露天矿爆破粉尘图像智能降噪优化决策系统设计及应用OA

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.