Due to customer individual difference, limitation of cognitive process and insufficient realtime 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. INDEX TERMS Remanufacturing service (RMS), customer demand discovery, edge computing, situational semantic network.

The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

Accurate acquisition of retired mechanical products demand (RMPD) is the basis for realizing effective utilization of remanufacturing service data and improving the feasibility of remanufacturing schemes. Some studies have explored product demands, making product demands an important support for product design and development. However, these studies are obtained through the transformation of customer and market demand information, and few studies are studied from a product perspective. However, remanufacturing services for retired mechanical products (RMP) must consider the impact of the failure characteristics. Consequently, based on the generalized growth of RMP driven by the failure characteristics, the concept of RMPD is proposed in this paper. Then, the improved ant colony algorithm is proposed to mine the generalized growth evolution law of RMP from the empirical data of remanufacturing services, and the RMPD is deduced based on the mapping relationship between the product and its attributes. Finally, the feasibility and applicability of the proposed method are verified by obtaining the demand for retired rolls. In detail, the results show that the proposed method can obtain the RMPD accurately and efficiently, and the performance of the method can be continuously optimized with the accumulation of empirical data.

This paper investigates the win-win commercialization mode of aggregating electric vehicles (EVs) in demand side for ancillary service. We have conducted a half-year-long incentive verification experiment covering 10,066 electric vehicle owners in Beijing. Based on the experimental results, we develop an incentive-based mechanism that enables electric vehicles to participate the wholesale capacity market through an aggregator. The aggregator, which is held by charging service operators can make a profit by designing a smart pricing policy. In this process, not only the electric vehicle owners but also the utility can gain benefits.