Garment customization design method and system based on body shape data analysis

Abstract

This invention discloses a method and system for customized clothing design based on body shape data analysis, belonging to the field of clothing design technology. The method includes the following steps: receiving body size measurement data from the user to be customized; selecting active reference groups from multiple pre-stored body shape reference benchmarks; combining all mixed positioning point coordinates to generate a preliminary outline of a mixed pattern; constructing a unified adjustment model and processing the unified adjustment model through an optimization algorithm; synchronously updating the coordinates of all mixed positioning points of the preliminary outline of the mixed pattern, and outputting customized clothing pattern data. This invention solves the problems in existing technologies, such as structural conflicts caused by forced classification of a single master pattern, chain contradictions in adjustment logic, and repeated oscillations and eventual collapse during iterative solutions, by weighted fusion of multiple body shape reference benchmarks and overall optimization of the unified adjustment model.

Description

Technical Field

[0001] This invention relates to the field of clothing design technology, and in particular to a method and system for customized clothing design based on body shape data analysis. Background Technology

[0002] With the increasing popularity of small-batch, multi-variety personalized clothing customization, automatically generating garment cutting patterns that conform to individual body shapes directly from end-user anthropometric data has become a crucial link connecting anthropometric data collection with subsequent automated cutting and production. Existing automatic pattern generation solutions typically pre-classify body shapes into several fixed categories based on large-scale anthropometric data statistics and prepare a set of standard basic patterns for each category. During operation, the acquired user's body size data is compared with the benchmarks of each category, forcibly assigning the user to the most similar category. Then, the corresponding basic pattern for that category is retrieved, and based on the deviation between the user's size and the standard size of that category, local modifications are made through a series of pre-set adjustment rules. Finally, after repeated verification and iteration, the customized pattern is obtained.

[0003] In actual engineering execution, the above-mentioned method of assigning a single category and adjusting according to rules exposed several cascading technical problems. When the dimensions of different parts of a user belong to different body shape characteristics, forcibly classifying them into a single category causes conflicts in the basic pattern structure. At the same time, during adjustment, the stitching relationship reverses and damages the dimensions of another part, causing differences in the stitching edge length. Furthermore, it causes repeated oscillations during iterative verification, making it impossible to converge to a feasible solution and causing the process to collapse. Summary of the Invention

[0004] This application provides a clothing customization design method and system based on body shape data analysis, which solves the problems in the prior art such as structural conflicts caused by forced classification of a single master pattern, chain contradictions in adjustment logic, and repeated oscillations in iterative solution that fail to converge to a feasible solution. It achieves stable convergence of pattern generation and global coordination of pattern structure in fuzzy regions with mixed body shape features.

[0005] This application provides a method for customized clothing design based on body shape data analysis, including: receiving body size measurement data of the user to be customized; calculating the degree of closeness between the body size measurement data and multiple pre-stored body shape reference benchmarks, generating a weight list, and selecting active reference groups from the pre-stored multiple body shape reference benchmarks; obtaining the coordinates of positioning points on the pattern outline corresponding to each body shape reference benchmark in the active reference group, generating mixed positioning point coordinates, and combining all mixed positioning point coordinates to generate a preliminary mixed pattern outline; constructing a unified adjustment model for the preliminary mixed pattern outline, and processing the unified adjustment model through an optimization algorithm; synchronously updating all mixed positioning point coordinates of the preliminary mixed pattern outline until the requirements of the unified adjustment model are met, and outputting customized clothing pattern data.

[0006] Furthermore, the steps for calculating the degree of closeness between body size measurement data and multiple pre-stored body shape reference benchmarks include: calculating the difference between the measurement value of each body part and the value of each standard body part, and converting the difference value into an initial degree of closeness value; then summarizing the initial degree of closeness values ​​corresponding to all measured body parts under the same body shape reference benchmark to generate a comprehensive degree of closeness value for that body shape reference benchmark; iterating through all pre-stored body shape reference benchmarks one by one to obtain a comprehensive degree of closeness value corresponding to each body shape reference benchmark independently; and organizing all comprehensive degree of closeness values ​​as the degree of closeness value.

[0007] Furthermore, the steps of generating a weight list and selecting active reference groups from multiple pre-stored body shape reference benchmarks include: obtaining the similarity values ​​corresponding to each body shape reference benchmark, and performing overall normalization and conversion on all similarity values; using the converted similarity values ​​as weight values, arranging and combining all weight values ​​according to the binding relationship of body shape reference benchmarks to generate a weight list; filtering out weight values ​​greater than the pre-set retention threshold value; extracting the body shape reference benchmarks corresponding to these filtered weight values, and combining these extracted body shape reference benchmarks together to establish and output an active reference group record set.

[0008] Furthermore, the steps for obtaining the coordinates of the positioning points on the pattern outlines corresponding to each body type reference benchmark in the active reference group include: locating each body type reference benchmark record included in the active reference group, retrieving a complete set of basic garment pattern outline data pre-bound to each body type reference benchmark; identifying all line positioning points in the basic garment pattern outline data, and reading the horizontal and vertical coordinate values ​​of each line positioning point; reading different basic garment pattern outline data, and identifying the unified name number commonly bound to line positioning points in the same structural semantic position; for a specific local position on the new pattern to be generated, extracting the horizontal and vertical coordinate values ​​of positioning points with the same unified name number; and centrally grouping and classifying all extracted coordinate values ​​to establish a set of coordinate data to be mixed.

[0009] Furthermore, the steps for generating mixed positioning point coordinates include: extracting coordinate values ​​of the same structural position from the dataset of coordinates to be mixed; searching for weight values ​​from the generated weight list according to the unified name number, and calculating the horizontal and vertical coordinate values ​​after multiplication and weighting; merging and accumulating all weighted horizontal coordinate values ​​corresponding to the same structural position to obtain a new mixed horizontal coordinate value, and similarly merging and accumulating all weighted vertical coordinate values ​​corresponding to the same structural position to obtain a new mixed vertical coordinate value; combining the new mixed horizontal coordinate value and the new mixed vertical coordinate value to form the mixed positioning point coordinates of the specific structural position, and obtaining all mixed positioning point coordinates for all structural positions on the garment pattern.

[0010] Furthermore, the steps for constructing a unified adjustment model for the preliminary outline of the mixed pattern include: extracting the coordinates of all mixed positioning points of the preliminary outline of the mixed pattern, calculating the estimated size values ​​based on the mixed positioning point coordinates; establishing size matching constraint calculation rules, calculating the specific change value of the curve formed by the coordinates of the sequentially connected mixed positioning points; establishing smoothness constraint calculation rules, identifying the edge lines of two garment pieces that need to be spliced ​​together, and calculating the total path length; and establishing edge stitching matching constraint calculation rules, configuring different weight parameter values ​​for each constraint calculation rule to form a unified adjustment model.

[0011] Furthermore, the steps for establishing the size fit constraint calculation rules include: extracting the estimated size values ​​one by one, and simultaneously retrieving the target body measurement values ​​to obtain the basic size deviation data values; performing a numerical square transformation calculation on the basic size deviation data values ​​to obtain the squared deviation data values, and then generating the total deviation index value; and writing the judgment logic and judgment program segment of the total deviation index value as the target evaluation code segment into the internal module of the size fit constraint calculation rules.

[0012] Furthermore, the steps for establishing smoothness constraint calculation rules include: finding line pairing combinations in the internal data record of the preliminary outline of the mixed pattern; extracting the coordinates of all mixed positioning points contained in the first edge of the line pairing combination to obtain the total length data of the first edge path; extracting the coordinates of all mixed positioning points contained in the second edge of the line pairing combination to obtain the total length data of the second edge path; subtracting the total length data of the first edge path from the total length data of the second edge path to extract the actual seam length difference data; establishing a penalty feedback mechanism logic segment, whereby when the seam length difference data is greater than the set negligible tolerance range, an independent code block for calculating edge seam matching constraints is constructed.

[0013] Furthermore, the steps for establishing edge stitching matching constraint calculation rules include: inputting the coordinates of all mixed positioning points contained in the preliminary outline of the mixed pattern into the unified adjustment model, and calculating the total value of the overall adjustment feedback; finding the guiding value of the coordinate movement direction, modifying the displacement position of all mixed positioning point coordinates in the same calculation batch, and generating a candidate coordinate data set; substituting the candidate coordinate data set back into the unified adjustment model, and recalculating the total value of the overall adjustment feedback.

[0014] This application provides a clothing customization design system based on body shape data analysis, used to implement a clothing customization design method based on body shape data analysis, including: a data receiving module, a closeness calculation module, a preliminary outline generation module, a unified adjustment model construction module, and a data output module; wherein, the data receiving module is used to receive the body size measurement data of the user to be customized; the closeness calculation module is used to calculate the closeness value between the body size measurement data and multiple pre-stored body shape reference benchmarks, generate a weight list, and filter out the active reference group from the multiple pre-stored body shape reference benchmarks; the preliminary outline generation module is used to obtain the coordinates of the positioning points on the paper outline corresponding to each body shape reference benchmark in the active reference group, generate mixed positioning point coordinates, and combine all mixed positioning point coordinates to generate a mixed paper pattern preliminary outline; the unified adjustment model construction module is used to construct a unified adjustment model for the mixed paper pattern preliminary outline, and process the unified adjustment model through an optimization algorithm; the data output module is used to synchronously update all mixed positioning point coordinates of the mixed paper pattern preliminary outline until the requirements of the unified adjustment model are met, and output the customized clothing paper pattern data.

[0015] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: In the process of generating clothing patterns based on body size data, the degree of closeness between body size and multiple body shape reference benchmarks is calculated and a weight list is formed. Then, active reference groups are selected, and the coordinates of pattern positioning points under different reference benchmarks are weighted and mixed to generate a preliminary outline of the mixed pattern. This allows the initial pattern to flexibly adapt to different parts of the user's body that are close to different body shape characteristics, avoiding internal structural conflicts caused by forcibly selecting a single benchmark and reducing potential contradictions in subsequent adjustments.

[0016] Furthermore, after obtaining the preliminary outline of the mixed paper pattern, a unified adjustment model is constructed that simultaneously covers the requirements of size matching, outline smoothness, and seam edge length matching. This allows for the overall coordination of the size of each part and the mutual seam edge during the adjustment process, effectively avoiding geometric contradictions caused by the conflicting adjustment directions between different parts due to individual local modifications, and ensuring that the seam edge lengths of each part of the paper pattern are consistent and the outline is smooth.

[0017] Furthermore, when processing the unified adjustment model using the optimization algorithm, all paper pattern positioning points are treated as a whole candidate data set. In each iteration, the coordinates of all positioning points are updated synchronously according to the overall adjustment decrease direction, and this process is repeated until the adjustment stops changing. This allows the entire paper pattern adjustment process to converge stably to a solution that meets all requirements in one go, avoiding repeated oscillations and process crashes that occur in conventional iterative correction of each part, and ensuring the stable and reliable output of customized paper pattern data. Attached Figure Description

[0018] Figure 1 A flowchart of a clothing customization design method based on body shape data analysis provided in this application embodiment; Figure 2 A schematic diagram of the structure of the clothing customization design system based on body shape data analysis provided in this application embodiment. Detailed Implementation

[0019] This application provides a clothing customization design method and system based on body shape data analysis, which solves the problems in the prior art where forced classification of a single master pattern leads to structural conflicts, chain contradictions in adjustment logic, and repeated oscillations in iterative solutions that fail to converge to a feasible solution. By calculating the closeness of body size with multiple body shape reference benchmarks and weightedly fusing them to generate a preliminary outline of a mixed pattern, a unified adjustment model that takes into account size fit, smoothness, and seam matching is constructed and an overall optimization solution is obtained. This achieves stable convergence of pattern generation and global coordination of pattern structure in the fuzzy region where multiple body shape features are mixed.

[0020] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0021] like Figure 1The diagram shows a flowchart of a clothing customization design method based on body shape data analysis provided in this application embodiment. This method is applied to a clothing customization design system based on body shape data analysis and includes the following steps: receiving body size measurement data from the user to be customized; calculating the degree of closeness between the body size measurement data and multiple pre-stored body shape reference benchmarks, generating a weight list containing multiple degree of closeness values, and selecting active reference groups from the pre-stored multiple body shape reference benchmarks; obtaining the coordinates of positioning points on the pattern outline corresponding to each body shape reference benchmark in the active reference group, performing weighted mixed calculation on the obtained positioning point coordinates using the weight list, generating mixed positioning point coordinates, and combining all mixed positioning point coordinates to generate a preliminary mixed pattern outline; constructing a unified adjustment model for the preliminary mixed pattern outline that simultaneously includes size matching requirements, smoothness requirements, and edge stitching matching requirements, and processing the unified adjustment model through an optimization algorithm; synchronously updating all mixed positioning point coordinates of the preliminary mixed pattern outline until the requirements of the unified adjustment model are met, and outputting customized clothing pattern data.

[0022] In this embodiment, a clothing customization design method based on body shape data analysis is provided. When facing a customer, it does not rigidly assume that the customer is only fat or thin, but considers the customer to be a mixture of multiple standard body shapes.

[0023] This method first receives the body size measurement data of the user to be customized, including at least chest circumference and waist circumference; then, it calculates how closely the customer's data matches the various standard body size data stored in the computer, generates a weight for each standard body size, and selects the top few standard body sizes with high weights to form an active reference group.

[0024] Then, take out the paper patterns corresponding to these selected standard body shapes, and calculate the coordinates of each key point on their drawings according to the scores just scored, and piece together a brand new preliminary drawing, namely the preliminary outline of the mixed paper pattern. Finally, to prevent problems such as uneven lines or seams from being pieced together from the original drawings, a unified adjustment framework was constructed to uniformly adjust the model. This framework addresses the three requirements of correct dimensions, smooth lines, and even seams, moving all points on the drawings to the most suitable positions at once to output the final garment pattern data.

[0025] Furthermore, the step of calculating the degree of closeness between the body size measurement data and multiple pre-stored body shape reference benchmarks includes: for each body part measurement value in the body size measurement data, extracting the corresponding standard body part values ​​of multiple body shape reference benchmarks from a pre-set storage space. The difference between the measured value of each body part and the value of each standard body part is calculated separately. Following a reverse correspondence where a larger difference corresponds to a smaller similarity value, the difference is converted into an initial similarity value. During this data conversion process, a pre-defined continuous smoothing value conversion logic is invoked, ensuring that any changes in the difference are continuously reflected in the initial similarity value. Abrupt judgment logic that directly sets the difference to zero or one is excluded. Then, the initial similarity values ​​corresponding to all measured body parts under the same body type reference are aggregated to generate a comprehensive similarity value for that reference. This calculation method is used to iterate through all pre-stored body type references, obtaining a comprehensive similarity value for each reference independently. All comprehensive similarity values ​​are then compiled into a single similarity value and output to the next calculation step.

[0026] In this embodiment, after receiving the customer's various measurement values, body shape reference standards are extracted from a pre-stored database, such as 10 preset standard body shapes, each with corresponding standard chest circumference, waist circumference, etc. For each body part, the difference between the customer's values ​​and the standard values ​​is calculated.

[0027] To avoid the traditional approach of abruptly resetting the value to zero once the difference exceeds a certain threshold, this invention employs a smooth calculation logic. Specifically, the initial similarity value can be calculated using the following logical formula. : ; In the formula, This represents the actual measurement value of a certain part of the customer's body (such as the actual chest circumference). Standard values ​​for a particular body part (such as standard chest circumference) represent a specific body type. That is, the magnitude of the difference. This is a sensitivity adjustment parameter, typically ranging from 0.1 to 0.5. Companies can adjust it according to the required looseness of different garment styles. It's worth it; the more fitted the style, the better. The larger the value.

[0028] It can be seen that the closer the customer's size is to the standard size, the smaller the difference. The closer the value is to 1, the greater the difference. The value decreases smoothly without suddenly dropping to 0.

[0029] Next, add up all the initial fit values ​​for all parts (such as neck circumference, bust circumference, waist circumference, hip circumference, etc.) under the current standard body shape and calculate the average to get the overall fit value of this standard body shape. Then, process all the standard body shapes stored in the computer in the same way as above.

[0030] Further, the steps of generating a weight list and selecting active reference groups from multiple pre-stored body shape reference benchmarks include: obtaining the similarity values ​​corresponding to each body shape reference benchmark; normalizing and converting all similarity values ​​to a single value, so that the sum of all converted similarity values ​​equals one; using the converted similarity values ​​as weight values; arranging and combining all weight values ​​according to the binding relationship of body shape reference benchmarks to generate a weight list containing multiple similarity values; pre-setting a retention threshold value for intercepting data within the system; comparing each weight value in the weight list with the retention threshold value to filter out weight values ​​greater than the retention threshold value; extracting the body shape reference benchmarks corresponding to these filtered weight values; combining these extracted body shape reference benchmarks to establish and output an active reference group record set; and removing and intercepting the body shape reference benchmarks corresponding to weight values ​​whose values ​​are not greater than the retention threshold value from the subsequent mixed calculation process.

[0031] In this embodiment, the overall similarity scores of all standard body types are summed up, and then the overall similarity score of each standard body type is divided by this sum. This operation is the normalization conversion of the similarity scores. At this point, the similarity score of each standard body type is recorded as the corresponding weight score, and these weight scores are output as a weight list.

[0032] Next, using a pre-set retention threshold, such as 0.05 or 5%, each weight value is checked. If a standard body shape has a weight of only 2%, which is less than the retention threshold of 5%, it means the customer's body shape is almost unrelated to that value, and it is discarded. If the weight is greater than 5%, the standard body shape is retained. These retained standard body shapes form an active reference group. The advantage of this is that it filters out useless and distracting data, making subsequent calculations faster and more accurate.

[0033] Furthermore, the steps for obtaining the coordinates of the positioning points on the pattern outlines corresponding to each body type reference benchmark in the active reference group include: locating each body type reference benchmark record included in the active reference group in the system's basic graphic database, and retrieving a complete set of basic garment pattern outline data pre-bound to each body type reference benchmark; identifying all line positioning points connected in a prescribed order in the basic garment pattern outline data, and reading the horizontal and vertical coordinate values ​​of each line positioning point in the two-dimensional plane space; reading different basic garment pattern outline data, and identifying the unified name number commonly bound to line positioning points in the same structural semantic position; following the reading guide of the unified name number, for a specific local position on the new pattern to be generated, extracting the horizontal and vertical coordinate values ​​of positioning points with the same unified name number from the basic garment pattern outline data of each body type reference benchmark in the active reference group; and centrally grouping and classifying all extracted coordinate values ​​from the same structural position of different body type reference benchmarks to establish a set of coordinate data to be mixed for performing mixed weighted calculation.

[0034] In this embodiment, after determining the active reference group, the basic drawings (pattern outline data) corresponding to these standard body shapes are retrieved from the graphics database.

[0035] All paper pattern drawings are made up of connected points. In order to match them, the points at the same position on different drawings in the database are named and numbered exactly the same (for example, the "center point of the front neckline" is uniformly numbered "Neck_F_01").

[0036] For a specific point on the new drawing to be generated (e.g., finding the new center point of the front neckline), based on the unified name number, extract the horizontal (X coordinate) and vertical (Y coordinate) positions of the "Neck_F_01" point on the computer screen from the selected standard drawings and place them on the set of coordinate data to be mixed, ready for the next step of mixing.

[0037] Furthermore, the steps for generating mixed positioning point coordinates include: extracting coordinate values ​​from the same structural position from different body type reference bases, categorized in the dataset of coordinates to be mixed; finding the weight values ​​independently corresponding to each body type reference base from the generated weight list according to the unified name number; multiplying the horizontal and vertical coordinate values ​​provided by each body type reference base by the weight value corresponding to that body type reference base, and calculating a series of horizontal and vertical coordinate values ​​after multiplication and weighting calculation; merging and accumulating all weighted horizontal coordinate values ​​corresponding to the same structural position to obtain a new mixed horizontal coordinate value; similarly merging and accumulating all weighted vertical coordinate values ​​corresponding to the same structural position to obtain a new mixed vertical coordinate value; combining the new mixed horizontal coordinate value and the new mixed vertical coordinate value to form the mixed positioning point coordinates for that specific structural position; and performing multiplication and weighting calculations on all structural positions on the garment pattern one by one to obtain all mixed positioning point coordinates.

[0038] In this embodiment, after obtaining the coordinates of several standard drawings at the same location, the mixing process begins.

[0039] The dataset of coordinates to be mixed is combined with weight values. Let's assume we've selected standard body type A (weight 60%) and standard body type B (weight 40%).

[0040] Perform the following multiplication and accumulation operations: New horizontal coordinates ; New vertical coordinates .

[0041] In the formula, , For a standard body type A, the horizontal and vertical coordinates are the coordinates of a specific structural position (such as the "center point of the front neckline") on the basic pattern outline. , For a standard body type B, the horizontal and vertical coordinates are the coordinates of a specific structural position (such as the "center point of the front neckline") on the basic pattern outline. , The coordinates of the newly obtained hybrid positioning point.

[0042] Through this proportional mixing calculation, a completely new set of coordinates for the mixed positioning point is obtained. By calculating all the points on the drawing using this "proportionally combined" method and connecting them, a preliminary drawing that takes into account different body characteristics is produced, namely the preliminary outline of the mixed pattern.

[0043] Furthermore, the steps for constructing a unified adjustment model for the preliminary outline of the mixed pattern include: extracting the coordinates of all mixed positioning points of the preliminary outline of the mixed pattern; calculating the estimated size values ​​of each specific part of the customized garment based on the straight-line connection distance between the mixed positioning point coordinates or the cumulative length along the curve; establishing size matching constraint calculation rules for the deviation between the calculated estimated size values ​​and the actual body size measurement data input by the user; calculating the specific change value of the curvature of the curve along the line extension direction for the curve composed of the coordinates of the sequentially connected mixed positioning points; establishing smoothness constraint calculation rules to limit the abrupt changes in the change value to promote the smooth and natural transition of the line; identifying the edge lines of two garment pieces that need to be spliced ​​and sewn together, and calculating the total path length of these edge lines that need to be spliced ​​and sewn together; establishing edge sewing matching constraint calculation rules that force the difference in the total length of these edge lines that need to be spliced ​​and sewn together to continuously decrease; configuring different weight parameter values ​​for the constraint calculation rules in these three directions respectively, and integrating them into a comprehensive numerical calculation adjustment equation to form a unified adjustment model.

[0044] In this embodiment, the preliminary drawing pieced together through the above steps is generally correct in shape, but the details may not stand up to scrutiny. The current traditional approach is to correct only the parts that are found to be incorrect, resulting in the bust and neckline becoming crooked, easily leading to localized inconsistencies. Therefore, this invention constructs a unified adjustment model that calculates the overall cost at once. This model simultaneously monitors three indicators: First, is the size correct (size meets requirements): calculate the length of the line segments on the drawing to see how much the bust and waist measurements differ from the customer's actual measurements after the garment is made.

[0045] Second, check if the lines are smooth (smoothness requirement): see if the connected lines suddenly turn a corner or shake like waves.

[0046] Third, whether they fit together (edge ​​stitching matching requirements): For example, the sides of the front and back pieces of the garment need to be sewn together, and the lengths of these two sides on the drawing must be the same.

[0047] Assign an importance level, i.e., a weight parameter value, to each of the three indicators. This value can be adjusted according to process requirements. Then, based on the weighted fusion method, the three indicators and their corresponding weight parameter values ​​are integrated into a comprehensive formula.

[0048] Furthermore, the steps for establishing the size fit constraint calculation rules include: extracting the estimated size value corresponding to each specific designated part, and simultaneously retrieving the target body measurement value of the corresponding part from the received body size data record of the user to be customized; subtracting the target body measurement value from the estimated size value of the same specific part to obtain the basic size deviation data value of each individual inspection part; performing a square transformation calculation on the basic size deviation data value of each individual inspection part by multiplying itself to obtain the square deviation data value of each corresponding part; then performing a unified accumulation and summarization operation on the square deviation data values ​​obtained from all inspection parts to generate a total deviation index value to reflect the overall size deviation; and writing the judgment logic and judgment program segment that require the total deviation index value to approach zero in the subsequent optimization calculation process as the core target evaluation code segment into the internal module of the size fit constraint calculation rules, and the constraint optimization solution algorithm finding the coordinate position combination that makes the total deviation index value show a shrinking trend.

[0049] In this embodiment, the actual dimensions of the garment are estimated by calculating the distances between points on the drawing. Subtracting the customer's requested dimensions from this estimated dimensions yields the basic size deviation.

[0050] To prevent positive and negative deviations from canceling each other out—for example, if the bust circumference is 2 cm larger and the waist circumference is 2 cm smaller, the sum becomes 0—this deviation value is squared to obtain the squared deviation value. The formula is as follows: ;in, It is the total deviation index value of the overall size deviation. These are estimated dimensions calculated from the drawings. This is the customer's target size. The primary task during subsequent adjustments is to ensure this... The values ​​are squeezed to be infinitely close to zero to ensure the clothes fit perfectly.

[0051] Further, the steps for establishing smoothness constraint calculation rules include: finding line pairings with interlocking association marks in the internal data record of the preliminary outline of the mixed pattern; extracting the coordinates of all mixed positioning points contained in the first edge of the line pairing; calculating the distance values ​​between adjacent mixed positioning points and summing them uniformly to obtain the total length data of the first edge path; extracting the coordinates of all mixed positioning points contained in the second edge of the line pairing; using the same distance calculation method to obtain the total length data of the second edge path; subtracting the total length data of the first edge path from the total length data of the second edge path; extracting the absolute value of the subtraction result as the actual seam length difference data; establishing a penalty feedback mechanism logic segment; when the seam length difference data is greater than the set negligible tolerance range, adding an additional penalty term accumulation item inside the unified adjustment model; encapsulating the penalty term accumulation item to construct an independent code block for edge seam matching constraint calculation rules.

[0052] In this embodiment, matching marks are pre-marked on the two sides that need to be sewn together. The total length of the first side is calculated by measuring along the marks on the first side and adding them together; the total length of the second side is calculated in the same way.

[0053] Then, subtract the two lengths and take the absolute value to obtain the actual difference in seam length. Here, a penalty mechanism is set: if this difference exceeds the tailor's tolerance tolerance, a hefty fine is added to the overall adjustment model, with the penalty value accumulating. The tailor's tolerance tolerance is adjustable by the company; for example, a negligible tolerance range of 2 millimeters is set. The higher the fine, the more unqualified the drawing, forcing the system to trim the two sides to the same length.

[0054] Furthermore, the steps for establishing edge stitching matching constraint calculation rules include: inputting the coordinates of all mixed positioning points contained in the preliminary outline of the mixed pattern as an indivisible overall candidate coordinate data set into the unified adjustment model; calculating the total value of the overall adjustment feedback generated by the current candidate coordinate data set under the unified adjustment model calculation rules; finding the coordinate movement direction guidance value that can make the total value of the overall adjustment feedback show a decreasing trend; modifying the displacement position of all mixed positioning point coordinates in the same calculation batch according to the change requirements of the coordinate movement direction guidance value, generating an updated candidate coordinate data set; substituting the updated candidate coordinate data set back into the unified adjustment model, recalculating the total value of the overall adjustment feedback; repeatedly executing the above-mentioned evaluation feedback value and the cyclic process of moving all coordinates in the decreasing direction of the feedback value. During the entire iterative calculation, all mixed positioning points remain combined and execute the movement operation command in the same step until the total value of the overall adjustment feedback remains unchanged, then ending the synchronous update operation and directly outputting the final mixed positioning point coordinates.

[0055] like Figure 2 The diagram shows a structural schematic of a clothing customization design system based on body shape data analysis provided in this application embodiment. The system includes: a data receiving module, a proximity calculation module, a preliminary outline generation module, a unified adjustment model construction module, and a data output module. The data receiving module receives body size measurement data from the user to be customized. The proximity calculation module calculates the proximity values ​​between the body size measurement data and multiple pre-stored body shape reference benchmarks, generates a weight list, and selects active reference groups from the pre-stored reference benchmarks. The preliminary outline generation module obtains the coordinates of positioning points on the pattern outline corresponding to each body shape reference benchmark in the active reference group, generates mixed positioning point coordinates, and combines all mixed positioning point coordinates to generate a mixed pattern preliminary outline. The unified adjustment model construction module constructs a unified adjustment model for the mixed pattern preliminary outline and processes the unified adjustment model using an optimization algorithm. The data output module synchronously updates all mixed positioning point coordinates of the mixed pattern preliminary outline until the requirements of the unified adjustment model are met, and outputs customized clothing pattern data.

[0056] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0057] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0058] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for customized clothing design based on body shape data analysis, characterized in that, The process includes the following steps: receiving body size measurement data from the user to be customized; Calculate the degree of closeness between body size measurement data and multiple pre-stored body shape references, generate a weight list, and filter out active reference groups from the multiple pre-stored body shape references; Obtain the coordinates of the positioning points on the paper pattern outline corresponding to each body type reference benchmark in the active reference group, generate mixed positioning point coordinates, and combine all mixed positioning point coordinates to generate a preliminary mixed paper pattern outline; construct a unified adjustment model for the preliminary mixed paper pattern outline, and process the unified adjustment model through an optimization algorithm; synchronously update all mixed positioning point coordinates of the preliminary mixed paper pattern outline until the requirements of the unified adjustment model are met, and output the customized garment paper pattern data.

2. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for calculating the degree of closeness between body size measurement data and multiple pre-stored body shape references include: calculating the difference between the measurement value of each body part and the value of each standard body part, and converting the difference value into an initial degree of closeness value; then summarizing the initial degree of closeness values ​​corresponding to all measured body parts under the same body shape reference to generate a comprehensive degree of closeness value for that body shape reference; iterating through all pre-stored body shape references one by one to obtain a comprehensive degree of closeness value corresponding to each body shape reference independently; and finally compiling all comprehensive degree of closeness values ​​into a single degree of closeness value.

3. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for generating a weight list and selecting active reference groups from multiple pre-stored body shape reference benchmarks include: obtaining the similarity values ​​corresponding to each body shape reference benchmark, and performing overall normalization and conversion on all similarity values; using the converted similarity values ​​as weight values, arranging and combining all weight values ​​according to the binding relationship of body shape reference benchmarks to generate a weight list; filtering out weight values ​​greater than a pre-set retention threshold value; extracting the body shape reference benchmarks corresponding to these filtered weight values, and combining these extracted body shape reference benchmarks together to establish and output an active reference group record set.

4. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for obtaining the coordinates of the positioning points on the pattern outlines corresponding to each body type reference benchmark in the active reference group include: locating each body type reference benchmark record in the active reference group and retrieving a complete set of basic garment pattern outline data pre-bound to each body type reference benchmark; identifying all line positioning points in the basic garment pattern outline data and reading the horizontal and vertical coordinate values ​​of each line positioning point; reading different basic garment pattern outline data and identifying the unified name number bound to line positioning points in the same structural semantic position; extracting the horizontal and vertical coordinate values ​​of positioning points with the same unified name number from a specific local position on the new pattern to be generated; and grouping and classifying all extracted coordinate values ​​to establish a set of coordinate data to be mixed.

5. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for generating mixed positioning point coordinates include: extracting coordinate values ​​of the same structural position from the dataset of coordinates to be mixed; searching for weight values ​​from the generated weight list according to the unified name number, and calculating the horizontal and vertical coordinate values ​​after multiplication and weighting; merging and accumulating all weighted horizontal coordinate values ​​corresponding to the same structural position to obtain a new mixed horizontal coordinate value, and similarly merging and accumulating all weighted vertical coordinate values ​​corresponding to the same structural position to obtain a new mixed vertical coordinate value; combining the new mixed horizontal coordinate value and the new mixed vertical coordinate value to form the mixed positioning point coordinates of that specific structural position, and obtaining all mixed positioning point coordinates for all structural positions on the garment pattern.

6. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for constructing a unified adjustment model for the preliminary outline of the mixed pattern include: extracting the coordinates of all mixed positioning points of the preliminary outline of the mixed pattern, calculating the estimated size values ​​based on the mixed positioning point coordinates; establishing size matching constraint calculation rules, calculating the specific change value of the curve formed by the coordinates of the sequentially connected mixed positioning points; establishing smoothness constraint calculation rules, identifying the edge lines of two garment pieces that need to be spliced ​​together, and calculating the total path length; and establishing edge stitching matching constraint calculation rules, configuring different weight parameter values ​​for each constraint calculation rule to form a unified adjustment model.

7. The clothing customization design method based on body shape data analysis as described in claim 6, characterized in that, The steps for establishing size fit constraint calculation rules include: extracting the estimated size values ​​one by one, and simultaneously retrieving the target body measurement values ​​to obtain the basic size deviation data values; performing a numerical square transformation calculation on the basic size deviation data values ​​to obtain the squared deviation data values, and then generating the total deviation index value; and writing the judgment logic and judgment program segment of the total deviation index value as the target evaluation code segment into the internal module of the size fit constraint calculation rules.

8. The clothing customization design method based on body shape data analysis as described in claim 1, characterized in that, The steps for establishing smoothness constraint calculation rules include: finding line pairings in the internal data record of the preliminary outline of the mixed pattern; extracting the coordinates of all mixed positioning points contained in the first edge of the line pairing to obtain the total length data of the first edge path; extracting the coordinates of all mixed positioning points contained in the second edge of the line pairing to obtain the total length data of the second edge path; subtracting the total length data of the first edge path from the total length data of the second edge path to extract the actual seam length difference data; and establishing a penalty feedback mechanism logic segment, whereby an independent code block for edge seam matching constraint calculation rules is constructed when the seam length difference data exceeds a set negligible tolerance range.

9. The clothing customization design method based on body shape data analysis as described in claim 6, characterized in that, The steps for establishing edge stitching matching constraint calculation rules include: inputting the coordinates of all mixed positioning points contained in the preliminary outline of the mixed pattern into the unified adjustment model, and calculating the total value of the overall adjustment feedback; finding the guiding value of the coordinate movement direction, modifying the displacement position of all mixed positioning point coordinates in the same calculation batch, and generating a candidate coordinate data set; substituting the candidate coordinate data set back into the unified adjustment model, and recalculating the total value of the overall adjustment feedback.

10. A clothing customization design system based on body shape data analysis, used to implement the clothing customization design method based on body shape data analysis as described in any one of claims 1-9, characterized in that, include: The system comprises a data receiving module, a proximity calculation module, a preliminary contour generation module, a unified adjustment model construction module, and a data output module. The data receiving module receives body size measurement data from the user to be customized. The proximity calculation module calculates the proximity values ​​between the body size measurement data and multiple pre-stored body shape references, generates a weight list, and selects active reference groups from the pre-stored multiple body shape references. The preliminary outline generation module is used to obtain the coordinates of the positioning points on the paper pattern outline corresponding to each body type reference benchmark in the active reference group, generate mixed positioning point coordinates, and combine all mixed positioning point coordinates to generate a mixed paper pattern preliminary outline; the unified adjustment model construction module is used to construct a unified adjustment model for the mixed paper pattern preliminary outline, and process the unified adjustment model through an optimization algorithm; the data output module is used to synchronously update all mixed positioning point coordinates of the mixed paper pattern preliminary outline until the requirements of the unified adjustment model are met, and output the customized garment paper pattern data.

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