AI intelligent reloading and synchronizing system based on multi-terminal collaboration

Abstract

The invention relates to the technical field of artificial intelligence and electronic commerce, and discloses an AI intelligent reloading and synchronizing system based on multi-terminal cooperation, and the system comprises an acquisition module which collects user image data through user terminal equipment; the construction module is used for recognizing key points of a human body by using an AI image processing technology and constructing a personalized three-dimensional virtual image of the user; the database building module is used for building an online and offline clothing commodity database which comprises commodity three-dimensional models, material textures and size data; an adaptation module; a synchronization module; a terminal synchronization module; a recommendation module; a sales module; a sales module; and an analysis module. According to the invention, seamless fusion of online and offline shopping experiences is realized through a multi-terminal collaborative architecture, the system can accurately capture the body characteristics of the user through an AI image processing technology, a highly real virtual fitting effect is generated, and the accuracy and reality sense of virtual fitting are remarkably improved.

Description

Technical Field

[0001] This invention patent relates to the fields of artificial intelligence and e-commerce technology, specifically an AI-powered intelligent dress-up and synchronization system based on multi-terminal collaboration. Background Technology

[0002] With the rapid development of e-commerce, virtual try-on technology has become an important tool for online shopping. However, existing virtual try-on systems suffer from the following major problems: A severe disconnect between online and offline experiences; users' online try-on records and favorites cannot be shared with offline stores, preventing users from continuing their online experience in physical stores, and vice versa. This data isolation significantly impacts the user's shopping experience and decision-making continuity. Inadequate data synchronization mechanisms; existing systems experience significant delays in multi-terminal data synchronization, making it difficult to guarantee data consistency across different devices. User actions on mobile devices often take a long time to synchronize with in-store terminals, affecting the timely and accurate service provided by store staff. Insufficient realism and accuracy in virtual try-on; existing human key point recognition algorithms have limited accuracy, resulting in unrealistic virtual clothing fit that fails to accurately reflect the actual wearing effect, reducing user trust in virtual try-on. Poor personalized recommendations; recommendation systems fail to fully consider users' real-time try-on behavior and preference changes, often resulting in recommendations that deviate from actual user needs, impacting the shopping experience and conversion rates. Inconsistent system performance and user experience; system response speed, interface operation, and visual effects vary across different terminal devices, failing to provide a consistent and smooth user experience.

[0003] In view of this, we propose an AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration.

[0004] Invention Patent Content

[0005] The purpose of this invention is to provide an AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration to solve the problems mentioned in the background.

[0006] To achieve the above objectives, this invention provides the following technical solution:

[0007] An AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration, the system comprising:

[0008] The acquisition module acquires user image data through user terminal devices, including smartphones, tablets, personal computers, VR glasses, and projectors;

[0009] The module uses AI image processing technology to identify key points of the human body and build a personalized 3D virtual avatar for the user.

[0010] The database creation module establishes an online and offline apparel product database, including product 3D models, material textures, and size data.

[0011] The adaptation module enables virtual try-on functionality, automatically adapting selected clothing to the user's virtual avatar;

[0012] The synchronization module synchronizes user fitting data via a cloud server, including fitting records, outfit combinations, and favorites lists.

[0013] The terminal synchronization module supports real-time data synchronization across multiple terminals, synchronizing user online data to store terminals and store fitting data to user personal accounts.

[0014] The recommendation module provides intelligent recommendation functionality, generating outfit suggestions based on the user's fitting history and preferences;

[0015] The sales module enables real-time sales and pre-sales, allowing users to complete purchases directly through the system.

[0016] The protection module establishes a data security and privacy protection mechanism to ensure user data security;

[0017] The analytics module provides data analysis capabilities, offering merchants sales forecasts and inventory optimization suggestions.

[0018] Preferably, the human key point recognition in the construction module adopts a multimodal fusion algorithm, and the specific calculation formula is as follows:

[0019] ;

[0020] in, This represents the confidence level of the i-th key point. This represents the weight of the k-th mode. This represents the feature extraction function for the k-th mode. Indicates time continuity constraints, This is the adjustment coefficient.

[0021] Preferably, the data synchronization in the synchronization module adopts a differential transmission mechanism, and the synchronization priority calculation formula is as follows:

[0022] ;

[0023] in, For data freshness factor, As a data importance factor, For data urgency factor, , , These are the weighting coefficients.

[0024] Preferably, the real-time sales function in the sales module adopts a distributed transaction processing mechanism, and the inventory update strategy is as follows:

[0025] ;

[0026] in, For the updated inventory, Current inventory Let be the weight factor for the i-th item. For the quantity purchased.

[0027] Preferably, the sales forecasting in the analysis module uses a time series analysis model, and the forecasting formula is as follows:

[0028] ;

[0029] in, Let be the predicted sales volume at time t. These are the autoregressive coefficients. The moving average coefficient is... This is the historical error term.

[0030] Preferably, the virtual try-on in the adaptation module uses a physical simulation engine, and the clothing deformation model is implemented through an energy minimization algorithm to ensure the realism and naturalness of clothing deformation.

[0031] Preferably, the intelligent recommendation in the recommendation module adopts a hybrid recommendation algorithm that combines collaborative filtering and content filtering, which can dynamically update the recommendation results based on the user's real-time try-on behavior.

[0032] Preferably, the data security protection in the protection module adopts a multi-layer encryption mechanism, including transmission layer encryption, storage layer encryption and access control encryption, to ensure the security of user data at all levels.

[0033] Preferably, the system further includes a network optimization module, which employs an adaptive bitrate adjustment algorithm to dynamically adjust the data transmission strategy according to network conditions, ensuring the stability of the system under high traffic conditions.

[0034] Preferably, the system further includes a user experience optimization module, which uses a QoE evaluation model to quantitatively evaluate various performance indicators of the system to ensure the consistency of user experience.

[0035] By employing the above technical solution, this invention patent provides an AI-powered intelligent dress-up and synchronization system based on multi-terminal collaboration. It possesses at least the following beneficial effects:

[0036] This invention achieves seamless integration of online and offline shopping experiences through a multi-terminal collaborative architecture. Utilizing AI image processing technology, the system accurately captures user body characteristics, generating highly realistic virtual try-on effects, significantly improving the accuracy and realism of virtual try-on. Through an intelligent data synchronization mechanism, the system ensures real-time consistency and integrity of data across multiple terminal devices. User try-on records, favorite preferences, and other data can be instantly synchronized across mobile phones, in-store tablets, and other devices, providing precise service support for store staff and facilitating user decision-making or sharing at home. Through advanced recommendation algorithms, the system dynamically adjusts its recommendation strategy based on users' real-time try-on behavior and preference changes, providing increasingly accurate clothing matching suggestions, significantly improving the shopping experience and conversion rate. A multi-layered data security protection mechanism ensures the security of user privacy data at all levels, including comprehensive encryption protection at the transmission, storage, and access control layers, effectively preventing data leakage risks. Through network optimization and user experience optimization modules, the system maintains stable performance under different network conditions and provides a consistent user experience across all terminal devices, ensuring system reliability and ease of use. Attached Figure Description

[0037] The accompanying drawings, which are included to provide a further understanding of the invention, form part of this application:

[0038] Figure 1 This is a schematic diagram of the overall structure of an AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration, as per this invention patent. Detailed Implementation

[0039] The technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0040] Please see Figure 1 , Figure 1 This is a schematic diagram of the overall structure of an AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration, as per this invention patent. The AI-powered intelligent dress-changing and synchronization system based on multi-terminal collaboration of this invention includes:

[0041] The acquisition module acquires user image data through user terminal devices, including smartphones, tablets, personal computers, VR glasses, and projectors;

[0042] The module uses AI image processing technology to identify key points of the human body and build a personalized 3D virtual avatar for the user.

[0043] The database creation module establishes an online and offline apparel product database, including product 3D models, material textures, and size data.

[0044] The adaptation module enables virtual try-on functionality, automatically adapting selected clothing to the user's virtual avatar;

[0045] The synchronization module synchronizes user fitting data via a cloud server, including fitting records, outfit combinations, and favorites lists.

[0046] The terminal synchronization module supports real-time data synchronization across multiple terminals, synchronizing user online data to store terminals and store fitting data to user personal accounts.

[0047] The recommendation module provides intelligent recommendation functionality, generating outfit suggestions based on the user's fitting history and preferences;

[0048] The sales module enables real-time sales and pre-sales, allowing users to complete purchases directly through the system.

[0049] The protection module establishes a data security and privacy protection mechanism to ensure user data security;

[0050] The analytics module provides data analysis capabilities, offering merchants sales forecasts and inventory optimization suggestions.

[0051] It should be noted that the user image data acquisition in the acquisition module adopts multi-source heterogeneous data fusion technology. It acquires multi-angle image data of users through the high-definition cameras of terminal devices such as smartphones and tablets. It also supports importing existing photos from the album. The image acquisition process includes automatic light correction, background removal and image enhancement processing to ensure the quality and consistency of the input image.

[0052] The human body key point recognition in the construction module adopts a deep learning-based multimodal fusion algorithm. This algorithm can accurately identify 21 key points, including joint positions and body contours, to build an accurate three-dimensional human body model. Through temporal continuity constraints and spatial consistency verification, the accuracy and stability of the model construction are ensured.

[0053] The apparel product database in the database creation module adopts a distributed architecture design, supports dynamic expansion and real-time updates. The database contains complete 3D models of products, material texture information and size data, and also supports multi-level of detail (LOD) rendering to ensure the best display effect on different devices.

[0054] The virtual try-on function in the adaptation module uses a physically based rendering engine and achieves realistic deformation and natural draping effects of clothing through an energy minimization algorithm. During the try-on process, collision detection and material simulation are performed in real time to ensure the accuracy and realism of clothing adaptation.

[0055] The synchronization module employs an incremental synchronization strategy, using differential transmission technology to reduce data transmission volume. The synchronization process includes intelligent conflict detection and resolution mechanisms to ensure the consistency and integrity of data across multiple terminals.

[0056] The terminal synchronization module enables real-time synchronization across multiple terminals and supports offline mode operation, ensuring basic functions remain operational even under poor network conditions. Upon reconnecting to the network, it automatically performs data synchronization and conflict resolution, ensuring a continuous user experience.

[0057] The recommendation module employs an intelligent recommendation system with a hybrid recommendation algorithm, combining the advantages of collaborative filtering and content filtering. This allows it to dynamically adjust recommendation strategies based on the user's real-time try-on behavior. Recommendation results include multi-dimensional matching scores and explanatory information to help users make better purchasing decisions.

[0058] The sales module's sales system supports multiple payment methods and delivery options, and provides complete shopping cart and order management functions. The pre-sale function supports arrival reminders and priority purchase rights management, offering users flexible shopping options.

[0059] The data security mechanism in the protection module employs end-to-end encryption protection, including TLS encryption at the transport layer, AES encryption at the storage layer, and RBAC access control. Regular security audits and vulnerability scans are performed to ensure system security and reliability.

[0060] The data analysis system in the analytics module offers rich data visualization dashboards and reporting features, supporting sales trend analysis, user behavior analysis, and inventory optimization suggestions. It uses machine learning algorithms to analyze historical data, providing merchants with precise decision support.

[0061] The human key point recognition in the construction module adopts a multimodal fusion algorithm, and the specific calculation formula is as follows:

[0062] ;

[0063] in, This represents the confidence level of the i-th key point. This represents the weight of the k-th mode. This represents the feature extraction function for the k-th mode. Indicates time continuity constraints, This is the adjustment coefficient.

[0064] It should be noted that the multimodal fusion algorithm employed in the construction module integrates multiple data sources, including visual sensors, depth information, and motion capture devices. By extracting and weighting features from different modalities, it significantly improves the robustness and accuracy of human keypoint recognition. This algorithm effectively handles interference from varying lighting conditions, occlusion, and changes in user posture, ensuring that the constructed 3D virtual avatar maintains high fidelity and stability in different environments. Furthermore, this module introduces temporal consistency constraints, utilizing the correlation information between consecutive frames to further optimize keypoint localization, avoiding jumps or distortions caused by noise in a single frame, thus providing users with a smoother and more natural virtual avatar construction experience.

[0065] The data synchronization in the synchronization module adopts a differential transmission mechanism, and the synchronization priority calculation formula is as follows:

[0066] ;

[0067] in, For data freshness factor, As a data importance factor, For data urgency factor, , , These are the weighting coefficients.

[0068] It's worth noting that the differential transmission mechanism employed by the synchronization module significantly reduces the amount of data transmitted over the network by synchronizing only the changed data, thereby improving synchronization efficiency and reducing latency. This mechanism intelligently identifies the freshness, importance, and urgency of data, dynamically adjusting the synchronization strategy to ensure that critical data is synchronized first, guaranteeing a consistent user experience across multiple devices. Furthermore, the module also features conflict detection and automatic resolution capabilities. When multiple devices simultaneously modify the same data, the system can automatically merge the changes or prompt the user for action based on its strategy, effectively maintaining data consistency and integrity.

[0069] The real-time sales function in the sales module adopts a distributed transaction processing mechanism, and the inventory update strategy is as follows:

[0070] ;

[0071] in, For the updated inventory, Current inventory Let be the weight factor for the i-th item. For the quantity purchased.

[0072] It's worth noting that the sales module employs a distributed transaction processing mechanism that encapsulates multiple operations, such as inventory management, order processing, and payment confirmation, into atomic transactions, ensuring transaction consistency and reliability in high-concurrency scenarios. This mechanism supports multi-node collaboration; even if a service node fails, eventual data consistency can be guaranteed through transaction rollback or retry mechanisms, preventing overselling or order errors. Furthermore, this module implements dynamic inventory awareness and real-time updates, adjusting inventory allocation strategies for different products based on weighting factors, effectively improving inventory utilization and order fulfillment efficiency.

[0073] The sales forecast in the analysis module uses a time series analysis model, and the forecast formula is as follows:

[0074] ;

[0075] in, Let be the predicted sales volume at time t. These are the autoregressive coefficients. The moving average coefficient is... This is the historical error term.

[0076] It should be noted that the analysis module employs a time series analysis model that uses autoregression and moving average analysis on historical sales data to capture seasonal, cyclical, and random factors in sales trends, thereby generating highly accurate sales forecasts. This model can dynamically adjust parameters based on real-time data to adapt to market changes and provide businesses with reliable decision-making support. Furthermore, this module can incorporate external factors such as promotional activities and holidays for multivariate modeling, further enhancing the comprehensiveness and practicality of the forecasts.

[0077] The virtual try-on feature in the adaptation module uses a physics simulation engine, and the clothing deformation model is implemented through an energy minimization algorithm to ensure the realism and naturalness of clothing deformation.

[0078] It should be noted that the physical simulation engine used in the adaptation module simulates the deformation behavior of fabrics under gravity, friction, and collision, and optimizes the fit between clothing and virtual avatar by combining the principle of energy minimization. This engine can realistically reproduce the characteristics of clothing of different materials, such as elasticity, thickness, and drape, thus presenting a natural and realistic dynamic effect during virtual try-on, greatly enhancing the user's immersion and trust.

[0079] The intelligent recommendation module employs a hybrid recommendation algorithm that combines collaborative filtering and content filtering, which can dynamically update recommendation results based on the user's real-time try-on behavior.

[0080] It's worth noting that the recommendation module employs a hybrid recommendation algorithm that combines the advantages of collaborative filtering and content filtering. It generates recommendations based on user history and similar user preferences, while also matching content at the content level using product attributes and tags. This algorithm can respond in real-time to user try-on behavior and feedback, dynamically adjusting the recommendation strategy to achieve personalized, contextualized, and accurate recommendations, effectively promoting user decision-making and purchase conversion.

[0081] The data security protection in the protection module adopts a multi-layer encryption mechanism, including transport layer encryption, storage layer encryption and access control encryption, to ensure the security of user data at all levels.

[0082] It should be noted that the protection module employs a multi-layered encryption mechanism covering data security at every stage of transmission, storage, and access control. Transport layer encryption ensures the confidentiality and integrity of data during network transmission; storage layer encryption prevents unauthorized access to data during persistent storage; and access control encryption, through role-based access control and dynamic token verification, ensures that only authorized users can operate on the corresponding data, comprehensively constructing a defense line for user privacy and data security.

[0083] The system also includes a network optimization module, which uses an adaptive bitrate adjustment algorithm to dynamically adjust the data transmission strategy according to network conditions, ensuring the system's stability under high traffic conditions.

[0084] It should be noted that the network optimization module monitors network bandwidth, latency, and packet loss rate in real time through an adaptive bitrate adjustment algorithm, dynamically adjusting data encoding and transmission strategies to effectively reduce network resource consumption while ensuring image quality and smooth interaction. This module is particularly suitable for mobile networks and unstable connection environments, significantly improving system availability and user experience under weak network conditions.

[0085] The system also includes a user experience optimization module, which uses the QoE evaluation model to quantitatively evaluate various performance indicators of the system to ensure the consistency of user experience.

[0086] It's worth noting that the User Experience Optimization module constructs a comprehensive User Experience Evaluation (QoE) system by quantitatively evaluating key indicators such as system performance, interface responsiveness, and interaction smoothness. This module can periodically generate evaluation reports, identify experience bottlenecks, and drive optimization iterations to ensure that users can obtain a consistent, smooth, and enjoyable user experience across different devices and scenarios.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0088] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI intelligent changing and synchronization system based on multi-terminal cooperation, characterized in that, The system comprises: a collection module that collects user image data through a user terminal device, including smartphones, tablets, personal computers, VR glasses, and projection; a construction module that uses AI image processing technology to identify human key points and construct a user's personalized three-dimensional virtual image; a database construction module that establishes an online and offline clothing commodity database, including three-dimensional models, material textures, and size data; an adaptation module that realizes virtual try-on function and automatically adapts selected clothing to the user's virtual image; a synchronization module that synchronizes user try-on data through a cloud server, including try-on records, matching schemes, and a collection list; a terminal synchronization module that supports real-time data synchronization of multiple terminals, with user online data synchronized to store terminals and store try-on data synchronized to user personal accounts; a recommendation module that provides intelligent recommendation function and generates matching suggestions based on user try-on history and preferences; a sales module that realizes instant sales and pre-sale functions, allowing users to complete purchases directly through the system; a protection module that establishes data security and privacy protection mechanisms to ensure user data security; an analysis module that provides data analysis function and provides sales prediction and inventory optimization suggestions for businesses.

2. The AI intelligent changing and synchronizing system based on multi-terminal cooperation according to claim 1, characterized in that, The human key point recognition in the construction module uses a multi-modal fusion algorithm, with the specific calculation formula being: ； wherein, denotes a confidence of the i-th keypoint, denotes a weight of the k-th modality, denotes a feature extraction function of the k-th modality, denotes a temporal continuity constraint, is a tuning coefficient.

3. The AI intelligent changing and synchronizing system based on multi-terminal cooperation according to claim 1, characterized in that, The data synchronization in the synchronization module uses a differential transmission mechanism, with the synchronization priority calculation formula being: ； wherein, is a data freshness factor, is a data importance factor, is a data urgency factor, , , is a weight coefficient.

4. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The instant sales function in the sales module uses a distributed transaction processing mechanism, with the inventory update strategy being: ； wherein, is the updated inventory, is the current inventory, is the weight factor for the ith item, is the purchase quantity.

5. The AI intelligent changing and synchronizing system based on multi-terminal cooperation according to claim 1, characterized in that, The sales prediction in the analysis module uses a time series analysis model, with the prediction formula being: ； wherein, is the predicted sales at time t, is the autoregressive coefficient, is the moving average coefficient, is the historical error term.

6. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The virtual try-on in the adaptation module uses a physical simulation engine, with the clothing deformation model realized through an energy minimization algorithm to ensure the authenticity and naturalness of clothing deformation.

7. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The intelligent recommendation in the recommendation module uses a hybrid recommendation algorithm combining collaborative filtering and content filtering, which can dynamically update the recommendation results according to the user's real-time try-on behavior.

8. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The data security protection in the protection module uses a multi-layer encryption mechanism, including transmission layer encryption, storage layer encryption, and access control encryption, to ensure the security of user data at various levels.

9. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The system also includes a network optimization module that uses an adaptive code rate adjustment algorithm to dynamically adjust data transmission strategies based on network conditions, ensuring the stability of the system under high traffic conditions.

10. The AI intelligent changing and synchronizing system based on multi-terminal cooperation of claim 1, wherein, The system also includes a user experience optimization module that uses a QoE evaluation model to quantitatively evaluate various performance indicators of the system, ensuring the consistency of user experience.

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