区域防灾气象监测信息空间结构演化仿真

Simulation of Spatial Structure Evolution of Regional Disaster Prevention Meteorological Monitoring Information

针对当前区域防灾气象监测信息空间结构演化方法精度低、能耗高的问题,提出基于分段权重的区域防灾气象监测信息空间结构演化方法。设置风速、风向、温湿度和雨量传感器,并将不同传感器采集到的信息量化。针对风气象的特殊性,通过滑动平均法分别对风的瞬时值、1min、2min、10min平均风速与风向值进行计算,得到最终的风气象监测信息。将温湿度信息、雨量信息与风信息结合,构建气象监测信息集合。计算气象监测信息传感器的某个通道于某个校准点示值误差,获取校正后的区域防灾气象监测信息集合。根据校正后的区域防灾气象监测信息,提取出气象特征日当天最大负荷与最小负荷的真实值修正峰谷值。利用分段权重演化方法将气象监测信息空间结构演化模型分成最小值演化段与最大值演化段,获取初步演化结果,并对其进行修正。依据修正结果得到气象特征日负荷值增速,引入降维函数,构建区域防灾气象监测信息空间结构演化模型。实验表明,上述方法演化结果与实际情况吻合度高、演化能耗低。

At present, spatial structure evolution method of meteorological monitoring information in regional disaster prevention often leads to low precision and high energy consumption. Therefore, we focus on a method of spatial structure evolution for meteorological monitoring information in regional disaster prevention based on segmentation weight. Firstly, we set up wind speed, wind direction, humiture and rain sensor, and then quantized the information collected by different sensors. For the meteorological particularity of wind, we calculated the instantaneous wind speed, the average wind speed for 1 min, 2 mins and 10 mins and the value of wind direction by the sliding average method, so that the final meteorological monitoring information of wind was obtained. Then, we used humiture information ,rain information and wind information to construct meteorological monitoring information set. In addition, we calculated indication error of a channel of meteorological monitoring information sensor at a calibration point, so as to obtain the corrected set of regional disaster prevention meteorological monitoring information. Based on the corrected regional disaster prevention meteorological monitoring information, we extracted true value of the maximum load and the minimum load on the day of meteorological feature to correct peak-to-valley value. Moreover, we used the segmentation weight evolution method to divide the spatial structure evolution model of meteorological monitoring information into the minimum evolution segment and the maximum evolution section, so as to get the preliminary evolution result and modify it. According to the modified result, we obtained the increasing speed of daily load value of meteorological features. Finally, we introduced the dimensionality reduction function to construct the spatial structure evolution model of regional disaster prevention meteorological monitoring information. Simulation results show that the evolutionary result of proposed method is coincided with the actual situation. Meanwhile, the energy consumption of evolution is low.