Clothing customization shopping guide method based on non-measurement intelligent size matching

Abstract

The invention relates to the technical field of clothing matching and customization, and discloses a clothing customization shopping guide method based on non-body-measurement intelligent size matching, and the method comprises the following steps: S101, obtaining non-body-measurement basic information inputted by a user; s102, on the basis of the non-body-measurement basic information of the user, calculating and correcting through a preset physical sign parameter mapping model to obtain the net body size of the user; s103, calling a dynamic adding and releasing rule base according to the clothes category attributes, and dynamically adjusting the net body size in combination with the style preference selected by the user to generate a first finished product size; s104, verifying the generated first finished product size, prompting a user to input a specific part adjustment parameter after judging that the first finished product size is abnormal, and correcting the first finished product size through a nonlinear compensation algorithm to obtain a second finished product size; and S105, outputting the second finished product size to the commodity shopping guide system and the customization processing system. According to the invention, the complexity and cost of the body measurement process are reduced, and the intelligent level of clothing matching and customization is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of clothing matching and customization, and in particular relates to a clothing customization shopping guide method based on non-measured intelligent size matching. Background Art

[0002] With the development of intelligent clothing industry, consumers' demand for personalized customization and accurate size recommendation is growing. However, there are still many shortcomings in the current clothing shopping guide and customization methods in the market, which leads to inaccurate size matching and cumbersome customization process, affecting user experience.

[0003] Existing clothing shopping guide methods are mainly based on fixed size recommendations or historical purchase data analysis, but these methods cannot take into account individual physical differences and brand size differences, and it is difficult to accurately match the needs of different users. On the other hand, clothing customization usually relies on manual measurement, the measurement steps are cumbersome, remote customization is costly, and it is impossible to quickly respond to market demand, affecting customization efficiency and popularity. Summary of the invention

[0004] The present invention provides a clothing customization shopping guide method based on non-measured intelligent size matching, which solves the technical problems in the related art that the size recommendation accuracy is low, it is difficult to meet personalized clothing needs, the customization measurement process is complicated, the remote operation cost is high, the ready-made clothing shopping guide is separated from the customized production, and it is difficult to achieve integrated application.

[0005] The present invention provides a clothing customization shopping guide method based on non-measured intelligent size matching, comprising the following steps:

[0006] S101, obtaining non-body measurement basic information input by the user, including: height, weight, age and physical parameters, the physical parameters including: waist circumference, shoulder width, chest circumference and arm span;

[0007] S102, based on the non-physical basic information of the user, the user's predicted physical signs are calculated through a preset physical sign parameter mapping model, and the physical sign parameters are corrected and missing complemented to obtain a net body size, wherein the physical sign parameter mapping model is generated through machine learning training, and the predicted physical signs, net body size and physical sign parameters are consistent in dimension;

[0008] S103, calling a dynamic addition rule library according to clothing category attributes, dynamically adjusting the net body size in combination with the style preference selected by the user, and generating a first finished product size;

[0009] S104, performing a rationality check on the generated first finished product size, and issuing an early warning prompt based on the first preset judgment rule. If an abnormality is detected, adjusting parameters according to the specific part input by the user, and correcting the first finished product size through a nonlinear compensation algorithm to obtain a second finished product size;

[0010] S105, outputting the second finished product size to a target application end, wherein the target application end includes a product shopping guide system and a customized processing system.

[0011] Furthermore, the physical sign parameter mapping model is constructed based on a deep neural network, which includes: an implicit body shape inference module, a gating network structure, and a sub-model coordination mechanism;

[0012] The implicit body shape inference module is used to extract the body mass index based on the user's non-measurement basic information, and implicitly divide the user into different body shape categories through the pre-trained clustering algorithm combined with distance inversion, and obtain the preliminary probability vector of the user belonging to each body shape category, where the body shape categories include: high fat density type, low muscle density type and standard balanced type; the gated network structure includes: input layer, hidden layer and output layer;

[0013] The sub-model collaboration mechanism means that each body shape category corresponds to an independent deep neural network sub-model, and each sub-model outputs a predicted net body size based on the user's non-body basic information.

[0014] Furthermore, the K-Means clustering algorithm is combined with distance inversion to implicitly divide the users into body shape categories. The specific steps include:

[0015] S301, setting the number of clusters to 3, and randomly selecting three center points as body shape categories;

[0016] S302, calculating the Euclidean distance between each user data point and the center point;

[0017] S303, assigning the user to the nearest cluster, updating the center point, and recalculating the mean of each cluster;

[0018] S304, repeating steps S302 to S303 until the position change range of the center point is smaller than a preset threshold;

[0019] S305, calculating the Euclidean distance between the user's non-body-size basic information and the three cluster center points, and using the distance inversion method to calculate the preliminary probability vector of the user belonging to each body type category. The calculation formula of the distance inversion method is: ,in, represents the probability that the user belongs to the jth cluster center, represents the Euclidean distance from the user to the jth cluster center, and j and k both represent the index of the cluster center.

[0020] Furthermore, the input layer of the gated network structure is used to receive the preliminary probability vector and body mass index of the user belonging to each body type category; the hidden layer includes 2 fully connected layers; and the output layer is used to generate a weight vector containing the weights of each body type category through an activation function, and the sum of the weights of each body type category is 1.

[0021] Furthermore, the predicted net body size output by each sub-model is combined with the weight vector output by the gated network structure for weighted summation to obtain the predicted physical sign. The calculation formula of the predicted physical sign includes:

[0022] ;

[0023] Among them, CBS represents the predicted physical sign, i represents the body type index, HFD, LMD and STD represent high fat density type, low muscle density type and standard balanced type, respectively. represents the weight of body type i, It represents the predicted value when the body size is i output by the sub-model coordination mechanism.

[0024] Furthermore, the fields of the dynamic addition rule library include: clothing category code, addition part code, style preference code, basic addition coefficient, BMI correction coefficient, age correction coefficient and gender applicability, and the calculation formula of the first finished product size is: ;

[0025] in, Indicates the first finished product size, Indicates the net size, Indicates the basic addition coefficient, represents the BMI correction factor, Indicates the preset standard body mass index, represents the age correction factor, A represents age, Indicates the preset standard age.

[0026] Furthermore, the specific steps of S104 include:

[0027] S401, when the first finished product size is detected to be abnormal, prompting the user to input the adjustment amount of the adjustment part;

[0028] S402, when the adjustment amount does not exceed the preset adjustment threshold, correcting other parts based on a first empirical formula, wherein the first empirical formula is: ,in, Indicates the adjustment amount of the part a that needs to be corrected, Indicates the adjustment amount of the adjustment part b input by the user, Indicates the human body proportion coefficient, a and b are the index of the adjustment part input by the user and the index of the part to be corrected respectively;

[0029] S403, when the adjustment amount exceeds the preset adjustment threshold, a nonlinear exponential formula is used for correction, wherein the nonlinear exponential formula is: ,in, Indicates the second finished product size, Indicates the maximum compensation limit, represents the exponential convergence coefficient, Indicates adjusting the threshold.

[0030] Furthermore, the product shopping guide system matches market standard clothing sizes based on a brand size normalization model and generates cross-brand size recommendation results.

[0031] Furthermore, the customized processing system converts the second finished product size into clothing pattern data, and completes the customized clothing production through automatic cutting equipment.

[0032] The beneficial effects of the present invention are as follows: the present invention only requires the user to input non-body measurement data such as height, weight, age, etc. that do not require professional tools, and constructs a body sign parameter mapping model through a deep neural network and a clustering algorithm to achieve accurate prediction of net body size. The model uses an implicit body shape inference method to divide body shape categories such as high fat density type and low muscle density type, and combines the prediction results of multiple sub-models with a gated network to dynamically weighted fusion, thereby solving the problem of poor adaptability of traditional single models to different body shapes. Compared with the method that relies on manual body measurement, the user operation threshold is significantly reduced;

[0033] The present invention calls a dynamic addition rule library based on clothing categories and style preferences, dynamically adjusts the net body size through a formula, and introduces BMI and age correction coefficients to make the version adapt to the physical characteristics and wearing habits of different users. When the user manually adjusts, a nonlinear compensation algorithm is adopted: conventional adjustments correct related parts through the linkage of the human body proportion coefficient, and when the threshold is exceeded, the exponential function is used to suppress the size out of control, taking into account both personalized needs and ergonomic rationality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flow chart of a clothing customization shopping guide method based on non-measured intelligent size matching. DETAILED DESCRIPTION

[0035] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0036] like Figure 1 As shown, a clothing customization shopping guide method based on non-measured intelligent size matching includes the following steps:

[0037] S101, obtaining non-body measurement basic information input by the user, including: height, weight, age and physical parameters, the physical parameters including: waist circumference, shoulder width, chest circumference and arm span;

[0038] S102, based on the non-physical basic information of the user, the user's predicted physical signs are calculated through a preset physical sign parameter mapping model, and the physical sign parameters are corrected and missing complemented to obtain a net body size, wherein the physical sign parameter mapping model is generated through machine learning training, and the predicted physical signs, net body size and physical sign parameters are consistent in dimension;

[0039] S103, calling a dynamic addition rule library according to clothing category attributes, dynamically adjusting the net body size in combination with the style preference selected by the user, and generating a first finished product size;

[0040] S104, performing a rationality check on the generated first finished product size, and issuing an early warning prompt based on the first preset judgment rule. If an abnormality is detected, adjusting parameters according to the specific part input by the user, and correcting the first finished product size through a nonlinear compensation algorithm to obtain a second finished product size;

[0041] S105, outputting the second finished product size to a target application end, wherein the target application end includes a product shopping guide system and a customized processing system.

[0042] In one embodiment of the present invention, the user's non-body measurement basic information is collected through a user interaction interface, and the non-body measurement basic information includes: height, weight, age, waist circumference, shoulder width, chest circumference and arm span.

[0043] In one embodiment of the present invention, after receiving the non-body measurement basic information of the user, the system pre-processes the information. The specific steps include:

[0044] S201, when missing values ​​are detected, prompt the user to complete them;

[0045] S202, prompting and rejecting input that does not conform to conventional human dimensions, for example, height 50 cm, weight 200 kg;

[0046] S203, normalizing the non-body-measurement basic information input by the user.

[0047] In one embodiment of the present invention, the physical sign parameter mapping model is constructed based on a deep neural network, and the model includes: an implicit body shape inference module, a gated network structure, and a sub-model coordination mechanism;

[0048] The implicit body shape inference module is used to extract the body mass index based on the user's non-measurement basic information, and implicitly divide the user into different body shape categories through the pre-trained clustering algorithm combined with distance inversion, and obtain the preliminary probability vector of the user belonging to each body shape category, where the body shape categories include: high fat density type, low muscle density type and standard balanced type; specifically, the body mass index calculation formula is: , where BMI stands for body mass index, W stands for weight, and H stands for height;

[0049] The gated network structure includes: input layer, hidden layer and output layer;

[0050] The sub-model coordination mechanism means that each body type category corresponds to an independent deep neural network sub-model. Each sub-model outputs a predicted net body size based on the user's non-measurement basic information. Each sub-model is trained using samples from the nearest cluster center. For example, the high fat density sub-model is specifically designed to optimize the prediction accuracy for people with a high BMI.

[0051] In one embodiment of the present invention, the K-Means clustering algorithm is combined with distance inversion to implicitly divide the body types of users. The specific steps include:

[0052] S301, setting the number of clusters to 3, and randomly selecting three center points as body shape categories;

[0053] S302, calculating the Euclidean distance between each user data point and the center point;

[0054] S303, assigning the user to the nearest cluster, updating the center point, and recalculating the mean of each cluster;

[0055] S304, repeating steps S302 to S303 until the position change range of the center point is smaller than a preset threshold;

[0056] S305, calculating the Euclidean distance between the user's non-body-size basic information and the three cluster center points, and using the distance inversion method to calculate the preliminary probability vector of the user belonging to each body type category. The calculation formula of the distance inversion method is: ,in, represents the probability that the user belongs to the jth cluster center, represents the Euclidean distance from the user to the jth cluster center, and j and k both represent the index of the cluster center.

[0057] In one embodiment of the present invention, the input layer of the gated network structure is used to receive the preliminary probability vector and body mass index of the user belonging to each body type category; the hidden layer includes 2 fully connected layers; the output layer is used to generate a weight vector containing the weights of each body type category through an activation function, and the sum of the weights of each body type category is 1, for example, the weight vector , it means that the weights of the user's high fat density type, low muscle density type and standard balanced type are 0.2, 0.1 and 0.7 respectively, and the user is closer to the standard balanced type.

[0058] In one embodiment of the present invention, the predicted net body size output by each sub-model is combined with the weight vector output by the gated network structure for weighted summation to obtain the predicted physical sign. The calculation formula of the predicted physical sign includes: ;

[0059] Among them, CBS represents the predicted physical sign, i represents the body type index, HFD, LMD and STD represent high fat density type, low muscle density type and standard balanced type, respectively. represents the weight of body type i, It represents the predicted value when the body size is i output by the sub-model coordination mechanism.

[0060] In one embodiment of the present invention, a sub-model collaboration mechanism is adopted to construct independent deep neural network sub-models for different body types, and weighted fusion is performed to calculate the net body size based on a gated network structure. Compared with the traditional single model method, this mechanism can dynamically adapt to users of different body types such as high fat density, low muscle density, and standard balanced types, ensuring that each user obtains a more accurate net body size prediction.

[0061] In one embodiment of the present invention, for the physical sign parameters input by the user, the present invention compares them with the predicted physical signs obtained by the physical sign parameter mapping model. If the absolute value of the deviation exceeds 10%, it is determined to be abnormal, and the physical sign parameters input by the user are corrected to the predicted physical signs and expressed by the net body size.

[0062] In one embodiment of the present invention, the fields of the dynamic addition rule library include: clothing category code, addition part code, style preference code, basic addition coefficient, BMI correction coefficient, age correction coefficient and gender applicability. Specifically, the clothing category code is used to indicate the category of clothing, for example, 001 indicates formal wear, 002 indicates casual wear, 003 indicates sportswear, 004 indicates outdoor wear, etc.; the addition part code is used to indicate the specific addition part, and the addition part includes: chest circumference, waist circumference, shoulder width, arm span, etc.; the style preference code is used to indicate the user's dressing style preference, and the style preference includes: slim, standard and loose; the basic addition coefficient indicates the basic addition ratio under the clothing category, addition part and style preference, BMI indicates the correction coefficient of BMI affecting the part, the age correction coefficient indicates the correction coefficient of age affecting the part, and the gender applicability indicates that the data is applicable to men, women or general; the calculation formula of the first finished product size is: ;

[0063] in, Indicates the first finished product size, Indicates the net size, It represents the basic addition coefficient obtained from the dynamic addition rule library according to the clothing category and style preference selected by the user. It represents the BMI correction factor, which is used to indicate the influence of BMI on the added part. BMI stands for body mass index. Indicates the preset standard body mass index, such as 22, Indicates the age correction factor, which is used to indicate the influence of age on the added part. A indicates age. Indicates the preset standard age, such as 30 years old.

[0064] In one embodiment of the present invention, when calculating the size of the first finished product, not only the attributes of the clothing category are taken into consideration, but also the size is dynamically adjusted in combination with the user's personalized style preference. By adjusting the addition coefficient, the clothing pattern is made more in line with the user's wearing habits. Compared with traditional static size recommendations, this method can flexibly adapt to the personalized needs of different users, reduce the discomfort caused by fixed addition rules, improve matching accuracy, and provide users with more accurate and personalized clothing recommendations and customization services.

[0065] In one embodiment of the present invention, the specific steps of S104 include:

[0066] S401, when it is determined based on the first preset determination rule that the first finished product size is abnormal, prompting the user to input the adjustment amount of the adjustment part. Specifically, the first preset determination rule includes: for the first finished product size, it is compared with the net body size item by item, and a threshold determination is performed according to the deviation value of each physical sign dimension. When it exceeds the preset threshold range of each item, it is determined to be abnormal. The present invention will issue a warning prompt to the user, mark the abnormal part, and guide the user to input the adjustment amount of the adjustment part;

[0067] S402, when the adjustment amount does not exceed the preset adjustment threshold, correcting other parts based on a first empirical formula, wherein the first empirical formula is: ,in, Indicates the adjustment amount of the part a that needs to be corrected, Indicates the adjustment amount of the adjustment part b input by the user, It represents the human body proportion coefficient, which is extracted from large-scale measurement data. a and b are the index of the adjustment part input by the user and the index of the part to be corrected respectively;

[0068] S403, when the adjustment amount exceeds the preset adjustment threshold, a nonlinear exponential formula is used for correction, wherein the nonlinear exponential formula is: ,in, Indicates the second finished product size, Indicates the maximum compensation limit, used to prevent the garment size from getting out of control. Represents the exponential convergence coefficient, which is used to adjust the nonlinear convergence speed. Indicates the adjustment threshold, for example 5 cm.

[0069] In one embodiment of the present invention, step S103 is used to dynamically adjust the net body size of the user as a whole according to the characteristics of the clothing category and the overall style preference selected by the user, and generate a preliminary first finished product size; step S104 is used to further use a nonlinear compensation algorithm based on the first finished product size, aiming at the user's personalized local or overall fine-tuning needs, and optimize the personalized adjustment parameters of the user to ensure that the size still conforms to the human body proportions after adjustment, and automatically correct the relevant parts through the first empirical formula to prevent the local adjustment from causing overall imbalance; when the adjustment exceeds the threshold, an exponential correction formula is used to avoid overcompensation and improve wearing comfort. Compared with the traditional linear adjustment method, the present invention can dynamically adapt to the user's adjustment needs, ensuring that the finished product size not only conforms to the personalized preferences, but also maintains the structural rationality and ergonomic coordination of the clothing.

[0070] In one embodiment of the present invention, the product shopping guide system is used to receive the second finished product size, and match the standard sizes of different brands on the market through a brand size normalization model to generate a cross-brand size recommendation result. The specific steps include:

[0071] S501, establishing a brand size normalization database, specifically, collecting size data of multiple brands and establishing a brand size conversion relationship table, the database stores clothing size data of different brands and different versions, and optimizes the conversion relationship between brand sizes in combination with large-scale user purchase feedback information;

[0072] S502, inputting the second finished product size into the product shopping guide system;

[0073] S503, based on the brand size normalization model, converting the size of the second finished product into the recommended size of each brand. Specifically, using a size conversion method based on multiple linear regression, the size of the second finished product is fitted and matched with the historical size data of each brand.

[0074] S504, recommending appropriate size options to the user based on the normalized mapping relationship between sizes of different brands. For example, the size of the second finished product is calculated as: waist circumference 82cm, shoulder width 45cm, chest circumference 96cm, arm span 170cm. The system may recommend: Brand A: size L, Brand B: size M, Brand C: size L.

[0075] In one embodiment of the present invention, the customization processing system converts the second finished product size into clothing pattern data, and completes the customized clothing production through automatic cutting equipment.

[0076] In one embodiment of the present invention, the customization processing system converts the second finished product size into clothing pattern data, and completes the customized clothing production through the automatic cutting device, and the specific steps include:

[0077] S601, establishing a clothing pattern database, specifically, pre-storing clothing templates of different patterns, including different pattern structures such as slim fit, loose fit, and standard fit, each pattern including basic structural parameters of key parts, and can be personalized according to the user's second finished product size;

[0078] S602, performing garment pattern conversion based on the second finished product size, specifically, inputting the second finished product size into a garment pattern conversion model, and converting it into a CAD garment pattern making file of a corresponding pattern;

[0079] S603, automatic cutting data is generated by CAD conversion software, and the automatic cutting device reads the cutting data to complete the cutting of the cloth;

[0080] S604, the cut fabric is transferred to the sewing process through the production system for garment production.

[0081] The embodiments of the present invention are described above, but the present invention is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, which are all within the protection of the present embodiment.

Claims

1. A clothing customization shopping guide method based on non-measured intelligent size matching, characterized in that: The following steps are involved: S101, obtaining non-body measurement basic information input by the user, including: height, weight, age and physical parameters, the physical parameters including: waist circumference, shoulder width, chest circumference and arm span; S102, based on the non-physical basic information of the user, the user's predicted physical signs are calculated through a preset physical sign parameter mapping model, and the physical sign parameters are corrected and missing complemented to obtain a net body size, wherein the physical sign parameter mapping model is generated through machine learning training, and the predicted physical signs, net body size and physical sign parameters are consistent in dimension; S103, calling a dynamic addition rule library according to clothing category attributes, dynamically adjusting the net body size in combination with the style preference selected by the user, and generating a first finished product size; S104, performing a rationality check on the generated first finished product size, and issuing an early warning prompt based on the first preset judgment rule. If an abnormality is detected, adjusting parameters according to the specific part input by the user, and correcting the first finished product size through a nonlinear compensation algorithm to obtain a second finished product size; S105, outputting the second finished product size to a target application end, wherein the target application end includes a product shopping guide system and a customized processing system.

2. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 1 is characterized in that: The physical sign parameter mapping model is built based on a deep neural network, which includes: an implicit body shape inference module, a gating network structure, and a sub-model coordination mechanism; The implicit body shape inference module is used to extract the body mass index based on the user's non-measurement basic information, and implicitly divide the user into different body shape categories through the pre-trained clustering algorithm combined with distance inversion, and obtain the preliminary probability vector of the user belonging to each body shape category, where the body shape categories include: high fat density type, low muscle density type and standard balanced type; the gated network structure includes: input layer, hidden layer and output layer; The sub-model collaboration mechanism means that each body shape category corresponds to an independent deep neural network sub-model, and each sub-model outputs a predicted net body size based on the user's non-body basic information.

3. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 2 is characterized in that: The K-Means clustering algorithm combined with distance inversion is used to implicitly divide users into body shape categories. The specific steps include: S301, setting the number of clusters to 3, and randomly selecting three center points as body shape categories; S302, calculating the Euclidean distance between each user data point and the center point; S303, assigning the user to the nearest cluster, updating the center point, and recalculating the mean of each cluster; S304, repeating steps S302 to S303 until the position change range of the center point is smaller than a preset threshold; S305, calculating the Euclidean distance between the user's non-body-size basic information and the three cluster center points, and using the distance inversion method to calculate the preliminary probability vector of the user belonging to each body type category, wherein the distance inversion method exponentially processes the Euclidean distance from the user to the cluster center, and combines it with a normalized calculation method to obtain the probability of the user belonging to each body type category.

4. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 2 is characterized in that: The input layer of the gated network structure is used to receive the preliminary probability vector and body mass index of the user belonging to each body type category; the hidden layer includes 2 fully connected layers; the output layer is used to generate a weight vector containing the weights of each body type category through an activation function, and the sum of the weights of each body type category is 1.

5. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 4 is characterized in that: The predicted net body size output by each sub-model is combined with the weight vector output by the gated network structure for weighted summation to obtain the predicted physical sign. The predicted values ​​of each dimension of the predicted physical sign are output by the sub-models of different body shape categories respectively, and the predicted physical sign is calculated based on the weight ratio of the user in each body shape category.

6. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 5, characterized in that: The fields of the dynamic addition rule library include: clothing category code, addition part code, style preference code, basic addition coefficient, BMI correction coefficient, age correction coefficient and gender applicability. The first finished product size is calculated based on the basic addition coefficient of the dynamic addition rule library, combined with the user's BMI correction coefficient, age correction coefficient and gender, and integrated with the net body size.

7. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 6, characterized in that: The specific steps of S104 include: S401, when the first finished product size is detected to be abnormal, prompting the user to input the adjustment amount of the adjustment part; S402, when the adjustment amount does not exceed the preset adjustment threshold, based on the human body proportion relationship, the first empirical formula is used to perform linkage correction on the relevant parts, wherein the first empirical formula is obtained by multiplying the human body proportion coefficient of other relevant parts by the adjustment amount of the adjustment part input by the user to obtain the adjustment amount of other parts that need to be corrected; S403, when the adjustment amount exceeds a preset adjustment threshold, a nonlinear exponential formula is used for correction, wherein the nonlinear exponential formula dynamically controls the adjustment amount by setting a maximum compensation limit and an exponential convergence coefficient.

8. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 1, characterized in that: The product shopping guide system matches market standard clothing sizes based on a brand size normalization model and generates cross-brand size recommendation results.

9. The clothing customization shopping guide method based on non-measured intelligent size matching according to claim 1, characterized in that: The customized processing system converts the second finished product size into clothing pattern data and completes the customized clothing production through automatic cutting equipment.

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