Development of AI (Artificial Intelligence) standing cutting system and conversion application in fashion document travel management system

Abstract

The invention relates to the technical field of clothing digitalization, and particularly discloses an intelligent digital clothing three-dimensional cutting system based on artificial intelligence and conversion application of the intelligent digital clothing three-dimensional cutting system in a fashion document travel management environment. The system comprises a three-dimensional human body dynamic scanning module, an AI version generation engine, a virtual fabric physical engine and a text travel scene adaptation interface. The method comprises the following steps: capturing dynamic deformation data of a human body in real time through a multi-modal sensor, and generating a self-adaptive model through a deep learning model (adopting an improved ResNet-Transform hybrid architecture); a fabric mechanical simulation algorithm is combined to realize tailoring path optimization; cross-platform interaction between clothing data and scenes such as a virtual museum and a competition venue is innovatively realized through a text travel scene management API (Application Program Interface). The problems that traditional three-dimensional cutting is low in efficiency, insufficient in personalized adaptation and disjointed with text travel scenes are solved. According to actual measurement of professional sports teams, customization efficiency is improved by 300%, material waste is reduced by 45%, and a virtual fitting guide function is verified in scenes such as Shenzhen Weibo. The system is a key conversion project for the Diye-northern suit industry research institute, and has large-scale popularization conditions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intersection of clothing engineering and digital technology, and specifically relates to an artificial intelligence driven three-dimensional cutting technology and systematic application in a travel and tourism scene. BACKGROUND

[0002] Defects of prior art: - The VR teaching system only solves the visualization of training, and lacks real physical interaction - Traditional three-dimensional cutting relies on manual experience and cannot adapt to dynamic deformation of the moving human body - Clothing display in the travel and tourism scene is mostly static models, lacking personalized interaction - Suit technology accumulation: - A sports biomechanics database accumulated by a winter sports event project (Liu Li team) - Digital production verification system of Dior-Suit Research Institute (Lan Suiqin team) - XR rendering engine developed by Shenzhen Polytechnic in cooperation SUMMARY

[0003] Core innovation points: 1. Dynamic human body modeling technology: - Multi-sensor fusion scheme (millimeter wave radar + electronic skin) is adopted; - Breakthrough: can capture 0.1mm muscle deformation, precision is 3 times higher than industry standard.

[0004] 2. AI pattern generation method: - Build a "motion-pattern" mapping model (network structure shown in FIG. 1); - Example: adaptive algorithm for speed skating suit crotch structure reduces friction damage by 80%.

[0005] 3. Conversion architecture in travel and tourism scene: - Develop clothing digital twin middleware, support Unreal / Unity dual-engine; - Virtual Hanfu customization tour will be realized in the project of Pingyao Ancient City in Shanxi.

[0006] Technical effects: - Customization cycle: from 72 hours to 8 hours; - Material utilization rate: from 65% to 92%; - User stay time in travel and tourism scene is increased by 40% (actual measurement data of Shenzhen Happy Valley). DETAILED DESCRIPTION

[0007] 1. Hardware deployment: - Lightweight scanning cabin (size: 2m x 1.5m x 2.2m) jointly developed by Beifu and the resource party of Xueheng College; - The robotic arm cutting unit adopts a modular design, supporting the modification of existing production lines in garment enterprises; 2. Software process: - Step 1: User completes 15-second dynamic scanning (including 8 standard movements); - Step 2: AI engine generates pattern and renders 3D try-on effect; - Step 3: User adjusts design details through travel APP; - Step 4: System automatically generates production instructions and virtual scene display package; 3. Industrialization cases: - Production of winter sports event project speed skating suits: 482 sets of customized uniforms for 12 professional teams; - Palace cultural and creative project: development of digital Ming-style clothing experience system, expected to increase single product price by 300%. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 AI pattern generation network: - Input layer: human point cloud data + motion acceleration matrix; - Feature extraction layer: parallel CNN and LSTM modules; - Fusion layer: feature splicing based on attention weight; - Output layer: pattern parameter correction vector ΔP; Figure 2 Travel application process: [Visitor scanning] → [Generate digital clothing] → [VR scene matching] → [Social media sharing] → [Offline production and distribution]. Figure 3 Mechanical arm cutting path optimization schematic. INNOVATION DECLARATION

[0009] 1. Technological breakthrough: ① First "motion biomechanics-AI generation-scene transformation" three-in-one architecture; ② Solving the problem of large-scale customization of high-performance clothing (part of the achievements have been accepted by the State Sports General Administration).

[0010] Innovation points: ① Dynamic biomechanics data-driven AI modeling; ② Cutting-travel dual system data closed loop; ③ Application of Beifu's proprietary high-performance clothing knowledge base.

[0011] Industrial value: ① Has been industrialized through the Dior Group (2024 mass production line renovation put into production); ② Listed as a key promotion technology by the Beijing Future Industry Research Institute.

[0012] Academic basis: Fusion of Liu Li's "Ice and Snow Sportswear Heat and Moisture Transfer Model" (2023) and Lan Suqin's "Digital Clothing Cross-Platform Interaction Protocol" (2024) core achievements.

Claims

1. An AI-based intelligent digital garment 3D cutting system, characterized in that... include: - Three-dimensional dynamic acquisition unit: integrates millimeter-wave radar and flexible pressure sensor array to capture human kinematic parameters and micro-strain of body surface in real time; -AI pattern generation engine: It adopts a neural network model that fuses spatiotemporal features, takes dynamically acquired data as input, and outputs a parameterized pattern base model; - Intelligent cutting optimization module: Optimizes fabric utilization based on genetic algorithm and generates cutting paths that can be executed by the robotic arm; - Cultural and Tourism Scene Interface: Enables cross-platform rendering of clothing models in AR / VR environments via the OpenXR protocol.

2. The system as described in claim 1, characterized in that: The neural network model includes a two-branch feature extraction structure. The first branch processes static shape data, and the second branch processes motion and biomechanics data. After being fused by an attention mechanism, the model outputs a pattern correction coefficient.

3. The system as described in claim 1, characterized in that: The cultural tourism scene interface supports dynamic switching of clothing model LOD (Level of Detail), with a latency of less than 20ms in a 5G environment.

4. The system as described in claim 1, characterized in that: The system integrates 5,000 sets of high-performance clothing parameters from the competition uniform database of a winter sports event at Beijing Institute of Fashion Technology as a prior knowledge base.

5. A fashionable cultural tourism management environment system, characterized in that... It includes the three-dimensional cutting system as described in any one of claims 1-4, and uses blockchain technology to realize digital copyright management of clothing.

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