面向TRIZ理論的深度知識獲取及應(yīng)用研究
發(fā)布時間：2018-12-31 21:33
【摘要】：傳統(tǒng)的產(chǎn)品設(shè)計過程對概念設(shè)計階段創(chuàng)新力不足，設(shè)計人員往往由于自身單一的專業(yè)知識很難產(chǎn)生真正創(chuàng)新的產(chǎn)品。TRIZ理論雖然能夠指導(dǎo)設(shè)計人員創(chuàng)新，但在實際應(yīng)用中不易被完全掌握。專利是產(chǎn)品創(chuàng)新的主要知識資源，但是傳統(tǒng)專利庫中包含了不計其數(shù)的專利文件，且是以學(xué)科作為分類基礎(chǔ)的，難以被設(shè)計人員查找和利用。因此按照TRIZ理論相關(guān)知識提取、重組以及分析，以及對已有知識庫的擴(kuò)充和更新有利于TRIZ理論的實際應(yīng)用和自身完善，從創(chuàng)新理論的角度上輔助人們掌握創(chuàng)新設(shè)計的普遍規(guī)律，在類比以往成功案例的基礎(chǔ)上激發(fā)設(shè)計人員的發(fā)散思維是非常有意義的。 提出了基于TRIZ理論的深度知識獲取模型。該模型是以TRIZ理論為基礎(chǔ)，采用數(shù)據(jù)挖掘技術(shù)為手段，利用中文專利文獻(xiàn)資源來獲取深度知識的。專利的深度知識獲取研究有助于專利知識跨學(xué)科應(yīng)用和知識發(fā)現(xiàn)與重用研究，以及TRIZ理論由理論的高度變成被普通設(shè)計者接受的一般的理解并實際應(yīng)用過程的探索。 論文按照基于TRIZ理論中文專利深度知識獲取模型的各子模塊的實現(xiàn)方法和關(guān)鍵技術(shù)進(jìn)行論述，包括以下幾個部分：專利文本抽取模塊、文本分類器模塊和深度知識挖掘模塊。 首先，專利文本抽取模塊介紹了如何在國家知識產(chǎn)權(quán)局這樣的網(wǎng)頁中獲取所需要的專利庫信息，并將之保存到數(shù)據(jù)庫當(dāng)中。專利文本抽取是整個知識挖掘過程的前提，如果不能正確抽取所需要的專利摘要和基本信息，就無法構(gòu)建專利庫，進(jìn)行下一步的分析，因此，本模塊的研究對于后續(xù)的研究至關(guān)重要。 其次，文本分類器模塊主要實現(xiàn)了對專利文本的分類過程。將從人工分類和計算機(jī)分類兩個方面闡述專利分類的原理及過程。人工輔助專利分類是建立在人工仔細(xì)閱讀專利說明書的基礎(chǔ)上進(jìn)行的，要求分類人員掌握一定的TRIZ知識和相關(guān)的領(lǐng)域知識。而計算機(jī)分類的對象都是專利文檔的摘要部分，摘要既能基本上代替專利全文的基本內(nèi)容，而且對于計算機(jī)計算的難度大大簡化，對于分類器在測試階段是非常簡便和實用的。本模塊的專利分類主要是指運(yùn)用發(fā)明原理為分類背景對專利進(jìn)行分類和分析的。 再次，深度知識挖掘模塊主要利用了分類的結(jié)果進(jìn)行深度知識提取。挖掘過程是在閱讀專利說明書的基礎(chǔ)上，按照深度知識模板向?qū)нM(jìn)行分析的，最后將分析的結(jié)果存入實例庫中。 最后，構(gòu)建了該深度知識獲取模型的軟件系統(tǒng)——DKMining。該軟件系統(tǒng)實現(xiàn)了各模塊的功能，，并且能對專利庫和實例庫中的信息實現(xiàn)檢索、刪除、修改、更新功能。論文結(jié)合具體專利實例驗證上述理論研究，因此軟件系統(tǒng)具有一定的可行性。
[Abstract]:The traditional product design process has insufficient innovation ability to the conceptual design stage, the designer is often difficult to produce the truly innovative product because of their own single professional knowledge. Although the TRIZ theory can guide the designer's innovation, But in the practical application is not easy to be fully grasped. Patent is the main knowledge resource of product innovation, but the traditional patent library contains countless patent files, and is based on disciplines, so it is difficult to be searched and utilized by designers. Therefore, according to the relevant knowledge extraction, reorganization and analysis of TRIZ theory, as well as the expansion and updating of the existing knowledge base, it is beneficial to the practical application and self-improvement of the TRIZ theory. From the perspective of innovation theory, it helps people to grasp the general law of innovative design. It is very meaningful to stimulate the divergent thinking of designers on the basis of analogizing past successful cases. A deep knowledge acquisition model based on TRIZ theory is proposed. The model is based on TRIZ theory and uses data mining technology to acquire deep knowledge by using Chinese patent literature resources. The research of patent in-depth knowledge acquisition is helpful to the interdisciplinary application of patent knowledge and the research of knowledge discovery and reuse, as well as the exploration of TRIZ theory from a theoretical height to a general understanding and practical application accepted by ordinary designers. According to the implementation method and key technology of the Chinese patent depth knowledge acquisition model based on TRIZ theory, this paper discusses the following parts: patent text extraction module, text classifier module and depth knowledge mining module. First, the patent text extraction module introduces how to obtain the required patent library information in a web page such as the State intellectual property Office, and store it in the database. Patent text extraction is the premise of the whole process of knowledge mining. If the patent abstract and basic information can not be extracted correctly, the patent database can not be constructed and the next step can be analyzed. The research of this module is very important for further research. Secondly, the text classifier module mainly realizes the patent text classification process. This paper expounds the principle and process of patent classification from two aspects: manual classification and computer classification. Artificial assistant patent classification is based on manual careful reading of patent specification, which requires classifier to master certain TRIZ knowledge and related domain knowledge. The objects of computer classification are abstracts of patent documents, which can not only replace the basic contents of patent full text, but also simplify the difficulty of computer calculation greatly, and it is very simple and practical for classifiers in the testing stage. The patent classification of this module mainly refers to the classification and analysis of patents using the invention principle as the classification background. Thirdly, the deep knowledge mining module mainly uses the classification results to extract the depth knowledge. On the basis of reading patent specification, mining process is analyzed according to the deep knowledge template guide. Finally, the results of the analysis are stored in the case library. Finally, a software system named DKMining. is constructed for the deep knowledge acquisition model. The software system realizes the function of each module, and can retrieve, delete, modify and update the information in patent library and instance library. The paper verifies the above theory research with the concrete patent example, therefore the software system has certain feasibility.
【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.1;TB472
本文編號：2397140
[Abstract]:The traditional product design process has insufficient innovation ability to the conceptual design stage, the designer is often difficult to produce the truly innovative product because of their own single professional knowledge. Although the TRIZ theory can guide the designer's innovation, But in the practical application is not easy to be fully grasped. Patent is the main knowledge resource of product innovation, but the traditional patent library contains countless patent files, and is based on disciplines, so it is difficult to be searched and utilized by designers. Therefore, according to the relevant knowledge extraction, reorganization and analysis of TRIZ theory, as well as the expansion and updating of the existing knowledge base, it is beneficial to the practical application and self-improvement of the TRIZ theory. From the perspective of innovation theory, it helps people to grasp the general law of innovative design. It is very meaningful to stimulate the divergent thinking of designers on the basis of analogizing past successful cases. A deep knowledge acquisition model based on TRIZ theory is proposed. The model is based on TRIZ theory and uses data mining technology to acquire deep knowledge by using Chinese patent literature resources. The research of patent in-depth knowledge acquisition is helpful to the interdisciplinary application of patent knowledge and the research of knowledge discovery and reuse, as well as the exploration of TRIZ theory from a theoretical height to a general understanding and practical application accepted by ordinary designers. According to the implementation method and key technology of the Chinese patent depth knowledge acquisition model based on TRIZ theory, this paper discusses the following parts: patent text extraction module, text classifier module and depth knowledge mining module. First, the patent text extraction module introduces how to obtain the required patent library information in a web page such as the State intellectual property Office, and store it in the database. Patent text extraction is the premise of the whole process of knowledge mining. If the patent abstract and basic information can not be extracted correctly, the patent database can not be constructed and the next step can be analyzed. The research of this module is very important for further research. Secondly, the text classifier module mainly realizes the patent text classification process. This paper expounds the principle and process of patent classification from two aspects: manual classification and computer classification. Artificial assistant patent classification is based on manual careful reading of patent specification, which requires classifier to master certain TRIZ knowledge and related domain knowledge. The objects of computer classification are abstracts of patent documents, which can not only replace the basic contents of patent full text, but also simplify the difficulty of computer calculation greatly, and it is very simple and practical for classifiers in the testing stage. The patent classification of this module mainly refers to the classification and analysis of patents using the invention principle as the classification background. Thirdly, the deep knowledge mining module mainly uses the classification results to extract the depth knowledge. On the basis of reading patent specification, mining process is analyzed according to the deep knowledge template guide. Finally, the results of the analysis are stored in the case library. Finally, a software system named DKMining. is constructed for the deep knowledge acquisition model. The software system realizes the function of each module, and can retrieve, delete, modify and update the information in patent library and instance library. The paper verifies the above theory research with the concrete patent example, therefore the software system has certain feasibility.
【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.1;TB472
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 占向輝,李彥,賈愛軍,孫玉帥;面向創(chuàng)新設(shè)計的科學(xué)效應(yīng)庫研究[J];工程設(shè)計學(xué)報;2005年01期
2 呂桂志;任工昌;丁濤;;基于TRIZ技術(shù)進(jìn)化分析點膠機(jī)的演進(jìn)[J];工程設(shè)計學(xué)報;2008年05期
3 周寬久;仇鵬;王磊;;基于實踐論的隱性知識獲取模型研究[J];管理學(xué)報;2009年03期
4 Maryam Alavi;Dorothy E.Leidner;鄭文全;;知識管理和知識管理系統(tǒng):概念基礎(chǔ)和研究課題[J];管理世界;2012年05期
5 張惠;邱清盈;馮培恩;王朝霞;;產(chǎn)品專利設(shè)計知識獲取方法研究[J];哈爾濱工程大學(xué)學(xué)報;2009年07期
6 蔣云良;知識表示綜述[J];湖州師專學(xué)報;1995年05期
7 李善平,尹奇椺,胡玉杰,郭鳴,付相君;本體論研究綜述[J];計算機(jī)研究與發(fā)展;2004年07期
8 劉群,張華平,俞鴻魁,程學(xué)旗;基于層疊隱馬模型的漢語詞法分析[J];計算機(jī)研究與發(fā)展;2004年08期
9 劉向東 ,駱斌 ,陳兆乾;支持向量機(jī)最優(yōu)模型選擇的研究[J];計算機(jī)研究與發(fā)展;2005年04期
10 何國斌;趙晶璐;;漢語文本自動分詞算法的研究[J];計算機(jī)工程與應(yīng)用;2010年03期
相關(guān)博士學(xué)位論文 前3條
1 梁艷紅;基于專利挖掘的創(chuàng)新設(shè)計關(guān)鍵技術(shù)研究[D];河北工業(yè)大學(xué);2011年
2 王朝霞;專利知識獲取及其支持概念創(chuàng)新設(shè)計的方法研究[D];浙江大學(xué);2009年
3 譚可欣;高技術(shù)企業(yè)R&D知識管理的模型與機(jī)制研究[D];華僑大學(xué);2009年
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