A product requirement development method based on multi-layer heterogeneous networks

The rapid advancement of emerging technologies in the information age has resulted in intricate, diverse, and rapidly changing user requirements, thereby reducing the effectiveness of traditional requirement development approaches. This paper proposes a multi-layer heterogeneous network-based method for product requirement development to identify key requirements in the product development process and address the complex relationships among users, requirements, products, and requirement development teams. By incorporating the theory of hesitant fuzzy into the expert decision-making process, this method constructs four single-layer networks: user attention network, expert trust network, requirement correlation network, and component combination network, which assigns realistic significance to nodes in each network layer and establishes connections between different networks, forming a multi-layer heterogeneous network model for product requirement development. Furthermore, this paper defines indicators such as layer significance and node comprehensive significance and proposes a calculation method for node comprehensive significance based on biased random walk, which enables deep integration of node’s realistic significance and topological centrality. The proposed method is applied to a data experiment of product requirement development for a specific type of new energy vehicle. By analyzing the calculation results of node comprehensive significance, high-quality recommendations are provided for the development team from the perspectives of functional requirements, product components, and user groups. Moreover, comparative experiments to assess algorithm performance demonstrate that the average accuracy of the proposed algorithm is 0.95, which is significantly higher than that of the commonly used single-layer network node centrality measurement methods and recently proposed multi-layer network node centrality measurement methods. These results reflect the advancement and applicability of the proposed method in product development scenarios.