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基于隱私保護(hù)的電解鋁生產(chǎn)決策方法
網(wǎng)絡(luò)安全與數(shù)據(jù)治理
曾凡鋒,,楊玉麗
北方工業(yè)大學(xué)信息學(xué)院
摘要: 在電解鋁生產(chǎn)過程中,傳統(tǒng)的人工控制決策方式已經(jīng)難以適應(yīng)現(xiàn)代鋁電解生產(chǎn)要求,,當(dāng)下深度學(xué)習(xí)算法處理此類時間序列數(shù)據(jù)得到廣泛應(yīng)用,,決策是否高效影響鋁電解槽的穩(wěn)定運行和高效產(chǎn)出鋁,。同時,,數(shù)據(jù)隱私問題也不容忽視,隱私安全既影響電解鋁生產(chǎn)過程又影響其正常使用,,誤用,、濫用數(shù)據(jù)挖掘可能導(dǎo)致用戶數(shù)據(jù)特別是敏感信息的泄露,而信息一旦丟失或泄漏將造成重大的損失,。針對以上問題,,提出一種利用改進(jìn)LSTM模型結(jié)構(gòu)結(jié)合優(yōu)化ElGamal算法的電解鋁決策方法:首先針對數(shù)據(jù)隱私問題提出了優(yōu)化后的ElGamal算法;再針對電解鋁數(shù)據(jù)特性改進(jìn)LSTM模型結(jié)構(gòu)與優(yōu)化ElGamal算法的雙結(jié)合,。實驗結(jié)果表明,,本方法可以在保證決策隱私安全的情況下,性能優(yōu)于傳統(tǒng)方法,,在實際情況中有參考的價值,。
中圖分類號:TF821;TP18文獻(xiàn)標(biāo)識碼:ADOI:10.19358/j.issn.2097-1788.2024.09.005
引用格式:曾凡鋒,,楊玉麗.基于隱私保護(hù)的電解鋁生產(chǎn)決策方法[J].網(wǎng)絡(luò)安全與數(shù)據(jù)治理,,2024,43(9):26-32.
Privacy-preserving decision-making method for electrolytic aluminum production
Zeng Fanfeng,,Yang Yuli
College of Information Technology,North China University of Technology
Abstract: In the process of electrolytic aluminum production, the traditional manual control decision-making method has been difficult to adapt to the requirements of modern aluminum electrolysis production.Deep learning algorithms have been widely used to process such time series data, and the efficiency of decision-making affects the stable operation of the aluminum electrolytic cell and the efficient output of aluminum. At the same time, data privacy issues can not be ignored.Privacy security not only affects the production process of electrolytic aluminum,but also affects its normal use.Misuse and abuse of data mining may lead to the leakage of user data, especially sensitive information.Once the information is lost or leaked, it will cause significant losses.In order to solve the above problems, this paper proposes an electrolytic aluminum decision-making method based on the improved LSTM model structure combined with the optimized ElGamal algorithm. Firstly, the optimized ElGamal algorithm is proposed to solve the problem of data privacy. Then according to the characteristics of electrolytic aluminum data, the LSTM model structure is improved and the ElGamal algorithm is optimized.Experimental results show that the performance of this method is better than that of traditional methods under the condition of ensuring the privacy and security of decision-making.It has reference value in actual situations.
Key words : electrolytic aluminum; LSTM; privacy protection; production decisions

引言

鋁電解過程是一個非線性,、多變量耦合、時變和大時滯的工業(yè)過程體系[1],,在傳統(tǒng)鋁電解槽的控制中,,一直面臨著許多挑戰(zhàn),難以實現(xiàn)智能優(yōu)化控制,。生產(chǎn)人員在實際生產(chǎn)時,工藝參數(shù)的生產(chǎn)決策方案主要采用人工經(jīng)驗進(jìn)行設(shè)置,,具有強(qiáng)烈的個人主觀性[2],而沒有充分利用現(xiàn)有鋁電解生產(chǎn)過程中遺留的大量歷史數(shù)據(jù),,沒有發(fā)現(xiàn)其中蘊(yùn)含的對企業(yè)生產(chǎn)和管理具有重要指導(dǎo)作用的規(guī)律和最佳決策方案,。鋁電解生產(chǎn)與檢測裝備的自動化與信息化水平不斷提升[3], 為此,利用數(shù)據(jù)挖掘技術(shù)挖掘電解槽工藝參數(shù)之間的關(guān)系以及工藝參數(shù)之間隱藏的規(guī)律,,智能化地指導(dǎo)決策生產(chǎn),,提高生產(chǎn)設(shè)備生產(chǎn)效率,就顯得尤為重要,,高效率的決策生產(chǎn)不僅可以使企業(yè)長期穩(wěn)定地高效產(chǎn)鋁,,還能夠延長電解槽的使用壽命,并且對能源消耗也有著重要的影響,。

與此同時,,數(shù)據(jù)挖掘也面臨著很多挑戰(zhàn),其中,數(shù)據(jù)挖掘的個人隱私與信息安全問題尤其受到關(guān)注,,大數(shù)據(jù)一定來源多樣,,本身存在隱私范圍擴(kuò)大、隱私權(quán)利歸屬復(fù)雜,、隱私保護(hù)難度大的問題,,高性能計算一般以云計算和分布式計算為特征,用戶數(shù)據(jù)脫離本地計算,,數(shù)據(jù)的訪問控制,、隱私保護(hù)難度增大。信息一旦丟失或泄漏將造成重大的損失,。越來越多的人們對此表示擔(dān)憂,,甚至拒絕提供真實的數(shù)據(jù)從而造成“數(shù)據(jù)孤島”現(xiàn)象,如何在不暴露用戶隱私的前提下進(jìn)行數(shù)據(jù)挖掘就非常重要,。

因此,,對于上述的問題,本文根據(jù)電解鋁數(shù)據(jù)的特性對LSTM(Long Short Term Memory)模型結(jié)構(gòu)進(jìn)行改進(jìn),,同時由于密碼學(xué)在電解鋁等工業(yè)生產(chǎn)過程中應(yīng)用較少,,本文便添加了優(yōu)化后的的ElGamal算法,保證了電解鋁生產(chǎn)決策過程的隱私安全,,同時其性能得到提升,,改變了之前傳統(tǒng)的決策方法,提高了效率,,在實際生產(chǎn)中有利用價值,。


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作者信息:

曾凡鋒,,楊玉麗

(北方工業(yè)大學(xué)信息學(xué)院,,北京100144)


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