本文選題：嵌入式軟件　切入點：VxWorks　出處：《哈爾濱工程大學》2013年碩士論文

【摘要】：隨著計算機技術與微電子技術的高速發(fā)展，人們對嵌入式系統(tǒng)的應用已經(jīng)普及到社會的各個角落。伴隨著嵌入式軟件復雜性的不斷提高，嵌入式系統(tǒng)自身的隱患也越來越多，嵌入式軟件在運行的過程中一旦出現(xiàn)系統(tǒng)故障或軟件失效，將很難完成使命，甚至有可能對用戶造成巨大的財產損失。因此，對嵌入式軟件可靠性的研究，已經(jīng)成為當下的熱門研究方向。本文以VxWorks下的嵌入式軟件為研究對象，對嵌入式軟件狀態(tài)監(jiān)測與自恢復技術進行研究。論文的主要工作如下： 首先，闡述了嵌入式軟件狀態(tài)監(jiān)測與自恢復技術的國內外研究現(xiàn)狀，，簡要的介紹了本課題的研究背景及意義，給出了論文的主要研究內容；主要介紹了嵌入式軟件自恢復相關技術，包括介紹了嵌入式軟件自恢復的必要性和研究過程中存在的主要問題；分別講述了兩種常用的嵌入式軟件自恢復策略以及各自的優(yōu)缺點；詳細介紹了軟件自恢復策略中最常用的修復技術——微重啟技術。 其次，分別從嵌入式系統(tǒng)性能和嵌入式軟件行為兩個方面對嵌入式的軟件狀態(tài)監(jiān)測方法進行了研究。在系統(tǒng)性能監(jiān)測方法研究方面，分別對系統(tǒng)內存可用量、任務自身的運行狀態(tài)、CPU和任務堆棧的占用情況四個指標的監(jiān)測方法進行了深入研究；同時提出了一種嵌入式軟件衰老趨勢分析的方法；并利用最小二乘法進行曲線擬合，采用了一種基于閾值的嵌入式軟件失效時間估計方法。在軟件行為監(jiān)測方法研究方面，運用了一種構建軟件行為樹的方法來進行軟件行為監(jiān)測，并介紹了軟件行為樹中節(jié)點的分類、構建軟件行為樹的方法和軟件行為樹中行為的匹配規(guī)則。 再次，結合VxWorks操作系統(tǒng)自身的特點，提出了一種適合嵌入式軟件的嵌套式自恢復策略；分別介紹了基于系統(tǒng)性能監(jiān)測的嵌入式軟件自恢復策略和基于軟件行為監(jiān)測的嵌入式軟件自恢復策略的詳細流程。在第三章嵌入式軟件狀態(tài)監(jiān)測結果的基礎上，用所選擇的監(jiān)測指標構建狀態(tài)空間；根據(jù)所選擇指標的異常狀態(tài)所對應的修復行為，構建動作空間，給出了修復動作評價模型；采用SARSA(λ)算法進行自恢復決策規(guī)則強化學習，在此基礎上采用了一種基于規(guī)則的嵌入式軟件自恢復決策方法。 最后，對前面提出的嵌入式軟件狀態(tài)監(jiān)測方法和嵌入式軟件自恢復策略進行實驗驗證，證實了方法的有效性和可行性；最后通過比較，證明了本文采用的嵌套式自恢復策略的優(yōu)越性。
[Abstract]:With the rapid development of computer technology and microelectronics technology, the application of embedded system has been popularized to every corner of the society.With the increasing complexity of embedded software, there are more and more hidden dangers of embedded system itself. Once there is a system failure or software failure in the running process of embedded software, it will be very difficult to complete the mission.It is even possible to cause huge property losses to users.Therefore, the research of embedded software reliability has become a hot research direction.In this paper, the state monitoring and self-recovery technology of embedded software based on VxWorks is studied.The main work of the thesis is as follows:First of all, the status quo of embedded software state monitoring and self-recovery technology at home and abroad is described, the research background and significance of this topic are briefly introduced, and the main research content of this paper is given.This paper mainly introduces the related technologies of embedded software self-recovery, including the necessity of embedded software self-recovery and the main problems in the research process.In this paper, two commonly used self-recovery strategies of embedded software and their respective advantages and disadvantages are described respectively, and the most commonly used repair technology of software self-recovery strategy, micro-restart technology, is introduced in detail.Secondly, the embedded software state monitoring method is studied from two aspects: embedded system performance and embedded software behavior.In the aspect of system performance monitoring methods, the monitoring methods of the system memory consumption, the running status of the task itself, the CPU and the occupation of the task stack are studied.At the same time, an embedded software aging trend analysis method is proposed, and the least square method is used for curve fitting, and an embedded software failure time estimation method based on threshold is proposed.In the research of software behavior monitoring method, a method of constructing software behavior tree is used to monitor software behavior, and the classification of nodes in software behavior tree is introduced.The method of constructing software behavior tree and the matching rules of behavior in software behavior tree.Thirdly, according to the characteristics of VxWorks operating system, a nested self-recovery strategy for embedded software is proposed.The detailed flow chart of embedded software self-recovery strategy based on system performance monitoring and embedded software self-recovery strategy based on software behavior monitoring is introduced respectively.Based on the results of embedded software state monitoring in the third chapter, the state space is constructed with the selected monitoring index, and the action space is constructed according to the repair behavior corresponding to the abnormal state of the selected index, and the evaluation model of the repair action is given.SARSAA (位) algorithm is used for reinforcement learning of self-recovery decision rules, and a rule-based self-recovery decision method for embedded software is proposed.Finally, the experimental verification of the state monitoring method and the self-recovery strategy of embedded software is carried out, which proves the effectiveness and feasibility of the method.The superiority of the nested self-recovery strategy is proved.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP368.1

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