A group clothing intelligent size generation method and system based on multi-template dynamic adaptation

Abstract

The present application relates to the technical field of group clothing intelligent size generation based on multi-template dynamic adaptation, and discloses a group clothing intelligent size generation method and system based on multi-template dynamic adaptation. The height and weight of each member in the group are first standardized, then three template systems are set: 5cm interval template, 10cm interval template and comprehensive template, and mapping functions and thresholds are set respectively. Through these steps, the problems of non-standard data collection, fixed template parameters, non-specific threshold setting and non-precise size division in the prior art are solved, thereby realizing size dynamic adaptation and standardized output, significantly improving the size adaptation accuracy and production efficiency of group clothing, and improving the situation that clothing sizes are not unified and individual differences cannot be fully reflected, ultimately improving the wearing comfort and aesthetics of clothing.

Description

Technical Field

[0001] This invention relates to the field of intelligent size generation technology for group clothing based on multi-template dynamic adaptation, specifically to a method and system for intelligent size generation of group clothing based on multi-template dynamic adaptation. Background Technology

[0002] Currently, in the field of apparel production and design, the generation of sizes for group uniforms mainly relies on traditional size charts and fixed template designs. Existing technologies, most companies use a size classification method based on static data. The core of this method is to determine fixed height and weight ranges based on historical statistical data and generate a uniform size chart accordingly. While this method has some applicability in the production of single groups and a small number of clothing styles, in the production of group uniforms, due to significant differences in body shape among different members, it easily leads to problems such as low individual fit and insufficient comfort when wearing the clothing.

[0003] Traditional size generation methods often rely on a single template or a simple piecewise function to map height and weight data to predetermined size ranges. Generally, commonly used templates in existing technologies include 5cm or 10cm interval templates. The mapping formula typically involves subtracting a fixed value from the height, dividing by a constant, and then rounding down to determine the size level. While this method is simple to operate, its fixed constant and piecewise approach lack fine-tuning for individual differences, easily overlooking subtle deviations in body shape. Meanwhile, some technologies use a comprehensive template to simply linearly superimpose height and weight before mapping, but this method fails to fully reflect the individual influencing factors of height and weight in clothing fit and lacks a robust body shape correction mechanism. In existing technological systems, group clothing size generation mainly relies on empirical data and traditional statistical methods for grouping and classification. Data collection typically involves manual entry or measurement using basic measuring equipment, with data records stored in simple numerical formats and analyzed using fixed formats. This method suffers from high data entry error rates and inconsistent data formats, thus affecting the accuracy of size generation. For individual body shape deviations, existing technologies mostly rely on static indicators for judgment, such as comparing weight with a fixed standard weight, but fail to introduce more intuitive mathematical models for dynamic classification and fine-tuning of body shape. Furthermore, existing technologies typically use pre-defined fixed mapping functions and static threshold sets for template mapping and dynamic adaptation rule formulation. For example, some technologies map height data to different size levels by setting fixed 5cm or 10cm interval thresholds, but this method cannot dynamically respond to the actual distribution of body shape data among group members, nor can it meet the individual's need for fine-tuning body shape deviations. In most systems, template mapping functions and body shape correction methods use traditional mathematical methods or simple linear formulas, failing to fully consider the individual differences and diversity among group members during actual clothing wearing.

[0004] Therefore, this case aims to propose a method and system for intelligent size generation of group clothing based on multi-template dynamic adaptation. It employs entirely new designs in template design, data acquisition, mapping function construction, body shape correction, personalized real-time fine-tuning, and final data output, aiming to overcome the shortcomings of existing technologies in dynamic size adaptation, individual body shape classification, and data processing. This method, through explicit mathematical formulas and defined numerical parameters, ensures high accuracy and operability in size generation for each group member, while providing standardized and structured production guidance data for subsequent clothing production, thereby improving the production efficiency and wearing comfort of group clothing. Summary of the Invention

[0005] This invention provides a method for generating intelligent sizes for group clothing based on dynamic adaptation of multiple templates, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: a method for generating intelligent sizes for group clothing based on multi-template dynamic adaptation, comprising:

[0007] Record the height of any member in the group as The range of values ​​is The unit is centimeters;

[0008] Record the weight of any member in the group as The range of values ​​is The unit is kilogram;

[0009] Set template At the same time, set the template mapping function to 5cm interval. ;

[0010] Set template At the same time, set the template mapping function to 10cm interval. ;

[0011] Set template At the same time, set the comprehensive template mapping function as ;

[0012] For template Set the threshold set as follows: Each threshold corresponds to a 5cm interval level.

[0013] For template Set the threshold set as follows: Each threshold corresponds to a 10cm interval level.

[0014] Collect user data and then classify individuals;

[0015] Define template mapping and dynamic adaptation rules;

[0016] Users can select templates and make personalized, real-time size adjustments.

[0017] Integration and statistical generation of group size data;

[0018] Output the final data and production guidance documents.

[0019] Optionally, the setting of the 5cm interval template mapping function is as follows: Set the template mapping function to 10cm interval. and set the comprehensive template mapping function as Specifically, it includes:

[0020] Set the template mapping function to 5cm interval. , ;

[0021] in, To round down; the denominator 5 represents the fixed interval of height difference;

[0022] Set the template mapping function to 10cm interval. , ;

[0023] The denominator 10 represents the fixed interval of height difference;

[0024] Set the comprehensive template mapping function to , ;

[0025] The denominator 10 is used to unify the mapping scale.

[0026] Optionally, the process of collecting user data and then classifying individuals specifically involves:

[0027] Obtain the height of each group member;

[0028] Obtain the weight of each group member;

[0029] Record the data for each group member as follows: ;

[0030] in, For the first A unique identifier for each member; For the first The height of each member; For the first The weight of each member;

[0031] For the first Each member sets a baseline weight. Specifically:

[0032] ;

[0033] Wherein, denominator 2 is the standard constant for human body weight growth;

[0034] Set classification tags to reflect body shape deviations among group members, specifically:

[0035] ;

[0036] in, For the first Category tags for each member.

[0037] Optionally, the template mapping and dynamic adaptation rule formulation are specifically as follows:

[0038] Each group member determines the template based on their selection. Calculate the preliminary size index as follows:

[0039] ;

[0040] in, For the first Each member selects a specific template; , indicating that a template is selected ; , indicating that a template is selected ; , indicating that a template is selected ; For the first Preliminary size index for each member;

[0041] Set a body shape correction function to calculate the adjusted size index, specifically:

[0042] ;

[0043] in, For the first Adjusted size index for each member.

[0044] Optionally, the user can select a template and make personalized real-time size adjustments, specifically as follows:

[0045] In the user interface, each member confirms their selected template type. Size Index ;

[0046] Set the adjustment range in the size adjustment input box to ,in, Adjust the minimum value that can be entered in the size input box; Adjust the maximum value that can be entered in the size input box;

[0047] Get the first in the group Each member adjusts the value entered in the size adjustment input box. ;

[0048] Based on user confirmation and minor adjustments, the final size index is calculated as follows:

[0049] ;

[0050] in, For the first The final size index for each member.

[0051] Optionally, the integration and statistical generation of group size data specifically includes:

[0052] For the group For each member, construct a record, specifically:

[0053] ;

[0054] And store all records in the database;

[0055] Set the group size set as ;

[0056] in, The total number of group members;

[0057] The average size of group members is calculated as follows: ;

[0058] in, The arithmetic mean of the group members' sizes;

[0059] To obtain the smallest size among the group members' sizes, specifically:

[0060] ;in, The smallest size among the group members' sizes;

[0061] To retrieve the largest size among the group members' sizes, specifically:

[0062] ;in, The largest size among the group members' sizes;

[0063] For any unique size Set a calculation function to count the number of occurrences, specifically:

[0064] ;

[0065] in, To count the records that meet the conditions; Only one size available The count.

[0066] Optionally, the output final data and production guidance document are specifically as follows:

[0067] S61. Generate the final size list:

[0068] Construct a size details table, with each row containing the following information: ;

[0069] The output file uses a comma-separated value format, with the first line being the title. The rest will be assigned according to the group members' numbers. From 1 to Sort the records;

[0070] S62. Generate production quantity guidance information:

[0071] For each unique size Generate production instruction data:

[0072] ;

[0073] Record the recommended production quantity for each size;

[0074] S63. Compile comprehensive production guidance documents:

[0075] The document contains: size details table data; group data. , and All sizes Corresponding production quantity Template mapping function , and ;

[0076] S64. Data Output Interface Specification:

[0077] The output file format is specified as CSV, with the first row containing fixed field headers.

[0078] A system for implementing the intelligent size generation method for group clothing based on multi-template dynamic adaptation includes:

[0079] The template management module is used to set up templates and template mappings;

[0080] The rules engine module is used for the classification and labeling of height and weight data;

[0081] The dynamic interaction module is used for users to input fine-tuning values ​​and select template types and sizes.

[0082] The data export module is used to generate the final size chart and output production data.

[0083] The present invention has the following beneficial effects:

[0084] 1. By first recording and fixing the height and weight of any member of the group within a fixed range (height in centimeters, weight in kilograms), this solution addresses the issues of non-standard data collection and unclear data ranges in existing technologies. Next, by establishing a multi-template system, including 5cm interval templates, 10cm interval templates, and a comprehensive template, each template has a defined mapping function. The 5cm interval template mapping function maps height to a size index using a fixed formula, solving the problem of imprecise size classification in traditional methods. The 10cm interval template mapping function, with its fixed 10cm interval, is more suitable for clothing fitting for taller or shorter groups. The comprehensive template mapping function combines height and weight values ​​for mapping, unifying the mapping scale and addressing the limitation of a single dimension in fully reflecting individual body shape differences. By setting threshold sets for the templates, with the 5cm and 10cm interval threshold sets clearly corresponding to their respective levels, this solution eliminates the problem of inaccurate size fitting caused by unclear threshold settings and inflexible parameter adjustments in traditional technologies. Meanwhile, for the comprehensive template, the threshold is directly determined by the value in the formula, eliminating the need for a separate threshold set. This simplifies system design and ensures data consistency and standardization. In summary, through standardized data collection, explicit multi-template mapping function settings, and strict threshold set settings, this solution solves the problems of unreasonable clothing size generation and low individual matching caused by non-standard data, fixed template parameters, and inaccurate threshold settings in existing technologies. It significantly improves the accuracy and efficiency of group clothing size matching, while providing accurate and standardized data output for subsequent production stages. This effectively addresses the problems of inconsistent sizes and the inability to fully reflect individual differences in clothing production, thereby improving the comfort and aesthetics of the clothing.

[0085] 2. By acquiring the height and weight data of each group member and recording it as a standardized data record containing a unique identifier, height, and weight, this solution effectively solves the problems of inconsistent data collection standards and unclear data formats in existing technologies. First, the system acquires the height and weight of each member, ensuring that each data item has a fixed value range and unit, thus avoiding deviations caused by measurement errors or non-standard recording. Next, the system formats each member's data record into a record containing a unique identifier (identifying each member), height, and weight, giving the entire dataset good structure and traceability, solving the problem of subsequent processing difficulties caused by chaotic data recording methods in traditional systems. Based on this, this solution sets a baseline weight for each group member. This step constructs a standardized baseline weight using height data, providing a clear reference for subsequent individual classification. Through this calculation, the actual weight can be intuitively compared with the baseline weight, thereby determining the body shape deviation of each member. Subsequently, the system further sets classification tags to qualitatively classify the body shape of each member. The classification markers, based on a comparison between a member's actual weight and a calculated baseline weight, clearly identify whether a member is underweight, average, or overweight. This effectively solves the problem of inaccurate size generation caused by the lack of dynamic and clear classification standards in traditional methods. The introduction of classification markers allows the system to implement refined management based on individual differences, thereby improving the accuracy and personalization of size allocation. Overall, through four steps—data collection, standardized recording, baseline weight calculation, and classification marker setting—this solution addresses the problems of non-standard data, inconsistent records, lack of unified reference, and unclear individual body type judgment in existing technologies. This step not only ensures the accurate collection and reasonable recording of each member's data but also provides a reliable foundation for subsequent size generation and template mapping, ultimately achieving precise size classification and dynamic adaptation for group clothing, improving overall production and wearing effects.

[0086] 3. Through a series of steps involving template mapping and dynamic adaptation rules, this solution first requires each group member to select a predefined template type based on their own situation and then calculate a preliminary size index using the corresponding template mapping function. This step solves the problems of fixed templates and inflexible size index calculation in traditional size generation methods. Specifically, by calling the template mapping function, the member's height or a combination of height and weight is directly mapped to a preliminary size index, allowing each member to obtain a preliminary size value calculated based on a fixed formula. Since each template has clear mapping rules, such as differences in intervals between different templates, the system can perform more refined hierarchical mapping for different body types, thereby improving the accuracy of size calculation. Next, the solution sets up a body type correction function to dynamically adjust the preliminary size index. This function combines the body type classification labels obtained from the data collection stage with the preliminary size index, performs mathematical operations, and generates an adjusted size index. This process solves the problem that traditional techniques cannot fully reflect individual body type differences and dynamic deviations. Specifically, the body shape correction function can add or subtract from the initial size index based on the actual body shape deviation of the member, such as being underweight or overweight. This ensures that the final mapped size reflects both the member's physiological parameters and individual body shape characteristics, achieving precise quantification of the size index. Overall, through template mapping and dynamic adaptation rule formulation, the solution solves the problems of inaccurate individual size adaptation, lack of flexibility in size index calculation, and insufficient consideration of body shape deviations caused by fixed templates in traditional methods. By employing the steps of pre-selecting a template, calculating the initial size index using a mapping function, and then adjusting it using the body shape correction function, it ensures that each member's physiological data and body shape characteristics can be accurately and directly mapped to a unique integer size index. This method not only improves the accuracy of size generation but also makes the size calculation process operable and adaptive, thereby providing standardized and refined production guidance data for subsequent garment production and effectively improving the shortcomings of uneven size allocation and insufficient fit in traditional methods.

[0087] 4. Through a series of steps including template selection and personalized real-time size fine-tuning, this solution allows users to further confirm and adjust the automatically generated sizes during actual operation, thus solving the problem of incomplete matching between size calculation results and individual actual wearing needs in traditional size generation methods. First, in the user interface, each group member needs to confirm the size index pre-calculated by the system based on template mapping and body shape correction, and select the corresponding template type. This step ensures that each member can intuitively understand their preliminary size based on physiological data and perform preliminary verification, avoiding potential deviations that may occur from solely relying on automatic calculation results. Second, the system sets up a size adjustment input box in the interface and clearly defines the adjustment range, such as the minimum and maximum values ​​of the input range. This provides users with a safe and standardized fine-tuning space and avoids system output errors caused by users inputting values ​​outside the reasonable range. Next, the system obtains the fine-tuning values ​​input by each member in the group through the adjustment input box in real time, and adds these fine-tuning values ​​to the preliminary size index to calculate the final size index. This calculation process employs direct numerical addition, ensuring that all fine-tuning operations are based on clear and fixed values, thus avoiding errors caused by complex models or unclear weight settings in traditional techniques. Through this operation, users can directly influence the final size index based on their own wearing experience, making size generation both highly efficient through automation and retaining the flexibility and accuracy of personalized customization. In summary, by having the user confirm the template type and initial size in the user interface, set the numerical range of the size adjustment input box, obtain fine-tuning values ​​in real time, and accumulate the fine-tuning results with the automatically calculated size index, this solution successfully solves the problem that traditional systems cannot fully reflect individual needs and lack personalized customization capabilities. Ultimately, this step achieves a precise size generation process combining automatic calculation and real-time user fine-tuning, improving the accuracy of size data, enhancing user participation and the customized experience, and providing more scientific and standardized production guidance data for subsequent garment production, ensuring a significant improvement in the fit and wearing comfort of group garments.

[0088] 5. Through a series of steps including group size data integration and statistical generation, this solution first records detailed data for each member of the group. Each member's data record includes a unique identifier, height, weight, template selection, and the final generated size index. All data records are stored in a unified database, thus solving the problems of scattered data and inconsistent records in traditional methods. Next, the system sets up a group size set, aggregating the final size indices of all members, and calculating the average size, minimum size, and maximum size of the group members. This solves the problems of lacking overall data analysis and failing to reflect the balance of size distribution among group members in traditional methods. Specifically, calculating the average size can intuitively reflect the overall size level, while obtaining the minimum and maximum sizes effectively reveals the differences between individuals, thus providing an intuitive statistical basis for subsequent production. In addition, by setting a calculation function for any unique size and counting the frequency of each size, this solution solves the problems of inaccurate size statistics and unclear production quantity recommendations in traditional technologies. This calculation function counts records that meet the conditions, ensuring that the production quantity recommendation for each size is accurate and reliable. Through this series of data integration and statistical steps, the system not only improves the accuracy and standardization of data processing, but also provides precise size distribution data and production quantity guidance for the production process, thereby effectively reducing the waste of production resources and inventory risks caused by uneven size distribution. Overall, by constructing group data records, storing them in a database, building size sets, calculating average, minimum, and maximum sizes, and counting and statistically analyzing unique sizes, the system solves the problems of scattered data collection, inaccurate statistics, and insufficient production guidance data in existing technologies. This makes the allocation of group clothing sizes more scientific and reasonable, providing a rigorous and reliable basis for clothing production, and ultimately improving the comfort of wearing clothing and overall production efficiency.

[0089] 6. Through the series of steps including final data output and production guidance document generation, this solution achieves seamless information integration from data collection and analysis to production execution, solving the problems of scattered size data, inconsistent output formats, and insufficient production guidance information in traditional methods. First, step S61 constructs a detailed size table, recording each group member's data (including unique identifier, height, weight, template selection, final size index, etc.) in a strict order and outputting it as a comma-separated value (CSV) file. The first line has a fixed header, and subsequent lines are sorted by member number. This solves the problem of difficult subsequent processing caused by non-standard data formats or disordered recording order in existing technologies, ensuring accurate, standardized, and easily queryable and retrievalable data output. Subsequently, in step S62, the system calculates the frequency of each unique size and generates corresponding production instruction data based on the statistical results, specifying a recommended production quantity for each size. This step effectively solves the problems of unclear size production quantities and unscientific production arrangements in traditional production guidance. By automatically generating accurate production instruction data, it reduces manual statistical errors and makes the allocation of production resources more reasonable, which helps to improve production efficiency and reduce inventory backlog. In step S63, the system integrates all key information into a comprehensive production guidance document. The document includes detailed size tables, group data statistics (such as average size, smallest size, and largest size), suggested production quantities for each size, and the template mapping functions used. This document provides the production line with a comprehensive and standardized guidance document, enabling strict adherence to predetermined data during production and avoiding production deviations caused by incomplete information or inconsistent data, thus ensuring the stability of final product quality and wearing effect. Finally, step S64 clearly defines the interface specifications for output files, ensuring that all generated files are in CSV format with a fixed field header in the first row. This not only facilitates internal data integration but also makes subsequent interface integration with other production systems or information management platforms simple and efficient. Overall, through these steps, the solution achieves standardized data output, comprehensive production guidance information, and convenient interface integration, thereby greatly improving the scientific nature and execution efficiency of production guidance. Attached Figure Description

[0090] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0091] 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.

[0092] Example, refer to Figure 1 A method for generating intelligent sizes for group clothing based on multi-template dynamic adaptation, comprising:

[0093] Record the height of any member in the group as The range of values ​​is The unit is centimeters;

[0094] Record the weight of any member in the group as The range of values ​​is The unit is kilogram;

[0095] Set template At the same time, set the template mapping function to 5cm interval. ;

[0096] Set template At the same time, set the template mapping function to 10cm interval. ;

[0097] Set template At the same time, set the comprehensive template mapping function as ;

[0098] For template Set the threshold set as follows: Each threshold corresponds to a 5cm interval level.

[0099] For template Set the threshold set as follows: Each threshold corresponds to a 10cm interval level.

[0100] For template The threshold is directly determined by the value in the formula, without the need for a separate threshold set;

[0101] Collect user data and then classify individuals;

[0102] Define template mapping and dynamic adaptation rules;

[0103] Users can select templates and make personalized, real-time size adjustments.

[0104] Integration and statistical generation of group size data;

[0105] Output the final data and production guidance documents.

[0106] The setting of the 5cm interval template mapping function is as follows: Set the template mapping function to 10cm interval. and set the comprehensive template mapping function as Specifically, it includes:

[0107] Set the template mapping function to 5cm interval. , ;

[0108] in, To round down; the denominator 5 represents the fixed interval of height difference;

[0109] Set the template mapping function to 10cm interval. , ;

[0110] The denominator 10 represents the fixed interval of height difference;

[0111] Set the comprehensive template mapping function to , ;

[0112] The denominator 10 is used to unify the mapping scale.

[0113] By first recording and fixing the height and weight of any member of the group within a fixed range (height in centimeters, weight in kilograms), this solution addresses the issues of non-standard data collection and unclear data ranges in existing technologies. Next, by establishing a multi-template system, including 5cm interval templates, 10cm interval templates, and a comprehensive template, each template has a defined mapping function. The 5cm interval template mapping function maps height to a size index using a fixed formula, solving the problem of imprecise size classification in traditional methods. The 10cm interval template mapping function, with its fixed 10cm interval, is more suitable for clothing fitting for taller or shorter groups. The comprehensive template mapping function combines height and weight values ​​for mapping, unifying the mapping scale and addressing the limitation of a single dimension in fully reflecting individual body shape differences. By setting threshold sets for the templates, with the 5cm and 10cm interval threshold sets clearly corresponding to their respective levels, this solution eliminates the problem of inaccurate size fitting caused by unclear threshold settings and inflexible parameter adjustments in traditional technologies. Meanwhile, for the comprehensive template, the threshold is directly determined by the value in the formula, eliminating the need for a separate threshold set. This simplifies system design and ensures data consistency and standardization. In summary, through standardized data collection, explicit multi-template mapping function settings, and strict threshold set settings, this solution solves the problems of unreasonable clothing size generation and low individual matching caused by non-standard data, fixed template parameters, and inaccurate threshold settings in existing technologies. It significantly improves the accuracy and efficiency of group clothing size matching, while providing accurate and standardized data output for subsequent production stages. This effectively addresses the problems of inconsistent sizes and the inability to fully reflect individual differences in clothing production, thereby improving the comfort and aesthetics of the clothing.

[0114] The process of collecting user data and then classifying individuals involves the following steps:

[0115] Obtain the height of each group member;

[0116] Obtain the weight of each group member;

[0117] Record the data for each group member as follows: ;

[0118] in, For the first A unique identifier for each member; For the first The height of each member; For the first The weight of each member;

[0119] For the first Each member sets a baseline weight. Specifically:

[0120] ;

[0121] Among them, denominator 2 is the standard constant for human body weight growth, which has been experimentally proven to be applicable to group standards and is existing technology;

[0122] Set classification tags to reflect body shape deviations among group members, specifically:

[0123] ;

[0124] in, For the first Category tags for each member.

[0125] By acquiring the height and weight data of each group member and recording it as a standardized data record containing a unique identifier, height, and weight, this solution effectively solves the problems of inconsistent data collection standards and unclear data formats in existing technologies. First, the system acquires the height and weight of each member, ensuring that each data item has a fixed value range and unit, thus avoiding deviations caused by measurement errors or non-standard recording. Next, the system formats each member's data record into a record containing a unique identifier (identifying each member), height, and weight, giving the entire dataset good structure and traceability, solving the problem of subsequent processing difficulties caused by chaotic data recording methods in traditional systems. Based on this, this solution sets a baseline weight for each group member. This step constructs a standardized baseline weight using height data, providing a clear reference for subsequent individual classification. Through this calculation, the actual weight can be intuitively compared with the baseline weight to determine the body shape deviation of each member. Subsequently, the system further sets classification labels to qualitatively classify the body shape of each member. The classification labels, based on the comparison results between the member's actual weight and the calculated baseline weight, clearly identify whether the member is underweight, average, or overweight. This effectively solves the problem of inaccurate size generation caused by the lack of dynamic and clear classification standards in traditional methods. The introduction of classification tags enables the system to implement refined management based on individual differences, thereby improving the accuracy and personalization of size allocation. Overall, through four steps—data collection, standardized recording, baseline weight calculation, and classification tag setting—this solution addresses the problems of non-standard data, inconsistent records, lack of unified reference, and unclear individual body type judgment in existing technologies. This step not only ensures the accurate collection and reasonable recording of data for each member but also provides a reliable foundation for subsequent size generation and template mapping, ultimately achieving precise size division and dynamic adaptation for group clothing, improving overall production and wearing performance.

[0126] The template mapping and dynamic adaptation rule formulation are as follows:

[0127] Each group member determines the template based on their selection. Calculate the preliminary size index as follows:

[0128] ;

[0129] in, For the first Each member selects a specific template; , indicating that a template is selected ; , indicating that a template is selected ; , indicating that a template is selected ; For the first Preliminary size index for each member;

[0130] Set a body shape correction function to calculate the adjusted size index, specifically:

[0131] ;

[0132] in, For the first The adjusted size index for each member; this operation ensures that each member's physiological data and body shape deviation are accurately mapped to a unique integer size index.

[0133] Through a series of steps involving template mapping and dynamic adaptation rules, this solution first requires each group member to select a predefined template type based on their own situation and then calculate a preliminary size index using the corresponding template mapping function. This step solves the problems of fixed templates and inflexible size index calculations in traditional size generation methods. Specifically, by calling the template mapping function, the member's height or a combination of height and weight is directly mapped to a preliminary size index, allowing each member to obtain a preliminary size value calculated based on a fixed formula. Since each template has clear mapping rules, such as differences in intervals between different templates, the system can perform more refined hierarchical mapping for different body types, thereby improving the accuracy of size calculation. Next, the solution sets up a body type correction function to dynamically adjust the preliminary size index. This function combines the body type classification labels obtained from the data collection phase with the preliminary size index, performs mathematical operations, and generates an adjusted size index. This process solves the problem that traditional techniques cannot fully reflect individual body type differences and dynamic deviations. Specifically, the body shape correction function can add or subtract from the initial size index based on the actual body shape deviation of the member, such as being underweight or overweight. This ensures that the final mapped size reflects both the member's physiological parameters and individual body shape characteristics, achieving precise quantification of the size index. Overall, through template mapping and dynamic adaptation rule formulation, the solution solves the problems of inaccurate individual size adaptation, lack of flexibility in size index calculation, and insufficient consideration of body shape deviations caused by fixed templates in traditional methods. By employing the steps of pre-selecting a template, calculating the initial size index using a mapping function, and then adjusting it using the body shape correction function, it ensures that each member's physiological data and body shape characteristics can be accurately and directly mapped to a unique integer size index. This method not only improves the accuracy of size generation but also makes the size calculation process operable and adaptive, thereby providing standardized and refined production guidance data for subsequent garment production and effectively improving the shortcomings of uneven size allocation and insufficient fit in traditional methods.

[0134] The user's template selection and personalized real-time size adjustment are as follows:

[0135] In the user interface, each member confirms their selected template type. Size Index ;

[0136] Set the adjustment range in the size adjustment input box to ,in, Adjust the minimum value that can be entered in the size input box; Adjust the maximum value that can be entered in the size input box; for example ;

[0137] Get the first in the group Each member adjusts the value entered in the size adjustment input box. ;

[0138] Based on user confirmation and minor adjustments, the final size index is calculated as follows:

[0139] ;

[0140] in, For the first The final size index for each member.

[0141] Through a series of steps including template selection and personalized real-time size fine-tuning, this solution allows users to further confirm and adjust automatically generated sizes during actual operation, thus solving the problem of incomplete matching between size calculation results and individual actual wearing needs in traditional size generation methods. First, in the user interface, each group member needs to confirm the size index pre-calculated by the system based on template mapping and body shape correction, and select the corresponding template type. This step ensures that each member can intuitively understand their preliminary size based on physiological data and perform initial verification, avoiding potential deviations from relying solely on automatic calculation results. Second, the system includes a size adjustment input box in the interface, clearly defining the adjustment range, such as the minimum and maximum values. This provides users with a safe and standardized fine-tuning space and prevents system output errors caused by users inputting values ​​outside the reasonable range. Next, the system acquires the fine-tuning values ​​input by each member in the group through the adjustment input box in real time, and adds these fine-tuning values ​​to the preliminary size index to calculate the final size index. This calculation process employs direct numerical addition, ensuring that all fine-tuning operations are based on clear and fixed values, thus avoiding errors caused by complex models or unclear weight settings in traditional techniques. Through this operation, users can directly influence the final size index based on their own wearing experience, making size generation both highly efficient through automation and retaining the flexibility and accuracy of personalized customization. In summary, by having the user confirm the template type and initial size in the user interface, set the numerical range of the size adjustment input box, obtain fine-tuning values ​​in real time, and accumulate the fine-tuning results with the automatically calculated size index, this solution successfully solves the problem that traditional systems cannot fully reflect individual needs and lack personalized customization capabilities. Ultimately, this step achieves a precise size generation process combining automatic calculation and real-time user fine-tuning, improving the accuracy of size data, enhancing user participation and the customized experience, and providing more scientific and standardized production guidance data for subsequent garment production, ensuring a significant improvement in the fit and wearing comfort of group garments.

[0142] The integration and statistical generation of group size data specifically includes:

[0143] For the group For each member, construct a record, specifically:

[0144] ;

[0145] And store all records in the database;

[0146] Set the group size set as ;

[0147] in, The total number of group members;

[0148] The average size of group members is calculated as follows: ;

[0149] in, The arithmetic mean of the group members' sizes;

[0150] To obtain the smallest size among the group members' sizes, specifically:

[0151] ;in, The smallest size among the group members' sizes;

[0152] To retrieve the largest size among the group members' sizes, specifically:

[0153] ;in, The largest size among the group members' sizes;

[0154] For any unique size Set a calculation function to count the number of occurrences, specifically:

[0155] ;

[0156] in, To count the records that meet the conditions; Only one size available The count.

[0157] Through a series of steps including group size data integration and statistical generation, this solution first records detailed data for each member of the group. Each member's data record includes a unique identifier, height, weight, template selection, and the final generated size index. All data records are stored in a unified database, thus solving the problems of scattered data and inconsistent records in traditional methods. Next, the system sets up a group size set, aggregating the final size indices of all members and calculating the group's average size, minimum size, and maximum size. This addresses the lack of overall data analysis and inability to reflect the balanced size distribution of group members in traditional methods. Specifically, calculating the average size intuitively reflects the overall size level, while obtaining the minimum and maximum sizes effectively reveals individual differences, providing a clear statistical basis for subsequent production. Furthermore, by setting a calculation function for any unique size and counting the frequency of each size, this solution solves the problems of inaccurate size statistics and unclear production quantity recommendations in traditional technologies. This calculation function counts records that meet the conditions, ensuring that the production quantity recommendations for each size are accurate and reliable. Through this series of data integration and statistical steps, the system not only improves the accuracy and standardization of data processing, but also provides precise size distribution data and production quantity guidance for the production process, thereby effectively reducing the waste of production resources and inventory risks caused by uneven size distribution. Overall, by constructing group data records, storing them in a database, building size sets, calculating average, minimum, and maximum sizes, and counting and statistically analyzing unique sizes, the system solves the problems of scattered data collection, inaccurate statistics, and insufficient production guidance data in existing technologies. This makes the allocation of group clothing sizes more scientific and reasonable, providing a rigorous and reliable basis for clothing production, and ultimately improving the comfort of wearing clothing and overall production efficiency.

[0158] The final output data and production guidance documents are specifically as follows:

[0159] S61. Generate the final size list:

[0160] Construct a size details table, with each row containing the following information: ;

[0161] The output file uses a comma-separated value format, with the first line being the title. The rest will be assigned according to the group members' numbers. From 1 to Sort the records;

[0162] S62. Generate production quantity guidance information:

[0163] For each unique size Generate production instruction data:

[0164] ;

[0165] Record the recommended production quantity for each size;

[0166] S63. Compile comprehensive production guidance documents:

[0167] The document contains: size details table data; group data. , and All sizes Corresponding production quantity Template mapping function , and ;

[0168] S64. Data Output Interface Specification:

[0169] The output file format is specified as CSV, with the first row containing fixed field headers.

[0170] Through a series of steps including final data output and production guidance document generation, this solution achieves seamless information integration from data collection and analysis to production execution, solving the problems of scattered size data, inconsistent output formats, and insufficient production guidance information in traditional methods. First, step S61 constructs a detailed size table, recording each group member's data (including unique identifier, height, weight, template selection, final size index, etc.) in a strict order and outputting it as a comma-separated values ​​(CSV) file. The first line has a fixed header, and subsequent lines are sorted by member number. This solves the problem of difficult subsequent processing caused by non-standard data formats or disordered recording order in existing technologies, ensuring accurate, standardized, and easily searchable and retrieval data output. Subsequently, in step S62, the system calculates the frequency of each unique size and generates corresponding production instruction data based on the statistical results, specifying a recommended production quantity for each size. This step effectively solves the problems of unclear size production quantities and unscientific production arrangements in traditional production guidance. By automatically generating accurate production instruction data, it reduces manual statistical errors and makes the allocation of production resources more reasonable, which helps to improve production efficiency and reduce inventory backlog. In step S63, the system integrates all key information into a comprehensive production guidance document. The document includes detailed size tables, group data statistics (such as average size, smallest size, and largest size), suggested production quantities for each size, and the template mapping functions used. This document provides the production line with a comprehensive and standardized guidance document, enabling strict adherence to predetermined data during production and avoiding production deviations caused by incomplete information or inconsistent data, thus ensuring the stability of final product quality and wearing effect. Finally, step S64 clearly defines the interface specifications for output files, ensuring that all generated files are in CSV format with a fixed field header in the first row. This not only facilitates internal data integration but also makes subsequent interface integration with other production systems or information management platforms simple and efficient. Overall, through these steps, the solution achieves standardized data output, comprehensive production guidance information, and convenient interface integration, thereby greatly improving the scientific nature and execution efficiency of production guidance.

[0171] This embodiment also provides a system for intelligent size generation of group clothing based on multi-template dynamic adaptation, including:

[0172] The template management module is used to set up templates and template mappings;

[0173] The rules engine module is used for the classification and labeling of height and weight data;

[0174] The dynamic interaction module is used for users to input fine-tuning values ​​and select template types and sizes.

[0175] The data export module is used to generate the final size chart and output production data.

[0176] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0177] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent size generation of group clothing based on multi-template dynamic adaptation, characterized in that, include: Record the height of any member in the group as The range of values ​​is The unit is centimeters; Record the weight of any member in the group as The range of values ​​is The unit is kilogram; Set template At the same time, set the template mapping function to 5cm interval. ; Set template At the same time, set the template mapping function to 10cm interval. ; Set template At the same time, set the comprehensive template mapping function as ; For template Set the threshold set as follows: Each threshold corresponds to a 5cm interval level. For template Set the threshold set as follows: Each threshold corresponds to a 10cm interval level. Collect user data and then classify individuals; The process of collecting user data and then classifying individuals involves the following steps: Obtain the height of each group member; Obtain the weight of each group member; Record the data for each group member as follows: ; in, For the first A unique identifier for each member; For the first The height of each member; For the first The weight of each member; For the first Each member sets a baseline weight. Specifically: ； Wherein, denominator 2 is the standard constant for human body weight growth; Set classification tags to reflect body shape deviations among group members, specifically: ； in, For the first Classification tags for each member; Define template mapping and dynamic adaptation rules; The template mapping and dynamic adaptation rule formulation are as follows: Each group member determines the template based on their selection. Calculate the preliminary size index as follows: ； in, For the first Each member selects a specific template; , indicating that a template is selected ; , indicating that a template is selected ; , indicating that a template is selected ; For the first Preliminary size index for each member; Set a body shape correction function to calculate the adjusted size index, specifically: ； in, For the first Adjusted size index for each member; Users can select templates and make personalized, real-time size adjustments. Integration and statistical generation of group size data; Output the final data and production guidance documents.

2. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 1, characterized in that, The setting of the 5cm interval template mapping function is as follows: Set the template mapping function to 10cm interval. and set the comprehensive template mapping function as Specifically, it includes: Set the template mapping function to 5cm interval. , ; in, To round down; the denominator 5 represents the fixed interval of height difference; Set the template mapping function to 10cm interval. , ; The denominator 10 represents the fixed interval of height difference; Set the comprehensive template mapping function to , ; The denominator 10 is used to unify the mapping scale.

3. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 2, characterized in that, The user's template selection and personalized real-time size adjustment are as follows: In the user interface, each member confirms their selected template type. Size Index ; Set the adjustment range in the size adjustment input box to ,in, Adjust the minimum value that can be entered in the size input box; Adjust the maximum value that can be entered in the size input box; Get the first in the group Each member adjusts the value entered in the size adjustment input box. ; Based on user confirmation and minor adjustments, the final size index is calculated as follows: ； in, For the first The final size index for each member.

4. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 3, characterized in that, The integration and statistical generation of group size data specifically includes: For the group For each member, construct a record, specifically: ； And store all records in the database; Set the group size set as ; in, The total number of group members; The average size of group members is calculated as follows: ; in, The arithmetic mean of the group members' sizes; To obtain the smallest size among the group members' sizes, specifically: ;in, The smallest size among the group members' sizes; To retrieve the largest size among the group members' sizes, specifically: ;in, The largest size among the group members' sizes; For any unique size Set a calculation function to count the number of occurrences, specifically: ； in, To count the records that meet the conditions; Only one size available The count.

5. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 4, characterized in that, The final output data and production guidance documents are specifically as follows: S61. Generate the final size list: Construct a size details table, with each row containing the following information: ; The output file uses a comma-separated value format, with the first line being the title. The rest will be assigned according to the group members' numbers. From 1 to Sort the records; S62. Generate production quantity guidance information: For each unique size Generate production instruction data: ； Record the recommended production quantity for each size; S63. Compile comprehensive production guidance documents: The document contains: size details table data; group data. , and All sizes Corresponding production quantity Template mapping function , and ; S64. Data Output Interface Specification: The output file format is specified as CSV, with the first row containing fixed field headers.

6. A system employing the intelligent size generation method for group clothing based on multi-template dynamic adaptation as described in claim 5, characterized in that, include: The template management module is used to set up templates and template mappings; The rules engine module is used for the classification and labeling of height and weight data; The dynamic interaction module is used for users to input fine-tuning values ​​and select template types and sizes. The data export module is used to generate the final size chart and output production data.

Images