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今天小編為大家帶來文章：83研讀分享論文5-4：精讀博士論文-《不确定性分析視角下大資料資訊服務定價研究》研究方法與路線 。
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Today's small series for you to bring the article:83 Reading and sharing papers 5-4: Intensive doctoral thesis - "Research on the pricing of big data information services from the perspective of uncertainty analysis" research method and route .
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今日内容摘要Abstract：
閱讀并分析博士論文《不确定性分析視角下大資料資訊服務定價研究》中研究内容、方法和路線設計部分的寫作。
Read and analyze the writing of the research content, method and route design in the doctoral dissertation "Research on Big Data Information Service Pricing from the Perspective of Uncertainty Analysis".
正文 body part：
研究内容方面，作者以問題為導向建構了論文的五層架構（圖1、2、3）。第一層：緒論章節為提出問題的章節，交代了研究背景、意義、目的等。第二層：第二章章節文獻綜述為分析問題章節，首先從學術角度去分析解剖問題，其次探讨了大資料資訊服務的定義、特點、影響定價的因素（第三章）、價值傳導模型。此外分析下了傳統定價方法難以勝任的原因，并提出新的定價方法。第三層：第四、五、六章節論證如何解決問題。分别解決了應對大資料資訊源的不确定性的問題、消除大資料資訊服務交易雙方對服務品質效果的不确定性的問題以及原始資料來源着的補償問題。第四層：第七章為實證研究，主要探讨了第五章中提出的在大資料資訊服務的成熟期基于bp神經網絡的定價方法的實用性。研究結果驗證了該方法的有效性。第五層：第八章對本研究做出總結。
In terms of research content, the author constructs the five-layer structure of the paper based on the problem (Figures 1, 2, and 3). The first layer: the introduction chapter is the chapter that asks the question, explaining the research background, meaning, purpose, etc. The second layer: The second chapter, the literature review, is a chapter of analyzing problems. First, it analyzes the anatomical problems from an academic perspective, and then discusses the definition, characteristics, factors affecting pricing (chapter 3), and value transmission models of big data information services. In addition, it analyzes the reasons why traditional pricing methods are incompetent, and proposes new pricing methods. Level 3: Chapters 4, 5, and 6 demonstrate how to solve the problem. It solves the problem of dealing with the uncertainty of the big data information source, the problem of eliminating the uncertainty of the service quality effect between the two parties of the big data information service transaction, and the compensation problem of the original data source. The fourth layer: The seventh chapter is an empirical study, which mainly discusses the practicability of the pricing method based on bp neural network proposed in the fifth chapter in the mature stage of big data information service. The results of the study verified the effectiveness of the method. Level 5: Chapter 8 summarizes this research.
圖1 Figure 1
圖2 Figure 2
圖3 Figure 3
研究方法（圖4）總體采用了文獻研究法（第二章）、問卷調查法（第四章）、模型建構與仿真（第四、五、六章）、實證研究法（第七章）。在模型建構與仿真法中，包含了層次分析法、德爾菲法、蒙特卡洛模拟法、bp神經網絡法。
The research method (Figure 4) generally adopts the literature research method (Chapter 2), the questionnaire survey method (Chapter 4), the model construction and simulation (Chapters 4, 5, and 6), and the empirical research method (Chapter 7). In the model construction and simulation method, the AHP, Delphi method, Monte Carlo simulation method and bp neural network method are included.
圖4 Figure 4
研究路線設計（圖5）方面，作者也做了圖。對比研究内容的圖，可以看出研究路線更加具體和可操作性變強。研究内容會遵循每個章節的順序來描述，而研究路線更像是做研究的操作手冊。此外，研究路線圖偏向樹狀圖和流程圖，而研究内容則是子產品圖。作者把大資料資訊服務的定價問題分為資料型、方案型，并分别設計研究思路。對于資料型的定價，不确定性來自于資料集的屬性，采用專家打分；對于方案型的定價，不确定性來自于交易過程和交易規律。針對交易過程的不确定性，設計了結果導向定價法，同時設計了合約管理機制。針對交易規律的不确定性，采用bp神經網絡，訓練定價參考體系。
In terms of research route design (Figure 5), the author also made a diagram. Comparing the graph of the research content, it can be seen that the research route is more specific and more operable. The research content will be described in the order of each chapter, and the research route is more like a manual for doing research. Furthermore, research roadmaps are biased towards tree diagrams and flowcharts, while research content is a block diagram. The author divides the pricing problem of big data information service into data type and scheme type, and designs research ideas respectively. For data-based pricing, the uncertainty comes from the attributes of the data set, and experts are used to score; for program-based pricing, the uncertainty comes from the transaction process and transaction rules. Aiming at the uncertainty of the transaction process, a result-oriented pricing method is designed, and a contract management mechanism is also designed. Aiming at the uncertainty of transaction rules, the bp neural network is used to train the pricing reference system.
圖5 Figure 5
進一步地，作者總結了研究的創新點（圖6）。第四章的創新在于設計資料型大資料資訊服務定價體系時結合使用了專家打分、層次分析法和蒙特卡羅模拟法，依靠專家經驗設計屬性權重降低任意性的同時避免專家過少的不确定性。第五章提出解決方案型大資料資訊服務定價的結果導向法，創新性地提出智能合約，用以提高交易過程和交易後的可追蹤性、不可抵賴性。第六章采用區塊鍊和數字摘要技術，保障了大資料原始資料提供者的合法權益。第七章運用bp神經網絡方法，對實際資料進行大量的訓練，得到了大資料資訊服務的定價和影響因素的關系，在研究方法方面進行了創新。
Further, the authors summarize the innovative points of the study (Figure 6). The innovation of the fourth chapter is that when designing the data-based big data information service pricing system, expert scoring, analytic hierarchy process and Monte Carlo simulation method are used in combination, relying on expert experience to design attribute weights to reduce arbitrariness and avoid the uncertainty of too few experts sex. The fifth chapter proposes the result-oriented method of solution-based big data information service pricing, and innovatively proposes smart contracts to improve the traceability and non-repudiation of the transaction process and post-transaction. The sixth chapter adopts blockchain and digital summarization technology to protect the legitimate rights and interests of the original data providers of big data. The seventh chapter uses the bp neural network method to conduct a large number of training on the actual data, and obtains the relationship between the pricing of the big data information service and the influencing factors, and innovates in the research method.
圖6 Figure 6
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參考文獻：
郭春芳. 不确定性分析視角下大資料資訊服務定價研究[D]. 北京交通大學, 2019.
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