面向TRIZ理論的深度知識(shí)獲取及應(yīng)用研究
發(fā)布時(shí)間：2018-12-31 21:33
【摘要】：傳統(tǒng)的產(chǎn)品設(shè)計(jì)過(guò)程對(duì)概念設(shè)計(jì)階段創(chuàng)新力不足，設(shè)計(jì)人員往往由于自身單一的專業(yè)知識(shí)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(jì)人員創(chuàng)新，但在實(shí)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(shí)資源，但是傳統(tǒng)專利庫(kù)中包含了不計(jì)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(jì)人員查找和利用。因此按照TRIZ理論相關(guān)知識(shí)提取、重組以及分析，以及對(duì)已有知識(shí)庫(kù)的擴(kuò)充和更新有利于TRIZ理論的實(shí)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(jì)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(jì)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(shí)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(lái)獲取深度知識(shí)的。專利的深度知識(shí)獲取研究有助于專利知識(shí)跨學(xué)科應(yīng)用和知識(shí)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(jì)者接受的一般的理解并實(shí)際應(yīng)用過(guò)程的探索。 論文按照基于TRIZ理論中文專利深度知識(shí)獲取模型的各子模塊的實(shí)現(xiàn)方法和關(guān)鍵技術(shù)進(jìn)行論述，包括以下幾個(gè)部分：專利文本抽取模塊、文本分類器模塊和深度知識(shí)挖掘模塊。 首先，專利文本抽取模塊介紹了如何在國(guó)家知識(shí)產(chǎn)權(quán)局這樣的網(wǎng)頁(yè)中獲取所需要的專利庫(kù)信息，并將之保存到數(shù)據(jù)庫(kù)當(dāng)中。專利文本抽取是整個(gè)知識(shí)挖掘過(guò)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(wú)法構(gòu)建專利庫(kù)，進(jìn)行下一步的分析，因此，本模塊的研究對(duì)于后續(xù)的研究至關(guān)重要。 其次，文本分類器模塊主要實(shí)現(xiàn)了對(duì)專利文本的分類過(guò)程。將從人工分類和計(jì)算機(jī)分類兩個(gè)方面闡述專利分類的原理及過(guò)程。人工輔助專利分類是建立在人工仔細(xì)閱讀專利說(shuō)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(shí)和相關(guān)的領(lǐng)域知識(shí)。而計(jì)算機(jī)分類的對(duì)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(duì)于計(jì)算機(jī)計(jì)算的難度大大簡(jiǎn)化，對(duì)于分類器在測(cè)試階段是非常簡(jiǎn)便和實(shí)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(duì)專利進(jìn)行分類和分析的。 再次，深度知識(shí)挖掘模塊主要利用了分類的結(jié)果進(jìn)行深度知識(shí)提取。挖掘過(guò)程是在閱讀專利說(shuō)明書的基礎(chǔ)上，按照深度知識(shí)模板向?qū)нM(jìn)行分析的，最后將分析的結(jié)果存入實(shí)例庫(kù)中。 最后，構(gòu)建了該深度知識(shí)獲取模型的軟件系統(tǒng)——DKMining。該軟件系統(tǒng)實(shí)現(xiàn)了各模塊的功能，，并且能對(duì)專利庫(kù)和實(shí)例庫(kù)中的信息實(shí)現(xiàn)檢索、刪除、修改、更新功能。論文結(jié)合具體專利實(shí)例驗(yàn)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.1;TB472
本文編號(hào)：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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.1;TB472
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