Textile and garment design business management system based on model analysis

Abstract

The present application relates to the technical field of textile and garment design and business management, in particular to a textile and garment design business management system based on model analysis, comprising: a multi-source data acquisition module, used for acquiring market trend data corresponding to a target design scheme, design attribute data containing three-dimensional design model parameters and fabric physical attributes, and supply chain timeliness data; a trend prediction and evaluation module, used for calculating the survival heat of a specific design element at a specific time node in the future; a fidelity analysis module, used for evaluating the physical restoration degree of the target design scheme; a scheduling and resource consumption accounting module, used for calculating the target listing path, time length and target resource occupation total amount under a plurality of candidate material schemes; an implementation failure risk evaluation module, used for calculating a comprehensive market success probability score; a strategy generation module, used for generating an optimal listing time window and material combination suggestion and performing a high-redundancy failure risk early warning.

Description

Technical Field

[0001] This invention relates to the field of textile and apparel design and business management technology, specifically a textile and apparel design business management system based on model analysis. Background Technology

[0002] Currently, textile and apparel design business management is typically based on a separation of market research, design review, and supply chain scheduling. When companies make market launch decisions for target design schemes, the existing management methods cannot conduct collaborative analysis of the survival status of fashion trends, the effect of physical design reproduction, and the timeliness of the supply chain and the consumption of system resources. Each decision may rely on experience-based judgment and be accompanied by significant timing and implementation deviations, which reduces the accuracy of design scheme evaluation and market launch decisions and increases the probability of missing sales windows or creating high redundancy failure risks. Summary of the Invention

[0003] The purpose of this invention is to provide a model-based textile and apparel design business management system to address the following technical problems: Existing textile and apparel design business management technologies have significant shortcomings in the collaborative analysis of the popularity and viability of target design schemes, the effectiveness of physical design reproduction, and the timeliness of the supply chain and the consumption of system resources. There is an urgent need for a model-based textile and apparel design business management system that can more accurately quantify and assess the risks of full-chain commercial implementation and dynamically generate optimal launch time windows and material combination suggestions. The purpose of this invention can be achieved through the following technical solutions:

[0004] The multi-source data acquisition module is used to collect multi-source business data, including market trend data, design attribute data including 3D design model parameters and fabric physical properties, and supply chain timeliness data.

[0005] The trend prediction and evaluation module is used to input the market trend data into a preset popular trend time series prediction model constructed by an autoregressive integral moving average model or a long short-term memory artificial neural network; extract specific design elements, determine specific future time nodes in combination with the set listing cycle, and calculate the survival popularity of the specific design elements at that time node.

[0006] The fidelity analysis module is used to map the physical properties of the fabric to the three-dimensional mesh features based on the parameters of the three-dimensional design model and the physical properties of the fabric to perform physical simulation calculations and evaluate the fidelity to the real object.

[0007] The scheduling and resource consumption calculation module is used to calculate the target market launch path, time length, and total target resource consumption under multiple candidate material schemes;

[0008] Implement a failure risk assessment module to calculate the overall market success probability score of the target design scheme under each candidate material scheme by combining survival heat, physical reproduction degree, time length and total target resource consumption;

[0009] The strategy generation module is used to generate the optimal time-to-market window and material combination suggestions based on the score, and to provide a high redundancy failure risk warning for the target design scheme, and output the results to the display terminal.

[0010] Optionally, the multi-source data acquisition module is specifically used to perform the following operations:

[0011] The data on the popularity and decay curves of trending elements on social media platforms are collected through a pre-set web crawler interface, along with historical sales lifecycle data of similar models, and combined to form the market trend data.

[0012] The three-dimensional design model parameters corresponding to the target design scheme are extracted from the preset design database. The three-dimensional design model parameters include preset ideal shape features, and the corresponding fabric physical properties are obtained from the preset fabric physical property library and combined into the design attribute data.

[0013] The delivery date data, factory production cycle, and logistics timeliness of each fabric supplier are retrieved from the preset supplier business system and combined to form the supply chain timeliness data.

[0014] Optionally, the trend prediction and evaluation module is specifically used to perform the following operations:

[0015] The popularity and decay curve data of the aforementioned popular elements are subjected to time series smoothing to obtain smooth trend data;

[0016] Input the smoothed trend data and historical sales lifecycle data of similar models into the popular trend time series prediction model, and output the survival popularity prediction curve;

[0017] Based on the specific future time nodes set according to business needs, the values ​​corresponding to the specific time nodes are extracted from the survival popularity prediction curve and used as the survival popularity.

[0018] Optionally, the fidelity analysis module is specifically used to perform the following operations:

[0019] The parameters of the three-dimensional design model are analyzed in a mesh format to obtain the three-dimensional mesh features;

[0020] The physical properties of the fabric are mapped to the three-dimensional mesh features. Physical simulation calculations are performed using a preset design physical fidelity analysis model. The design physical fidelity analysis model uses a finite element analysis algorithm. By setting the material constitutive equation transformed from the physical properties of the fabric, the strain and displacement data of the three-dimensional mesh features under the simulated gravity field are calculated, thereby obtaining the simulated morphological features.

[0021] Calculate the spatial distance deviation between the feature vectors corresponding to the simulated morphological features and the preset ideal morphological features in the parameters of the three-dimensional design model, and use this as the morphological deviation value;

[0022] The reciprocal of the sum of the morphological deviation value and a preset non-zero constant is taken as the physical reproduction degree.

[0023] Optionally, the scheduling and resource consumption calculation module is specifically used to perform the following operations:

[0024] Based on the physical properties of the fabric and the supply chain timeliness data, a material supply network topology diagram containing multiple candidate material solutions is constructed.

[0025] The material supply network topology is input into a preset supply chain scheduling and resource consumption accounting model. The shortest path is optimized for each node using a preset Dijkstra algorithm or A* pathfinding algorithm. The shortest delivery time sequence corresponding to each candidate material solution is obtained as the target listing path, and the total duration of the shortest delivery time sequence is extracted as the time length.

[0026] Extract the resource usage parameters of each node in the target listing path and sum them up to obtain the total target resource usage.

[0027] Optionally, the failure risk assessment module is specifically used to perform the following operations:

[0028] The survival heat and the physical reduction degree are normalized to obtain standard heat and standard reduction degree;

[0029] The time length and the total target resource usage are added to a preset non-zero constant used to prevent computational overflow anomalies, and the reciprocal is taken. Then, normalization is performed to obtain the standard timeliness and standard resource evaluation.

[0030] Obtain a preset set of rating weights, the set of rating weights including the standard popularity, the standard fidelity, the standard timeliness, and the weight coefficients corresponding to the standard resource evaluation;

[0031] Based on the set of scoring weights, the standard popularity, standard fidelity, standard timeliness, and standard resource evaluation are weighted and summed to obtain the comprehensive market success probability score of the target design scheme under the candidate material scheme.

[0032] Optionally, the operation of the strategy generation module in generating the optimal launch time window and material combination suggestions specifically includes:

[0033] Obtain the comprehensive market success probability score for all candidate material solutions, and select the candidate material solution with the highest comprehensive market success probability score as the optimal material combination.

[0034] Extract the target market launch path corresponding to the optimal material combination and the survival popularity prediction curve;

[0035] Obtain a preset popularity threshold, and determine the optimal listing time window as the continuous time interval in the survival popularity prediction curve where the popularity is greater than or equal to the preset popularity threshold and the time node is later than the end time of the target listing path; if there is no continuous time interval that meets the requirements, output a re-evaluation instruction.

[0036] The optimal launch time window and the optimal material combination are packaged together to generate the optimal launch time window and material combination suggestion.

[0037] Optionally, the operation of the strategy generation module to perform high redundancy failure risk warning on the target design scheme specifically includes:

[0038] Obtain the preset risk threshold;

[0039] The overall market success probability score corresponding to the optimal material combination is compared with the preset risk threshold;

[0040] If the overall market success probability score is greater than or equal to the preset risk threshold, the target design scheme is determined to be in a safe state and no warning instruction is triggered.

[0041] If the overall market success probability score is less than the preset risk threshold, the target design scheme is determined to have a high risk of redundancy failure, and an early warning instruction is generated.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. This system collects market trend data corresponding to the target design scheme, design attribute data including 3D design model parameters and fabric physical properties, and supply chain timeliness data. It can structurally aggregate the originally scattered data from the market, design, and supply chain under the same design scheme, thereby bringing three types of questions—market acceptance, design feasibility, and supply chain responsiveness—into a unified analytical perspective, improving the completeness and consistency of design scheme evaluation and market launch decisions. By simultaneously introducing the popularity and decay curve data of trending elements and historical sales lifecycle data of similar styles into the market trend data, it can distinguish between superficial topic popularity and actual purchasing power, thereby reducing evaluation bias caused by relying solely on single popularity data.

[0044] 2. This system performs time-series smoothing on the popularity and decay curve data of trending elements, and combines this with historical sales lifecycle data of similar models into a trend time-series prediction model. It can output a survival popularity prediction curve for the future timeline, thus transforming the judgment of design elements from current popularity rankings to assessing their survival ability at future launch points, improving the accuracy of predicting whether the sales window will still have market attention. By extracting corresponding values ​​from the survival popularity prediction curve as survival popularity at specific future time points set according to business needs, it can directly serve the formulation of specific launch schedules, thereby reducing time-series deviations caused by short-term public opinion spikes.

[0045] 3. This system analyzes the parameters of the 3D design model into a mesh and maps the physical properties of the fabric to the 3D mesh features. With the help of the design physical fidelity analysis model, it performs physical simulation calculations to evaluate the simulated morphological characteristics of the target design scheme under specific fabric conditions. This allows it to determine whether the pattern, drape, and silhouette in the design drawings can be stably converted into the actual garment effect. By calculating the spatial distance deviation between the simulated morphological characteristics and the preset ideal morphological characteristics, and obtaining the actual garment reproduction degree, the system can transform the sample garment production risk, which originally relied on subjective aesthetic judgment, into a quantifiable indicator. This reduces the production deviation and repeated sampling problems caused by the inconsistency between the artwork and the actual garment. Attached Figure Description

[0046] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0048] like Figure 1 As shown, the model-based textile and apparel design business management system includes:

[0049] The multi-source data acquisition module is used to collect multi-source business data, including market trend data, design attribute data including 3D design model parameters and fabric physical properties, and supply chain timeliness data.

[0050] The trend prediction and evaluation module is used to input market trend data into a preset popular trend time series prediction model constructed by an autoregressive integral moving average model or a long short-term memory artificial neural network; extract specific design elements, combine them with the set listing cycle to determine specific future time nodes, and calculate the survival popularity of specific design elements at that time node.

[0051] The fidelity analysis module is used to map the physical properties of the fabric to the physical properties of the fabric based on the parameters of the 3D design model and the physical properties of the fabric, to perform physical simulation calculations and evaluate the fidelity to the real object.

[0052] The scheduling and resource consumption calculation module is used to calculate the target market launch path, time length, and total target resource consumption under multiple candidate material schemes;

[0053] Implement a failure risk assessment module to calculate the overall market success probability score of the target design scheme under each candidate material scheme by combining survival heat, physical reproduction degree, time length and total target resource consumption;

[0054] The strategy generation module is used to generate optimal time-to-market windows and material combination suggestions based on scores, and to provide high redundancy failure risk warnings for target design schemes, which are then output to the display terminal.

[0055] This embodiment provides a model-based management mechanism for textile and apparel design business. Specifically, the system is deployed between the design management platform, supplier collaboration platform, and product planning terminal of an apparel company to conduct full-link analysis of the same target design scheme, from project initiation, fabric selection, pre-sampling evaluation to market launch decision. In this embodiment, a women's autumn trench coat project of an apparel company is used as the recurring scenario. The project has already produced a 3D design draft, and the design language includes a wide shoulder silhouette, a draped hem, and a commuter-friendly light business style. The company plans to launch the product before the autumn season window, so there is a strong coupling relationship between the design effect, market popularity, and supply chain timeliness.

[0056] Specifically, the multi-source data acquisition module connects data sources from the market, design, and supply chain. Market data reflects the speed of evolution of consumer aesthetic preferences, design data reflects whether the 3D design effect can be stably presented on real fabrics, and supply chain data reflects the time and system resource load required from fabric ordering to garment delivery. After receiving market data, the trend prediction and evaluation module does not simply count the current popularity, but judges whether the design elements will still be attractive at the target launch point in the future.

[0057] After receiving the parameters of the 3D design model and the physical properties of the fabric, the fidelity analysis module does not stop at the level of 2D visual aesthetics evaluation, but determines whether the sample garment still retains the pattern, drape and silhouette of the design drawings after it is implemented; the scheduling and resource consumption accounting module connects the supplier delivery time, factory production schedule and logistics links into an executable path, thereby identifying the impact of different material combinations on the time to market; the failure risk assessment module further puts market popularity, recovery capability, timeliness performance and resource load into the same evaluation framework, so that design decisions can be transformed from experience-based judgment to result-oriented comprehensive scoring;

[0058] The strategy generation module outputs executable suggestions, such as retaining the original design but changing the fabric, locking in a supplier's capacity in advance, or directly triggering an alert for high-risk solutions. For ease of understanding, a simplified data model can be used to illustrate how data flows between modules. Assume that there are two candidate material options for this trench coat project: Option A is an imported high-drape blended fabric, and Option B is a domestically available functional fabric. After data collection, the system generates three types of data packages: market data package M, design data package D, and supply chain data package S.

[0059] The trend prediction and assessment module identifies two core elements of the wide-shouldered, lightweight trench coat for commuting from M; the fidelity analysis module identifies morphological features such as sleeve cap three-dimensionality, hem spread, and waist dart curve from D; the scheduling and resource consumption accounting module identifies the following paths from S: Option A is overseas fabric preparation—import customs clearance—factory production scheduling—regional distribution, and Option B is local spot order locking—factory production scheduling—regional distribution; the implementation failure risk assessment module does not directly favor minimizing resource consumption or minimizing delivery time, but rather compares which option is more likely to maintain the design effect and be within the sales window before the heat fades at the target launch point;

[0060] As an anomaly handling mechanism, if market data is temporarily unavailable, such as due to social media interface outages or insufficient historical sales of a certain style, the system can switch the trend prediction and evaluation module to a conservative mode, prioritizing the use of recent season's internal sales records and publicly available industry reports, and temporarily suspending the output of high-confidence launch recommendations. If the design side lacks complete 3D model parameters, the system can perform local fidelity analysis only on key parts with available parameters and mark the results as local evaluations. If a supplier's delivery time fluctuates abnormally in the supply chain, the system can automatically remove the unstable node from the target path to prevent a single abnormal node from lowering the overall reliability of the recommendations. If the credibility of any core module's output is lower than the preset standard, the final recommendation in the terminal will be marked with a manual review requirement, rather than directly giving a definitive conclusion.

[0061] The women's trench coat project for autumn was entered into the system after the planning meeting. The system found that the current popular trend of light commuter silhouettes is still in its diffusion phase, but the popularity of extra-long floor-length trench coats is nearing the end of its peak. At the same time, the draping effect of the hem emphasized in the design draft is quite sensitive to the fabric's drape coefficient. If Option A with a longer lead time is adopted, although the visual appearance is closer to the initial design draft, the launch time may be close to the decline in popularity. If Option B is adopted, the draping effect may be slightly sacrificed, but it can enter the core sales window first. Based on this, the system centrally displays the results of each module to the design manager, product manager, and supply chain manager, allowing multiple roles to complete collaborative decision-making on the same interface.

[0062] The purpose of this step is to bring together the previously separate design review, fabric review, production scheduling and product launch judgment into the same business management closed loop, thereby achieving proactive business risk identification for clothing design schemes and reducing the high risk of redundancy failure caused by missing the trend window or insufficient physical reproduction.

[0063] In this embodiment, the multi-source data acquisition module is specifically used to perform the following operations:

[0064] Data on the popularity and decay curves of trending elements on social media platforms are collected through a pre-set web crawler interface, along with historical sales lifecycle data of similar models, and combined to form market trend data.

[0065] Extract the 3D design model parameters corresponding to the target design scheme from the preset design database. The 3D design model parameters contain preset ideal shape features and obtain the corresponding fabric physical properties from the preset fabric physical property library, and combine them into design attribute data.

[0066] The system retrieves delivery date data, factory production schedule, and logistics timeliness of each fabric supplier from the pre-set supplier business system and combines them into supply chain timeliness data.

[0067] This embodiment provides a multi-source data acquisition step; specifically, after the above-mentioned autumn women's trench coat project enters the evaluation process, the system no longer relies solely on the style descriptions submitted by the designers, but simultaneously captures market trend data, design attribute data, and supply chain timeliness data to establish a cross-domain data foundation for the same design scheme from the source.

[0068] In detail, the comprehensive processing framework in the previous implementation still has obvious defects: if the input source is too singular, such as only looking at social media popularity, it is easy to mistakenly take short-term network data peaks as real purchasing trends; if only design drafts are looked at without reading the fabric properties, the system cannot determine whether the aesthetics of the design drafts can be transformed into the finished garment effect; if the actual production schedules of suppliers and factories are ignored, the subsequent scoring will be based on time assumptions that are not feasible.

[0069] Therefore, this embodiment further defines the scope and objects of data collection. Market trend data consists of two parts: one part is the popularity of trending elements on external platforms and their decline trajectory, and the other part is the historical sales lifecycle of similar styles in the company's internal or industry database. The former reflects the degree of discussion, and the latter reflects the persistence of purchases. The combination of the two can distinguish between short-term data fluctuations and the actual market absorption capacity. Design attribute data also consists of two parts: one is the three-dimensional design model parameters of the target design scheme, such as collar curvature, armhole structure, hem flare, waistline position, etc.; the other is the physical properties corresponding to the candidate fabric, such as weight, drape, elastic recovery ability, thickness grade, and shrinkage tendency, etc. Supply chain timeliness data consists of delivery date, production scheduling cycle, and logistics timeliness, which correspond to the raw material acquisition speed, manufacturing resource occupancy, and final distribution and transportation rhythm, respectively.

[0070] The data collection results can be illustrated using a simplified data package. Assume the market side captures three high-frequency element tags E1, E2, and E3, where E1 represents a wide-shouldered trench coat, E2 represents lightweight and wrinkle-resistant, and E3 represents an extra-long hem. Simultaneously, the system retrieves sales curves L1 and L2 for similar trench coats from the past three seasons. The design side reads three-dimensional structural fragments G1, G2, and G3, corresponding to the shoulder contour, waist darts, and hem spread, respectively. Two fabric attribute sets, F1 and F2, are then read, with F1 emphasizing drape and F2 emphasizing quick return. The supply chain side reads supplier nodes P1 and P2, and factory nodes... Points C1, C2, and logistics node T1 are combined to form the first market data package, which includes the first popular element, the second popular element, the third popular element, and the life cycle curves of the first and second similar styles; the first design data package includes shoulder grid features, waist grid features, hem grid features, and the first and second fabric attribute sets; the supply chain data package S={P1,P2,C1,C2,T1}; subsequent modules can then conduct analysis on a unified structure without having to retrieve data from the distributed systems one by one.

[0071] As an exception handling mechanism, if the web crawler interface fails to obtain the complete popularity curve due to platform limitations, the system can use public search indexes, e-commerce site click trends, or internal community feedback as supplementary sources; if the 3D design database has not yet completed the modeling of all patterns, the system can first extract the parameters of the completed key patterns and set the missing parts to be supplemented, and not include the missing parts in the fidelity conclusion; if a candidate fabric in the fabric physical property library lacks shrinkage rate or elasticity data, the system can require the supplier to upload a test report, or temporarily use historical test data from the same batch, but at the same time indicate the data source level in the results; if the delivery date in the supplier's business system conflicts with the manually maintained ledger, the actual performance record of the most recent confirmed order will be used as the verification basis to avoid static delivery dates misleading subsequent scheduling;

[0072] For this autumn women's trench coat project, the system crawled recent high-frequency content from social media, such as the waist-cinching silhouette of a lightweight functional trench coat for urban commuting, and retrieved sales decline records of similar styles in the four weeks after their launch from the company's autumn outerwear database over the past two years; extracted the three-dimensional pattern parameters of the target trench coat from the design database, identifying its core form and technical features as the three-dimensionality of the shoulders and the natural swing of the hem; read the differences in drape, weight, and wrinkle resistance between imported blended fabrics and local functional fabrics from the fabric library; and obtained the delivery dates, factory production windows, and warehousing and distribution timelines from the supplier system. At this point, the subsequent modules were no longer dealing with isolated data, but rather a complete business profile organized around the same trench coat.

[0073] The purpose of this step is to simultaneously bring three independent evaluation dimensions—market acceptance, design feasibility, and supply chain timeliness—into the system's view through structured data collection, thereby providing a unified input basis for subsequent forecasting, simulation, scheduling, and risk assessment.

[0074] In this embodiment, the trend prediction and evaluation module is specifically used to perform the following operations:

[0075] Time series smoothing was performed on the popularity and decay curve data of popular elements to obtain smoothed trend data;

[0076] Input smoothed trend data and historical sales lifecycle data of similar models into the popular trend time series prediction model, and output the survival popularity prediction curve;

[0077] Based on specific future time points set according to business needs, the values ​​corresponding to those specific time points are extracted from the survival popularity prediction curve and used as the survival popularity.

[0078] This embodiment provides a trend prediction and evaluation step; specifically, after completing data collection, the system further judges the future popularity of the popular elements involved in the target design scheme, thereby answering the core question of whether the target audience will maintain their preference for the design elements when the product is actually launched.

[0079] In detail, raw popularity data alone is still insufficient; popularity in social media is often affected by instantaneous events such as specific preset event nodes, strong characteristic interactions of specific user nodes, and targeted pushes from platform algorithms, which manifest as short-cycle spikes; if such raw fluctuations are used directly to judge the listing window, it is easy to mistake short-term noise for a stable trend, causing the system to output suboptimal design evaluation directions in order to track instantaneous data spikes.

[0080] Therefore, this embodiment first smooths the popularity and decay curves of trending elements to make the trend line closer to the continuous changes in real market aesthetics. The smoothing here does not pursue absolute mathematical smoothness, but rather filters out instantaneous anomalies that do not match the actual purchase cycle, so that the data can better reflect the real state of whether consumers are willing to continue to pay attention. After obtaining the smoothed trend data, the system then introduces historical sales life cycle data of similar models, because being discussed and being purchased are not always synchronized.

[0081] For example, some visually striking design elements have high-frequency data interaction on social networks, but the structural acceptance of the target audience in actual application scenarios is insufficient. The historical sales life cycle can make up for this bias, as it reflects the acceptance speed, peak duration, and tail decline rhythm of similar styles after their launch. After inputting the smoothed popularity trajectory and the historical sales life cycle into the trend time series prediction model, the model outputs not a simple current ranking, but a survival popularity prediction curve for the future time axis. Business personnel can extract the popularity value corresponding to the target launch point as the survival popularity of the design element at that time.

[0082] A simplified deduction can be made; assuming the system predicts three elements: E1 wide shoulder contour, E2 lightweight anti-wrinkle, and E3 extra-long hem; in the original heat sequence, the value of E3 rises in a pulse-like manner after the intervention of a specific strong influencing factor, but falls back significantly after two weeks; after smoothing, the curve of E3 changes from a peak to a short rise followed by a decline.

[0083] Meanwhile, historical trench coat sales lifecycle data shows that excessively long hemlines are effective for social media marketing, but have a short lifespan in actual sales. Based on this, the model outputs three future popularity curves C1, C2, and C3. If a company plans to launch its product in four weeks, the system extracts the curve value at the corresponding node of the four weeks as the survival popularity at that point in time. There is no need to show the formula calculation process here; it can be understood that the system identifies how much market attention will remain in the future, rather than the instantaneous popularity value at the current time.

[0084] As an anomaly handling mechanism, if the number of historical samples for a popular element is lower than the preset first sample size threshold, the system can expand the reference range and use similar styles, similar consumer groups, or similar seasons' sales lifecycles as supplements. However, the confidence level of the prediction results for that element should be lowered. If the difference between the data before and after smoothing is too large, it indicates that there is a lot of event-driven noise in the original popularity. The system can prompt the display terminal that the current public opinion is fluctuating greatly and it is not recommended to determine the style based solely on short-term popularity. If the company has not yet determined a specific future time node, such as the launch week still under discussion, the system can first output multiple candidate time nodes' popularity slices for the planning department to compare. If the market environment changes suddenly, such as abnormal weather causing a delay in the seasonal transition, the system can refresh the prediction curve after re-collecting recent data to avoid using invalid judgments.

[0085] For this autumn women's trench coat project, the system found that the popularity of lightweight wrinkle-resistant elements in commuting scenarios is relatively stable, and past sales records show that these features can sustainably drive repeat purchases after launch. Conversely, although the extra-long hem is a hot topic, its actual purchase conversion rate in the trench coat category is weak. If the project is planned to be launched around National Day, the system reads the element survival status at that time point from the prediction curve and judges that retaining the wide shoulder silhouette and lightweight wrinkle resistance is more conducive to continuing sales, while continuing to emphasize the extra-long hem may cause the design focus to become out of touch with the actual market.

[0086] The purpose of this step is to transform the judgment of design elements from static popularity ranking to a survival ability judgment for future listing nodes, thereby realizing a preliminary assessment of whether the popularity can be converted into sales. In specific implementation, the time series smoothing processing in this embodiment can adopt the exponential moving average algorithm to give higher weight to recent popularity data, so as to effectively filter out short-term data spike noise caused by accidental events. The popularity trend time series prediction model can rely on the time series analysis mechanism to perform window sliding calculation on the smoothed historical time series, thereby fitting the decay or growth trend curve of future time nodes, clearly defining the data flow rules, and breaking the business blind spot of judging the popularity cycle by experience alone.

[0087] In this embodiment, the fidelity analysis module is specifically used to perform the following operations:

[0088] The parameters of the 3D design model are analyzed into a mesh to obtain the 3D mesh features;

[0089] The physical properties of the fabric are mapped to the three-dimensional mesh features. Physical simulation calculations are performed through a preset design physical fidelity analysis model. The design physical fidelity analysis model uses a finite element analysis algorithm. By setting the material constitutive equation transformed from the physical properties of the fabric, the strain and displacement data of the three-dimensional mesh features under the simulated gravity field are calculated, thereby obtaining the simulated morphological features.

[0090] The spatial distance deviation between the feature vectors corresponding to the ideal morphological features preset in the parameters of the 3D design model and the simulated morphological features is calculated and used as the morphological deviation value.

[0091] The reciprocal of the sum of the morphological deviation value and the preset non-zero constant is used as the physical reproduction degree.

[0092] This embodiment provides a fidelity analysis step; specifically, after knowing whether the target design element will still have market appeal in the future, the system further answers another key question, namely, whether the ideal form on the design drawing can be stably transformed into the actual garment effect under specific fabric conditions;

[0093] In detail, relying solely on trend assessment still has significant limitations; even if a design element remains popular in the future, it does not necessarily mean that the current solution is worth producing; clothing design, especially trench coats and outerwear, is highly dependent on the actual performance of the fabric under gravity, stretching, bending, and human body dynamics; if the wide shoulder silhouette, hem swing, and waist cinching in the design drawing are based on an idealized digital model, but the selected fabric cannot support the silhouette, the physical sample garment may have problems such as shoulder deformation exceeding the preset structural threshold, hem smoothness below the preset physical parameter lower limit, or waistline rebound deformation rate not meeting the standard, ultimately resulting in a deviation between the three-dimensional shape of the design and the physical shape of the actual garment.

[0094] Therefore, this embodiment introduces a simulation mechanism that couples three-dimensional mesh features with fabric physical properties. During implementation, the system first performs mesh analysis on the parameters of the three-dimensional design model to form three-dimensional mesh features that are easy to analyze. This can be understood as decomposing the continuous garment shape into several interconnected local structural pieces, so that the stress and deformation characteristics of areas such as the shoulder, neckline, armhole, and hem can be observed separately.

[0095] The system maps the physical properties of the fabric to these grid features. For example, higher drape makes the hem fall more naturally, greater stiffness helps maintain the shoulder contour, and elastic recovery affects the rebound stability of the waist structure after wearing. Based on this, a physical fidelity analysis model is designed to perform physical simulation and output simulated morphological features. After comparing the simulated morphology with the preset ideal morphology in the design draft, a morphological deviation state is obtained. When the morphological deviation value is less than a preset threshold, the garment morphology is determined to have reached the preset restoration standard. When the morphological deviation value is greater than or equal to the preset threshold, the risk of morphological distortion is triggered.

[0096] Furthermore, the system maps morphological deviations to physical fidelity through preset transformation methods, allowing it to be included in subsequent scoring along with other business indicators; a simplified deduction can be performed; assuming the 3D model of the trench coat is resolved into shoulder mesh G1, waist mesh G2, and hem mesh G3; in the ideal form, G1 needs to maintain an outward support, G2 needs to form a natural contraction, and G3 needs to exhibit continuous swaying when walking; if fabric F1 is used, the simulation results show that G1 is close to the ideal form, and G3 sways continuously; if fabric F2 is used, although it has better wrinkle resistance, G3 cannot form the natural drooping curve in the design draft because the material hardness parameter exceeds the preset range; at this time, the system will identify the morphological deviation corresponding to F2 as exceeding the preset threshold, and the fidelity will be reduced accordingly; the so-called spatial distance deviation value here can be understood as the comprehensive difference between the simulated form and the ideal form. It is not necessary to elaborate on complex calculation details in the implementation method, but its engineering significance needs to be clarified: it describes the degree of distortion of the garment's outline;

[0097] As an anomaly handling mechanism, if the 3D model only completes the static standing posture without including dynamic gait parameters, the system can first perform a static fidelity analysis and mark the conclusions involving dynamic features such as swaying and collar rebound as dynamic effects to be supplemented. If a candidate fabric lacks complete mechanical parameters, the system can only simulate the dimensions supported by the existing parameters, such as only analyzing the contour changes caused by drape and thickness, without outputting conclusions about resilience. If the simulation results show significant differences in different key parts, such as the shoulder height being reproduced but the hem being obviously distorted, the system can output a regional fidelity report, prompting designers to adopt local splicing, lining adjustment, or structural modification, rather than abandoning the target design scheme entirely. If topological anomalies occur in the mesh analysis, such as the model uploaded to the design database having broken surfaces or missing pieces, the system can revert to the model repair process to avoid simulating on an incorrect geometry.

[0098] For this autumn women's trench coat project, the design team wanted to maintain a structured shoulder line but a smooth hem. When simulating the imported blended fabric, the system found that the fabric was more likely to reproduce the natural drape and waist cinching in the design draft, and the final sample garment had a high degree of consistency with the 3D visual design. However, when simulating the local functional fabric, the hem undulation was reduced, causing the style of the finished garment to shift from a soft yet sharp silhouette to a more functional, straight-line shape. This does not necessarily mean that the latter is unusable, but it means that if the current pattern structure is continued to be used, the style positioning of the final product will deviate from the original plan.

[0099] The purpose of this step is to shift the design aesthetic judgment to the coupling level of fabric properties and garment form, thereby achieving a quantitative description of the risks in physical reproduction and reducing repeated modifications and ineffective sampling in subsequent sample garments. From a quantitative calculation perspective, the feature vector corresponding to the simulated form characteristics can be set as follows: The feature vector corresponding to the preset ideal morphological features is Spatial distance deviation value is also known as morphological deviation value It can be obtained through the formula: ;

[0100] Furthermore, the accuracy of the reproduction of the actual object This can be expressed by the formula: ,in, A preset non-zero constant; used to adjust the sensitivity of the reduction value and ensure calculation stability; to make Result normalization is usually pre-defined. Alternatively, it can be dynamically set based on the deviation threshold; this constant not only avoids the calculation overflow anomaly caused by the denominator being zero when the shapes are completely identical, but also plays a role in smoothly adjusting the mapping range of the degree of restoration values.

[0101] In this embodiment, the scheduling and resource consumption calculation module is specifically used to perform the following operations:

[0102] Based on the physical properties of the fabric and supply chain timeliness data, a material supply network topology diagram containing multiple candidate material solutions is constructed.

[0103] Input the material supply network topology into the preset supply chain scheduling and resource consumption accounting model, optimize the shortest path for each node using the preset Dijkstra algorithm or A* pathfinding algorithm, obtain the shortest delivery time series corresponding to each candidate material solution as the target listing path, and extract the total duration of the shortest delivery time series as the time length.

[0104] Extract the resource usage parameters of each node in the target listing path and sum them up to obtain the total target resource usage.

[0105] This embodiment provides a scheduling and cost accounting step; specifically, after understanding the future survival status of trends and the degree to which different fabrics reproduce the design effect, the system continues to evaluate whether the solution can be processed and output to the business terminal at an appropriate time and with a controlled resource consumption threshold.

[0106] In detail, simply knowing that a certain fabric can better reproduce the design intent is not enough to form an actionable decision. In real business, apparel companies often face the following contradiction: the closer the fabric is to the ideal design, the longer the procurement cycle often exceeds the preset first time threshold, the fewer the number of candidate suppliers are than the preset first quantity threshold, and the less the production resource reserve is than the preset first reserve threshold. On the other hand, the easier it is to quickly implement ready-made fabrics, the more compromises may be made in terms of style expression. If supplier delivery time, factory capacity window and logistics route are not included in the unified analysis, the high-fidelity fabric solution may cause the delivery cycle to exceed the optimal launch time window.

[0107] Therefore, this embodiment introduces a material supply network topology diagram to describe the entire link from the source of the fabric to the warehouse of the finished garment. During implementation, the system constructs a network structure of multiple candidate material solutions based on the physical properties of the fabric and the timeliness data of the supply chain. The reason for considering the physical properties of the fabric at the same time is that some seemingly substitutable fabrics cannot be interchanged in real business. For example, although a type of fabric has a short delivery time, it should not be regarded as an equivalent candidate path because its weight or stiffness does not meet the pattern requirements.

[0108] The nodes in the network can include fabric suppliers, dyeing and finishing processes, factory production scheduling units, quality inspection nodes, logistics nodes, and warehousing nodes. The system performs path optimization among these nodes to obtain the shortest delivery time sequence from the current time to the marketable state for each candidate solution. The total duration corresponding to this sequence is the time length. At the same time, the system extracts the resource usage parameters of each node in the path, such as material acquisition resources, production scheduling switching resources, cross-regional transfer resources, and storage throughput resources, and summarizes them to form the target total resource usage.

[0109] A simplified deduction can be made; assume there are scheme A and scheme B; the path of scheme A is P1→D1→C1→T1→W1, where P1 is the overseas fabric supplier, D1 is the dyeing and finishing, C1 is the main factory, T1 is the cross-regional transportation, and W1 is the finished product warehousing; the path of scheme B is P2→C2→T2→W1, where P2 is the domestic spot supplier; after system comparison, it is found that although scheme A has better physical performance, it has more nodes, and any delay at any node may compress the market launch window; scheme B has a shorter path and a better time length, but may be accompanied by increased resource consumption or style deviation; the so-called shortest path optimization in this embodiment should be understood as finding the market launch route with a shorter or more stable total delivery time in the executable link under the premise of satisfying design adaptability, rather than pursuing the shortest transportation distance in isolation.

[0110] As an exception handling mechanism, if a candidate solution has only a single supplier for its critical nodes, the system should mark the path as vulnerable to prevent over-reliance on single-point fulfillment. If the factory's production window conflicts with the target production cycle, even if the fabric is ready, the solution can be judged as an unusable path. If logistics nodes are significantly affected by seasonal factors, such as peak season warehouse shortages or cross-border customs clearance fluctuations, the system can add a timeliness fluctuation range to the path and reduce the timeliness stability level of the solution. If the total time of different paths is similar but the cost difference is greater than a preset ratio, the system will not draw a conclusion directly, but will leave the timeliness and system resource consumption to the subsequent comprehensive scoring stage for judgment. If all candidate paths cannot be completed before the popular window, the system can suggest to the front end to postpone the project, reduce the process complexity, or change to a small-batch test version.

[0111] For this autumn women's trench coat project, the imported blended fabric solution requires overseas warehousing, customs clearance, secondary finishing, and main factory production scheduling, resulting in a longer overall chain, but it can maintain the high drape effect of the original design; the local functional fabric solution can directly lock in the spot goods and connect with the backup factory, significantly shortening the path to market; based on this, the system provides two executable paths and their corresponding time lengths and comprehensive resource consumption, providing an objective supply chain basis for subsequent judgment on whether the trench coat should adhere to the ideal style or prioritize seizing the market window;

[0112] The purpose of this step is to unify fabric selection and supply chain execution constraints into the same launch path for management, thereby achieving simultaneous accounting of whether the product can be launched on time and the consumption of system materials and logistics resources.

[0113] In this embodiment, the failure risk assessment module is specifically used to perform the following operations:

[0114] The survival temperature and physical reproduction degree are normalized to obtain standard temperature and standard reproduction degree;

[0115] The standard timeliness and standard resource evaluation are obtained by adding the time length and the total target resource usage to a preset non-zero constant used to prevent computational overflow anomalies, taking the reciprocal, and then normalizing.

[0116] Obtain a preset set of rating weights, which includes the weight coefficients corresponding to standard popularity, standard fidelity, standard timeliness, and standard resource evaluation.

[0117] Based on the set of scoring weights, the standard popularity, standard fidelity, standard timeliness, and standard resource evaluation are weighted and summed to obtain the comprehensive market success probability score of the target design scheme under the candidate material scheme.

[0118] This embodiment provides a business risk assessment step; specifically, after obtaining the survival popularity, physical reproduction degree, time length and total target resource consumption, the system further transforms these data with different dimensions and different business meanings into a comparable comprehensive market success probability score, which is used to judge the commercial feasibility of each candidate solution;

[0119] In detail, relying solely on the independent outputs of the aforementioned modules still presents decision-making obstacles; the multi-dimensional system indicators each reflect different physical or execution constraints, and without a unified data evaluation standard, it is difficult to integrate the indicators of each node, which may ultimately lead to the evaluation process degenerating into subjective human experience judgment.

[0120] Therefore, this embodiment uses standardization and weighted summarization to converge the four types of indicators into the same scoring framework. The engineering significance of the normalization process here is to eliminate the dimensional differences of multi-source data, so that the heat index, morphology index, timeliness index, and resource consumption index can all be converted into standardized contributions to the system's comprehensive score. The reciprocal correlation processing method is used for time length and comprehensive resource consumption because in the decision-making of apparel market launch, the shorter the time length and the smaller the resource consumption, the higher the corresponding evaluation gain is usually. However, the gain should not be infinitely amplified. Therefore, it is necessary to maintain the stability of the evaluation through preset constants and standardization steps. The set of scoring weights reflects the company's business strategy preferences.

[0121] For example, in high-fidelity evaluation scenarios, the weight of physical reproduction is usually higher because structural consistency directly affects the output of product attributes; for high-responsiveness scenarios, timeliness and popularity are given higher weight because the matching degree of execution sequence is more critical than subtle differences in physical parameters; the system does not require the weights to remain fixed, but allows configuration based on category, season, brand positioning, or project type; the final comprehensive market success probability score can be understood as a business feasibility indicator after balancing multiple factors. Its core purpose is not to provide an absolute prediction of subsequent conversion volume, but to help companies identify the relatively better choice among multiple candidate solutions;

[0122] A simplified deduction can be made; assuming that Option A performs better in terms of popularity and fidelity, but its duration exceeds the set period threshold and the total target resource consumption exceeds the preset budget limit; Option B has an advantage in terms of timeliness and system resource consumption, but its design effect is slightly sacrificed; after standardization, the system maps these four types of indicators to comparable standard popularity, standard fidelity, standard timeliness, and standard resource evaluation; then, based on the weights set by the enterprise for this project, such as prioritizing timeliness and popularity for the core traffic-driving product in autumn, and prioritizing fidelity for the image product, the system obtains the comprehensive scores of the two candidate options respectively; thus, the system does not need to rely on manual qualitative measurement of the subjective weight difference between timeliness indicators and form indicators, but instead allows the scoring results to intuitively reflect organizational preferences through preset business strategies;

[0123] As an anomaly handling mechanism, if a certain indicator is missing, for example, if the fabric testing is incomplete and the reproduction degree has not yet been generated, the system can temporarily not output the final score, but instead display that the key indicator is incomplete; if the same solution has an extreme imbalance among the four indicators, such as extremely high popularity but a delivery cycle that is seriously out of window, the system can add a structural risk warning next to the comprehensive score to prevent a single high score from covering up major shortcomings; if the company temporarily adjusts its business focus, for example, by increasing the sensitivity weight to resource consumption due to limited resource supply, the system can reload the weight set and recalculate the score in real time without having to recollect front-end data; if the scores of multiple solutions are close, the system can prompt the user to enter the manual review mode, where the design, product, and procurement departments jointly review the source of the differences.

[0124] For this autumn women's trench coat project, the imported blended fabric option is more ideal in terms of design reproduction and style consistency, but the launch time is slower; the local functional fabric option is more conducive to falling into the high-frequency range of the preset audience demand, and has a lower resource consumption volatility; based on the company's strategy of defining this trench coat as the main autumn sales item, the system appropriately increases the role of popularity and timeliness in the comprehensive evaluation, and finally obtains the comprehensive market success probability score of each candidate option; in this way, the strategy output is no longer the result dominated by a single dimension indicator, but a unified output formed around the comprehensive goals set for the project;

[0125] The purpose of this step is to establish a cross-departmental shared standard for quantifying business risks, thereby enabling horizontal comparisons of different material solutions and providing a clear ranking basis for subsequent strategy generation; in terms of specific calculation logic, the time length is set as... The total target resource usage is The specific process of taking the reciprocal can be represented by an intermediate variable. and ,in, This is a preset non-zero constant, which plays a role in dimensional smoothing and numerical conversion in the calculation, ensuring that timeliness and system resource consumption can be converted into dimensionless positive evaluation scores after taking the reciprocal; through this mapping, the constraints of timeliness and system resource consumption, which were originally better the smaller they were, are reversed into positive evaluation indicators that are better the larger they are.

[0126] The system uniformly adopts a normalization algorithm to combine survival popularity, physical reproduction accuracy, and as well as All values ​​are mapped to a standard data range and denoted as indicators. Combined with the corresponding weight coefficients in the weight set The overall market success probability score The specific deduction process is as follows: This eliminates the dimensional barriers of multi-source data in the underlying business model, forming a clear and reproducible cross-departmental decision-making benchmark.

[0127] In this embodiment, the operation of the strategy generation module in generating the optimal launch time window and material combination suggestions specifically includes:

[0128] Obtain the comprehensive market success probability score for all candidate material solutions, and select the candidate material solution with the highest comprehensive market success probability score as the optimal material combination.

[0129] Extract the target market launch path and survival popularity prediction curve corresponding to the optimal material combination;

[0130] Obtain a preset popularity threshold, and determine the optimal listing time window as the continuous time interval in the survival popularity prediction curve where the popularity is greater than or equal to the preset popularity threshold and the time node is later than the end time of the target listing path; if there is no continuous time interval that meets the requirements, output a re-evaluation instruction.

[0131] The optimal time-to-market window and the optimal material combination are packaged together to generate an optimal time-to-market window and material combination recommendation.

[0132] This embodiment provides a strategy generation step; specifically, after the system has completed the comprehensive scoring of each candidate solution, the scoring results are further transformed into listing suggestions that the enterprise can directly implement, and integrated strategy conclusions such as specific material combination configurations and corresponding execution cycle suggestions are output.

[0133] In detail, ranking based on comprehensive scores alone is still insufficient. Even if the optimal solution has been obtained, the system still needs to handle the matching of two execution dimensions: first, which specific execution path the optimal solution corresponds to; second, even if the path can be delivered, it does not mean that it matches the trend peak at any point in time. If there is a lack of a precise time-series matching mechanism, there may be a problem where a high-scoring solution is determined, but its execution cycle is misaligned with the trend node.

[0134] Therefore, this embodiment further links the target market launch path and the survival popularity prediction curve based on the scoring results; the system first finds the scheme with the highest comprehensive market success probability score from all candidate schemes and regards it as the current optimal material combination; extracts the target market launch path end time point corresponding to the scheme, that is, the earliest time from now when qualified garments can be delivered to the predetermined sales node;

[0135] The system searches for a continuous interval on the survival popularity prediction curve associated with the scheme that meets two conditions: first, the popularity in the interval is higher than the preset popularity threshold, indicating that consumers still have sufficient attention to the relevant design elements; second, the start time of the interval is later than the end time of the target market launch path, indicating that the system can meet the preconditions for launching the product to the market in terms of physical execution timing; the continuous interval that meets the above conditions can be identified as the optimal market launch time window.

[0136] The system combines the selected time window with the chosen materials and packages them into a deliverable proposal. Simplified deductions and explanations are possible. Assuming that Plan A and Plan B rank first and second respectively in terms of comprehensive scores, after selecting Plan A, the system reads its target market entry completion node as Wk4, meaning warehousing can be completed in four weeks. Then, it reads the survival heat prediction curve of relevant design elements and finds that the heat consistently exceeds the business-set standard from week 5 to week 8, while the heat declines significantly after week 9. Therefore, week 5 to week 8 can be identified as a recommended market entry window. If another interval also has high heat but occurs before week 4, it lacks practical feasibility due to the supply chain not yet completing delivery and is not included in the window recommendation. The preset heat threshold here is not an abstract threshold but rather a minimum market attention level set by the company based on experience, used to exclude market entry points that have already entered a decline phase.

[0137] As an anomaly handling mechanism, if the solution with the highest overall score is optimal, but its target market launch date is nearing the end of its life cycle decline, the system can suggest shortening the process or switching to a suboptimal quick-turnaround solution. If there are no consecutive high-heat intervals after the target market launch date, it indicates that the design solution has missed its opportunity, and the system can not generate a recommendation window, but instead output alternative suggestions such as suspending regular production, switching to test batches, or adjusting design elements. If there are multiple scattered windows, the system can perform secondary sorting based on window length, heat stability, or holiday sales nodes. If the score differences between different material solutions are very small, but the market launch window differences are significant, the system can display two alternative suggestions side by side on the display terminal for management to make a final choice based on channel strategy.

[0138] For the autumn women's trench coat project, after comprehensive comparison, the system concluded that although the local functional fabric solution was slightly inferior to the imported blend in terms of hem drape, it received a higher overall score because it could be delivered earlier. The system further found that the solution could still meet the continued high demand for seasonal commuter coats after it was put into storage. Therefore, the system packaged the local functional fabric with the launch window of consecutive preset time intervals to generate the optimal launch time window and material combination suggestions, which were provided to the planning department to arrange the new product launch schedule, marketing material shooting and store distribution plans.

[0139] The purpose of this step is to move the analysis results from simple score ranking to a combination of solutions with time-series and material execution characteristics, so as to achieve market-ready decision outputs that can be directly implemented.

[0140] In this embodiment, the operation of the strategy generation module to perform high redundancy failure risk warning on the target design scheme specifically includes:

[0141] Obtain the preset risk threshold;

[0142] Compare the overall market success probability score corresponding to the optimal material combination with the preset risk threshold;

[0143] If the success probability score of the comprehensive market is greater than or equal to the preset risk threshold, the target design scheme is determined to be in a safe state and no warning instruction is triggered.

[0144] If the overall market success probability score is less than the preset risk threshold, the target design scheme is determined to have a high risk of redundancy failure, and an early warning instruction is generated.

[0145] This embodiment provides a high redundancy failure risk warning step; specifically, after the optimal market launch window and material combination suggestions have been output, the system also performs a risk gate on the target design scheme to determine whether the scheme meets the minimum safety standards that the enterprise allows for mass production.

[0146] In detail, even if a candidate solution ranks the highest in a horizontal comparison, it does not necessarily mean that the solution is worth entering mass production. In actual evaluation scenarios, there are often situations where the comprehensive scores of all candidate solutions are relatively low, that is, each solution is not ideal from the perspective of overall multi-dimensional indicators, and only the relatively optimal one exists. If the system only provides the optimal solution without setting an absolute risk bottom line, the system may still push the design solution that does not have the execution benefits into the full production process, ultimately causing redundant risks such as idle physical resources, ineffective use of production line capacity, and overloaded warehouse resources.

[0147] Therefore, this embodiment introduces a risk threshold mechanism. This threshold reflects the system's judgment boundary for the upper limit of fault tolerance. When the comprehensive market success probability score corresponding to the optimal material combination does not reach this boundary, even if it ranks first among the candidates, it should be considered unsuitable for proceeding according to the conventional process. During implementation, the system first reads the preset risk threshold and then compares it with the comprehensive market success probability score corresponding to the optimal material combination. If the score reaches or exceeds the threshold, the project is determined to be in a safe state, indicating that under the current market, design, timeliness, and cost conditions, the project has basic feasibility and can continue to proceed according to the established process. If the score is lower than the threshold, a high risk of redundancy failure is determined, and an early warning instruction is generated. This early warning instruction can be sent to the relevant business ports in the form of a project-level red icon, email reminder, or task flow on the display terminal, prompting a re-examination of the fabric, pattern, execution rhythm, or production scale.

[0148] A simplified deduction can be made: Suppose the system selects option B as the current optimal option among multiple candidate material options, but its comprehensive score still falls below the safety line set by the system; This means that although option B is more suitable for the current conditions than option A, it is still insufficient to support standard batch production; At this time, the system does not recommend issuing batch instructions directly, but triggers an early warning, and can provide an explanation of the source of risk, such as the market launch window being too short, the morphological reproduction degree being lower than the set benchmark, or the resource consumption exceeding the preset upper limit; In this way, the early warning mechanism focuses not on the relative ranking between options, but on whether the project has crossed the basic feasibility threshold under multi-dimensional constraints;

[0149] As an exception handling mechanism, if the risk threshold has not been configured, the system can call the default threshold based on the category template, but the threshold must be marked as the default in the terminal. If a lower success rate is tolerated in a special application scenario, such as a gray-scale testing phase to verify a new process, a temporary threshold can be enabled for a specific project, but the system should retain adjustment records. If the score is right on the edge of the threshold, the system can classify the project into the manual review area, where managers can make supplementary judgments based on brand strategy, channel characteristics, and small-batch rapid response capabilities, rather than relying solely on a single hard threshold for rigid blocking. If a high redundancy failure risk warning is triggered, and the relevant instruction port modifies the fabric scheme, shortens the execution path, or adjusts the design parameters, the system should allow the evaluation process to be re-initiated, and the new results should overwrite the old warning status.

[0150] For the autumn women's trench coat project, if the local functional fabric solution has an advantage in speed, but still falls below the safety standards set by the company for the main autumn style after comprehensive consideration, the system will identify the project as having a high risk of redundancy failure and issue a warning instruction. Management can then decide to convert it to a small-scale test flow or further optimize the pattern and material combination before re-evaluating. Conversely, if the score reaches the safety line, the project can enter the production and execution flow stage without additional threshold interception.

[0151] The purpose of this step is to establish a final closed-loop risk gate for the system process, thereby preventing the potential for redundant inventory and ineffective resource utilization from being directly pushed into the production stage, and avoiding the direct implementation of a relatively optimal but absolutely risky solution.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A textile and apparel design business management system based on model analysis, characterized in that, include: The multi-source data acquisition module is used to collect multi-source business data, including market trend data, design attribute data including 3D design model parameters and fabric physical properties, and supply chain timeliness data. The trend prediction and evaluation module is used to input the market trend data into a preset popular trend time series prediction model constructed by an autoregressive integral moving average model or a long short-term memory artificial neural network; extract specific design elements, determine specific future time nodes in combination with the set listing cycle, and calculate the survival popularity of the specific design elements at that time node. The fidelity analysis module is used to map the physical properties of the fabric to the three-dimensional mesh features based on the parameters of the three-dimensional design model and the physical properties of the fabric to perform physical simulation calculations and evaluate the fidelity to the real object. The scheduling and resource consumption calculation module is used to calculate the target market launch path, time length, and total target resource consumption under multiple candidate material schemes; Implement a failure risk assessment module to calculate the overall market success probability score of the target design scheme under each candidate material scheme by combining survival heat, physical reproduction degree, time length and total target resource consumption; The strategy generation module is used to generate the optimal time-to-market window and material combination suggestions based on the score, and to provide a high redundancy failure risk warning for the target design scheme, and output the results to the display terminal.

2. The textile and apparel design business management system based on model analysis according to claim 1, characterized in that, The multi-source data acquisition module is specifically used to perform the following operations: The data on the popularity and decay curves of trending elements on social media platforms are collected through a pre-set web crawler interface, along with historical sales lifecycle data of similar models, and combined to form the market trend data. The three-dimensional design model parameters corresponding to the target design scheme are extracted from the preset design database. The three-dimensional design model parameters include preset ideal shape features, and the corresponding fabric physical properties are obtained from the preset fabric physical property library and combined into the design attribute data. The delivery date data, factory production cycle, and logistics timeliness of each fabric supplier are retrieved from the preset supplier business system and combined to form the supply chain timeliness data.

3. The textile and apparel design business management system based on model analysis according to claim 2, characterized in that, The trend prediction and evaluation module is specifically used to perform the following operations: The popularity and decay curve data of the aforementioned popular elements are subjected to time series smoothing to obtain smooth trend data; Input the smoothed trend data and historical sales lifecycle data of similar models into the popular trend time series prediction model, and output the survival popularity prediction curve; Based on the specific future time nodes set according to business needs, the values ​​corresponding to the specific time nodes are extracted from the survival popularity prediction curve and used as the survival popularity.

4. The textile and apparel design business management system based on model analysis according to claim 3, characterized in that, The fidelity analysis module is specifically used to perform the following operations: The parameters of the three-dimensional design model are analyzed in a mesh format to obtain the three-dimensional mesh features; The physical properties of the fabric are mapped to the three-dimensional mesh features. Physical simulation calculations are performed using a preset design physical fidelity analysis model. The design physical fidelity analysis model uses a finite element analysis algorithm. By setting the material constitutive equation transformed from the physical properties of the fabric, the strain and displacement data of the three-dimensional mesh features under the simulated gravity field are calculated, thereby obtaining the simulated morphological features. Calculate the spatial distance deviation between the feature vectors corresponding to the simulated morphological features and the preset ideal morphological features in the parameters of the three-dimensional design model, and use this as the morphological deviation value; The reciprocal of the sum of the morphological deviation value and a preset non-zero constant is taken as the physical reproduction degree.

5. The textile and apparel design business management system based on model analysis according to claim 4, characterized in that, The scheduling and resource consumption calculation module is specifically used to perform the following operations: Based on the physical properties of the fabric and the supply chain timeliness data, a material supply network topology diagram containing multiple candidate material solutions is constructed. The material supply network topology is input into a preset supply chain scheduling and resource consumption accounting model. The shortest path is optimized for each node using a preset Dijkstra algorithm or A* pathfinding algorithm. The shortest delivery time sequence corresponding to each candidate material solution is obtained as the target listing path, and the total duration of the shortest delivery time sequence is extracted as the time length. Extract the resource usage parameters of each node in the target listing path and sum them up to obtain the total target resource usage.

6. The textile and apparel design business management system based on model analysis according to claim 5, characterized in that, The failure risk assessment module is specifically used to perform the following operations: The survival heat and the physical reduction degree are normalized to obtain standard heat and standard reduction degree; The time length and the total target resource usage are added to a preset non-zero constant used to prevent computational overflow anomalies, and the reciprocal is taken. Then, normalization is performed to obtain the standard timeliness and standard resource evaluation. Obtain a preset set of rating weights, the set of rating weights including the standard popularity, the standard fidelity, the standard timeliness, and the weight coefficients corresponding to the standard resource evaluation; Based on the set of scoring weights, the standard popularity, standard fidelity, standard timeliness, and standard resource evaluation are weighted and summed to obtain the comprehensive market success probability score of the target design scheme under the candidate material scheme.

7. The textile and apparel design business management system based on model analysis according to claim 6, characterized in that, The strategy generation module generates the optimal launch time window and material combination suggestions, specifically including the following operations: Obtain the comprehensive market success probability score for all candidate material solutions, and select the candidate material solution with the highest comprehensive market success probability score as the optimal material combination. Extract the target market launch path corresponding to the optimal material combination and the survival popularity prediction curve; Obtain a preset popularity threshold, and determine the optimal listing time window as the continuous time interval in the survival popularity prediction curve where the popularity is greater than or equal to the preset popularity threshold and the time node is later than the end time of the target listing path; if there is no continuous time interval that meets the requirements, output a re-evaluation instruction. The optimal launch time window and the optimal material combination are packaged together to generate the optimal launch time window and material combination suggestion.

8. The textile and apparel design business management system based on model analysis according to claim 7, characterized in that, The operation of the strategy generation module to perform high redundancy failure risk warning for the target design scheme specifically includes: Obtain the preset risk threshold; The overall market success probability score corresponding to the optimal material combination is compared with the preset risk threshold; If the overall market success probability score is greater than or equal to the preset risk threshold, the target design scheme is determined to be in a safe state and no warning instruction is triggered. If the overall market success probability score is less than the preset risk threshold, the target design scheme is determined to have a high risk of redundancy failure, and an early warning instruction is generated.

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