AI driven dual constraint cooptimization of affective semantics and engineering parameters for biomimetic product design

In response to the limitation of the separation between emotion and rationality in traditional bionic design of product forms, an artificial intelligence-based dual-constraint generative design framework (AI-DCF) is proposed, which deeply integrates computer vision and visual dynamics modeling technology to achieve systematic coupling of biological semantics and engineering parameters. Firstly, an emotional semantic graph is constructed through the TextRank algorithm in natural language processing (NLP), combined with OpenCV contour detection to generate biological form tensor representations, forming a cross-modal semantic-morphological knowledge graph, breaking through the barrier of traditional design’s reliance on subjective experience. Secondly, a dual-channel optimization engine based on deep reinforcement learning (DRL) is developed, where the Alpha channel fusion technology realizes shape matching based on visual attention, and the visual dynamics model completes the inference of structural parameters guided by mechanics, and a real-time gradient feedback mechanism is embedded to form a data-algorithm-decision closed loop. Finally, taking Mingyu forklift as the experimental carrier, a comparative experiment is conducted to verify the significant advantages of the dual-constraint model over traditional methods in terms of biological feature recognizability (+ 37.5%), comprehensive performance score (+ 25%), and design iteration efficiency (+ 31.25%). These findings demonstrate case-level feasibility of aligning emotional semantics with engineering parameterization within the study’s constraints; it provides a preliminary feasibility verification for the mapping relationship between the emotional semantic descriptors based on artificial intelligence and engineering parameterization.