熵权集结多维数据的事故网络节点重要度评估

Evaluation of accident network node importance based on entropy weights integrated multi-dimensional data

为控制多组元复杂系统的风险,确保其安全运行,用牵连控制重要节点方式抑制系统崩溃。基于静态事故网络中的节点度参数和专家经验获得的节点中心度参数,以及动态仿真所得的风险传递数据,建立熵权集结多维数据的事故网络节点重要度评估算法。从备用风险处置资源视角,采用Arena软件验证4种节点重要度排序结果的优劣情况。结果表明:基于熵权集结多维数据的节点重要度评估结果最优,通过赋予该排序下的重要节点一定的性能可大幅提高系统风险处置能力。

In order to control risk of a complex system consisting of a large number of elements,ensure its safe operation,the important nodes implicated control method can be used to suppress the collapse of complex system. Based on node degree parameter of static accident network,node centrality parameter from expertise,risk transfer data from dynamic simulation,a entropy weights integrated multi-dimensional data method was worked out for evaluating accident network node importance. From perspective of spare risk treatment resources,Arena software was used to test and verify four node importance sort results. The simulation results show that node importance sort results based on entropy weights integrated multi-dimensional data is the optimum,under such situation improving performance of important node can gain better risk control results of system.