危险品仓库有害气体泄漏智能识别预警研究

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中图分类号：TQ086.5 文献标志码：A

文章编号：1001-5922（2026）1-0188-04

Abstract：To meet the early warning demand for chemical harmful gas leakage in hazardous goods warehouses，an SVM-SSA model based on principal component analysis of chemical harmful gases in hazardous goods warehouses was established.Meanwhile，random forest（RF），extreme learning machine（ELM），and backpropagationartificial neural network（BP）models were adopted to conduct a comparative analysis on the dataset verification results of chemical harmful gas leakage in hazardous goods warehouses.On this basis，based on the principal component analysis of chemical harmful gases in hazardous goods warehouses，the optimal parameter combination was trainedand subjected to regresson processing in te SSA-SVM model，and theconcentrationofchemical harmful gases in hazardous goods warehouses was predicted.The resultsshow that compared with the RF，ELM，and BP models，the SSA-SVM model achieves the highest accuracy in identifying methane，ethylene，air，and mixed gases.The prediction fitting degree of the SSA-SVM model for single chemical harmful gases in hazardous goods warehouses is above 97% ，and that for mixed chemical harmful gases is above 92% ：

Key words：component analysis ; chemical harmful gases ;leak ;early warning;handle

智能仓储和物流智能化技术的快速发展，给化 工仓储行业带来了良好发展机遇，随之而来的化工自动化仓储管理系统、货物追踪与监控技术、仓储机械化设备的智能化等技术在化工行业中都在不断应用，很大程度上提高了化工行业的仓储管理效率、保障了产品质量和安全[1]，然而，实际应用过程中基于化工行业的有害特性，在存放和运输过程中经常出现化工有害气体泄漏等现象，如果不进行气体泄漏预警，一方面会危及接触人员的生命安全，另一方面还可能造成环境污染等危害[3]。（剩余4744字）