No abstract

Taking Baidu search index as the data source, this research collects yoga-related data in various provinces in China, analyzes the public’s attention to yoga on the Internet from the perspective of the complex network theory, so as to dig out characteristics of those who are interested in yoga as well as the temporal and spatial change of yoga attention from 2011 to 2020. Then, by transforming the time series into a network through the VG model and HVG model, the paper analyzes the network characteristics and predicts the popularity of yoga. Furthermore, the publicity of yoga and the public’s attention to it are analyzed, considering the policy, national education level, the influence of TV, mobile phone and other communication equipment, so as to help the market to provide corresponding products and services in a targeted manner and to promote the healthy development of the yoga industry.

This paper works out relationship between visibility and near-surface meteorological factors. The formation of heavy fog is affected by meteorological factors near the ground and fog in the past period. In this paper, we abstract and simplify the problem as a time series problem. First, the airport AWOS observation data is reprocessed, and some missing and incorrect data are supplemented and corrected. Then draw a distribution map of “Visibility-Near-surface Meteorological Factors” to intuitively grasp the correlation between them. Finally, model the classic VARIMAX to fit the mapping relationship between visibility and near-surface meteorological factors. The results show temperature has the greatest impact on visibility index, positively correlated with it; secondly, dew point temperature index negatively correlated with it. The results show that, with the temperature low and the humidity high, the water vapor in the atmosphere is more likely to condense into mist, which is not easy to dissipate, resulting in reduced visibility. The indicators related to air pressure and wind speed are positively correlated with visibility, indicating that the increase in air pressure and the increase in wind speed will promote the dissipation of heavy fog. Generally speaking, the MOR index fits better with near-surface meteorological factors.

In this paper, a visibility detection model based on the dark channel prior and image entropy is established to improve the lane line detection algorithm. Our algorithm does not need to preset the target, nor is it affected by the camera calibration parameters and position. It transforms the visibility calculation problem into the atmosphere transmittance calculation problem and refines the required results through the guided filter, achieving more accurate and stable visibility estimation results. In addition, based on the changing regularity of the visibility over time obtained by the detection model, a mathematical model is established to predict the change of heavy fog. We use ADF to test the visibility obtained in the visibility detection model and calculate the autocorrelation and partial autocorrelation functions. Finding the original sequence non-stationary, we perform the difference on the data, remove all insignificant factors and then incorporate the data into ARIMA model for fitting, finally getting the fitting and prediction results. The results are found similar to the actual situation, indicating that the results obtained by the visibility prediction model are robust and reliable.