During product conceptualization, capturing the non-linear history and cognitive intent is crucial. Traditional sketching tools often lose this context. We introduce DIMES (Design Idea Management and Evolution capture System), a web-based environment featuring sGIT (SketchGit), a custom visual version control architecture, and Generative AI. sGIT includes AEGIS, a module using hybrid Deep Learning and Machine Learning models to classify six stroke types. The system maps Git primitives to design actions, enabling implicit branching and multi-modal commits (stroke data + voice intent). In a comparative study, experts using DIMES demonstrated a 160% increase in breadth of concept exploration. Generative AI modules generated narrative summaries that enhanced knowledge transfer; novices achieved higher replication fidelity (Neural Transparency-based Cosine Similarity: 0.97 vs. 0.73) compared to manual summaries. AI-generated renderings also received higher user acceptance (Purchase Likelihood: 4.2 vs 3.1). This work demonstrates that intelligent version control bridges creative action and cognitive documentation, offering a new paradigm for design education.

翻译：在产品概念化过程中，捕捉非线性的历史记录与认知意图至关重要。传统草图工具常丢失此类上下文。我们介绍了DIMES（设计理念管理与演化捕捉系统），这是一个基于网络的环境，其特色包括sGIT（SketchGit）——一种定制化的视觉版本控制架构，以及生成式人工智能。sGIT包含AEGIS模块，该模块采用混合深度学习与机器学习模型对六种笔画类型进行分类。该系统将Git原语映射至设计操作，实现了隐式分支与多模态提交（笔画数据+语音意图）。在一项对比研究中，使用DIMES的专家在概念探索广度上展现出160%的提升。生成式人工智能模块生成的叙述性摘要增强了知识传递；与人工摘要相比，新手获得了更高的复现保真度（基于神经透明度的余弦相似度：0.97对比0.73）。人工智能生成的渲染图也获得了更高的用户接受度（购买可能性：4.2对比3.1）。本研究表明，智能版本控制在创造性行为与认知记录之间架起了桥梁，为设计教育提供了新范式。