化工矿物与加工2026，Vol.55Issue(5)：16-22,7.DOI:10.16283/j.cnki.hgkwyjg.2026.05.003
露天矿爆破粉尘图像智能降噪优化决策系统设计及应用
Design and application of intelligent noise reduction optimization decision system for blasting dust image in open-pit mine
摘要
Abstract
In order to efficiently optimize the noise reduction method to accurately identify blasting dust,this paper used Python to design and develop a set of intelligent noise reduction optimization decision system for blasting dust images,and establishes an evaluation model of noise reduction method based on range-based weighting algorithm.Through interactive intelligent methods,the functions of target video selection,dust original image extraction,noise addition,noise reduction method selection and noise reduction processing,noise reduction effect index calculation and noise reduction method optimization were completed.The system was applied to the noise reduction optimization of blasting dust images in an open-pit mine.The results show that the time of 640 blasting dust noise reduction proces-sing is only 85.56 s by using four noise reduction methods,such as mean filtering,median filtering,Gaussian filtering and bilateral filtering.The probability of bilateral filtering as the best noise reduction method is 99.38%.The deci-sion-making system can dynamically adapt image features,calculate noise reduction effect indicators,determine the best noise reduction method and the best noise reduction image,which provides an effective way to accurately identify blasting dust and objectively describe the temporal and spatial distribution characteristics of blasting dust.关键词
露天矿/爆破粉尘/图像提取/降噪方法/智能决策系统/极差加权算法Key words
open-pit mine/blasting dust/image extraction/noise reduction method/intelligent decision-making system/range-based weighting algorithm分类
矿业与冶金引用本文复制引用
吴雪黎,柯丽华,王其虎,黄兆云..露天矿爆破粉尘图像智能降噪优化决策系统设计及应用[J].化工矿物与加工,2026,55(5):16-22,7.基金项目
湖北省技术创新计划项目(2024BCB102). （2024BCB102）
