Garment fashion trend prediction and intelligent design platform

Abstract

The invention discloses a clothing fashion trend prediction and intelligent design platform, and the platform comprises a data collection module which is used for obtaining original data related to clothing; the data processing module is used for carrying out preprocessing and variational mode decomposition on the collected original data and extracting effective features in the data; the data analysis and prediction module is used for predicting the trend of the processed data by using a gating circulation unit model; the intelligent design module is used for recommending design elements according to the fashion trend prediction result and generating a costume design drawing by utilizing a generative adversarial network; and the platform interface module is used for providing a user visual interface, displaying a trend analysis result and a design scheme and allowing the user to carry out design adjustment. According to the clothing fashion trend prediction and intelligent design platform, the accuracy of fashion trend prediction and the efficiency of intelligent design are effectively improved, the design cost is reduced, the product marketing period is shortened, and the market competitiveness of a brand is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of clothing design and manufacturing, and specifically relates to a clothing fashion trend prediction and intelligent design platform. Background Art

[0002] Against the backdrop of rapid globalization and informatization, the apparel industry is facing unprecedented challenges and opportunities. Consumer demands are becoming increasingly diversified and personalized, and traditional clothing design and manufacturing models are unable to respond quickly to market changes, putting companies at a disadvantage in the competition. Therefore, how to use advanced technology to improve design efficiency, predict market trends, and shorten product launch cycles has become an urgent issue for the apparel industry.

[0003] At present, the forecast of fashion trends mainly relies on the experience of designers and market research, but this method has defects such as strong subjectivity, incomplete data, and long forecasting cycle, and it is impossible to quickly and accurately grasp the market trend. In addition, the traditional clothing design process is often time-consuming and costly, and the creativity and market adaptability of the design plan are difficult to guarantee.

[0004] In order to overcome these problems, big data and artificial intelligence technologies have been widely used in the clothing industry in recent years. Nevertheless, existing technologies still have many deficiencies in data processing, model selection, and system integration. Especially in the feature extraction and trend prediction of time series data, traditional statistical methods and simple machine learning models cannot fully mine the deep information in the data, and the prediction accuracy needs to be improved. In addition, how to effectively combine the prediction results with clothing design to achieve automatic generation and optimization of design schemes is also a hot spot and difficulty in current research. Summary of the invention

[0005] The present invention provides a clothing fashion trend prediction and intelligent design platform to solve the above-mentioned technical problems, and specifically adopts the following technical solutions:

[0006] A clothing fashion trend prediction and intelligent design platform, including:

[0007] Data acquisition module, used to obtain raw data related to clothing;

[0008] The data processing module is used to pre-process and perform variational mode decomposition on the collected raw data and extract effective features from the data;

[0009] Data analysis and prediction module, used to predict the popularity trend of processed data using the gated recurrent unit model;

[0010] Intelligent design module, which is used to recommend design elements based on the results of fashion trend prediction and generate clothing design drawings using generative adversarial networks;

[0011] The platform interface module is used to provide users with a visual interface, display trend analysis results and design solutions, and allow users to make design adjustments.

[0012] Furthermore, the data acquisition module performs data acquisition through the following steps:

[0013] Collect sales data from e-commerce platforms, including product sales volume, sales revenue, and hot-selling products;

[0014] Collecting comment data from the user comment area, wherein the comment data includes the user's evaluation, rating, and comment content of the product;

[0015] Collect discussion data from social media platforms, including topics, tags, post content, and user interaction data related to clothing;

[0016] The press conference information is collected from the fashion press conference, and the press conference information includes the press conference time, brand, display clothing style, color, and material.

[0017] Furthermore, the data collection module collects data regularly through web crawler technology and API interface, and stores the data in a database.

[0018] Furthermore, the data processing module includes:

[0019] Data cleaning unit, used to remove noise data, duplicate data, incomplete data from the collected raw data, unify the data format, and fill in missing values;

[0020] The variational modal decomposition unit is used to perform multi-scale decomposition on the cleaned data and extract various modal components.

[0021] Furthermore, the formula of the variational mode decomposition is:

[0022]

[0023] in, For the modal, is its center frequency.

[0024] Furthermore, the gated recurrent unit model includes:

[0025] Update gate, used to control the impact of the previous state on the current state;

[0026] Reset gate, used to control the impact of current input on current state;

[0027] The current state unit is used to calculate the state at the current moment.

[0028] Furthermore, the mathematical formula of the gated recurrent unit model is:

[0029]

[0030]

[0031]

[0032]

[0033] in, To update the gate, To reset the gate, is the current state, is the current state, W and b are model parameters.

[0034] Furthermore, the intelligent design module includes:

[0035] Design element library, which is used to store a database containing design elements such as colors, styles, materials, patterns, etc.;

[0036] A design element recommendation unit, used to select elements that meet the predicted trend from the design element library according to the trend prediction results;

[0037] Generative adversarial networks for generating clothing designs based on recommended design elements.

[0038] Furthermore, the mathematical formula of the generative adversarial network is:

[0039]

[0040] Among them, D is the discriminator, G is the generator, is the real data sample, z is the noise sample, is the distribution of real data, is the distribution of noise samples.

[0041] Furthermore, the platform interface module includes:

[0042] Trend analysis result display unit, used to visually display the trend prediction results;

[0043] Design scheme display unit, used to display automatically generated clothing design schemes for users to preview, edit and select the final design;

[0044] The design adjustment unit is used to provide online design tools for users to fine-tune and customize the design plan according to their needs.

[0045] The invention is beneficial in that it provides a clothing fashion trend prediction and intelligent design platform, which realizes high-precision prediction of clothing fashion trends and intelligent design generation, significantly improves design efficiency and market response speed, reduces design costs and error risks, and enhances the market competitiveness and brand influence of enterprises. The details are as follows:

[0046] 1. Improve the accuracy of trend prediction:

[0047] Multi-source data integration: The platform obtains data from multiple channels such as e-commerce platforms, user reviews, social media and fashion shows. The data sources are wide and rich, ensuring the comprehensiveness and representativeness of the data.

[0048] Advanced time series analysis: Use variational mode decomposition (VMD) to perform multi-scale decomposition of time series data, extract different modal components, effectively capture the complex features in the data, and improve the accuracy of data processing and feature extraction.

[0049] Deep learning prediction model: The gated recurrent unit (GRU) model is used to effectively utilize historical information and current inputs through the update and reset gate mechanisms, thereby improving the accuracy and robustness of trend predictions.

[0050] 2. Realize intelligent design:

[0051] Automated design element recommendation: Based on the trend prediction results, the design element library intelligently recommends design elements such as colors, styles, materials, and patterns that are in line with current trends, reducing the workload and time cost of designers.

[0052] Generative Adversarial Network (GAN): GAN technology is used to generate clothing design drawings that conform to the recommended design elements, ensuring the novelty and diversity of the design scheme while retaining the creativity and market adaptability of the design.

[0053] 3. Improve design efficiency and market response speed:

[0054] Full process automation: The platform realizes full process automation from data collection, processing, analysis to design generation, greatly improving work efficiency and shortening the product design and market launch cycle.

[0055] User participation and adjustment: A visual interface is provided to display trend analysis results and design solutions. Users can preview, edit and select the final design solution online, and make fine adjustments and customizations through design adjustment tools, which improves user satisfaction and the market adaptability of the design solution.

[0056] 4. Reduce costs and risks:

[0057] Reduce manual intervention: Through automated data processing and design generation, the reliance on designer experience and manual operations is reduced, which reduces labor costs and the risk of errors.

[0058] Improve market competitiveness: Accurate trend forecasts and efficient intelligent design enable companies to quickly respond to market changes, launch products that meet market demand, and enhance the brand's market competitiveness.

[0059] 5. Significant technical advantages:

[0060] Combination of VMD and GRU: The combination of variational mode decomposition (VMD) and gated recurrent unit (GRU) fully utilizes the time series decomposition ability of VMD and the deep learning prediction ability of GRU to provide accurate prediction of popular trends.

[0061] GAN generative design: The application of generative adversarial networks (GAN) in clothing design ensures the innovation and diversity of design schemes while taking into account market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0063] Figure 1 It is a flow chart of a clothing fashion trend prediction and intelligent design platform of the present invention. DETAILED DESCRIPTION

[0064] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0065] The present application discloses a clothing fashion trend prediction and intelligent design platform. The flowchart of the clothing fashion trend prediction and intelligent design platform is as follows: Figure 1 As shown. The clothing fashion trend prediction and intelligent design platform includes: data acquisition module, data processing module, data analysis and prediction module and platform interface module. Figure 1 The flowchart introduces each module of the clothing fashion trend prediction and intelligent design platform of this application.

[0066] The data collection module is used to obtain raw data related to clothing. In the embodiment of the present application, the data collection module regularly collects data through web crawler technology and API interface, and stores it in the database.

[0067] Specifically, web crawler technology is used to regularly collect data from e-commerce platforms and social media platforms. The crawler program simulates a user visiting the website and automatically extracts the required data. Structured data is obtained from various data sources through API interfaces. Many e-commerce platforms and social media platforms provide API interfaces that allow developers to obtain data on the platform. All data is stored in a centralized database for subsequent processing and analysis.

[0068] In the implementation manner of the present application, the data acquisition module performs data acquisition through the following steps:

[0069] Sales data is collected from e-commerce platforms, including product sales volume, sales revenue, and hot-selling products.

[0070] Review data is collected from the user review area, and the review data includes the user's evaluation, rating, and review content of the product.

[0071] Discussion data is collected from social media platforms, including clothing-related topics, tags, post content, and user interaction data.

[0072] Collect press conference information from fashion shows, including press conference time, brand, displayed clothing styles, colors, and materials.

[0073] The data processing module is used to preprocess and perform variational mode decomposition on the collected raw data and extract effective features from the data.

[0074] In an embodiment of the present application, the data processing module includes:

[0075] The data cleaning unit is used to remove noise data, duplicate data, and incomplete data from the collected raw data, unify the data format, and fill in missing values.

[0076] The variational mode decomposition (VMD) unit is used to perform multi-scale decomposition on the cleaned data (time series data) and extract various modal components. VMD can decompose complex time series into several intrinsic mode functions (IMFs), each of which represents a different frequency component, so as to better capture the characteristics of the data and finally output the processed data features ( ).

[0077] In the implementation manner of the present application, the formula for variational mode decomposition is:

[0078]

[0079] in, For the modal, is its center frequency.

[0080] The data analysis and prediction module is used to predict the popularity trend of the processed data using the gated recurrent unit (GRU) model.

[0081] In an embodiment of the present application, the gated recurrent unit model includes:

[0082] Update gate, used to control the impact of the previous state on the current state.

[0083] Reset gate, used to control the impact of current input on current state.

[0084] The current state unit is used to calculate the state at the current moment.

[0085] In an embodiment of the present application, the mathematical formula of the gated cycle unit model is:

[0086]

[0087]

[0088]

[0089]

[0090] in, To update the gate, To reset the gate, is the current state, is the current state, W and b are model parameters.

[0091] The intelligent design module is used to recommend design elements based on the results of fashion trend prediction and generate clothing design drawings using a generative adversarial network.

[0092] In an embodiment of the present application, the intelligent design module includes:

[0093] The design element library is used to store a database of design elements including colors, styles, materials, patterns, etc. Each design element has a corresponding attribute label to facilitate the recommendation system to filter and match.

[0094] The design element recommendation unit is used to select elements that meet the predicted trend from the design element library based on the trend prediction results. The recommendation system will filter out matching design elements based on the key features of the popular trend prediction results (such as popular colors, styles, materials, etc.).

[0095] Generative Adversarial Networks (GANs) are used to generate clothing designs based on recommended design elements.

[0096] In the implementation of the present application, the mathematical formula for generating an adversarial network is:

[0097]

[0098] Among them, D is the discriminator, G is the generator, is the real data sample, z is the noise sample, is the distribution of real data, is the distribution of noise samples.

[0099] The platform interface module is used to provide users with a visual interface, display trend analysis results and design solutions, and allow users to make design adjustments.

[0100] In an embodiment of the present application, the platform interface module includes:

[0101] The trend analysis result display unit is used to visualize the trend forecast results. Users can intuitively understand market trends and future trends through charts, statistical data, etc.

[0102] The design scheme display unit is used to display the automatically generated clothing design schemes for users to preview, edit and select the final design.

[0103] The design adjustment unit is used to provide online design tools for users to fine-tune and customize the design plan according to their needs.

[0104] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A clothing fashion trend prediction and intelligent design platform, It is characterized in that Include: Data acquisition module, used to obtain raw data related to clothing; The data processing module is used to pre-process and perform variational mode decomposition on the collected raw data and extract effective features from the data; Data analysis and prediction module, used to predict the popularity trend of processed data using the gated recurrent unit model; Intelligent design module, which is used to recommend design elements based on the results of fashion trend prediction and generate clothing design drawings using generative adversarial networks; The platform interface module is used to provide users with a visual interface, display trend analysis results and design solutions, and allow users to make design adjustments.

2. The clothing fashion trend prediction and intelligent design platform according to claim 1, It is characterized in that The data acquisition module performs data acquisition through the following steps: Collect sales data from e-commerce platforms, including product sales volume, sales revenue, and hot-selling products; Collecting comment data from the user comment area, wherein the comment data includes the user's evaluation, rating, and comment content of the product; Collect discussion data from social media platforms, including topics, tags, post content, and user interaction data related to clothing; The press conference information is collected from the fashion press conference, and the press conference information includes the press conference time, brand, display clothing style, color, and material.

3. The clothing fashion trend prediction and intelligent design platform according to claim 2, It is characterized in that The data collection module collects data regularly through web crawler technology and API interface, and stores the data in the database.

4. The clothing fashion trend prediction and intelligent design platform according to claim 1, It is characterized in that The data processing module comprises: Data cleaning unit, used to remove noise data, duplicate data, incomplete data from the collected raw data, unify the data format, and fill in missing values; The variational modal decomposition unit is used to perform multi-scale decomposition on the cleaned data and extract various modal components.

5. The clothing fashion trend prediction and intelligent design platform according to claim 4, It is characterized in that The formula for the variational mode decomposition is: in, For the modal, is its center frequency.

6. The clothing fashion trend prediction and intelligent design platform according to claim 1, It is characterized in that The gated recurrent unit model includes: Update gate, used to control the impact of the previous state on the current state; Reset gate, used to control the impact of current input on current state; The current state unit is used to calculate the state at the current moment.

7. The clothing fashion trend prediction and intelligent design platform according to claim 6, It is characterized in that The mathematical formula of the gated recurrent unit model is: , , , ,in, To update the gate, To reset the gate, is the current state, is the current state, W and b are model parameters.

8. The clothing fashion trend prediction and intelligent design platform according to claim 1, It is characterized in that The intelligent design module includes: Design element library, which is used to store a database containing design elements such as colors, styles, materials, patterns, etc.; A design element recommendation unit, used to select elements that meet the predicted trend from the design element library according to the trend prediction results; Generative adversarial networks for generating clothing designs based on recommended design elements.

9. The clothing fashion trend prediction and intelligent design platform according to claim 8, It is characterized in that The mathematical formula for generating the adversarial network is: Among them, D is the discriminator, G is the generator, is the real data sample, z is the noise sample, is the distribution of real data, is the distribution of noise samples.

10. The clothing fashion trend prediction and intelligent design platform according to claim 1, It is characterized in that The platform interface module includes: Trend analysis result display unit, used to visually display the trend prediction results; Design scheme display unit, used to display automatically generated clothing design schemes for users to preview, edit and select the final design; The design adjustment unit is used to provide online design tools for users to fine-tune and customize the design plan according to their needs.

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