## 5 Conclusions  
The purpose of this research is to find a relationship between on-site heating power and outdoor temperature, through neural network model training to obtain the optimal model for temperature prediction, combined with a heating strategy to ensure the stable operation of the on-site equipment. The following conclusions are drawn through the above results and experimental process:  
(1) In the algorithm training experiment, based on different error indicators, the prediction accuracy of Bi LSTM is higher than that of the other four neural networks, especially in the annual data. This shows that Bi LSTM improves the shortcomings of LSTM for time series prediction. (2) Field heating experiments have determined a set of experimental data of internal heating power corresponding to the air temperature when the outdoor wind speed is 10m/s. Equation (12) shows the relationship between input energy and temperature difference derived from the heat transfer model. The experimental data is combined to get Table 3, and combined with the optional internal heating method, the relationship between 8 groups of air temperature and power is determined. Besides, the discontinuous adjustment of heating power and the delay of remote-control satellite communications determine the necessity of predicting the temperature. Accurate predictions of environmental parameters are of considerable significance to many activities on the Antarctic site. Climate predictions in small Antarctic areas can provide strong support for local scientific research activities. The establishment of a heat conduction network in traditional modelling is a standard method to control the target temperature threshold. This paper proposes an environmental maintenance method for a space physics observation module based on  
Figure 17: Aerial imagery of the entire Remote Control, Image Acquisition, Operation Maintenance, and Document Management (RIOD) system. heat transfer physics model combined with a neural network prediction algorithm. In this paper, some advanced prediction models (such as Bi LSTM, SAE LSTM, etc.) are introduced into the experimental process, and the best prediction model is obtained by establishing evaluation indicators (such as RMSE). Combined with the traditional heat conduction network model, the energy of the system to maintain the system temperature can be determined, maintaining a constant temperature difference between indoor and outdoor per unit time. Combining these two models, a method for generating an on-site heating strategy operation ticket is proposed. This method and related hardware equipment can guarantee the operation of precision equipment (such as all-weather imagers) for astronomical observations and space physical observations in the Antarctic region. It provides an effective solution for other unmanned observation systems in the Antarctic area. The method is not limited to temperature prediction and heating. When combined with other physical models, the corresponding reference can also be provided based on the relevant observation data. The introduction of artificial intelligence algorithms can offer more solutions in this field. In future research, if more sample parameters (such as wind speed, and air pressure) are introduced to form a multiple-input single-output neural network and corresponding data processing (such as wavelet transform) is added, more accurate prediction and evaluation may be obtained. Conceptualization, Y.W., Y.D. and J.G. ; methodology, Y.W. ; software, Y.W. and D.H.; validation, Y.W., Y.D. and J.G. ; formal analysis, Y.W. and Y.D. ; investigation, J.G., D.H. and Y.W. ; resources, Y.D. and D.H.; data curation, Y.W. ; Writing--Original Draft Preparation, Y.W. ; Funding Acquisition, Y.D., D.H. and J.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China grant number 2018 YFB1307504 and the National Natural Science Foundation of China, grant number 41776199.