【摘要】：盡管我國(guó)煤礦死亡人數(shù)逐年下降，但形勢(shì)依然嚴(yán)峻。通過物聯(lián)網(wǎng)技術(shù)和多智能體理論將煤礦井下的物封裝成含有煤礦事故預(yù)警知識(shí)庫(kù)的智能體，可以有效降低煤礦事故的發(fā)生率。但目前尚缺乏基于事故致因機(jī)理的煤礦事故預(yù)警知識(shí)庫(kù)模型方面的研究。又近些年，本體在知識(shí)表示、人工智能方面獲得了長(zhǎng)足發(fā)展。因此，論文研究基于本體的煤礦事故預(yù)警知識(shí)庫(kù)模型構(gòu)成要素及相互關(guān)系，并實(shí)踐應(yīng)用。 論文首先對(duì)危險(xiǎn)源和隱患的概念進(jìn)行了重新界定，分析了二者的關(guān)系。然后，在此基礎(chǔ)上，結(jié)合時(shí)空邏輯，提出了抽象的、作為事故預(yù)警知識(shí)庫(kù)模型理論基石的時(shí)空視角下的基于根源危險(xiǎn)源的事故致因機(jī)理。接著，設(shè)計(jì)了適合描述具體事故發(fā)生機(jī)理的時(shí)空事故樹分析法。然后，根據(jù)根源危險(xiǎn)源智能體事故預(yù)警流程及本體論，，構(gòu)建了基于本體的煤礦事故預(yù)警知識(shí)庫(kù)模型。模型主要包括：基于根源危險(xiǎn)源的事故致因機(jī)理、時(shí)空邏輯、煤礦危險(xiǎn)源庫(kù)、具體事故致因機(jī)理表示法、具體事故致因機(jī)理描述和推理機(jī)。 事故預(yù)警知識(shí)庫(kù)的時(shí)空邏輯設(shè)計(jì)是在現(xiàn)有的時(shí)空邏輯研究的基礎(chǔ)上，研究時(shí)空實(shí)體的概念層次及其相互關(guān)系，為描述事故致因機(jī)理及預(yù)警規(guī)則提供了時(shí)空支持，時(shí)間邏輯采用了點(diǎn)段結(jié)合的時(shí)間表示，將時(shí)間實(shí)體分為時(shí)間點(diǎn)和時(shí)間段，時(shí)間實(shí)體間的關(guān)系分為13個(gè)大類�？臻g邏輯采用OGC空間數(shù)據(jù)模型的子集（點(diǎn)、線、面）構(gòu)建根源危險(xiǎn)源的空間表示，空間實(shí)體間的關(guān)系包括拓?fù)潢P(guān)系、方向關(guān)系、度量關(guān)系。 為了使智能體能夠理解事故預(yù)警知識(shí)庫(kù)模型并進(jìn)行推理，構(gòu)建了相應(yīng)的本體和推理算法。煤礦事故預(yù)警知識(shí)庫(kù)本體包括：用于描述基于根源危險(xiǎn)源的事故致因機(jī)理和煤礦領(lǐng)域概念及關(guān)系的煤礦風(fēng)險(xiǎn)本體、描述時(shí)間實(shí)體及其關(guān)系的時(shí)間本體、描述空間實(shí)體及其關(guān)系的空間本體、用以構(gòu)建具體的事故致因機(jī)理的時(shí)空事故樹表示本體。事故智能預(yù)警推理算法設(shè)計(jì)研究基于本體的描述邏輯推理算法、時(shí)間推理算法、空間推理算法及時(shí)空事故樹推理預(yù)警算法，為事故的智能預(yù)警提供算法支持。描述邏輯推理基于Tableau算法實(shí)現(xiàn)，基于時(shí)間約束網(wǎng)絡(luò)進(jìn)行時(shí)間的定性定量推理，基于組合表進(jìn)行空間拓?fù)洹⒎轿�、度量關(guān)系的推理，在現(xiàn)有的事故樹定性定量算法的基礎(chǔ)上，結(jié)合時(shí)空約束描述，實(shí)現(xiàn)了時(shí)空事故樹的定性定量推理計(jì)算。 最后，設(shè)計(jì)實(shí)現(xiàn)了四層架構(gòu)的王樓煤礦事故智能預(yù)警平臺(tái)，對(duì)構(gòu)建的基于本體的煤礦事故預(yù)警知識(shí)庫(kù)模型進(jìn)行了實(shí)踐應(yīng)用。應(yīng)用表明：論文所建立的基于本體的煤礦事故預(yù)警知識(shí)庫(kù)模型理論合理、實(shí)踐有效，可以為煤礦事故預(yù)警知識(shí)庫(kù)的構(gòu)建提供有益的參考。
[Abstract]:Although the number of coal mine deaths in China has decreased year by year, the situation is still grim. Through the technology of internet of things and the theory of multi-agent, the objects in coal mine can be encapsulated into an agent containing the knowledge base of mine accident warning, which can effectively reduce the incidence of coal mine accidents. However, there is still a lack of research on the knowledge base model of coal mine accident warning based on accident cause mechanism. In recent years, ontology has made great progress in knowledge representation and artificial intelligence. Therefore, this paper studies the knowledge base model of coal mine accident warning based on ontology and its relationship, and applies it to practice. Firstly, the concept of hazard and hidden danger is redefined and the relationship between them is analyzed. Then, based on the spatio-temporal logic, the mechanism of accident cause based on the source of hazard is proposed, which is the theoretical cornerstone of the knowledge base model of accident early warning, which is based on the theory of time and space. Then, a spatio-temporal accident tree analysis method is designed to describe the mechanism of specific accidents. Then, according to the process and ontology of agent accident warning, the knowledge base model of coal mine accident warning based on ontology is constructed. The model mainly includes: accident cause mechanism based on source hazard source, space-time logic, coal mine hazard source database, specific accident cause mechanism representation method, specific accident cause mechanism description and inference machine. The design of spatio-temporal logic of accident warning knowledge base is based on the existing research of spatio-temporal logic, which studies the concept level of space-time entity and its relationship, which provides spatio-temporal support for describing the mechanism of accident cause and the rules of early warning. Time logic is expressed by the combination of point and segment. Time entities are divided into time points and time periods, and the relationships between time entities are divided into 13 categories. Spatial logic uses the subsets (points, lines, surfaces) of the OGC spatial data model to construct the spatial representation of the source hazard source. The relationships among spatial entities include topological relations, directional relationships, and metric relationships. In order to make the agent understand the knowledge base model of accident warning and infer, the corresponding ontology and reasoning algorithm are constructed. The knowledge base ontology of coal mine accident warning includes: coal mine risk ontology which is used to describe the mechanism of accident cause based on the source of hazard and the concept and relation of coal mine domain, and the time ontology to describe the time entity and its relation. The spatial ontology which describes the spatial entity and its relationship is used to construct the spatio-temporal accident tree representation ontology of the specific accident cause mechanism. Design of reasoning algorithm for Intelligent accident early warning; description logic reasoning algorithm based on ontology, temporal reasoning algorithm, spatial reasoning algorithm and spatio-temporal accident tree reasoning early warning algorithm, which provide support for intelligent early warning of accident. The description of logic reasoning is based on Tableau algorithm, qualitative and quantitative reasoning based on time-constrained network, spatial topology, azimuth and metric relationship reasoning based on combinatorial table, based on the existing qualitative and quantitative algorithms of accident tree. The qualitative and quantitative inference calculation of spatio-temporal accident tree is realized by using spatio-temporal constraint description. Finally, a four-story structure of Wanglou coal mine accident intelligent early warning platform is designed and implemented, and the ontology based coal mine accident warning knowledge base model is applied in practice. The application shows that the ontology based model of coal mine accident warning knowledge base is reasonable in theory and effective in practice, which can provide a useful reference for the construction of mine accident early warning knowledge base.
【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TD76

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