Cosmetic efficacy multi-index comprehensive evaluation method and system

Abstract

The invention relates to the technical field of dynamic weight, and discloses a cosmetic efficacy multi-index comprehensive evaluation method and system, and the method comprises the steps: obtaining the data characteristics of a cross-modal index of a cosmetic efficacy; normalizing the data features of the cross-modal indexes to form cross-modal data of the same dimension; establishing a dynamic weight model by using cross-modal data of the same dimension, and introducing user personalized data to dynamically adjust the dynamic weight model; obtaining comprehensive efficacy scores through the dynamic weight model, comparing the comprehensive efficacy scores before and after the use of a single sample, and evaluating the effectiveness; and comparing the comprehensive efficacy scores of the plurality of samples, and evaluating differences. The limitation of single index evaluation is overcome, and the comprehensive effect of the cosmetics on the skin can be reflected more comprehensively; the dynamic weight model adjusts the weight according to the personalized data of the user, so that the evaluation result better meets the requirements of different users, and the efficacy and difference of cosmetics can be effectively evaluated by comparing different samples and comprehensive efficacy scores before and after use.

Description

Technical Field

[0001] The present invention relates to the technical field of weight models, and in particular to a method and system for comprehensively evaluating the efficacy of cosmetics using multiple indicators. Background Art

[0002] In the field of skin care product efficacy evaluation, in order to accurately measure the actual effect of the product, experts usually consider multiple key indicators comprehensively. These indicators include but are not limited to the skin's gloss, elasticity, roughness and moisture content. Gloss reflects the brightness and transparency of the skin and is an important indicator of skin health and vitality. Elasticity involves the firmness and recovery ability of the skin, which can reveal whether the skin has become loose due to aging or external factors. Roughness refers to the smoothness of the skin, which is related to the skin's texture and pore size. Moisture content is the key to measuring the skin's ability to retain moisture. Dry skin often lacks elasticity and luster.

[0003] Traditional evaluation methods often rely on a single indicator or a simple weighted average, which, although simple, often fails to fully reflect the comprehensive efficacy of skin care products. For example, a product may perform well in improving radiance, but if it causes skin moisture loss, it may have a negative impact on skin health in the long run. Therefore, in order to more accurately evaluate the effects of skin care products, researchers and dermatologists need a more effective way to evaluate the efficacy of cosmetics. Summary of the invention

[0004] The present invention provides a multi-index comprehensive evaluation method and system for cosmetic efficacy, which is used to solve the problem that the efficacy evaluation of skin care products needs to comprehensively consider indicators such as gloss, elasticity, roughness and moisture content, and the traditional single-index evaluation method is difficult to fully reflect the comprehensive efficacy.

[0005] The present invention provides a multi-index comprehensive evaluation method for cosmetic efficacy, comprising:

[0006] The data features of cross-modal indicators of cosmetic efficacy are obtained by using sensors; wherein the data features of cross-modal indicators include reflectivity of gloss, modulus of elasticity, texture parameters of roughness, and water content value;

[0007] Normalize the data features of cross-modal indicators to form cross-modal data of the same dimension;

[0008] A dynamic weight model is established using cross-modal data of the same dimension, and user personalized data is introduced to dynamically adjust the dynamic weight model;

[0009] The comprehensive efficacy score is obtained through the dynamic weight model, and the comprehensive efficacy scores of a single sample before and after use are compared to evaluate the effectiveness; the comprehensive efficacy scores of multiple samples are compared to evaluate the differences.

[0010] Preferably, the data features of the cross-modal indicators of cosmetic efficacy obtained by using sensors include:

[0011] The reflectance of gloss was measured by spectrometer;

[0012] The elastic modulus was obtained by a skin elasticity tester;

[0013] Obtain the texture parameters of roughness through a roughness meter;

[0014] The moisture content is measured by a moisture tester.

[0015] Preferably, normalizing the data features of the cross-modal indicators includes:

[0016] The Min-Max normalization formula is used to normalize the gloss reflectance, elastic modulus, roughness texture parameters and water content to the interval [0,1] to eliminate dimensional differences.

[0017] Preferably, obtaining cross-modal data of the same dimension through the Min-Max normalization formula includes:

[0018]

[0019] Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the indicator respectively.

[0020] Preferably, the establishing of a dynamic weight model using cross-modal data of the same dimension and the introduction of user personalized data to dynamically adjust the dynamic weight model includes:

[0021] The initial weight of cross-modal data of the same dimension is:

[0022] ω i =ω R ·X1+ω E ·X2+ω T ·X3+ω W X4

[0023] Among them, ω R ,ω E ,ω T ,ω W are the weights of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively; X1, X2, X3 and X4 are the normalized values ​​of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively;

[0024] The weight after introducing user personalized data is:

[0025]

[0026] Among them, α ij P is the influence coefficient of user personalized data on weight; j Normalized value for user personalized data.

[0027] Preferably, the user personalized data includes gender, skin type, age and season.

[0028] Preferably, the influence coefficient α of user personalized data on weight ij Includes fixed values ​​and matrices; the matrix is ​​represented as:

[0029]

[0030] Preferably, the comprehensive efficacy score obtained by the dynamic weight model includes:

[0031]

[0032] Among them, X i It measures reflectivity for gloss, modulus of elasticity, texture parameters for roughness, and moisture content.

[0033] Preferably, a multi-index comprehensive evaluation system for cosmetic efficacy includes:

[0034] A data acquisition module, used to obtain data features of cross-modal indicators of cosmetic efficacy using sensors; wherein the data features of cross-modal indicators include reflectivity of gloss, modulus of elasticity, texture parameters of roughness, and water content value;

[0035] The normalization module is used to normalize the data features of cross-modal indicators to form cross-modal data of the same dimension;

[0036] The dynamic weight module is used to establish a dynamic weight model using cross-modal data of the same dimension, and introduce user personalized data to dynamically adjust the dynamic weight model;

[0037] The comprehensive evaluation module is used to obtain a comprehensive efficacy score through a dynamic weight model, compare the comprehensive efficacy scores of a single sample before and after use, and evaluate the effectiveness; compare the comprehensive efficacy scores of multiple samples and evaluate the differences.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention discloses a multi-index comprehensive evaluation method and system for cosmetic efficacy, which overcomes the limitation of single-index evaluation and can more comprehensively reflect the comprehensive effects of cosmetics on skin, such as gloss, elasticity, roughness and moisture; introduces sensors to obtain cross-modal data, objectively and accurately obtains cosmetic efficacy indicators, and avoids the deviation of subjective evaluation; normalization processing ensures that different indicators have equal importance in the comprehensive score, and avoids the error caused by the difference in indicator dimensions; the introduction of a dynamic weight model adjusts the weight according to the personalized data of the user, so that the evaluation result is more in line with the needs of different users, and improves the pertinence and practicality of the evaluation result; by comparing different samples and the comprehensive efficacy scores before and after use, the efficacy and difference of cosmetics can be effectively evaluated, providing a scientific basis for cosmetics research and development and product improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic flow chart of a method for comprehensive evaluation of cosmetic efficacy using multiple indicators provided by an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of a module of a multi-index comprehensive evaluation system for cosmetic efficacy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, the present application provides a multi-index comprehensive evaluation method for cosmetic efficacy, including:

[0044] S1: Using sensors to obtain data features of cross-modal indicators of cosmetic efficacy; the data features of cross-modal indicators include gloss reflectance, elastic modulus, roughness texture parameters and water content value;

[0045] S2: Normalize the data features of cross-modal indicators to form cross-modal data of the same dimension;

[0046] S3: Use cross-modal data of the same dimension to establish a dynamic weight model, and introduce user personalized data to dynamically adjust the dynamic weight model;

[0047] S4: Obtain a comprehensive efficacy score through a dynamic weight model, compare the comprehensive efficacy scores of a single sample before and after use to evaluate effectiveness; compare the comprehensive efficacy scores of multiple samples to evaluate variability.

[0048] In the above scheme, the cross-modal indicator data obtained by sensors can comprehensively and objectively reflect the actual efficacy of cosmetics, avoiding the limitations of traditional subjective evaluation; normalization processing ensures the comparability of different indicators under the same dimension, improving the accuracy and reliability of data analysis; the introduction of dynamic weight models enables the evaluation process to be adjusted according to the personalized needs of users, enhancing the pertinence and practicality of the evaluation results. By comparing the comprehensive efficacy scores, the changes of cosmetics before and after use can be effectively evaluated to help consumers choose the products that best suit them; comparing the scores of multiple samples can reveal the differences between different products and provide a scientific basis for market competition.

[0049] Preferably, in step S1, the data features of the cross-modal indicators of cosmetic efficacy obtained by using sensors include:

[0050] The reflectance of gloss was measured by spectrometer;

[0051] The elastic modulus was obtained by a skin elasticity tester;

[0052] Obtain the texture parameters of roughness through a roughness meter;

[0053] The moisture content is measured by a moisture tester.

[0054] In the above scheme, the gloss reflectance measured by the spectrometer can intuitively reflect the improvement effect of cosmetics on skin gloss and provide direct visual feedback; the elastic modulus data obtained by the skin elasticity tester can quantify the improvement effect of cosmetics on skin elasticity and help evaluate the anti-aging effect of the product; the texture parameters measured by the roughness meter can reveal the degree of improvement of skin surface roughness by cosmetics and provide a scientific basis for the product's skin smoothing effect; the moisture content value measured by the moisture tester can directly reflect the moisturizing effect of cosmetics.

[0055] Preferably, in step S2, normalizing the data features of the cross-modal indicators includes:

[0056] The Min-Max normalization formula is used to normalize the gloss reflectance, elastic modulus, roughness texture parameters and water content to the interval [0,1] to eliminate dimensional differences.

[0057] In the above scheme, the collected indicators are normalized to eliminate the dimension effect. The Min-Max normalization formula is used:

[0058]

[0059] Among them, X is the original data, X min and X maxare the minimum and maximum values ​​of the indicator respectively;

[0060] In one embodiment provided according to the above scheme, there are unnormalized measurement data of the following cosmetics, as shown in the following table:

[0061]

[0062]

[0063] Normalized reflectance for glossiness:

[0064]

[0065] The modulus of elasticity is normalized:

[0066]

[0067] Normalization of the texture parameters of roughness:

[0068]

[0069] Normalization of moisture content:

[0070]

[0071] Preferably, in step S3, establishing a dynamic weight model using cross-modal data of the same dimension, and introducing user personalized data to dynamically adjust the dynamic weight model includes:

[0072] The initial weight of cross-modal data of the same dimension is:

[0073] ω i =ω R ·X1+ω E ·X2+ω T ·X3+ω W X4

[0074] Among them, ω R ,ω E ,ω T ,ω W are the weights of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively; X1, X2, X3 and X4 are the normalized values ​​of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively;

[0075] The weight after introducing user personalized data is:

[0076]

[0077] Among them, α ijP is the influence coefficient of user personalized data on weight; j Normalized value for user personalized data.

[0078] Preferably, the user personalized data includes gender, skin type, age and season.

[0079] Preferably, the influence coefficient α of user personalized data on weight ij Includes fixed values ​​and matrices; the matrix is ​​represented as:

[0080]

[0081] Preferably, in step S4, obtaining a comprehensive efficacy score through a dynamic weight model includes:

[0082]

[0083] Among them, X i It measures reflectivity for gloss, modulus of elasticity, texture parameters for roughness, and moisture content.

[0084] In one embodiment provided in this application, the data of a certain user is as follows:

[0085] Gender: female, normalized value P1 = 1;

[0086] Skin type: dry, normalized value P2 = 1;

[0087] Age: 30 years old, normalized value P3 = 0.5;

[0088] Season: Summer, normalized value P4 = 1;

[0089] Cross-modal indicator data:

[0090] Glossiness reflectivity: 0.8;

[0091] Elastic modulus: 0.7;

[0092] Roughness texture parameter: 0.6;

[0093] Moisture content value: 0.9;

[0094] Default initial weight ω R ,ω E ,ω T ,ω W Both are 0.25;

[0095] The adjusted weights are:

[0096] ω R=0.25×(1+0.1×1+0.2×1+0.1×0.5+0.1×1)=0.25×1.45=0.3625;

[0097] ω E =0.25×(1+0.1×1+0.1×1+0.2×0.5+0.1×1)=0.25×1.4=0.35;

[0098] ω T =0.25×(1+0.2×1+0.1×1+0.1×0.5+0.1×1)=0.25×1.45=0.3625;

[0099] ω W =0.25×(1+0.1×1+0.1×1+0.1×0.5+0.2×1)=0.25×1.45=0.3625;

[0100] The comprehensive efficacy score is:

[0101] S=0.3625×0.8+0.35×0.7+0.3625×0.6+0.3625×0.9=0.29+0.245+0.217.

[0102] In one embodiment provided in the present application, the effectiveness is evaluated by comparing the comprehensive efficacy scores of a single sample before and after use; specifically:

[0103] Select multiple indicators related to the efficacy of the sample, such as skin moisture content, elasticity, spot area, health index, etc.; assign different weights according to the importance of each indicator. For example, the weight of skin moisture content is 30%, elasticity is 25%, spot area is 25%, and health index is 20%; conduct a baseline assessment on the subjects, record the initial value of each indicator, and calculate the initial comprehensive efficacy score; the subjects use the sample according to the prescribed time and method, usually for 28 days or more; after the sample is used, the subjects are evaluated again, the final value of each indicator is recorded, and the final comprehensive efficacy score is calculated; calculate the difference between the comprehensive efficacy scores before and after, and evaluate the effectiveness of the sample. For example, if the final score is 15% higher than the initial score, the sample is considered to have a significant effect.

[0104] In one embodiment provided in the present application, the comprehensive efficacy scores of multiple samples are compared to evaluate the differences; specifically:

[0105] Select multiple indicators related to the efficacy of the sample, such as skin improvement, health improvement, etc.; assign different weights according to the importance of each indicator. For example, the weight of skin moisture content is 30%, elasticity is 25%, spot area is 25%, and health index is 20%; conduct a baseline assessment of the subjects, record the initial value of each indicator, and calculate the initial comprehensive efficacy score; the subjects are randomly assigned to different sample groups and use the samples according to the prescribed time and method, usually for 28 days or longer; after the sample use is completed, the subjects are evaluated again, the final value of each indicator is recorded, and the final comprehensive efficacy score is calculated; the final comprehensive efficacy score of each sample group is calculated, and inter-group comparison is performed.

[0106] Preferably, Figure 2 As shown, a multi-index comprehensive evaluation system for cosmetic efficacy includes:

[0107] A data acquisition module, used to obtain data features of cross-modal indicators of cosmetic efficacy using sensors; wherein the data features of cross-modal indicators include reflectivity of gloss, modulus of elasticity, texture parameters of roughness, and water content value;

[0108] The normalization module is used to normalize the data features of cross-modal indicators to form cross-modal data of the same dimension;

[0109] The dynamic weight module is used to establish a dynamic weight model using cross-modal data of the same dimension, and introduce user personalized data to dynamically adjust the dynamic weight model;

[0110] The comprehensive evaluation module is used to obtain a comprehensive efficacy score through a dynamic weight model, compare the comprehensive efficacy scores of a single sample before and after use, and evaluate the effectiveness; compare the comprehensive efficacy scores of multiple samples and evaluate the differences.

[0111] In the above scheme, the beneficial effects of the multi-index comprehensive evaluation system for cosmetic efficacy are mainly reflected in the following aspects:

[0112] Comprehensive evaluation: The system can simultaneously consider multi-dimensional indicators such as gloss, elasticity, roughness and moisture, provide a comprehensive evaluation of cosmetic efficacy, and ensure the accuracy and completeness of the results.

[0113] Data standardization: Through the normalization module, cross-modal data from different sources and units are converted into the same dimension, eliminating the inconsistency between the original data and improving the accuracy of the analysis.

[0114] Dynamic weight: The dynamic weight module adjusts the weight in real time according to the actual test data, making the evaluation closer to actual usage and better reflecting the true performance of the product.

[0115] User customization: Introduce user personalized data to make the evaluation results more targeted, meet the needs of different consumers, and improve user experience.

[0116] Effect comparison: The comprehensive evaluation module can not only evaluate the efficacy changes of a single sample, but also compare the performance differences of multiple samples, providing a strong basis for product development and market decision-making.

[0117] Scientific decision-making: The system helps cosmetics companies to scientifically evaluate product efficacy, optimize formulas, and improve product quality. It also helps consumers to rationally choose cosmetics that suit them and promote the healthy development of the market.

[0118] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-index comprehensive evaluation method for cosmetic efficacy, characterized in that: include: Using sensors to obtain data features of cross-modal indicators of cosmetic efficacy; The data features of the cross-modal indicators include the reflectivity of gloss, the modulus of elasticity, the texture parameters of roughness, and the water content value; Normalize the data features of cross-modal indicators to form cross-modal data of the same dimension; A dynamic weight model is established using cross-modal data of the same dimension, and user personalized data is introduced to dynamically adjust the dynamic weight model; The comprehensive efficacy score is obtained through the dynamic weight model, and the comprehensive efficacy scores of a single sample before and after use are compared to evaluate the effectiveness; the comprehensive efficacy scores of multiple samples are compared to evaluate the differences.

2. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 1, characterized in that: The data features of the cross-modal indicators of cosmetic efficacy obtained by using sensors include: The reflectance of gloss was measured by spectrometer; The elastic modulus was obtained by a skin elasticity tester; Obtain the texture parameters of roughness through a roughness meter; The moisture content is measured by a moisture tester.

3. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 1, characterized in that: The normalizing of the data features of the cross-modal indicators includes: The Min-Max normalization formula is used to normalize the gloss reflectance, elastic modulus, roughness texture parameters and water content to the [0,1] interval to eliminate dimensional differences.

4. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 3, characterized in that: The cross-modal data of the same dimension obtained by the Min-Max normalization formula include: Among them, X is the original data, X min and X max are the minimum and maximum values ​​of the indicator respectively.

5. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 3, characterized in that: The method of establishing a dynamic weight model by using cross-modal data of the same dimension and dynamically adjusting the dynamic weight model by introducing user personalized data includes: The initial weight of cross-modal data of the same dimension is: oh i =ω R ·X1+ω E ·X2+ω T ·X3+ω W ·X4 Among them, ω R ,ω E ,ω T ,ω W are the weights of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively; X1, X2, X3 and X4 are the normalized values ​​of gloss reflectance, elastic modulus, roughness texture parameter and water content respectively; The weight after introducing user personalized data is: Among them, α ij P is the influence coefficient of user personalized data on weight; j Normalized value for user personalized data.

6. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 5, characterized in that: The user personalized data includes gender, skin type, age and season.

7. A multi-index comprehensive evaluation method for cosmetic efficacy according to claim 6, characterized in that: The influence coefficient of user personalized data on weight α ij Includes fixed values ​​and matrices; the matrix is ​​represented as:

8. A method for comprehensive evaluation of cosmetic efficacy by multiple indicators according to claim 5, characterized in that: The comprehensive efficacy score obtained by the dynamic weight model includes: Among them, X i It measures reflectivity for gloss, modulus of elasticity, texture parameters for roughness, and moisture content.

9. A multi-index comprehensive evaluation system for cosmetic efficacy, characterized in that: include: A data acquisition module, used to obtain data features of cross-modal indicators of cosmetic efficacy using sensors; The data features of the cross-modal indicators include the reflectivity of gloss, the modulus of elasticity, the texture parameters of roughness, and the water content value; The normalization module is used to normalize the data features of cross-modal indicators to form cross-modal data of the same dimension; Dynamic weight module, which is used to establish a dynamic weight model using cross-modal data of the same dimension, and introduce user personalized data to dynamically adjust the dynamic weight model; The comprehensive evaluation module is used to obtain a comprehensive efficacy score through a dynamic weight model, compare the comprehensive efficacy scores of a single sample before and after use, and evaluate the effectiveness; compare the comprehensive efficacy scores of multiple samples and evaluate the differences.

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