目的 为了解决传统拉背器械固定负载铃片导致阻力与肌肉发力点不同步的问题,并突破创新设计阶段效率低与创新性差的瓶颈,提出TRIZ-专利数据- AIGC协同创新的方法。方法 首先,运用TRIZ理论系统分析传统器械的关键矛盾,通过矛盾矩阵求解获得动态化、预先作用等发明原理;其次,构建技术功效矩阵识别出负载调节的技术机会点,结合发明原理定义创新设计方向;最后,运用AIGC辅助完成平位拉背器械创新设计方案的快速产出。结果 提出电磁阻尼系统实现精准阻力控制、可变力臂机构产生自适应阻力曲线、智能驱动系统动态匹配阻力3大创新方向,并产出24个设计样本和9个最佳3D模型。结论 设计实践结果表明,融合TRIZ理论、专利数据、AIGC工具的方法建立了从挖掘问题矛盾、识别技术机会到高效产出方案的创新研究路径,为产品创新提供了理论-数据-AI协同的新范式。
Abstract
The work aims to propose a TRIZ-patent data-AIGC collaborative innovation method to solve the problem of resistance and muscle exertion points being out of sync caused by fixed load plates in traditional back-pulling equipment, and to break through the bottleneck of low efficiency and poor innovation in the design phase. Firstly, the TRIZ theory was used to systematically analyze the key contradictions of traditional equipment. Through contradiction matrix solutions, inventive principles such as dynamization and pre-action were derived. Secondly, a technical efficacy matrix was constructed to identify technical opportunity points for load adjustment. Combined with inventive principles, the direction for innovative design was defined. Finally, AIGC was employed to rapidly generate innovative design solutions for the horizontal lat back-pulling machine. Three major innovations were proposed: an electromagnetic damping system for precise resistance control, a variable lever mechanism generating adaptive resistance curves, and an intelligent drive system dynamically matching resistance. This process yielded 24 design samples and 9 optimal 3D models. Design practice results demonstrate that integrating the TRIZ theory, patent data, and AIGC tools establishes an innovation research pathway from mining problem contradictions and identifying technical opportunities to efficiently generating solutions, providing a new theory-data-AI collaborative paradigm for product innovation.
关键词
健身器械 /
TRIZ理论 /
专利分析 /
技术功效矩阵 /
生成式人工智能（AIGC）
Key words
fitness equipment /
TRIZ theory /
patent analysis /
technical efficacy matrix /
artificial intelligence generated content (AIGC)
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