Unmanned aerial vehicle air signal intensity prediction method and device based on neural network

The invention provides an unmanned aerial vehicle air signal intensity prediction method and device based on a neural network. The method comprises the following steps: acquiring an air signal test data packet at a preset height under the current weather condition; inputting the air signal test data packet into a signal prediction model, and outputting a signal intensity prediction result of a target weather condition and a target height corresponding to the air signal test data packet; wherein the signal prediction model is obtained by training air signal sample data packets of different weather conditions and different preset heights, and during testing, air coverage data sequences corresponding to other heights and other weather conditions can be output only by flying at one height and one weather condition. Therefore, the workload of aerial signal measurement can be reduced, and the signal strength can be accurately reflected. 本发明提供一种基于神经网络的无人机空中信号强度预测方法及装置，该方法包括：获取当前天气情况下在预设高度的空中信号测试数据包；将所述的空中信号测试数据包输入至信