摘要
为有效避免临时工不安全行为的产生,本文通过文献分析法提取出影响临时工不安全行为产生的主要因素,构建临时工不安全行为指标体系,制作调查问卷,共收集有效问卷205份。最后对收集到的数据进行处理,并通过模拟退火算法(SA)优化的BP神经网络对数据进行训练及预测。结果表明基于模拟退火算法的BP神经网络模型能对临时工不安全行为精确预测。
In order to effectively avoid the generation of temporary workers’unsafe behaviors,this paper extracts the main factors affecting the generation of temporary workers’unsafe behaviors through literature analysis method,constructs the index system of temporary workers’unsafe behaviors,makes questionnaires,and collects a total of 205 valid questionnaires.Finally,the collected data were processed and trained and predicted by BP neural network optimized by simulated annealing algorithm(SA).The results show that the BP neural network model based on simulated annealing algorithm can accurately predict the unsafe behaviors of temporary workers.
出处
《工程经济》
2022年第6期73-80,共8页
ENGINEERING ECONOMY
基金
2018年湖南省应急管理厅安全生产科技研究及推广项目(201801)
关键词
临时工
安全行为
模拟退火算法
神经网络
Temporary Workers
Safety Behavior
Simulated Annealing Algorithm
Neural Network
作者简介
陈赟,长沙理工大学,二级教授、博士生导师;蒋康洋,长沙理工大学,硕士研究生。
