基于大数据的嵌入式设备的超负荷状态估计

发布时间：2018-02-20 23:35

  本文关键词： 大数据 嵌入式 超负荷　出处：《计算机仿真》2016年03期 　论文类型：期刊论文

【摘要】：对嵌入式设备的超负荷状态进行准确估计,能够提高嵌入式设备的稳定性。由于嵌入式设备构造较为复杂,各个零件承担的电压电流负荷无法形成单一的稳定特征,会发现较大变化。传统的估计方法,仅仅以单个电路的负荷数据特征进行实时更新叠加,没有充分考虑负荷参数之间的相互影响,以及特征变化中的可识别周期性,计算的结果不准。提出采用大数据分析的嵌入式设备超负荷状态估计方法。对嵌入式设备的等效原理进行分析,建立嵌入式设备的等效模型;将各支路上的大数据功率数据作为超负荷状态估计的变量,得到稳定的嵌入式设备各支路上的电流,根据卡尔曼滤波原理建立嵌入式设备的超负荷状态估计的目标函数,将各支路电流作为卡尔曼滤波的输入量,进行泰勒级展开,最终获得准确的估计结果。仿真结果表明,改进算法能够提高超负荷估计的精度。
[Abstract]:Accurate estimation of the overload state of embedded devices can improve the stability of embedded devices. Because of the complex structure of embedded devices, the voltage and current load of each part can not form a single stable characteristic. The traditional estimation method only uses the load data features of a single circuit to update and stack in real time, without fully considering the interaction between load parameters and the identifiable periodicity of the characteristic changes. The method of overload state estimation of embedded equipment based on big data's analysis is put forward. The equivalent principle of embedded device is analyzed and the equivalent model of embedded device is established. Taking big data power data of each branch road as the variable of overload state estimation, the current of each branch of embedded equipment is obtained, and the objective function of overload state estimation of embedded equipment is established according to Kalman filter principle. Using each branch current as the input of Kalman filter, the Taylor stage expansion is carried out, and the accurate estimation results are obtained. The simulation results show that the improved algorithm can improve the accuracy of overload estimation.
【作者单位】： 南通大学;
【分类号】：TP368.1;TP311.13
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本文编号：1520352