In order to reveal the ship collision causation propagation law, a ship collision causation propagation model is proposed in this paper based on the complex network dynamics theory. The model improved the Susceptible—Infected—Recovered (SIR) model and makes it applicable to the dynamic process of risk propagation. In order to apply the model to the actual network, we construct a ship collision causation network. The network is used as the simulation object of this study. The simulation results show that the causative nodes of different importance have the ability to propagate the risk to the whole network. The impact of accident causation can be reduced by shortening the inspection time of accident causation, reducing the risk propagation rate, increasing the risk control rate, and selecting appropriate initial control factors. The model can help to simulate the propagation of the causes of ship collisions, and can quantitatively analyze the impact of the causes on ship collisions.
To determine the relationship between various factors leading to ship collision accidents and prevent ship collision accidents. This paper collected 100 reports of ship collision accidents, and 40 causative factors were determined from the four aspects of human, ship, environment, and management. The relationship among 136 kinds of causation factors was determined by extracting the causation chain from the accident report, and the causation network model of ship collision accidents was established. Python simulation was used to analyze the robustness of the causative network under deliberate and random attacks. By comparing the robustness of the causative network under degree value attack, betweenness centrality value attack, closeness centrality value attack, and PR value attack, the key factors in the causative network were identified, and corresponding prevention strategies were proposed. The simulation results show that the robustness of the causative network is weak under the deliberate attack, the robustness of the betweenness centrality value attack is the worst, and the node with a higher betweenness centrality value is the key node in the causal network, giving priority to the prevention of key nodes is conducive to the safe navigation of ships.
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