### Related Work  
The anomaly processing methods of large sensor data samples can be divided into the data fusion method, signal processing method, and multi-intelligence body fusion method [6; 7]. The data fusion method aims to infer abnormal values by combining the average values of some machine learning algorithms and multiple sensors [8; 9]. The idea of the signal processing method is to restore the signal to form the initial sample and then use the principal element analysis, wavelet analysis, and other methods to put forward the signal features [10]. A multi-agent for data processing method treats various processing methods as agents, gives the decision structure and route after fusion, and then judges whether the data are abnormal [11; 12]. Experts in specific fields (such as wind fields) have carried out much research on analyzing and predicting wind speed data, and most deal with their nonlinear characteristics based on time series [13; 14]. For example, [PERSON] [15] et al. aiming at the non-stationarity and randomness of wind speed data, developed a new hybrid 20 WSF model based on long-term memory (LSTM) network, 21 decomposition methods, and grey wolf optimizer (GWO). A hybrid wind speed prediction model is proposed, considering both the accuracy and stability of wind speed prediction. [PERSON] [16] used fully integrated empirical mode decomposition (CEEMD) to divide the original wind speed series into a set of intrinsic mode functions and then applies the extreme learning machine (ELM) optimized by multi-objective grey wolf optimization (MOGWO) to achieve excellent prediction performance. [PERSON] [17] used nonlinear autoregressive exogenous (NARX) neural networks to estimate the relationship between some parameters and wind speed. Based on the new combination method of two-stage data preprocessing technology, three-component prediction model, and multi-objective optimization algorithm, [PERSON] [18] proposed a unique combination forecasting system, which can decompose and reshape the original data to reduce noise and chaotic interference. [PERSON] [19] offered a two-way wind speed prediction and analysis system, which realizes the dual calculation of wind speed determination point prediction and interval prediction. Different from the characteristics of wind speed in other engineering fields, the change of mine wind speed is controlled by man as a whole, and its change trend changes with the evolution of ventilation facility control, and the information on ventilation facility variation may be implied in the wind speed data. Therefore, the analysis and processing of wind speed data are more critical [20]. The interpretation of mine wind speed can be divided into two categories: random variables and process variables, in which random variables can be understood as noise data in a local sense, including facility drop, train and personnel passage, abnormal detection equipment, and so on. The analysis of the uncommon degree of noise data can reveal the safety problem of randomness. The process variable has a definite change trend, which generally lasts for a specific time, including shaft extension, structure damage, roadway penetration, cage lift, air door installation, ventilation power regulation, and so on. The analysis of process variables is of global significance. The extraction and analysis of the characteristics of different periods, mining the ventilation state change information and comparing with the facility regulation measures, find the system's hidden danger. For data information mining in the global sense, most of the clustering methods are based on the data characteristics [21]. Many studies have used the mean C fuzzy clustering (FCM) algorithm. Mean C fuzzy clustering is an algorithm for clustering according to data characteristics. At present, there are many improved fuzzy C-means clustering algorithms.