Identification of core driving factors for product architecture design based on dual-layer complex network

doi:10.3785/j.issn.1008-973X.2026.08.004

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (8): 1650-1661 DOI: 10.3785/j.issn.1008-973X.2026.08.004

Identification of core driving factors for product architecture design based on dual-layer complex network

Shifeng LIU1(

),Jianning SU2,*(

),Shutao ZHANG2,Shijie WANG1,Kai QIU2,Wenjin YANG2

1. School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2. School of Architecture and Art Design, Lanzhou University of Technology, Lanzhou 730050, China

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Abstract

A method based on a dual-layer complex network was proposed to resolve two problems in product design: the difficulty in synergizing functional structure solutions with aesthetic appearance solutions, and the ambiguous and complex many-to-many mappings between multi-source driving factors and physical components. First, based on the ontological characteristics of product architecture and the natural language processing techniques, a physical component network was constructed to represent the topological relationships among physical components, and a driving factor network was built to reflect the semantic relationships among multi-source driving factors. Then, based on design cognitive characteristics, a dual-layer complex network was established to map the relationships between physical components and driving factors, enabling the quantitative representation of the complex influence of multi-source driving factors on physical components under design cognitive constraints. Finally, through multi-dimensional topological feature fusion analysis, the core driving factors and their weights for physical components were identified under the combined effects of intra-layer and inter-layer nodes on physical components. The results indicate that the influence of multi-source driving factors on the product architecture design exhibits characteristics of multi-level chain effects and synergistic interactions. Additionally, the synergistic relationship between functional structure and aesthetic appearance should be considered. A case study on automotive architecture demonstrates the feasibility of the proposed method.

Key words： product architecture multi-source driving factor physical component design cognition dual-layer complex network

Received: 10 July 2025 Published: 16 July 2026

CLC:

TB 472

Fund: 国家自然科学基金资助项目（52165033）；甘肃省青年科技计划资助项目（24JRRA968）；甘肃省教育厅高校教师创新基金资助项目（2023A-024）.

Corresponding Authors: Jianning SU E-mail: liusf@lut.edu.cn;sujn@lut.edu.cn

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Cite this article:

Shifeng LIU,Jianning SU,Shutao ZHANG,Shijie WANG,Kai QIU,Wenjin YANG. Identification of core driving factors for product architecture design based on dual-layer complex network. Journal of ZheJiang University (Engineering Science), 2026, 60(8): 1650-1661.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.08.004 OR https://www.zjujournals.com/eng/Y2026/V60/I8/1650

基于双层复杂网络的产品架构设计核心驱动因素识别

为了解决产品设计中物理组件的功能结构解与外观美学解难协同、多源驱动因素与物理组件之间“多对多”复杂映射关系不清晰的问题，提出双层复杂网络分析多源驱动因素对产品架构设计的复杂影响关系. 基于产品架构本体特性和自然语言处理技术，分别构建表征物理组件之间拓扑关联的物理组件网络和反映多源驱动因素之间语义关系的驱动因素网络；基于设计认知特性建立“物理组件-驱动因素”映射的双层复杂网络，定量表征设计认知约束下多源驱动因素对物理组件的复杂作用关系；通过多维拓扑特征融合分析，识别物理组件在层内与层间节点双重作用下的核心驱动因素及其权重. 结果表明，多源驱动因素对产品架构设计的影响具有多层级链式驱动和协同交互驱动的特征；应该关注功能结构和外观美学关系的协同作用. 以汽车架构为例进行分析，验证了提出方法的可行性.

关键词： 产品架构, 多源驱动因素, 物理组件, 设计认知, 双层复杂网络

Fig.1 Analysis framework for complex impact of multi-source driving factors on product architecture design

Tab.1 Functional structure relationship evaluation criteria^[34]

Tab.2 Image style relationship evaluation criteria

Tab.3 Comprehensive correlation strength between physical components

Tab.4 Correlation between driving factors

Tab.5 Mapping relationship between physical components and driving factors

Fig.2 Two-layer network of physical components and driving factors

Fig.3 Comprehensive importance of physical components

Fig.4 Influence strength of driving factors on modules

模块	组件	驱动因素1的路径	驱动因素2的路径	驱动因素3的路径	驱动因素4的路径	驱动因素5的路径	驱动因素6的路径
底盘模块	P₁	E₃→E₁₀→E₇→P₁ 权重：0.156 8	E₁₅→P₈→P₁ 权重：0.189 0	E₁₆→P₂₄→P₁ 权重：0.181 8	E₈→E₉→E₇→P₁ 权重：0.178 2	E₂→E₅→P₁ 权重：0.156 2	E₂₀→E₅→P₁ 权重：0.138 0
	P₃₂	E₃→E₁₀→P₃₂ 权重：0.154 6	E₁₅→P₃₂ 权重：0.194 5	E₁₆→E₁₈→P₃₂ 权重：0.174 0	E₈→P₂₆→P₃₂ 权重：0.180 5	E₂→P₃₂ 权重：0.155 5	E₂₀→E₅→P₃₂ 权重：0.140 9
	P₃₅	E₃→E₁₀→P₃₅ 权重：0.145 8	E₁₅→P₁₂→P₄→P₃₅ 权重：0.180 8	E₁₆→P₃₅ 权重：0.177 7	E₈→P₃₅ 权重：0.162 3	E₂→E₉→P₃₅ 权重：0.163 5	E₂₀→E₅→E₁₀→P₃₅ 权重：0.169 9
前脸模块	P₁₂	E₄→E₁→P₁₂ 权重：0.158 7	E₁₁→E₁→P₁₂ 权重：0.169 0	E₁₇→E₅→E₁→P₁₂ 权重：0.198 2	E₂₀→E₅→E₁→P₁₂ 权重：0.184 0	E₂→E₁→P₁₂ 权重：0.157 3	E₁₆→E₁₅→P₁₂ 权重：0.132 9
	P₂₄	E₄→E₁₀→P₂₄ 权重：0.156 2	E₁₁→E₁₀→P₂₄ 权重：0.156 0	E₁₇→P₂₄ 权重：0.191 7	E₂₀→E₅→E₁₀→P₂₄ 权重：0.182 7	E₂→P₂₄ 权重：0.156 8	E₁₆→P₂₄ 权重：0.156 8
	P₁₀	E₄→P₁₀ 权重：0.142 9	E₁₁→E₁₀→P₁₀ 权重：0.160 1	E₁₇→P₁₀ 权重：0.151 9	E₂₀→P₁₀ 权重：0.178 7	E₂→P₁₀ 权重：0.169 7	E₁₆→P₁₀ 权重：0.196 6
内室模块	P₆	E₁₆→P₂→P₄→P₆ 权重：0.199 2	E₂₀→E₅→P₁₉→P₆ 权重：0.168 0	E₁₈→E₁₀→E₇→P₆ 权重：0.164 2	E₄→P₂₈→P₆ 权重：0.147 4	E₈→P₃₀→P₆ 权重：0.161 9	E₃→P₆ 权重：0.159 3
	P₃	E₁₆→P₃₇→P₃ 权重：0.170 3	E₂₀→P₃₇→P₃ 权重：0.170 1	E₁₈→P₃₂→P₃ 权重：0.162 9	E₄→E₁₀→E₇→P₃ 权重：0.167 1	E₈→P₂₉→P₃ 权重：0.162 9	E₃→E₁₀→E₇→P₃ 权重：0.166 6
	P₅	E₁₆→E₁₈→E₁₂→P₅ 权重：0.199 5	E₂₀→E₅→P₅ 权重：0.172 7	E₁₈→E₁₂→P₅ 权重：0.148 8	E₄→E₁₀→E₇→P₅ 权重：0.156 6	E₈→P₂₀→P₅ 权重：0.166 3	E₃→E₁₀→E₇→P₅ 权重：0.156 1
侧身模块	P₁₉	E₁₆→P₂→P₁₉ 权重：0.183 5	E₄→E₁→P₁₉ 权重：0.160 8	E₁₈→E₁→P₁₉ 权重：0.160 0	E₁₇→E₅→P₁₉ 权重：0.159 4	E₂₀→E₅→P₁₉ 权重：0.145 7	E₈→E₉→E₇→P₁₉ 权重：0.190 6
	P₁₄	E₁₆→P₃₉→P₁₄ 权重：0.190 6	E₄→E₁₀→E₇→P₁₄ 权重：0.152 8	E₁₈→E₁₀→E₇→P₁₄ 权重：0.152 3	E₁₇→E₉→E₇→P₁₄ 权重：0.165 9	E₂₀→E₅→E₁₀→E₇→P₁₄ 权重：0.174 1	E₈→E₁₃→P₁₄ 权重：0.164 4
	P₁₈	E₁₆→P₁₁→P₁₈ 权重：0.187 6	E₄→E₁₀→P₁₈ 权重：0.148 4	E₁₈→E₁₀→P₁₈ 权重：0.147 9	E₁₇→E₉→E₇→P₁₈ 权重：0.168 7	E₂₀→E₅→E₁₀→P₁₈ 权重：0.171 0	E₈→E₉→E₇→P₁₈ 权重：0.176 4
尾部模块	P₂₅	E₄→P₂₅ 权重：0.170 7	E₂₀→E₅→E₉→P₂₅ 权重：0.181 8	E₁₆→P₂₅ 权重：0.170 7	E₈→E₉→P₂₅ 权重：0.169 5	E₁₉→E₉→P₂₅ 权重：0.146 1	E₁₇→E₉→P₂₅ 权重：0.161 2
	P₂₃	E₄→E₁₀→E₇→P₂₃ 权重：0.185 3	E₂₀→P₂₃ 权重：0.161 6	E₁₆→P₂₃ 权重：0.161 6	E₈→P₂₃ 权重：0.170 0	E₁₉→E₉→P₂₃ 权重：0.152 9	E₁₇→E₉→P₂₃ 权重：0.168 6
	P₂₀	E₄→E₁₀→P₂₀ 权重：0.161 2	E₂₀→E₅→E₁₄→P₂₀ 权重：0.176 7	E₁₆→E₁₈→E₁₀→P₂₀ 权重：0.214 8	E₈→P₂₀ 权重：0.124 9	E₁₉→P₂₀ 权重：0.139 6	E₁₇→E₁₄→P₂₀ 权重：0.182 8

Tab.6 Key drive paths and weights

Fig.5 Change of cumulative number of infected nodes over time

Fig.6 Degree centrality and cluster correlation coefficient of physical components

Tab.7 Partition results of physical component modules based on three types of association relationships


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