本文關(guān)鍵詞： 大數(shù)據(jù) 嵌入式 超負(fù)荷　出處：《計(jì)算機(jī)仿真》2016年03期 　論文類型：期刊論文

【摘要】：對(duì)嵌入式設(shè)備的超負(fù)荷狀態(tài)進(jìn)行準(zhǔn)確估計(jì),能夠提高嵌入式設(shè)備的穩(wěn)定性。由于嵌入式設(shè)備構(gòu)造較為復(fù)雜,各個(gè)零件承擔(dān)的電壓電流負(fù)荷無法形成單一的穩(wěn)定特征,會(huì)發(fā)現(xiàn)較大變化。傳統(tǒng)的估計(jì)方法,僅僅以單個(gè)電路的負(fù)荷數(shù)據(jù)特征進(jìn)行實(shí)時(shí)更新疊加,沒有充分考慮負(fù)荷參數(shù)之間的相互影響,以及特征變化中的可識(shí)別周期性,計(jì)算的結(jié)果不準(zhǔn)。提出采用大數(shù)據(jù)分析的嵌入式設(shè)備超負(fù)荷狀態(tài)估計(jì)方法。對(duì)嵌入式設(shè)備的等效原理進(jìn)行分析,建立嵌入式設(shè)備的等效模型;將各支路上的大數(shù)據(jù)功率數(shù)據(jù)作為超負(fù)荷狀態(tài)估計(jì)的變量,得到穩(wěn)定的嵌入式設(shè)備各支路上的電流,根據(jù)卡爾曼濾波原理建立嵌入式設(shè)備的超負(fù)荷狀態(tài)估計(jì)的目標(biāo)函數(shù),將各支路電流作為卡爾曼濾波的輸入量,進(jìn)行泰勒級(jí)展開,最終獲得準(zhǔn)確的估計(jì)結(jié)果。仿真結(jié)果表明,改進(jìn)算法能夠提高超負(fù)荷估計(jì)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.
【作者單位】： 南通大學(xué);
【分類號(hào)】：TP368.1;TP311.13
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本文編號(hào)：1520352