Discovery Strategy and Method for Remanufacturing Service Demand Using Situational Semantic Network

Due to customer individual difference, limitation of cognitive process and insufficient real-time response of cloud-based remanufacturing service platform, the problems such as disordered demand expression, difficulty in extracting implicit customer demand, and insufficient real-time performance of demand acquisition may be encountered. To this end, this paper presents an edge computing-based dynamic demand discovery and acquisition strategy. On the basis of existing methods and experimental results of implicit demand acquisition, a potential demand discovery method based on situational semantic network is proposed in this study. Firstly, the semantic similarity of ontology concept is used to calculate the correlation strength of registered keywords, and then registration keyword semantic network is constructed accordingly within the edge computing server. Afterwards, the keywords matrix of all web pages within single search behavior is obtained by data aggregation, the core attribute keywords of single search behavior are procured by the Kmeans algorithm and the retrieval keyword semantic network is constructed. After aggregating the two types of keywords semantic networks, the core semantics of aggregated semantic network are extracted by the pangrank method and customer situation semantic network reflecting current potential requirements is formed. Finally, an application example was demonstrated to verify the correctness and practicability of the remanufacturing service demand discovery strategy. This method has the potential to be applied in the intelligent management demand acquisition system of enterprises and urban communities, which provides reference for realization of intelligence technology in digital cities.