## 5 Conclusions and Future Works  
The main results are as follows: (1) the analysis and processing of mine wind speed data should be combined with local noise analysis and global air volume fluctuation. Based on the principles of local nonparametric optimization and fuzzy clustering calculation, the regression fuzzy C clustering algorithm can identify and deal with the local outliers and global outliers of underground wind speed. The algorithm comprehensively considers the location and times of outliers and the determination and analysis of air volume state, which makes establishing the model more reasonable; (2) the results of data preprocessing show that high, medium, and low-risk outliers account for 1%, 1.2%, and 2.8% of the data set, respectively. The setting of window width and weighted multiple is the critical factor affecting outlier identification. Regarding the abnormal global state, the local regression FCM algorithm determines that the number of clusters is three through the clustering validity function. Due to avoiding the influence of outliers, the clustering center is 4.4% lower than that of the FCM algorithm. Finally, the air volume state is determined to fluctuate around 4.979, 2.497, and 0.998 centers; (3) there is an implicit relationship between clustering results and local outliers, and the appearance of continuous outliers indicates the change in the overall state of air volume. When the data preprocessing creates an error in identifying outliers, it will increase the number of clusters, and outliers will be identified as one class separately, which is related to the robustness of the preprocessing algorithm.  
Future research will be carried out from the following aspects:  
1. We will analyze the data anomalies caused by different kinds of random variables, find the differences and relationships between them, and make a risk classification comparison table of various random variables, which provides a theoretical basis for checking the hidden dangers of the mine ventilation systems;
2. The uncertainty and sensitivity of parameters such as window width and weighted multiple in data preprocessing will be analyzed to solve the problems of data preprocessing failure and clustering errors in extreme cases;
3. According to the clustering results of this paper, the monitoring data of the mine will be deduced globally according to the law of air volume balance, which provides a theoretical basis for the intelligent ventilation of the mine.  
**Author Contributions:** Conceptualization, W.Z. and Y.L.; methodology, W.Z.; software, J.L.; validation, W.Z., Y.L. and J.L.; formal analysis, W.Z.; investigation, Y.L.; resources, Y.L.; data curation, J.L.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, J.L.; supervision, Y.L.; project administration, W.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.