發(fā)布時間：2018-08-28 08:26

【摘要】：泛在網(wǎng)絡(luò)的發(fā)展推動大量可聯(lián)可控的設(shè)備節(jié)點進(jìn)入網(wǎng)絡(luò),將這些設(shè)備節(jié)點的服務(wù)能力封裝并在統(tǒng)一平臺上管理,將產(chǎn)生面向用戶的個人服務(wù)云。SOA為通過簡單服務(wù)生成復(fù)雜業(yè)務(wù)提供了規(guī)范的機制,上下文計算可依據(jù)上下文信息按策略指導(dǎo)應(yīng)用或業(yè)務(wù)的執(zhí)行,SOA與上下文計算相結(jié)合使得動態(tài)的服務(wù)合成成為可能。面向個人服務(wù)云的動態(tài)服務(wù)合成的上下文計算擁有廣闊的應(yīng)用前景。但是,傳統(tǒng)上下文計算存在以下問題：(1)上下文情景推理模型定義不完善,不利于情景的更新與用戶的操作；(2)情景推理機制不完善,多停留于定性分析而不能得到定量結(jié)論；(3)指導(dǎo)業(yè)務(wù)邏輯的策略機械,不夠精確、靈活,且不能適應(yīng)用戶偏好的演變,通過自主學(xué)習(xí)更新策略。 為解決上述問題,本文首先給出了面向個人服務(wù)云的動態(tài)服務(wù)合成的上下文計算系統(tǒng)框架,將CBR引入上下文計算系統(tǒng)。其次,在此基礎(chǔ)上設(shè)計了基于CBR的情景模型和情景推理機制,將情景模型映射為CBR中的案例模型,通過CBR既有的案例管理機制實現(xiàn)情景的管理,將情景推理映射為CBR中的檢索環(huán)節(jié),通過相似性計算得到情景似然度。然后,本文設(shè)計了服務(wù)信息模型和相應(yīng)的服務(wù)評估機制與反饋處理機制,以前述定量的情景似然度和服務(wù)信息模型中的偏好為條件完成精確的服務(wù)評估和靈活的服務(wù)選擇,以用戶對服務(wù)評估結(jié)果的反饋為條件完成服務(wù)偏好的自主學(xué)習(xí)與更新。之后,本文還通過一個智能家居環(huán)境下的業(yè)務(wù)流程驗證了上述系統(tǒng)功能。 最后,本文總結(jié)了全文的主要工作,闡明了今后的主要研究方向。
[Abstract]:The development of ubiquitous network drives a large number of connected and controllable device nodes into the network, encapsulates the service capability of these device nodes and manages them on a unified platform. Will generate a user-oriented personal service cloud. SOA provides a canonical mechanism for generating complex business through simple services, Context computing can be based on context information to guide applications according to policy or the combination of business execution SOA and context computing makes dynamic service composition possible. The context computing of dynamic service composition for personal service cloud has a broad application prospect. However, the traditional context computing has the following problems: (1) the definition of contextual scenario reasoning model is not perfect, which is not conducive to the updating of the situation and the operation of the user; (3) the strategy machinery guiding business logic is not precise, flexible, and can not adapt to the evolution of user preferences, and update the strategy through autonomous learning. In order to solve the above problems, a context computing framework for dynamic service composition for personal service cloud is presented in this paper, and CBR is introduced into the context computing system. Secondly, the scenario model and scenario reasoning mechanism based on CBR are designed, the scenario model is mapped to the case model in CBR, and the scenario management is realized through the existing case management mechanism of CBR. The scenario inference is mapped to the retrieval link in CBR, and the likelihood degree of the scenario is obtained by the similarity calculation. Then, this paper designs the service information model, the corresponding service evaluation mechanism and the feedback processing mechanism, taking the above quantitative scenario likelihood degree and the preference in the service information model as the condition to complete the accurate service evaluation and the flexible service choice. Self-learning and updating of service preference is completed on the basis of user feedback on service evaluation results. After that, this paper verifies the above system function through a business process in a smart home environment. Finally, this paper summarizes the main work of the paper, and clarifies the main research direction in the future.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09

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本文編號：2208838