Optimization of Sensor Layout for Climate and Environmental Factors Based on Grey Correlation Degree

The environment in which modern weapons and equipment operate is complex and ever-changing, and environmental stress is an important cause of equipment failure. if the equipment is not able to apply to the environmental conditions in which it is located, it will produce corrosion, fatigue, or even structural damage on the structure or material, which will lead to its performance, life expectancy reduction and other issues. Therefore, monitoring environmental factors can better provide early warning of each failure.In order to accurately monitor different environmental factors, different types of sensors are needed, which requires a large number of sensors; it also makes the sensor network more and more complex. In order to simplify the sensor network at the same time, to obtain more and more accurate experimental data, the sensor layout needs to be studied. In this thesis, starting from the existing data, we study the spatio-temporal correlation between the data and propose a method to optimize the cost of the number of sensors, which provides a new research idea for the optimization of sensor layout.The research can be divided into three parts: (1) Time series data preprocessing. The seasonal variation and irregular fluctuation factors contained in the original data are removed by the seasonal adjustment method to test the smoothness of the data. (2) Research on spatio-temporal correlation of data series. The normalization of the pre-processed data series is completed by using the method of polar deviation. (3) Layout optimization of environmental factor sensors. By simplifying the process, construct a correlation network graph. Preprocess the special structure, and then quantify the degree of node importance based on the graph theory clustering method, combined with the key node identification method of the complex network. An optimization algorithm is proposed to finalize the sensor layout scheme.