消防钢瓶作为重要的灭火设备，在服役期间需要接受定期安全评价。为了对消防钢瓶安全状态进行高效且准确地评价，首先，基于层次分析法和模糊综合评价法，建立了一套适用于消防钢瓶的安全评价模型，并通过实例评价验证了该模型的可行性。其次，基于多种群遗传算法（multi-population genetic algorithm, MPGA）的BP神经网络优化消防钢瓶安全评价模型，该方法通过多种群遗传算法改进BP神经网络更新权重和阈值的过程，提高BP神经网络预测结果的准确度及消防钢瓶安全评价的效率。最后，通过python完成基于BP、GA-BP和MPGA-BP三种消防钢瓶安全评价模型的构建。通过对比分析三种模型的预测结果，发现MPGA-BP神经网络的预测误差最小，证明了所提出的MPGA-BP安全评价模型具有较高的准确度，能更加高效准确地进行消防钢瓶的安全评价。

As an important firefighting equipment, fire cylinders need to undergo regular safety evaluations during their service period. In order to efficiently and accurately evaluate the safety status of fire steel cylinders, a safety evaluation model suitable for fire steel cylinders was established based on the analytic hierarchy process and fuzzy comprehensive evaluation method. The feasibility of the model was verified through case evaluation. Secondly, the BP neural network based on MPGA (multi population genetic algorithm) is used to optimize the safety evaluation model of fire steel cylinders. This method improves the process of updating weights and thresholds of the BP neural network through multi population genetic algorithm, improving the accuracy of BP neural network prediction results and the efficiency of fire steel cylinder safety evaluation. Finally, the construction of safety evaluation models for fire steel cylinders based on BP, GA-BP, and MPGA-BP was completed using Python. By comparing and analyzing the prediction results of three models, it was found that the MPGA-BP neural network has the smallest prediction error, proving that the proposed MPGA-BP safety evaluation model has high accuracy and can more efficiently and accurately evaluate the safety of fire steel cylinders.