【摘要】：隨著機械自動化的迅猛發(fā)展，隨之而來頻發(fā)的附屬設施系統(tǒng)事故給非煤礦山行業(yè)拉響了安全警報。碎石加工系統(tǒng)作為附屬設施的重要組成部分。只有系統(tǒng)的分析風險、量化風險，找到行業(yè)存在問題，才能采取相應措施規(guī)避風險。 本文通過作業(yè)分解樹-風險分解樹（WBS-RBS）方法進行危險源辨識，引用支持向量機（SVM）對碎石加工系統(tǒng)的安全風險因素進行分類，并提出SVM方法對碎石加工系統(tǒng)進行安全生產(chǎn)標準化等級進行評估。 首先，在查閱碎石加工區(qū)域有關事故的基礎上，根據(jù)安全法規(guī)和行業(yè)標準，結合現(xiàn)場調(diào)研及專家咨詢，總結露天石灰?guī)r礦山行業(yè)碎石加工系統(tǒng)的安全風險現(xiàn)狀。 其次，針對碎石加工系統(tǒng)相關機械設備及生產(chǎn)工藝流程，對碎石加工系統(tǒng)進行分類；結合作業(yè)分解樹-風險分解樹（WBS-RBS）方法進行危險源辨識，確定安全風險因素指標體系。 再次，通過16個礦山企業(yè)危險源分類問卷調(diào)研并量化處理，在驗證數(shù)據(jù)適用性后，任意選取10個礦山調(diào)研數(shù)據(jù)作為訓練集，進行訓練學習，其余的6個礦山數(shù)據(jù)作為測試集。經(jīng)數(shù)據(jù)歸一化處理，核函數(shù)選取，尋求最優(yōu)參數(shù)c和g，得到基于SVM的碎石加工系統(tǒng)安全風險因素分類模型。經(jīng)測試集測試驗證后，得出碎石加工系統(tǒng)三種等級的危險源，，以此來判斷風險因素對安全生產(chǎn)目標的影響程度。 然后，結合危險源辨識結果和安全風險因素分類結果，建立碎石加工系統(tǒng)安全生產(chǎn)標準化等級評估指標體系。在此基礎上建立調(diào)查要素，對目前現(xiàn)有的已取得不同標準化等級的露天石灰?guī)r礦山碎石加工系統(tǒng)進行調(diào)研，根據(jù)不同標準化等級企業(yè)的安全生產(chǎn)現(xiàn)狀，對各指標要素投入到位情況進行賦值，以得到訓練樣本。將訓練集訓練學習，構造SVM碎石加工系統(tǒng)安全生產(chǎn)標準化等級評估模型。選任一石灰?guī)r礦山企業(yè)碎石加工系統(tǒng)進行測試，得到識別測試集輸出的結果，即該石灰?guī)r礦山企業(yè)碎石加工系統(tǒng)所處的安全生產(chǎn)標準化等級。 最后，經(jīng)測試驗證，建立的SVM安全風險評估模型與礦山企業(yè)實際現(xiàn)狀有較高的符合性。得出的高等級風險因素，有利于指導隱患排查與教育培訓。SVM不失為一種好的安全風險評價方法，并帶來一種新的安全生產(chǎn)標準化等級評定思路。
[Abstract]:With the rapid development of mechanical automation, the frequent accidents of auxiliary facilities alarm the non-coal mine industry. Gravel processing system is an important part of ancillary facilities. Only by systematically analyzing risks, quantifying risks and finding problems in the industry, can we take corresponding measures to avoid risks. In this paper, hazard source identification is carried out by job decomposition tree-risk tree (WBS-RBS) method, and support vector machine (SVM) is used to classify the safety risk factors of gravel processing system. The SVM method is put forward to evaluate the standardized grade of production safety of gravel processing system. First of all, on the basis of referring to the related accidents in the gravel processing area, according to the safety regulations and industry standards, combined with field investigation and expert consultation, the paper summarizes the current situation of safety risk of gravel processing system in open-pit limestone mining industry. Secondly, according to the related mechanical equipment and production process of gravel processing system, classification of gravel processing system is carried out; combined with job decomposition tree-risk decomposition tree (WBS-RBS) method, hazard source identification is carried out, and safety risk factor index system is determined. Thirdly, through the investigation and quantification of 16 mine enterprises' hazard source classification questionnaire, after verifying the applicability of the data, 10 mine survey data are chosen as the training set, and the remaining 6 mine data are used as the test set. After normalized data processing and kernel function selection, the optimal parameters c and g are obtained, and the classification model of safety risk factors of gravel processing system based on SVM is obtained. After testing and verification of the test set, three kinds of dangerous sources of gravel processing system are obtained to judge the influence of risk factors on the target of production safety. Then, combining the results of hazard source identification and the classification of safety risk factors, a standardized evaluation index system for production safety of gravel processing system is established. On this basis, the investigation elements are established, and the existing open-pit limestone mine gravel processing systems with different standardization grades are investigated, according to the safety production status of enterprises with different standardization grades. The input of each index element is assigned to get the training sample. The SVM gravel processing system safety standardization grade evaluation model is constructed by training and learning the training set. The lithotripsy processing system of a limestone mine enterprise is selected for testing, and the output result of the identification test set is obtained, that is, the standard grade of safety production of the lithotripsy processing system in the limestone mine enterprise. Finally, the SVM safety risk assessment model is proved to be in good agreement with the actual situation of mining enterprises. The high grade risk factors are helpful to guide hidden trouble detection and education training. SVM is a good method of safety risk evaluation and brings a new way of safety production standardization evaluation.
【學位授予單位】：重慶科技學院
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TD79;TD921.2

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