# Data Mining Based on Chinese Traditional Calendar in the Han Dynasty Yang Mansoleum Museum
## 1 Introduction  
Built in 2006, the outer burial pits of the Han Dynasty Yang Mansoleum is the first fully enclosed museum in China. In order to effectively evaluate the effect of the closed protection mode, 260 Internet of things (IoT) sensors have been installed in the site since 2009. Heterogeneous data such as temperature, humidity and carbon dioxide concentration are mainly monitored by IoT sensors. In the museum, heterogeneous data can be monitored remotely and in real time by IoT sensors [1; 2; 3; 4], which can be used to study the evolution law of the site and predict its development trend, so as to better guide the protection of the site. Also, IoT sensors can communicate autonomously with the base station to report the current condition of the soil surface, which greatly 1 reduces human interference and damage. Although a large number of IoT sensors have been deployed in the museum, the utilization rate of large-scale monitoring data is not relatively high. A deeper understanding of the data is needed to detect or determine anomalies. It mainly went through the following two stages. The processing of monitoring data still relies on traditional statistical models. These studies include studies on indoor museums and outdoor soil sites, involving museum air quality [5], temperature and humidity [6], microclimate models [7; 8], etc. Furthermore, there are also many studies on the outer burial pits of the Han Dynasty Yang Manusoleum, including the influence of indoor temperature change on cultural relics [9], prediction model [10; 11], weathering of the soil sites [12], the change rules of indoor air temperature and relative humidity [13; 14], indoor air environment at Hanyang Mausoleum museum [15]. These researches are based on the traditional statistical algorithm, which can only obtain the statistical rules of the data in a short time. In addition, these studies also don't mine the detailed characteristics of the data. The algorithm for processing data is not only relatively simple but also uses few data points. Compared with a large number of data, the algorithm uses less data. So it can not show the detailed characteristics of temperature change. Traditional machine learning is an attempt to predict the future behavior or trend of data based only on observed samples. Modern machine learning [16; 17] is a technology that studies how computers simulate or implement human learning behavior in order to acquire new knowledge or skills. Semi-supervised learning [18] can use big data to reorganize the existing knowledge structure to continuously improve its performance. Literature [19] proposes a time-frequency representation based on kernel-extreme learning machine(ELM), which solves non-uniform sampling. ELM is a single-hidden layer forward neural network (SLFNN) machine learning method, its main advantages are simple structure and fast learning. Literature [20] proposes a temperature data modeling and prediction algorithm by using ELM, which establishes a prediction model based on solar calendar label. Intelligent technology was initially introduced into the field of cultural relic protection by the model. Also, the prediction of temperature change in detail was also realized. The traditional Chinese lunar calendar can show information from both the sun and the moon. Natural data tend to be influenced by the sun and the moon at the same time, so using the lunar calendar as a time label would be more consistent with the changing law of the data itself. Based on the previous work, in this paper, the ELM is used to establish a temperature prediction model based on lunar calendar label. Compared with the processing of solar calendar labels, accuracy of this method is relatively higher. In addition, the superiority of the lunar calendar in recording natural data is preliminarily verified. Experiments show that compared with the solar calendar model, the lunar calendar model can better predict the temperature, humidity, and other environmental factors.