored in the database, and the humidity reference value and the air quality reference value can be obtained from the standard reference data released by the meteorological bureau stored in the database.

[0084] The specific formula for calculating the skin damage index, skin sensitivity index, and skin adverse feedback index for each age group of the cosmetics is as follows: ; in, To set the cosmetic Skin damage index for each age group, To set the cosmetic Skin oil secretion levels for each age group, To set the cosmetic Reference values ​​for skin oil secretion levels for each age group: is the oil secretion adjustment coefficient stored in the database, To set the cosmetic The erythema index of each age group, is the erythema adjustment factor stored in the database, To set the cosmetic Skin keratin thickness values ​​for each age group, To set the cosmetic Reference values ​​for skin keratin thickness for each age group: is the skin keratin adjustment coefficient stored in the database, To set the cosmetic Skin surface moisture content values ​​for each age group, To set the cosmetic Reference values ​​for skin surface moisture content for each age group: is the moisture content adjustment factor stored in the database, To set the cosmetic Skin sensitivity index for each age group, is the sensitive interaction coefficient stored in the database, To set the cosmetic Skin temperature gradient values ​​for each age group, is the temperature gradient adjustment coefficient stored in the database, To set the cosmetic Melanin index of each age group, is the melanin adjustment coefficient stored in the database, To set the cosmetic Skin conductivity values ​​for each age group, is the conductivity adjustment coefficient stored in the database, To set the cosmetic Environmental factors of each age group, is the environmental adjustment coefficient stored in the database, To set the cosmetic Skin adverse feedback index for each age group, is the damage coefficient stored in the database, is the damage adjustment factor stored in the database, is the sensitivity coefficient stored in the database, is the sensitivity adjustment coefficient stored in the database, , is a natural constant, and in this implementation example, the value is 2.71. 1, 2, 3, ..., , The number of age groups.

[0085] It needs to be explained that , , , It can be obtained through the following steps: using historical data, combined with skin oil secretion level values, erythema index, skin keratin thickness values, and skin surface moisture content values, statistical regression analysis is performed to quantify the specific impact of each factor on the skin damage index, thereby fitting the initial weight value; secondly, using the sensitivity analysis method, adjust the value range of each coefficient, observe its impact on the skin damage index, ensure the stability and rationality of the model, and based on the set cosmetic characteristics and actual conditions, correct and optimize the preliminary fitting coefficients, and finally determine the coefficient value applicable to specific cosmetics.

[0086] , , , , It can be obtained through the following steps: Based on historical data, determine the initial impact weight of each variable (such as skin temperature gradient value, melanin index, etc.) on the skin sensitivity index through statistical regression analysis, and then use the sensitivity analysis method to adjust the value range of the coefficient to evaluate the stability and applicability of these parameters to the formula output. Next, further fit the weights through model optimization (such as multi-objective optimization) to ensure that the formula can accurately reflect the skin sensitivity state, and fine-tune the coefficients based on the characteristics of different cosmetics to ensure that it is suitable for specific skin sensitivity assessment needs.

[0087] , It can be obtained through the following steps: read the skin sensitivity index and skin damage index of each age group of the set cosmetics, and perform mean processing to obtain the mean skin sensitivity index and the mean skin damage index of the set cosmetics, perform sum analysis to obtain the bad skin sum value, and perform proportion analysis on the mean skin sensitivity index and the mean skin damage index of the set cosmetics and the bad skin sum value, and use the proportion results as the corresponding coefficients.

[0088] , It can be obtained through the following steps: using historical data, combined with skin damage index and skin sensitivity index, statistical regression analysis is carried out to quantify the specific impact of each factor on the skin adverse feedback index, so as to fit the initial weight value. Secondly, the sensitivity analysis method is used to adjust the value range of each coefficient, and observe its impact on the skin adverse feedback index to ensure the stability and rationality of the model. Based on the set cosmetic characteristics and actual conditions, the preliminary fitting coefficients are corrected and optimized to finally determine the coefficient value applicable to specific cosmetics.

[0089] The specific implementation example of calculating the skin adverse feedback index for the first age group (e.g. 18-22 years old) of the cosmetics is as follows. The existing data is as follows: The skin oil secretion level value for the first age group of cosmetics is set to: 0.86.

[0090] The erythema index for the first age group of cosmetics is set to: 0.32.

[0091] The skin keratin thickness value for the first age group of cosmetics is set to: 0.38.

[0092] The skin surface moisture content value for the first age group of cosmetics is set to: 0.73.

[0093] Set the skin temperature gradient value of the first age group of cosmetics to: 0.53.

[0094] The melanin index for the first age group of cosmetics is set to: 0.34.

[0095] The skin conductivity value for the first age group of cosmetics is set to: 0.59.

[0096] The environmental factor for the first age group of cosmetics is approximately: 0.64.

[0097] The reference value for skin oil secretion level for the first age group for setting cosmetics is: 0.75.

[0098] The reference value for skin keratin thickness for the first age group of cosmetics is: 0.50.

[0099] The reference value for skin surface moisture content for cosmetics in the first age group is: 0.65.

[0100] The oil secretion adjustment factor stored in the database is approximately: 0.57.

[0101] The erythema adjustment factor stored in the database is approximately: 0.18.

[0102] The skin keratin adjustment coefficient stored in the database is approximately: 0.61.

[0103] The moisture content adjustment factor stored in the database is approximately: 0.52.

[0104] The sensitive interaction coefficient stored in the database is approximately: 1.25.

[0105] The temperature gradient adjustment coefficient stored in the database is approximately: 0.38.

[0106] The melanin adjustment factor stored in the database is approximately: 0.51.

[0107] The conductivity adjustment factor stored in the database is approximately: 0.74.

[0108] The environmental adjustment factor stored in the database is approximately: 0.47.

[0109] The damage factor stored in the database is approximately: 0.48.

[0110] The damage adjustment factor stored in the database is approximately: 0.26.

[0111] The sensitivity coefficient stored in the database is approximately: 0.52.

[0112] The sensitivity adjustment factor stored in the database is approximately: 0.21.

[0113] Substituting the above data into the specific formula for calculating the skin damage index, skin sensitivity index, and skin adverse feedback index for each age group of the cosmetics, we get: The skin damage index of the first age group of cosmetics is set to ≈0.58.

[0114] The skin sensitivity index of cosmetics for the first age group is set to ≈0.61.

[0115] The adverse skin feedback index of cosmetics for the first age group is set to ≈0.50.

[0116] In this embodiment, by comprehensively considering multiple skin physiological indicators such as skin oil secretion level, erythema index, stratum corneum thickness, moisture content, temperature gradient, etc., it is possible to more comprehensively reflect the overall impact of cosmetics on the skin, rather than relying solely on a certain indicator, which helps to accurately capture the advantages and disadvantages of the product, thereby avoiding erroneous evaluations caused by local data imbalance. Secondly, feedback data from different age groups are considered to conduct personalized analysis based on the skin characteristics of different age groups, and by subdividing the skin reactions of different age groups, it is possible to optimize the recommendation and use effects of cosmetics. At the same time, incorporating environmental factors into the analysis can better reflect the adaptability of products under different climate, temperature and humidity conditions. For example, a dry environment may aggravate the loss of skin moisture, or the problem of skin oil secretion in a hot and humid environment, which helps to provide more realistic skin reaction feedback. Finally, through a scientific comprehensive analysis method, the adverse reactions of the skin can be quantified, and the traditional perceptual evaluation can be converted into data-based feedback, which is convenient for improving the market adaptability of the product.

[0117] Specifically, Figure 2 As shown, the specific steps for obtaining the adverse feedback index of the set cosmetics for each age group are as follows: reading the usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index in the usage behavior evaluation feedback data set of the set cosmetics for each age group; and performing a comprehensive analysis (i.e., weighted processing) on ​​the usage frequency value, usage dosage value, duration value, and product mixing index of each age group of the set cosmetics to obtain the usage risk index of each age group of the set cosmetics; performing a comprehensive analysis on the comfort index, stickiness index, skin gloss index, and stinging index of each age group of the set cosmetics to obtain the sensory evaluation index of each age group of the set cosmetics, and performing a comprehensive analysis in combination with the usage risk index to obtain the adverse feedback index of the set cosmetics for each age group.

[0118] The specific formula for calculating the sensory evaluation index and adverse feedback index of cosmetics for each age group is as follows: ; in, To set the cosmetic Sensory evaluation index of each age group, To set the cosmetic The comfort index of each age group, To set the cosmetic Skin glossiness index for each age group, is the positive adjustment coefficient stored in the database, To set the cosmetic The stickiness index of each age group, To set the cosmetic The pain index of each age group, is the negative adjustment coefficient stored in the database, is the difference adjustment coefficient stored in the database, To set the cosmetic The negative feedback index for each age group, To set the cosmetic The use risk index for each age group, is the behavior adjustment coefficient stored in the database, is the sensory evaluation adjustment coefficient stored in the database, is the difference adjustment coefficient between behavior and sensory evaluation stored in the database, is the interaction adjustment coefficient between behavior and sensory evaluation stored in the database, 1, 2, 3, ..., , The number of age groups.

[0119] It should be explained that the formula This item was used to control the impact of the difference between behavior and sensory evaluation and the interaction between behavior and sensory evaluation on the use of adverse feedback index.

[0120] , , It can be obtained through the following steps: Based on historical data, determine the initial impact weight of each variable (comfort index, stickiness index, skin gloss index, and stinging index) on the sensory evaluation index through statistical regression analysis. Then, use the sensitivity analysis method to adjust the value range of the coefficient to evaluate the stability and applicability of these parameters to the formula output. Next, further fit the weights through model optimization (such as machine learning algorithms) to ensure that the formula can accurately reflect the sensory evaluation.

[0121] , , , It can be obtained through the following steps: using historical data, combined with risk index and sensory evaluation index, to conduct statistical regression analysis, quantify the specific impact of each factor on the use of adverse feedback index, so as to fit the initial weight value; secondly, using sensitivity analysis method, adjust the value range of each coefficient, observe its impact on the use of adverse feedback evaluation results, and ensure the stability and rationality of the model.

[0122] In this implementation scheme, by comprehensively analyzing factors such as frequency of use, dosage, duration, and mixing index, it can help identify the potential risks of cosmetics under different conditions of use, thereby providing important information on the safety and applicability of the product, especially for consumers of different age groups, avoiding adverse reactions that may be caused by excessive or improper use. Secondly, the sensory experience of different age groups (such as comfort, stickiness, tingling, etc.) is considered, and the feedback of consumers of different age groups is carefully divided, so as to help manufacturers adjust the formula or usage instructions according to the preferences of the target population and improve the market adaptability of the product. At the same time, the adjustment items of "differences in behavior and sensory evaluation" and "interaction effects of behavior and sensory evaluation" are added to the formula, which helps to more accurately quantify the impact of different user behaviors on sensory experience, and then accurately control this impact to avoid errors and improve the reliability of the evaluation. Finally, through regression analysis and sensitivity analysis based on historical data, it is possible to scientifically evaluate the impact of various indicators on the adverse feedback index, so that cosmetics manufacturers can adjust their product strategies based on specific feedback.

[0123] Specifically, the specific steps for obtaining the ingredient feedback safety index of the set cosmetics are as follows: obtain the concentration value, concentration reference value, irritation score value, allergenicity score value, and acne-causingness score value of each ingredient of the set cosmetics; and conduct a comprehensive analysis of the concentration value, concentration reference value, irritation score value, allergenicity score value, and acne-causingness score value of each ingredient of the set cosmetics to obtain the ingredient feedback safety index of the set cosmetics.

[0124] Among them, the specific formula for calculating the ingredient feedback safety index of cosmetics is as follows: ; in, To set the safety index of cosmetic ingredients, To set the cosmetic The concentration of the component, To set the cosmetic The concentration reference value of each component, is the concentration adjustment factor stored in the database, To set the cosmetic The irritation rating of the ingredients, is the stimulation gain coefficient stored in the database, is the stimulus adjustment coefficient stored in the database, To set the cosmetic The allergenicity score of each ingredient, is the sensitization gain coefficient stored in the database, is the sensitization adjustment coefficient stored in the database, To set the cosmetic The comedogenicity score of each ingredient is is the acne-causing gain coefficient stored in the database, is the acne adjustment factor stored in the database, 1, 2, 3, ..., , is the number of components.

[0125] It needs to be explained that , , , , , , It can be obtained through the following steps: Based on the historical monitoring data of the region, determine the initial impact weight of each variable (concentration value, irritation score value, allergenicity score value, acnegenicity score value) on the ingredient feedback safety index through statistical regression analysis, then use the sensitivity analysis method to adjust the range of coefficients to evaluate the stability and applicability of these parameters to the formula output, and then further fit the weights through model optimization (such as machine learning algorithms or multi-objective optimization) to ensure that the formula can accurately reflect the health status of the actual ingredient feedback.

[0126] The concentration value is obtained through the formula book of the cosmetic stored in the database.

[0127] Concentration reference values ​​are obtained through industry standards and regulations stored in the database.

[0128] Irritation score values, sensitization score values, and acnegenicity score values ​​can all be obtained through toxicology databases (e.g., PubChem).

[0129] In this implementation scheme, by quantifying the concentration value, irritation, sensitization, acnegenicity and other factors of each ingredient and combining them with historical monitoring data, a comprehensive score of each ingredient on product safety can be provided, thereby scientifically evaluating the adverse reactions caused by the potential hazards of cosmetic ingredients, and by comprehensively considering multiple factors such as ingredient concentration, irritation, sensitization, etc., manufacturers can more finely adjust and optimize the ingredient ratio of the product. For sensitive skin or special populations, safer and gentler formulas can be formulated, thereby improving the product's scope of application and consumer satisfaction. Secondly, through regression analysis and sensitivity analysis, the impact of each parameter on the ingredient safety assessment can be quantified, and adjustments can be made based on these impact weights, so as to more accurately identify ingredients that may have a negative impact on the skin and provide specific improvement directions for the R&D team. Finally, through comprehensive safety evaluation and optimization, it can ensure that the product is harmless to the consumer's skin, reduce the occurrence of adverse reactions such as allergies and irritation, thereby enhancing consumer trust and improving the product's market reputation.

[0130] Specifically, the specific steps of generating corresponding feedback suggestions based on the judgment and analysis results are as follows: if the comprehensive adverse feedback index of each age group of the set cosmetics is lower than or equal to the preset first comprehensive adverse feedback index threshold, then (the age group) is marked as no adverse reaction, and the first feedback suggestion is generated (i.e., providing the user with continued use, suggesting the user to continue using the cosmetics, because no adverse reaction occurs during the use of this age group; maintaining a normal frequency of use, maintaining a normal frequency of use according to the recommended method of use of the product; encouraging feedback, encouraging the user to continue to provide feedback on the experience during continued use, and ensuring the long-term safety of the cosmetics for the skin); if the comprehensive adverse feedback index of each age group of the set cosmetics is higher than the preset first comprehensive adverse feedback index threshold and lower than or equal to the preset second comprehensive adverse feedback index threshold, then (the age group) is marked as a mild adverse reaction, and the second feedback suggestion is generated (i.e., providing the user with the method of adjusting use, suggesting reducing the frequency of use or reducing the amount of use, or appropriately changing the time and method of use; paying attention to skin conditions, suggesting that the user pay close attention to skin reactions, and immediately stop using if there is further discomfort, and consult Consult a professional doctor for advice); if the comprehensive adverse feedback index of each age group of the cosmetics is set to be higher than the preset second comprehensive adverse feedback index threshold and lower than or equal to the preset third comprehensive adverse feedback index threshold, then (the age group) is marked as a moderate adverse reaction, and a third feedback suggestion is generated (i.e., an in-depth review of ingredients and formulas is provided to the manufacturer, and all ingredients of the cosmetics should be reviewed, especially those that may cause moderate adverse reactions, to assess whether the concentration of these ingredients needs to be reduced or replaced with milder ingredients); if the comprehensive adverse feedback index of each age group of the cosmetics is set to be higher than the preset third comprehensive adverse feedback index threshold, then (the age group) is marked as a severe adverse reaction, and a fourth feedback suggestion is generated (i.e., suggestions are provided to the manufacturer to stop sales and withdraw from the market, stop sales, and consider withdrawing the batch of cosmetics from the market to avoid affecting more users, and at the same time, it is recommended that the manufacturer conduct an ingredient and formula review to identify specific ingredients that may cause severe adverse reactions, especially negative effects on sensitive skin or specific groups; suggestions are made to improve product research and development and testing processes, strengthen the product research and development process, and increase multi-stage safety testing).

[0131] In this implementation plan, personalized feedback suggestions are formulated according to the comprehensive adverse feedback index of different age groups to help users adjust the usage of cosmetics according to their own circumstances, maximize the avoidance of adverse reactions, ensure skin safety, and for different degrees of adverse reactions, users can quickly obtain guidance to avoid further discomfort, take timely measures to alleviate symptoms, and enhance user confidence and experience. Secondly, by analyzing the feedback data of each age group and making judgments and analyses with preset thresholds, manufacturers can promptly identify possible risks in products, especially reactions to sensitive groups, and promote improvements in product design and research and development. Finally, through a scientific and phased feedback system, not only can user safety and continuous product optimization be ensured, but also the manufacturer's sense of responsibility and product quality control can be enhanced, ultimately forming a virtuous product improvement and market feedback cycle.

[0132] See also Figure 3 The embodiment of the present invention provides a technical solution: a cosmetic adverse reaction analysis feedback system based on active monitoring, comprising: a data acquisition module, used to acquire usage monitoring feedback information of several age groups of set cosmetics, and input it into a pre-trained multimodal recognition model for predictive analysis, so as to obtain a skin state feedback data set and a usage behavior evaluation feedback data set for each age group of the set cosmetics; a data analysis module, used to perform data analysis on the skin state feedback data set and the usage behavior evaluation feedback data set for each age group of the set cosmetics, respectively, to obtain a skin adverse feedback index and a usage adverse feedback index for each age group of the set cosmetics; a comprehensive analysis module, used to simultaneously acquire the ingredient feedback safety index of the set cosmetics, and perform comprehensive analysis in combination with the skin adverse feedback index and the usage adverse feedback index of each age group, so as to obtain a comprehensive adverse feedback index for each age group of the set cosmetics; a judgment feedback module, used to perform judgment analysis on the comprehensive adverse feedback index of each age group of the set cosmetics and a preset comprehensive adverse feedback index threshold set, wherein the comprehensive adverse feedback index threshold set includes a first comprehensive adverse feedback index threshold, a second comprehensive adverse feedback index threshold, and a third comprehensive adverse feedback index threshold, and mark based on the judgment analysis result, and generate corresponding feedback suggestions at the same time.

[0133] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0134] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring, characterized in that: The following steps are involved: Obtain usage monitoring feedback information of several age groups of the set cosmetics, and input it into a pre-trained multimodal recognition model for predictive analysis, to obtain a skin condition feedback data set and a usage behavior evaluation feedback data set for each age group of the set cosmetics, and perform data analysis respectively to obtain a skin adverse feedback index and a usage adverse feedback index for each age group of the set cosmetics; Obtain the ingredient feedback safety index of the set cosmetics, and combine it with the skin adverse feedback index of each age group and use the adverse feedback index for comprehensive analysis to obtain the comprehensive adverse feedback index of each age group of the set cosmetics, and perform judgment analysis with the preset comprehensive adverse feedback index threshold set, and mark it based on the judgment analysis results, and generate corresponding feedback suggestions.

2. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 1, characterized in that: The multimodal recognition model is specifically a visual-text joint model, which includes a visual encoder layer, a text encoder layer, a joint embedding layer, and a regression output layer. The specific steps of obtaining a skin state feedback dataset for each age group of a set cosmetic and a usage behavior evaluation feedback dataset are as follows: In the visual encoder layer of the visual-text joint model, image feature extraction is performed on the usage monitoring feedback information of each age group of the set cosmetics to obtain a high-dimensional image feature vector for each age group of the set cosmetics; In the text encoder layer of the visual-text joint model, text feature extraction is performed on the usage monitoring feedback information of each age group of the set cosmetics to obtain a text feature vector for each age group of the set cosmetics; In the joint embedding layer of the visual-text joint model, the high-dimensional image feature vector and the text feature vector of each age group of the set cosmetics are fused to obtain a joint feature vector of each age group of the set cosmetics; In the regression output layer of the visual-text joint model, the joint feature vector of each age group of the set cosmetics is subjected to regression prediction processing to obtain a skin condition feedback dataset and a usage behavior evaluation feedback dataset for each age group of the set cosmetics; The skin condition feedback data set includes skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value; The usage behavior evaluation feedback data set includes usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index.

3. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 1, characterized in that: The specific formula for calculating the comprehensive adverse feedback index for each age group of a given cosmetic is as follows: ； in, To set the cosmetic The comprehensive negative feedback index of each age group, , The first Skin adverse feedback index and usage adverse feedback index for each age group, To set the safety index of cosmetic ingredients, , , , The following are the skin adjustment coefficient, usage adjustment coefficient, ingredient adjustment coefficient, and adverse interaction coefficient stored in the database. 1, 2, 3, ..., , The number of age groups.

4. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 2, characterized in that: The specific steps to obtain the skin adverse feedback index for each age group of the set cosmetics are as follows: Read the skin oil secretion level value, erythema index, skin keratin thickness value, skin surface moisture content value, skin temperature gradient value, melanin index, and skin conductivity value in the skin condition feedback data set for each age group of the set cosmetics; And obtain the reference value of skin oil secretion level, skin keratin thickness, skin surface moisture content and environmental factors for each age group for setting cosmetics; Comprehensively analyze the reference value of skin oil secretion level, skin keratin thickness, skin surface moisture content, skin oil secretion level, erythema index, skin keratin thickness, and skin surface moisture content for each age group of the set cosmetics to obtain the skin damage index for each age group of the set cosmetics; A comprehensive analysis is conducted on the skin temperature gradient value, melanin index, skin conductivity value, and environmental factors for each age group for which the cosmetics are set to obtain the skin sensitivity index for each age group for which the cosmetics are set. A comprehensive analysis is then conducted in combination with the skin damage index to obtain the skin adverse feedback index for each age group for which the cosmetics are set to be set.

5. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 4, characterized in that: The specific formula for calculating the skin damage index, skin sensitivity index, and skin adverse feedback index for each age group of the cosmetics is as follows: ； in, To set the cosmetic Skin damage index for each age group, , , , , , , The first Skin oil secretion level value for each age group, skin oil secretion level reference value, erythema index, skin keratin thickness value, skin keratin thickness reference value, skin surface moisture content value, skin surface moisture content reference value, , , , They are the oil secretion adjustment coefficient, erythema adjustment coefficient, skin keratin adjustment coefficient, and moisture content adjustment coefficient stored in the database. To set the cosmetic Skin sensitivity index for each age group, , , , The first Skin temperature gradient values, melanin index, skin conductivity values, and environmental factors for each age group. , , , , They are the sensitive interaction coefficient, temperature gradient adjustment coefficient, melanin adjustment coefficient, skin conductivity value, conductivity adjustment coefficient, and environment adjustment coefficient stored in the database. To set the cosmetic Skin adverse feedback index for each age group, , , , They are the damage coefficient, damage adjustment coefficient, sensitivity coefficient, and sensitivity adjustment coefficient stored in the database. , is a natural constant, 1, 2, 3, ..., , The number of age groups.

6. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 2, characterized in that: The specific steps to obtain the adverse feedback index of the cosmetics used in each age group are as follows: Read the usage frequency value, usage dosage value, duration value, product mixing index, comfort index, stickiness index, skin gloss index, and stinging index in the usage behavior evaluation feedback data set of each age group of the set cosmetics; A comprehensive analysis is conducted on the usage frequency, dosage, duration, and product mixing index of the cosmetics for each age group to obtain the usage risk index of the cosmetics for each age group. A comprehensive analysis is conducted on the comfort index, stickiness index, skin gloss index and stinging index of the set cosmetics for each age group to obtain the sensory evaluation index of the set cosmetics for each age group, and a comprehensive analysis is conducted in combination with the use risk index to obtain the adverse feedback index of the set cosmetics for each age group.

7. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 6, characterized in that: The specific formula for calculating the sensory evaluation index and adverse feedback index of cosmetics for each age group is as follows: ； in, To set the cosmetic Sensory evaluation index of each age group, , , , The first The comfort index, skin gloss index, stickiness index, and tingling index of each age group. , , They are the positive adjustment coefficient, negative adjustment coefficient, and difference adjustment coefficient stored in the database, To set the cosmetic The negative feedback index for each age group, To set the cosmetic The use risk index for each age group, , , , They are the behavior adjustment coefficient, sensory evaluation adjustment coefficient, behavior and sensory evaluation difference adjustment coefficient, and behavior and sensory evaluation interaction adjustment coefficient stored in the database. 1, 2, 3, ..., , The number of age groups.

8. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 1, characterized in that: The specific steps to obtain the feedback safety index of the ingredients of the set cosmetics are as follows: Obtain the concentration value, concentration reference value, irritation score value, allergenicity score value, and acnegenicity score value of each ingredient of the set cosmetics; A comprehensive analysis is also conducted on the concentration value, concentration reference value, irritation score value, allergenicity score value, and acnegenicity score value of each ingredient of the set cosmetics to obtain the ingredient feedback safety index of the set cosmetics.

9. The method for analyzing and feedback of adverse reactions to cosmetics based on active monitoring according to claim 1, characterized in that: The comprehensive negative feedback index threshold set includes a first comprehensive negative feedback index threshold, a second comprehensive negative feedback index threshold, and a third comprehensive negative feedback index threshold. The specific steps of generating corresponding feedback suggestions based on the judgment and analysis results are as follows: If the comprehensive adverse feedback index of the cosmetics for each age group is set to be lower than or equal to the preset first comprehensive adverse feedback index threshold, it is marked as no adverse reaction and the first feedback suggestion is generated; If the comprehensive adverse feedback index of the cosmetics for each age group is higher than the preset first comprehensive adverse feedback index threshold and lower than or equal to the preset second comprehensive adverse feedback index threshold, it is marked as a minor adverse reaction and a second feedback suggestion is generated; If the comprehensive adverse feedback index of the cosmetics for each age group is higher than the preset second comprehensive adverse feedback index threshold and lower than or equal to the preset third comprehensive adverse feedback index threshold, it is marked as a moderate adverse reaction and a third feedback suggestion is generated; If the comprehensive adverse feedback index of the cosmetics for each age group is set to be higher than the preset third comprehensive adverse feedback index threshold, it is marked as a severe adverse reaction and a fourth feedback suggestion is generated.

10. A cosmetic adverse reaction analysis and feedback system based on active monitoring, using the cosmetic adverse reaction analysis and feedback method based on active monitoring according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to obtain usage monitoring feedback information of a set of cosmetics for a number of age groups, and input it into a pre-trained multimodal recognition model for predictive analysis to obtain a skin condition feedback data set and a usage behavior evaluation feedback data set for each age group of the set cosmetics; A data analysis module is used to analyze the skin condition feedback data set and the usage behavior evaluation feedback data set for each age group of the set cosmetics, and obtain the skin adverse feedback index and usage adverse feedback index for each age group of the set cosmetics; A comprehensive analysis module is used to simultaneously obtain the ingredient feedback safety index of the set cosmetics, and to perform a comprehensive analysis in combination with the skin adverse feedback index of each age group and the adverse feedback index, so as to obtain the comprehensive adverse feedback index of each age group of the set cosmetics; The judgment feedback module is used to judge and analyze the comprehensive adverse feedback index of each age group of the set cosmetics with a preset comprehensive adverse feedback index threshold set, wherein the comprehensive adverse feedback index threshold set includes a first comprehensive adverse feedback index threshold, a second comprehensive adverse feedback index threshold, and a third comprehensive adverse feedback index threshold, and mark based on the judgment and analysis results, and generate corresponding feedback suggestions.

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