A personalized makeup brush design management system

Abstract

This invention discloses a personalized makeup brush design and management system, relating to the field of product order technology. This system, through the organic collaboration of five modules, further achieves the precise capture and satisfaction of users' personalized needs. First, the user type locking module obtains relevant cognitive data based on user self-evaluation and accurately categorizes users into different types, making the subsequent customization process more precise and effective. The sample brush data monitoring module collects and records characteristic data and user feedback from multiple sets of sample brushes, providing a rich reference data foundation for personalized design. The brush bristle testing module conducts in-depth analysis and testing of the powder-grabbing and release abilities of makeup beginners, thereby accurately calculating the powder-grabbing ability coefficient and release ability coefficient, and effectively filtering based on set screening thresholds. Finally, the personalized screening module compares and analyzes the two results to select makeup brush products that relatively meet user needs.

Description

Technical Field

[0001] This invention relates to the field of product order technology, specifically to a makeup brush design and management system based on personalized customization. Background Technology

[0002] In recent years, with the rapid development of the beauty and personalized product market, consumers' demands for makeup tools have gradually shifted from basic functionality to more professional and personalized needs. In the field of makeup tools, makeup brushes, as one of the key application tools, have received widespread attention due to their direct impact on makeup results. In particular, the demand for personalized makeup brushes is growing, and the differences in user experiences when using makeup brushes make personalization an important way to improve user satisfaction.

[0003] Currently, personalized makeup brushes face many challenges. Traditional makeup brush design and production rely heavily on experience and market feedback. In the existing makeup brush market, most products fail to meet the increasingly personalized needs of consumers, especially makeup beginners. Choosing the right makeup brush often presents many challenges because different brushes may be easy or difficult to apply makeup to. Beginners often struggle to control the pressure applied, resulting in too much makeup and affecting the overall look. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a personalized makeup brush design and management system, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a personalized makeup brush design and management system, comprising a user type locking module, a sample brush data monitoring module, a data processing module, a brush bristle testing module, and a personalized screening module.

[0006] The user type locking module is used to obtain relevant cognitive data and determine the user type based on the user's self-evaluation;

[0007] The sample brush data monitoring module is used to collect and record relevant characteristic data information of several sets of sample brushes, and to collect data based on user feedback on each sample brush.

[0008] The data processing module is used to preprocess the relevant characteristic data information and feedback data obtained from the sample brush data monitoring module to construct a personalized design data set, and to eliminate the dimensions in the personalized design data set by combining dimensionless processing technology to form a dimensionless form.

[0009] The brush bristle testing module is used to analyze the powder-grabbing ability of sample brushes based on a personalized design data set when the user type is a makeup novice, so as to construct the powder-grabbing ability coefficient Zfxs of the corresponding sample brushes. The module also filters several groups of sample brushes through a pre-set screening threshold Q, and executes test commands on the filtered sample brushes to test the release ability coefficient Snxs of the corresponding sample brushes in turn.

[0010] The personalized screening module is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush in order to screen out the makeup brushes that meet the user's requirements and set them as the personalized order products for the user.

[0011] Preferably, the user type locking module includes a cognitive acquisition unit and a type positioning unit;

[0012] The cognitive acquisition unit is used to conduct a questionnaire survey on users in advance and obtain relevant cognitive data based on the users' self-evaluation. The relevant cognitive data includes the user's frequency of use of makeup brushes (Spz), years of makeup experience (Hnx), usage habits, and types of makeup tools.

[0013] Preferably, the type positioning unit is used to extract features from relevant cognitive data to obtain the user's years of makeup experience Hnx and usage frequency Spz, and calculates a proficiency value Sz based on the user's years of makeup experience Hnx and usage frequency Spz. The proficiency value Sz is obtained by the following formula: Where e represents the Euler number, and a and b represent the weight values ​​of frequency of use and years of makeup experience, respectively.

[0014] A threshold is preset, and the current user's makeup type is determined by comparing the proficiency value Sz with the threshold. The specific determination is as follows:

[0015] If the proficiency value Sz exceeds the threshold, it indicates that the current user's makeup type is "Experienced Makeup Artist".

[0016] If the proficiency value Sz does not exceed the threshold, it indicates that the current user's makeup type is "makeup novice".

[0017] Preferably, the sample brush data monitoring module includes a brush quality collection unit and a feedback information collection unit;

[0018] The brush collection unit is used to collect and record relevant characteristic data information of several sets of sample brushes. The relevant characteristic data information includes the bristle hardness Smyd, bristle roughness Schcd, bristle density Smd, bristle elasticity coefficient Txs, and adhesion Ffz of each sample brush.

[0019] The feedback information collection unit is used to record the user's feedback on each sample brush in order to obtain feedback data. The feedback data includes the user's feedback rating weight R for the bristle hardness Smyd of each sample brush.

[0020] Preferably, the data processing module includes a preprocessing unit and a standardization unit;

[0021] The preprocessing unit is used to use data preprocessing technology to identify and eliminate errors, outliers and noise data in the relevant characteristic data information and the feedback data, and to extract features to extract useful feature data to construct a personalized design dataset.

[0022] The standardization unit is used to standardize the feature data after feature extraction based on dimensionless processing technology, and to use standardization methods to make it have a uniform scale, including Z-score standardization.

[0023] Preferably, the bristle testing module includes a personalized screening unit, a preliminary analysis unit, and a matching analysis unit;

[0024] The personalized filtering unit is used to filter the bristle hardness Smyd to match the user's preferences based on the user's feedback data on each sample brush. Specifically, it is obtained in the following way:

[0025] ;

[0026] In the formula, i = 1, 2, 3, ..., n, where n represents the number of sample brushes. This represents the bristle hardness of the i-th sample brush. This represents the weight of the user's feedback rating for the bristle hardness of the i-th sample brush.

[0027] Preferably, the preliminary analysis unit is used to filter out sample brushes with the same bristle hardness Smyd as the user's preferred bristle hardness obtained from the personalized screening unit, and to perform statistics to obtain a set of sample brushes of the same type. Based on relevant characteristic data, the powder-grabbing ability coefficient Zfxs of the corresponding sample brush is constructed, specifically calculated as follows:

[0028] ;

[0029] In the formula, Smd represents the bristle density, Sccd represents the bristle roughness, and C represents the correction constant. and All are weight values, where 0 < ≤1, 0< ≤1, and + =1.

[0030] Preferably, based on the calculation method of the fan-grabbing ability coefficient Zfxs of the corresponding sample brush obtained in the preliminary analysis unit, the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set are obtained sequentially, and based on the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set, a screening threshold Q is set, the specific settings of which are as follows:

[0031] The average fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set was obtained by using a statistical mean-averaging algorithm. and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. ;

[0032] Based on the average powder-grabbing ability coefficient of sample brushes within the same sample brush set and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. Obtain the filtering threshold Q;

[0033] ;

[0034] Where k is a constant, and its specific value is set by the user;

[0035] The powder-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set is compared with the screening threshold Q to select the powder-grabbing ability coefficient Zfxs suitable for makeup beginners. If the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is less than or equal to the screening threshold Q, the corresponding sample brush is marked as a suitable sample brush; if the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is greater than or equal to the screening threshold Q, the corresponding sample brush is not marked as a suitable sample brush for the time being.

[0036] Preferably, the matching analysis unit is used to statistically analyze the compatible sample brushes and issue test commands, sequentially testing and analyzing the compatible sample brushes to obtain the release capacity coefficient Snxs of the corresponding sample brushes, specifically obtained in the following manner:

[0037] ;

[0038] In the formula, Txs represents the elasticity coefficient of the bristles. Ffz represents the displacement of the bristles when stretched, and Ffz represents the adhesion force.

[0039] Preferably, the personalized screening module includes a comparison unit and a locking unit;

[0040] The comparison unit is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush, so as to calculate the powder retention coefficient Fzzs of the corresponding sample brush, which is obtained in the following way:

[0041] .

[0042] The locking unit is used to compare the powder retention coefficient Fzzs with the value 1 to select makeup brushes that match the user's preferences. The specific details are as follows:

[0043] If the powder retention coefficient Fzzs = 1, it means that the brush bristles of the corresponding sample brush can balance the ability to pick up and release powder. At this time, the sample brush is set as a personalized order product for users, so as to customize an exclusive makeup brush for users.

[0044] If the powder retention coefficient Fzzs ≠ 1, it means that the powder held by the brush bristles of the corresponding sample brush has not been effectively released.

[0045] This invention provides a personalized makeup brush design and management system, which has the following advantages:

[0046] (1) Through the organic collaboration of five modules, the system further achieves the precise capture and satisfaction of users' personalized needs. First, the user type locking module obtains relevant cognitive data based on users' self-evaluation and accurately divides users into different types, making the subsequent customization process more accurate and effective. The sample brush data monitoring module collects and records the characteristic data of multiple sets of sample brushes and user feedback, providing a rich reference data foundation for personalized design. The brush bristle testing module conducts in-depth analysis and testing of the powder-grabbing and releasing abilities of makeup novices, thereby accurately calculating the powder-grabbing ability coefficient Zfxs and the releasing ability coefficient Snxs, and effectively screening according to the set screening threshold Q. Finally, the personalized selection module compares and analyzes the two options to select makeup brush products that best meet the user's needs. The beneficial effects of this system are that it not only enhances the user's personalized customization experience and further ensures the efficiency and comfort of makeup brushes in actual use, but also provides customized product suggestions for different user groups, especially makeup beginners, thereby improving user satisfaction. This systematic, data-driven design process not only simplifies the user's selection process but also enhances the matching degree between products and user needs, further avoiding the subjectivity and uncertainty of the traditional customization process.

[0047] (2) The personalized screening unit can accurately screen brushes with bristle hardness (Smyd) that match user preferences by using user feedback data on each sample brush. This personalized screening method utilizes the weight of user feedback ratings on the bristle hardness of each sample brush. This method not only comprehensively considers the user's personalized needs but also ensures the scientific validity and reliability of the screening results through comparative analysis of multiple samples. This screening process is particularly important for makeup beginners, as their needs for makeup tools rely more on personalized guidance and scientific analysis results, thereby reducing confusion and blind selection. In short, this process not only improves the matching degree of bristle hardness but also effectively enhances the user experience and satisfaction. This method further enhances the ability of personalized customization, enabling each makeup brush to meet the user's expectations as much as possible, thereby providing users with a higher quality makeup experience.

[0048] (3) Based on the brush bristle hardness Smyd obtained by the personalized screening unit, which is in line with the user's preferences, the system can further calculate the powder-grabbing ability of each sample brush. By marking the sample brushes that meet the conditions as suitable brushes, the marking process improves the accuracy of screening, ensuring that makeup novices can choose the right brush, and effectively improve the effect and experience of makeup.

[0049] (4) The matching analysis system can accurately test the release capability of the matching sample brush and obtain the release capability coefficient Snxs, thus realizing a scientific evaluation of the powder release capability of the sample brush in actual use. Secondly, in the personalized screening module, the powder-grabbing capability coefficient Zfxs of the sample brush is combined with the release capability coefficient Snxs to generate the powder retention coefficient Fzzs, which allows users to quickly screen out brushes that match their makeup habits through simple comparison and analysis. When the powder retention coefficient Fzzs equals 1, it indicates that the sample brush has reached a balance between powder grabbing and release, and can effectively grab and release powder, thereby achieving a uniform and natural makeup effect. Especially for makeup novices, such makeup brushes are easier to control, reducing uneven makeup or powder fallout, and making the makeup process smoother. In addition, the comparison mechanism of the powder retention coefficient Fzzs also enhances the system's personalized customization capability. When the powder retention coefficient Fzzs is not equal to 1, the system intelligently identifies deficiencies in powder release from the brush, such as powder accumulation or loss. This analysis helps users avoid using unsuitable brushes, improving powder utilization efficiency, reducing unnecessary waste, and significantly enhancing the precision and effectiveness of makeup application. In summary, through the synergy of the matching analysis unit and the personalized selection module, this system further optimizes the customization process for makeup brushes, providing a more accurate and scientific brush selection solution, particularly suitable for makeup beginners, and enhancing the overall makeup experience. Attached Figure Description

[0050] Figure 1 This is a block diagram of a makeup brush design and management system based on personalized customization according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] Please see Figure 1 This invention provides a personalized makeup brush design and management system, including a user type locking module, a sample brush data monitoring module, a data processing module, a brush bristle testing module, and a personalized filtering module.

[0054] The user type locking module is used to obtain relevant cognitive data and determine the user type based on the user's self-evaluation;

[0055] The sample brush data monitoring module is used to collect and record relevant characteristic data information of several sets of sample brushes, and to collect data based on user feedback on each sample brush.

[0056] The data processing module is used to preprocess the relevant characteristic data and feedback data obtained from the sample data monitoring module to construct a personalized design dataset. Combined with dimensionless processing technology, the dimensions in the personalized design dataset are eliminated to form a dimensionless form. In the data processing module, the dimensionless processing technology eliminates the difference in dimensions and constructs a dimensionless personalized design dataset, making the data analysis more standardized and normalized, and ensuring a unified comparison standard between different parameters.

[0057] The brush bristle testing module is used to analyze the powder-grabbing ability of sample brushes based on a personalized design data set when the user type is a makeup novice, so as to construct the powder-grabbing ability coefficient Zfxs of the corresponding sample brushes. The module also filters several groups of sample brushes through a pre-set screening threshold Q, and executes test commands on the filtered sample brushes to test the release ability coefficient Snxs of the corresponding sample brushes in turn.

[0058] The personalized screening module is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush in order to screen out the makeup brushes that meet the user's requirements and set them as the personalized order products for the user.

[0059] During operation, this system uses a user type locking module to accurately determine user types based on self-evaluation and cognitive data, especially for makeup beginners. This ensures that subsequent makeup brush customization better meets the user's actual needs, providing a foundation for personalized customization and improving user satisfaction and product applicability. Secondly, the combined use of the sample brush data monitoring and processing modules systematically collects and processes large amounts of sample brush data. Dimensionless processing technology eliminates dimensional differences in the data set, forming standardized and comparable data. This method not only improves data analysis efficiency but also ensures the comparability between different brush bristle characteristics, providing scientific data support for personalized design. Thirdly, the bristle testing module analyzes the powder-grabbing ability coefficient Zfxs and release ability coefficient Snxs required by makeup beginners, enabling precise screening and testing of different sample brushes. This refined testing process makes it easier for makeup beginners to find makeup brushes that effectively pick up powder while releasing it appropriately, enhancing the user's makeup experience. Finally, the personalized selection module compares and analyzes the powder-grabbing ability coefficient and the release ability coefficient to select makeup brushes that meet the user's needs. The introduction of this module further improves the accuracy and efficiency of personalized customization, enabling the final product to more accurately meet the user's personalized needs. In particular, for makeup novices, it can significantly reduce the confusion that may be encountered during the selection and use process.

[0060] Example 2

[0061] Please refer to Figure 1 Specifically: the user type locking module includes a cognitive acquisition unit and a type positioning unit;

[0062] The cognitive acquisition unit is used to conduct a questionnaire survey on users in advance and obtain relevant cognitive data based on the users' self-evaluation. The relevant cognitive data includes the user's frequency of use of makeup brushes (Spz), years of makeup experience (Hnx), usage habits, and types of makeup tools. Among them, the types of makeup tools include, but are not limited to, foundation brushes, concealer brushes, powder brushes, blush brushes, contour brushes, highlighter brushes, eyeshadow brushes, lip brushes, nose shadow brushes, and eyebrow brushes.

[0063] The type positioning unit is used to extract features from relevant cognitive data to obtain the user's years of makeup experience Hnx and usage frequency Spz, and calculates the proficiency value Sz based on the user's years of makeup experience Hnx and usage frequency Spz. The proficiency value Sz is obtained by the following formula: Where e represents the Euler number, with a value of approximately 2.71828, and a and b represent the weight values ​​for frequency of use and years of makeup experience, respectively.

[0064] A threshold is preset, and the current user's makeup type is determined by comparing the proficiency value Sz with the threshold. The specific determination is as follows:

[0065] If the proficiency value Sz exceeds the threshold, it indicates that the current user's makeup type is "Experienced Makeup Artist".

[0066] If the proficiency value Sz does not exceed the threshold, it indicates that the current user's makeup type is "makeup novice".

[0067] In this embodiment, firstly, through a pre-survey questionnaire conducted by the cognitive acquisition unit, the system can collect and understand users' usage of makeup brushes from multiple perspectives. This multi-dimensional data collection method ensures the comprehensiveness and accuracy of user cognitive data, laying a solid foundation for subsequent user type positioning. Secondly, the type positioning unit extracts features from relevant cognitive data, estimates the user's proficiency value Sz using a calculation formula, and compares it with a preset threshold. This exponential model calculation method based on Euler's number can further accurately reflect the user's makeup proficiency, making the system's judgment of user type more scientific and precise. Especially for distinguishing between makeup novices and experienced makeup users, this method can effectively avoid subjective misjudgments, further ensuring that the makeup brushes recommended by the system can accurately match the user's needs, thereby achieving personalized services for different user types. For makeup novices, the system can prioritize recommending makeup brushes that are easy to master and operate. This intelligent classification method not only improves the user's shopping experience but also improves the overall efficiency and recommendation accuracy of the system. In summary, through the precise user type locking module, this system not only enhances the system's intelligence but also enables personalized customization services to better meet the actual needs of different users, especially helping makeup novices better master makeup skills in the initial stage.

[0068] Example 3

[0069] Please refer to Figure 1 Specifically: the sample brush data monitoring module includes a brush quality collection unit and a feedback information collection unit;

[0070] The brush collection unit is used to collect and record relevant characteristic data information of several sets of sample brushes. The relevant characteristic data information includes the bristle hardness Smyd, bristle roughness Schcd, bristle density Smd, bristle elasticity coefficient Txs, and adhesion Ffz of each sample brush.

[0071] The bristle hardness Smyd mentioned above can be obtained by monitoring with a hardness tester;

[0072] The brush roughness, Sccd, can be obtained by measuring a surface roughness meter.

[0073] The bristle density Smd can be obtained by monitoring with a density analyzer;

[0074] Adhesion force Ffz can be measured by a tensile testing machine. In the test, the powder is applied to the brush bristles, and then a tensile force is gradually applied until the powder is detached from the brush bristles. The force value recorded by the testing machine is the adhesion force.

[0075] During use, the bristles are mainly subjected to bending forces. The elastic coefficient Txs of the bristles can be obtained using the following formula: Where E represents the Young's modulus of the bristles, which indicates the stiffness of the bristles under tension or compression. The unit of Young's modulus is Pascal; L represents the length of the bristles; and I represents the moment of inertia of the bristle cross section.

[0076] The elasticity coefficient Txs of a brush bristles describes the elastic recovery force of the bristles when bent, and is often used for softer bristles or in application scenarios where bending is the primary motion during makeup application.

[0077] The feedback information collection unit is used to record the user's feedback on each sample brush in order to obtain feedback data. The feedback data includes the user's feedback rating weight R for the bristle hardness Smyd of each sample brush.

[0078] The feedback rating weight R of the bristle hardness Smyd of each of the above sample brushes reflects the user's preference for different sample brushes. Here, the user's feedback rating can be assigned a corresponding value.

[0079] The data processing module includes a preprocessing unit and a standardization unit;

[0080] The preprocessing unit is used to use data preprocessing technology to identify and eliminate errors, outliers and noise data in the relevant characteristic data information and the feedback data, and to extract features to extract useful feature data to construct a personalized design dataset.

[0081] The standardization unit is used to standardize the feature data after feature extraction based on dimensionless processing technology, and to use standardization methods to make it have a uniform scale, including Z-score standardization.

[0082] In this embodiment, firstly, the sample brush data monitoring module records multiple key characteristics of the sample brushes in detail through the brush quality collection unit. This data collection provides rich foundational information for subsequent personalized design. Simultaneously, the feedback information collection unit records the user feedback rating weight R for each sample brush, allowing different users' actual usage experiences to directly influence personalized design optimization, further ensuring that the designed makeup brushes meet user needs as much as possible. Secondly, the data processing module preprocesses the collected data through the preprocessing unit. By identifying and eliminating errors, outliers, and noisy data, the system ensures the accuracy and reliability of the data. The feature extraction process further optimizes the data, making the personalized design dataset more targeted. This step not only improves data quality but also provides a reliable basis for subsequent design optimization. Finally, the standardization unit standardizes the processed data using dimensionless processing technology, enabling data from different dimensions to be compared and analyzed on a unified scale. In particular, the use of the Z-score standardization method makes the data processing process more scientific and standardized, avoiding analytical biases caused by inconsistent data scales. This standardization process significantly improves the accuracy and stability of the system, making personalized design more precise, ultimately achieving higher user satisfaction and a better product experience. Overall, the system further improves the precision and effectiveness of personalized makeup brush design through systematic data collection, precise preprocessing, and scientific standardization. In particular, it addresses different user needs and feedback, enabling the final product to better meet users' personalized requirements.

[0083] Example 4

[0084] Please refer to Figure 1 Specifically: the brush test module includes a personalized screening unit, a preliminary analysis unit, and a matching analysis unit;

[0085] The personalized filtering unit is used to filter the bristle hardness Smyd to match the user's preferences based on the user's feedback data on each sample brush. Specifically, it is obtained in the following way:

[0086] ;

[0087] In the formula, i = 1, 2, 3, ..., n, where n represents the number of sample brushes. This represents the bristle hardness of the i-th sample brush. This represents the weight of the user's feedback rating for the bristle hardness of the i-th sample brush.

[0088] In this embodiment, firstly, the personalized screening unit effectively performs personalized screening of bristle hardness (Smyd) by combining user feedback data for each sample brush. Using a mathematical model, the system can accurately screen bristle hardness (Smyd) that matches the user's preferences based on the user's feedback rating weights. This screening method not only improves the accuracy of the screening results and user satisfaction but also fully reflects each user's personalized needs in bristle hardness selection. Especially when there are a large number of sample brushes, this screening process can quickly and efficiently identify the bristle hardness suitable for the user, thus simplifying the user's selection process. Secondly, using this screening process, the system can dynamically adjust the recommended bristle hardness based on different user feedback, making personalized customization more flexible and accurate.

[0089] In summary, this data-driven filtering mechanism not only enhances the system's adaptability and responsiveness but also improves user satisfaction and overall experience with the final product. Through personalized filtering units, users can find makeup brushes that match their preferences more quickly and accurately, further reducing the cost and time of trial and error.

[0090] Example 5

[0091] Please refer to Figure 1 Specifically: The preliminary analysis unit is used to filter out sample brushes with the same bristle hardness Smyd as the user's preferred bristle hardness obtained from the personalized screening unit, and to perform statistics to obtain a set of sample brushes of the same type. Based on relevant characteristic data, the powder-grabbing ability coefficient Zfxs of the corresponding sample brush is constructed, which is specifically calculated as follows:

[0092] ;

[0093] In the formula, Smd represents the bristle density, Sccd represents the bristle roughness, and C represents the correction constant. and All are weight values, where 0 < ≤1, 0< ≤1, and + =1. The introduction of the correction constant C further improves the stability and accuracy of the calculation results.

[0094] Based on the calculation method of the fan-grabbing ability coefficient Zfxs of the corresponding sample brushes obtained in the preliminary analysis unit, the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set are obtained sequentially. Based on the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set, the screening threshold Q is set. The specific settings are as follows:

[0095] The average fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set was obtained by using a statistical mean-averaging algorithm. and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. ;

[0096] Based on the average powder-grabbing ability coefficient of sample brushes within the same sample brush set and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. Obtain the filtering threshold Q;

[0097] ;

[0098] Where k is a constant, usually taking values ​​of 1-3, corresponding to different confidence levels. The specific value is set by the user (according to the actual situation).

[0099] The powder-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set is compared with the screening threshold Q to select the powder-grabbing ability coefficient Zfxs suitable for makeup beginners. If the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is less than or equal to the screening threshold Q, the corresponding sample brush is marked as a suitable sample brush; if the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is greater than or equal to the screening threshold Q, the corresponding sample brush is not marked as a suitable sample brush for the time being.

[0100] In this embodiment, firstly, a preliminary analysis unit screens sample brushes whose bristle hardness (Smyd) matches the user's preference. Through statistical analysis and calculation of relevant characteristic data, the powder-grabbing ability coefficient (Zfxs) of the same set of sample brushes is obtained. This process accurately reflects the powder-grabbing ability performance of different sample brushes, allowing the system to scientifically analyze and select brushes more suitable for makeup beginners. Secondly, by setting a screening threshold Q and combining statistical methods to obtain the average powder-grabbing ability coefficient and standard deviation of the sample brushes, the system can further effectively screen brushes suitable for makeup beginners within the same set of sample brushes. The setting of this screening threshold Q considers the distribution of powder-grabbing ability of the sample brushes and flexibly controls the stringency of the screening by adjusting the confidence level, i.e., setting the k value. For makeup beginners, this screening mechanism ensures that the recommended brushes are neither too strong in powder-grabbing ability, avoiding excessive powder pickup at once, nor too weak, ensuring makeup application and thus improving the user experience and the success rate of makeup application. In summary, by systematically analyzing and thresholding the powder-grabbing ability of sample brushes, this system can effectively meet the special needs of makeup novices when using makeup brushes, and realize a more scientific and precise personalized customization process. This method not only improves the rationality of makeup brush selection, but also enhances users' confidence in makeup tools and their satisfaction with the tools.

[0101] Example 6

[0102] Please refer to Figure 1 Specifically: the matching analysis unit is used to statistically analyze the compatible sample brushes and issue test commands, sequentially testing and analyzing the compatible sample brushes to obtain the release capability coefficient Snxs of the corresponding sample brushes, specifically obtained in the following manner:

[0103] ;

[0104] In the formula, Txs represents the elasticity coefficient of the bristles. Ffz represents the displacement of the bristles when stretched, and Ffz represents the adhesion force.

[0105] The personalized filtering module includes a comparison unit and a locking unit;

[0106] The comparison unit is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush, so as to calculate the powder retention coefficient Fzzs of the corresponding sample brush, which is obtained in the following way:

[0107] .

[0108] The locking unit is used to compare the powder retention coefficient Fzzs with the value 1 to select makeup brushes that match the user's preferences. The specific details are as follows:

[0109] If the powder retention coefficient Fzzs = 1, it means that the brush bristles of the corresponding sample brush can balance the ability to pick up and release powder. At this time, the sample brush is set as a personalized order product for users, so as to customize an exclusive makeup brush for users. This indicates that the makeup brush can almost completely release the powder after picking it up. For beginners, such a makeup brush is easier to control and can apply makeup more evenly.

[0110] If the ratio is too low, the brush bristles will easily accumulate powder, making it difficult to apply makeup evenly; if the ratio is too high, the powder will easily be lost, resulting in uneven makeup. The closer the ratio is to 1, the higher the powder utilization efficiency, and at the same time, unnecessary waste is further avoided.

[0111] If the powder retention coefficient Fzzs ≠ 1, it means that the powder held by the brush bristles of the corresponding sample brush has not been effectively released. The powder may be trapped inside the brush bristles, resulting in uneven makeup. This means that the brush bristles have too strong an adsorption force or the release is insufficient, and the makeup is easy to accumulate, which is especially difficult for beginners to master.

[0112] In this embodiment, firstly, the matching analysis unit obtains the release capacity coefficient Snxs of the corresponding sample brush by statistically analyzing and testing the adapted sample brushes. Through comprehensive calculation of the bristle elasticity coefficient Txs, the displacement ΔL of the stretched bristles, and the adhesion force Ffz, the powder release capacity of each sample brush in actual use is further accurately evaluated. This process ensures the scientific validity and accuracy of the test data, providing a reliable foundation for subsequent comparative analysis. Secondly, the comparison unit in the personalized screening module further calculates the powder retention coefficient Fzzs of the sample brush by comparing the powder-grabbing capacity coefficient Zfxs with the release capacity coefficient Snxs. The introduction of the powder retention coefficient Fzzs not only intuitively reflects the balance between powder grabbing and powder release in the sample brush, but also provides a key parameter for the customized design of makeup brushes. By comparing the brushes with a value of 1, the system can quickly filter out makeup brushes suitable for user needs. For makeup beginners, brushes with a powder retention coefficient (Fzzs) close to 1 are particularly important because they ensure even powder distribution during application, reducing uneven makeup and powder buildup, thus improving makeup results and user experience. Finally, the locking unit, based on the powder retention coefficient (Fzzs), identifies brushes with a retention coefficient of 1 as the relatively better option. These brushes strive for an ideal balance between powder pickup and release, ensuring even application while minimizing powder waste. They are especially suitable for beginners, helping them better master makeup techniques and reducing makeup problems caused by uneven powder release. In conclusion, through a series of scientific and precise calculations and analyses, this system not only enhances the personalization capabilities of makeup brushes but also provides users with makeup tools that better meet their actual needs, thereby improving the makeup experience and results.

[0113] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A makeup brush design management system based on personalized customization, characterized in that: It includes a user type locking module, a sample brush data monitoring module, a data processing module, a brush bristle testing module, and a personalized filtering module; The user type locking module is used to obtain relevant cognitive data and determine the user type based on the user's self-evaluation; The sample brush data monitoring module is used to collect and record relevant characteristic data information of several sets of sample brushes, and to collect data based on user feedback on each sample brush. The data processing module is used to preprocess the relevant characteristic data information and feedback data obtained from the sample brush data monitoring module to construct a personalized design data set, and to eliminate the dimensions in the personalized design data set by combining dimensionless processing technology to form a dimensionless form. The brush bristle testing module is used to analyze the powder-grabbing ability of sample brushes based on a personalized design data set when the user type is a makeup novice, so as to construct the powder-grabbing ability coefficient Zfxs of the corresponding sample brushes. The module also filters several groups of sample brushes through a pre-set screening threshold Q, and executes test commands on the filtered sample brushes to test the release ability coefficient Snxs of the corresponding sample brushes in turn. The personalized screening module is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush in order to screen out the makeup brushes that meet the user's requirements and set them as the personalized order products for the user.

2. The makeup brush design and management system based on personalized customization according to claim 1, characterized in that: The user type locking module includes a cognitive acquisition unit and a type positioning unit; The cognitive acquisition unit is used to conduct a questionnaire survey on users in advance and obtain relevant cognitive data based on the users' self-evaluation. The relevant cognitive data includes the user's frequency of use of makeup brushes (Spz), years of makeup experience (Hnx), usage habits, and types of makeup tools.

3. The makeup brush design and management system based on personalized customization according to claim 2, characterized in that: The type positioning unit is used to extract features from relevant cognitive data to obtain the user's years of makeup experience Hnx and usage frequency Spz, and calculates the proficiency value Sz based on the user's years of makeup experience Hnx and usage frequency Spz. The proficiency value Sz is obtained by the following formula: Where e represents the Euler number, and a and b represent the weight values ​​of frequency of use and years of makeup experience, respectively. A threshold is preset, and the current user's makeup type is determined by comparing the proficiency value Sz with the threshold. The specific determination is as follows: If the proficiency value Sz exceeds the threshold, it indicates that the current user's makeup type is "Experienced Makeup Artist". If the proficiency value Sz does not exceed the threshold, it indicates that the current user's makeup type is "makeup novice".

4. The makeup brush design and management system based on personalized customization according to claim 1, characterized in that: The sample brush data monitoring module includes a brush quality collection unit and a feedback information collection unit; The brush collection unit is used to collect and record relevant characteristic data information of several sets of sample brushes. The relevant characteristic data information includes the bristle hardness Smyd, bristle roughness Schcd, bristle density Smd, bristle elasticity coefficient Txs, and adhesion Ffz of each sample brush. The feedback information collection unit is used to record the user's feedback on each sample brush in order to obtain feedback data. The feedback data includes the user's feedback rating weight R for the bristle hardness Smyd of each sample brush.

5. The makeup brush design and management system based on personalized customization according to claim 1, characterized in that: The data processing module includes a preprocessing unit and a standardization unit; The preprocessing unit is used to use data preprocessing technology to identify and eliminate errors, outliers and noise data in the relevant characteristic data information and the feedback data, and to extract features to extract useful feature data to construct a personalized design dataset. The standardization unit is used to standardize the feature data after feature extraction based on dimensionless processing technology, and to use standardization methods to make it have a uniform scale, including Z-score standardization.

6. The makeup brush design and management system based on personalized customization according to claim 4, characterized in that: The brush bristle testing module includes a personalized screening unit, a preliminary analysis unit, and a matching analysis unit. The personalized filtering unit is used to filter the bristle hardness Smyd to match the user's preferences based on the user's feedback data on each sample brush. Specifically, it is obtained in the following way: ； In the formula, i = 1, 2, 3, ..., n, where n represents the number of sample brushes. This represents the bristle hardness of the i-th sample brush. This represents the weight of the user's feedback rating for the bristle hardness of the i-th sample brush.

7. A makeup brush design and management system based on personalized customization according to claim 6, characterized in that: The preliminary analysis unit is used to filter out sample brushes with the same bristle hardness Smyd as the user's preferred bristle hardness obtained from the personalized screening unit, and to perform statistical analysis to obtain a set of sample brushes of the same type. Based on relevant characteristic data, the powder-grabbing ability coefficient Zfxs of the corresponding sample brush is constructed, specifically calculated as follows: ； In the formula, Smd represents the bristle density, Sccd represents the bristle roughness, and C represents the correction constant. and All are weight values.

8. A makeup brush design and management system based on personalized customization according to claim 7, characterized in that: Based on the calculation method of the fan-grabbing ability coefficient Zfxs of the corresponding sample brushes obtained in the preliminary analysis unit, the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set are obtained sequentially. Based on the fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set, the screening threshold Q is set. The specific settings are as follows: The average fan-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set was obtained by using a statistical mean-averaging algorithm. and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. ; Based on the average powder-grabbing ability coefficient of sample brushes within the same sample brush set and the standard deviation of the powder-grabbing ability coefficient of sample brushes within the same sample brush set. Obtain the filtering threshold Q; ； Where k is a constant, and its specific value is set by the user; The powder-grabbing ability coefficient Zfxs of multiple sample brushes within the same sample brush set is compared with the screening threshold Q to select the powder-grabbing ability coefficient Zfxs suitable for makeup beginners. If the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is less than or equal to the screening threshold Q, the corresponding sample brush is marked as a suitable sample brush; if the powder-grabbing ability coefficient Zfxs of the sample brushes within the same sample brush set is greater than or equal to the screening threshold Q, the corresponding sample brush is not marked as a suitable sample brush for the time being.

9. A makeup brush design and management system based on personalized customization according to claim 6, characterized in that: The matching analysis unit is used to statistically analyze the compatible sample brushes and issue test commands, sequentially testing and analyzing the compatible sample brushes to obtain the release capacity coefficient Snxs of the corresponding sample brushes, specifically obtained in the following manner: ； In the formula, Txs represents the elasticity coefficient of the bristles. Ffz represents the displacement of the bristles when stretched, and Ffz represents the adhesion force.

10. A makeup brush design and management system based on personalized customization according to claim 1, characterized in that: The personalized filtering module includes a comparison unit and a locking unit; The comparison unit is used to compare and analyze the powder-grabbing ability coefficient Zfxs of the sample brush with the release ability coefficient Snxs of the corresponding sample brush, so as to calculate the powder retention coefficient Fzzs of the corresponding sample brush, which is obtained in the following way: ； The locking unit is used to compare the powder retention coefficient Fzzs with the value 1 to select makeup brushes that match the user's preferences. The specific details are as follows: If the powder retention coefficient Fzzs = 1, it means that the brush bristles of the corresponding sample brush can balance the ability to pick up and release powder. At this time, the sample brush is set as a personalized order product for users, so as to customize an exclusive makeup brush for users. If the powder retention coefficient Fzzs ≠ 1, it means that the powder held by the brush bristles of the corresponding sample brush has not been effectively released.

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