Hyperdimensional Intelligent Engine for Personalized and Culturally Compliant Fashion Design

Globalization and the rapid growth of digital fashion demand intelligent systems capable of generating culturally compliant, personalized clothing designs across diverse traditions. Conventional deep learning models often rely on large labeled datasets and struggle with semantic–symbolic decoupling, cultural misalignment, and few-shot adaptation, resulting in aesthetic inconsistencies and risks of cultural appropriation. To overcome these limitations, a hyperdimensional intelligent engine is proposed that combines hyperdimensional computing, quantized self-supervised meta-learning, and differentiable fuzzy logic reasoning. Cultural semantics and visual symbols are embedded in a 10,000-dimensional vector space, where reversible binding and superposition enable precise, interpretable representation of complex cross-cultural styles.全球化浪潮与数字时尚的快速崛起，亟需能够适配多元文化传统、生成符合文化规范的个性化服饰设计的智能系统。传统深度学习模型往往依赖大规模标注数据集，且在语义-符号解耦、文化错位与少样本适配场景中存在显著局限，易导致美学风格不一致，甚至引发文化挪用风险。为破解上述瓶颈，本研究提出一种融合超维计算（hyperdimensional computing）、量化自监督元学习与可微模糊逻辑推理的超维智能引擎。该方案将文化语义与视觉符号嵌入至10000维向量空间中，借助可逆绑定与叠加操作，可实现复杂跨文化风格的精准且可解释的表征。