Design of Mine Ventilation Data Cleaning System Based on Machine Learning
In view of the common problems such as noise,outliers and redundancy in mine ventilation system data,a data cleaning method based on machine learning is proposed,aiming to provide reliable information for decision-making processes such as mine intelligent risk warning,ventilation strategy adjustment and environmental management.data.A data set contai-ning key parameters such as environmental monitoring parameters,wind turbine operating parameters,and safe operation pa-rameters was constructed.This data set supports the development of data cleaning algorithms and serves as a benchmark for e-valuating data cleaning methods.Based on the constructed dataset,a comprehensive machine learning-driven data cleaning framework is proposed.Firstly,an autoregressive model is used to estimate and fill missing values in time series data.This mod-el can effectively utilize the time correlation of data and improve the accuracy of missing data processing.Secondly,the isolation forest algorithm is introduced to isolate and identify data points by constructing multiple random trees.This model is suitable for dealing with anomaly detection problems in high-dimensional ventilation data and can effectively improve the recognition rate of outliers.Finally,the K-means clustering algorithm is used to aggregate similar data points by analyzing data characteristics to reduce duplicate or similar data records.Experimental results show that the proposed data cleaning method effectively improves the quality of mine ventilation data,provides high-quality data support for mine ventilation management,and shows good engi-neering application value.
万方数据
维普
