In order to better characterize the accident propagation mechanism in chemical industry park (CIP), a method combining mixed degree decomposition (MDD) algorithm with accident propagation probability is proposed to measure the accident propagation ability of nodes in the accident chain network of CIP in this paper. First, the accident chain network model of CIP is established by using complex network theory and the nodes in the network model are layered according to the accident propagation ability based on the MDD algorithm. Second, the accident propagation probability value of each node in the network is defined and calculated according to the characteristic factors of the node itself. Finally, the result of MDD algorithm is combined with the accident propagation probability value by using Euclidean distance formula, the nodes with strong accident propagation ability in the accident chain network of CIP are determined. The case analysis shows that the method has higher discrimination, can effectively measure the accident propagation ability of nodes in the accident chain network of CIP, and better identify the nodes with strong propagation ability.

中文翻译：

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基于混合度分解算法和事故传播概率的化工园区事故传播能力研究

为了更好地刻画化工园区（CIP）事故传播机制，提出了一种将混合度分解（MDD）算法与事故传播概率相结合的方法来衡量化工园区事故链网络中节点的事故传播能力。纸。首先，利用复杂网络理论建立CIP事故链网络模型，并基于MDD算法根据事故传播能力对网络模型中的节点进行分层。其次，根据节点本身的特征因素定义和计算网络中每个节点的事故传播概率值。最后，利用欧氏距离公式将MDD算法的结果与事故传播概率值相结合，确定CIP事故链网络中事故传播能力强的节点。案例分析表明，该方法具有较高的判别能力，能够有效衡量CIP事故链网络中节点的事故传播能力，更好地识别传播能力强的节点。