Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

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基于電力數(shù)據(jù)驅(qū)動(dòng)的云控平臺(tái)邊緣計(jì)算優(yōu)化策略

來(lái)源:電工電氣發(fā)布時(shí)間:2024-11-04 13:04瀏覽次數(shù):91

基于電力數(shù)據(jù)驅(qū)動(dòng)的云控平臺(tái)邊緣計(jì)算優(yōu)化策略

戴瑞海1,萬(wàn)燕珍2,羅曼2,洪達(dá)2,周國(guó)華1
(1 國(guó)網(wǎng)浙江省電力有限公司杭州市蕭山區(qū)供電公司,浙江 杭州 311200;
2 浙江中新電力工程建設(shè)有限公司,浙江 杭州 311200)
 
    摘 要:隨著工業(yè)互聯(lián)網(wǎng)和智能電網(wǎng)的發(fā)展,電力數(shù)據(jù)量呈指數(shù)級(jí)增長(zhǎng)。邊緣計(jì)算作為一種新型計(jì)算范式,通過(guò)將計(jì)算資源部署在靠近數(shù)據(jù)源的邊緣節(jié)點(diǎn)上,有效地緩解了中心服務(wù)器的壓力,提升了數(shù)據(jù)處理的實(shí)時(shí)性和可靠性。提出了一種電力數(shù)據(jù)驅(qū)動(dòng)的工業(yè)云控平臺(tái)邊緣計(jì)算優(yōu)化策略,從數(shù)據(jù)預(yù)處理、邊緣節(jié)點(diǎn)的合理分布以及動(dòng)態(tài)任務(wù)調(diào)度等進(jìn)行了系統(tǒng)分析。通過(guò)實(shí)際案例驗(yàn)證了所提出的邊緣計(jì)算優(yōu)化策略不僅顯著提高了系統(tǒng)的實(shí)時(shí)響應(yīng)能力及計(jì)算資源的利用,而且增強(qiáng)了數(shù)據(jù)的安全性,為智能電網(wǎng)的運(yùn)行和發(fā)展奠定了基礎(chǔ)。
    關(guān)鍵詞: 電力數(shù)據(jù);邊緣計(jì)算;云控平臺(tái);優(yōu)化策略;數(shù)據(jù)預(yù)處理;邊緣節(jié)點(diǎn);任務(wù)調(diào)度
    中圖分類號(hào):TM732 ;TM744     文獻(xiàn)標(biāo)識(shí)碼:B     文章編號(hào):1007-3175(2024)10-0037-05
 
Optimization Strategy for Edge Computing of Cloud
Control Platform Based on Power Data Driven
 
DAI Rui-hai1, WAN Yan-zhen2, LUO Man2, HONG Da2, ZHOU Guo-hua1
(1 State Grid Zhejiang Electric Power Co., Ltd. Hangzhou Xiaoshan District Power Supply Company, Hangzhou 311200, China;
2 Zhejiang Zhongxin Electric Power Engineering Construction Co., Ltd, Hangzhou 311200, China)
 
    Abstract: With the development of industrial internet and smart grid, the amount of power data is growing exponentially. Edge computing as a new computing paradigm, it effectively relieves the pressure on the central server and improves the real-time and reliability of data processing by deploying computing resources on edge nodes close to the data source. This paper proposes a edge computing optimization strategy of industrial cloud control platforms of power data driven, then systematically analyzes in terms of data preprocessing, reasonable distribution of edge nodes and dynamic task scheduling. It has been verified by practical cases that edge computing optimization strategy not only significantly improves the real-time response ability of the system and the utilization of computing resources, but also enhances the security of data, which lays a foundation for the operation and development of smart grid.
    Key words: power data; edge computing; cloud control platform; optimization strategy; data preprocessing; edge node; task scheduling
 
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