# Combining BioTRIZ and Multi-Factor Coupling for Bionic Mechatronic System Design
## 2 Related Works  
Both problem-driven and solution-driven approaches in bio-inspired design necessitate bridging the gaps between disciplines through knowledge cross-domain mapping. [PERSON] et al. [10] proposed BioTRIZ, which integrates classical TRIZ theory with bionic principles, to establish a \"bridge\" between engineering and biology. [PERSON] et al. [13] expanded the search scope for biologically inspired solutions by transforming engineering issues into biological ones based on the concept of \"functionality\". [PERSON] et al. [14] leveraged the pre-trained BERT model [15] to assess the semantic similarity between engineering and biological terms, facilitating bionic inference. [PERSON] et al. [16] proposed a human  
Figure 1: Bio-inspired design framework. The engineering issue is mapped onto the bio-space using BioTRIZ. Once the BioTRIZ solution and relevant biological instances are identified, the EM-MCB is constructed by examining the coupling elements that impact the biological function. Subsequently, the biological solution is translated back into the engineering domain to derive the engineering conceptual model.  
machine collaborative deep generative model for bionic design, visually depicting potential mappings between biomorphic forms and product shapes. [PERSON] et al. [17] developed the Focused Open Biology Information Extraction (FOBIE) dataset, which is aimed at discovering relevant cross-domain scientific literature for bionics research. Numerous other scholars [18; 19; 20; 21; 22; 23] have also contributed various computer-aided tools that rely on functional similarity to perform the crucial cross-domain mapping of knowledge in bionic design.  
The fundamental objective of bionics is to harness insights from biological instances to tackle engineering issues, and a thorough understanding of biological prototypes serves as the foundation for achieving this. In pursuit of this goal, [PERSON] et al. [11; 12] introduced the Extensive Model of Multi-factor Coupling Bionics (EM-MCB). This model offers an efficient tool for engineering biomimetic design by delving into the mechanisms of biological prototypes and elucidating the principles of biological coupling. [PERSON] et al. [24] developed AskNature, an online database that employs functional representation and abstraction techniques to identify and access biological prototypes. [PERSON] et al. [25] created a knowledge base focused on biological effects grounded in TRIZ principles. [PERSON] et al. [26] evaluated and selected biological prototypes based on topological, role, strategic, and structural similarity. [PERSON] et al. [27] proposed a hierarchical approach that organizes biological phenomena into form, behavior, and principle. This structure presents a latent analogy that can inspire design solutions. [PERSON] et al. [28] suggested abstracting biological prototypes based on their function, behavior, and structure. They further evaluated the fitness of these prototypes for engineering applications using a fuzzy triangular numbers-based algorithm. [PERSON] et al. [29] designed a knowledge base focused on multi-biological effects and integrated TRIZ to establish a design process model by combining product functions. [PERSON] et al. [30] combined BioTRIZ with biological coupling analysis, orthogonal analysis, and scheme merit value calculation to construct a multi-biological prototype bionic model.  
In brief, bionics research has frequently focused on particular links within the biomimetic chain. Scholars endeavor to refine tools and bridge disciplinary divides, rather than address the entirety of the complex process. Much of these works have revolved around single-factor bionics. With the focus gradually shifting towards multi-factor bionics, this paper aims to combine methodology and practical application to explain the bio-inspired design paradigm, and use a typical mechatronic system to elucidate this multi-factor bio-inspired design framework more clearly.