关键词: 概率神经网络, 蝙蝠算法, 数字孪生, 扰动事件

Abstract: With the progress of society,production safety and workshop management issues are receiving increasing attention.The management of traditional workshops mostly relies on manual work,and managers often fail to timely detect and identify disturbances in the workshop.This is not conducive to quickly resolving disturbance events and ensuring the safety of personnel and equipment.In order to improve management efficiency and ensure security,a disturbance determination method based on BA-PNN algorithm and digital twin technology was proposed.First,data were collected through sensors,the data were analyzed and preprocessed.Subsequently,the traditional PNN model and BA-PNN disturbance judgment model were built,and the latter aims at the optimization of algorithm recognition rate.BA-PNN model was integrated into twin platform by digital twin technology.Finally,through simulation and result analysis,compared with the model effect before optimization,the recognition effect of the model is significantly improved,while reflecting the characteristics of digital twins,proving the effectiveness of the method.

Key words: probabilistic neural network, bat algorithm, digital twin, disturbance event