Evolutionary Game Strategy Research on PSC Inspection Based on Knowledge Graphs

• Published in: Journal of marine science and engineering Vol. 12; no. 8; p. 1449
• Main Authors: Liu, Chengyong, Wang, Qi, Xiang, Banghao, Xu, Yi, Gan, Langxiong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Collusion; Computational linguistics; Cooperation; Cooperative learning; Crude oil; Decision making; Defense industry; Efficiency; Evolution; evolutionary game; Forecasts and trends; Game theory; Globalization; Graph theory; Graphs; Impact analysis; Inspection; inspection strategies; Inspections; Investigations; knowledge graphs; Knowledge representation; Language processing; Maritime safety; maritime supervision informatization; Natural language interfaces; Ontology; Parameter sensitivity; Population studies; port state control inspections; Ports; Recognition; Safety and security measures; Safety management; Semantic web; Semantics; Shipping industry; Standardization; Strategy; Supply chains; System theory; Traffic accidents & safety; Wavelet transforms; China
• DOI: 10.3390/jmse12081449
• ISSN: 2077-1312

Port state control (PSC) inspections, considered a crucial means of maritime safety supervision, are viewed by the industry as a critical line of defense ensuring the stability of the international supply chain. Due to the high level of globalization and strong regional characteristics of PSC inspections, improving the accuracy of these inspections and efficiently utilizing inspection resources have become urgent issues. The construction of a PSC inspection ontology model from top to bottom, coupled with the integration of multisource data from bottom to top, is proposed in this paper. The RoBERTa-wwm-ext model is adopted as the entity recognition model, while the XGBoost4 model serves as the knowledge fusion model to establish the PSC inspection knowledge graph. Building upon an evolutionary game model of the PSC inspection knowledge graph, this study introduces an evolutionary game method to analyze the internal evolutionary dynamics of ship populations from a microscopic perspective. Through numerical simulations and standardization diffusion evolution simulations for ship support, the evolutionary impact of each parameter on the subgraph is examined. Subsequently, based on the results of the evolutionary game analysis, recommendations for PSC inspection auxiliary decision-making and related strategic suggestions are presented. The experimental results show that the RoBERTa-wwm-ext model and the XGBoost4 model used in the PSC inspection knowledge graph achieve superior performance in both entity recognition and knowledge fusion tasks, with the model accuracies surpassing those of other compared models. In the knowledge graph-based PSC inspection evolutionary game, the reward and punishment conditions (n, f) can reduce the burden of the standardization cost for safeguarding the ship. A ship is more sensitive to changes in the detention rate β than to changes in the inspection rate α. To a certain extent, the detention cost CDC plays a role similar to that of the detention rate β. In small-scale networks, relevant parameters in the ship’s standardization game have a more pronounced effect, with detention cost CDC having a greater impact than standardization cost CS on ship strategy choice and scale-free network evolution. Based on the experimental results, PSC inspection strategies are suggested. These strategies provide port state authorities with auxiliary decision-making tools for PSC inspections, promote the informatization of maritime regulation, and offer new insights for the study of maritime traffic safety management and PSC inspections.