The emergency management of chemical accidents plays an important role in preventing the expansion of chemical accidents. In recent years, the evaluation and research of emergency management of chemical accidents has attracted the attention of many scholars. However, as an important part of emergency management, the professional rescue team of chemicals has few evaluation models for their capabilities. In this study, an emergency rescue capability assessment model based on the PCA-BP neural network is proposed. Firstly, the construction status of 11 emergency rescue teams for chemical accidents in Shanghai is analyzed, and an index system for evaluating the capabilities of emergency rescue teams for chemicals is established. Secondly, the principal component analysis (PCA) is used to perform dimension reduction and indicators’ weight acquisition on the original index system to achieve an effective evaluation of the capabilities of 11 rescue teams. Finally, the indicators after dimensionality reduction are used as the input neurons of the backpropagation (BP) neural network, the characteristic data of eight rescue teams are used as the training set, and the comprehensive scores of three rescue teams are used for verifying the generalization ability of the evaluation model. The result shows that the proposed evaluation model based on the PCA-BP neural network can effectively evaluate the rescue capability of the emergency rescue teams for chemical accidents and provide a new idea for emergency rescue capability assessment.

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基于PCA-BP的化学事故应急救援能力评估模型研究

化学事故的应急管理在防止化学事故扩大方面起着重要作用。近年来，化学事故应急管理的评估和研究引起了许多学者的关注。但是，作为应急管理的重要组成部分，专业的化学品救援团队对其能力缺乏评估模型。本文提出了一种基于PCA-BP神经网络的应急救援能力评估模型。首先，分析了上海市11个化学事故应急救援队的建设状况，建立了化学事故应急救援能力评估指标体系。其次，主成分分析（PCA）用于对原始指标系统进行降维和指标权重获取，以有效评估11个救援队的能力。最后，将降维后的指标用作BP神经网络的输入神经元，将八个救援队的特征数据用作训练集，并使用三个救援队的综合评分来验证泛化性。评估模型的能力。结果表明，所提出的基于PCA-BP神经网络的评估模型能够有效地评估化学事故应急救援队的救援能力，为应急救援能力评估提供新思路。