本文關(guān)鍵詞： 深度學(xué)習(xí) 行為安全 人-機-環(huán) 不安全因素 交互分析　出處：《中國安全科學(xué)學(xué)報》2017年04期 　論文類型：期刊論文

【摘要】：為解決行為安全領(lǐng)域不安全因素識別和交互分析困難的問題,構(gòu)建基于深度學(xué)習(xí)的不安全因素識別和交互分析模型。首先,從"人-機-環(huán)"3方面構(gòu)建不安全因素識別層,分別采用不同的深度學(xué)習(xí)結(jié)構(gòu)識別作業(yè)人員行為屬性、工作環(huán)境場景和操作設(shè)備工作狀態(tài)的不安全因素;然后,通過因素交互層,采用關(guān)聯(lián)和回歸多值算法完成對不安全因素的交互分析;最后,通過輸出顯示層實現(xiàn)分析結(jié)果的表征。以某煤礦綜采、掘進、通風(fēng)3個生產(chǎn)活動類別的視頻音頻數(shù)據(jù)為例,通過Matlab操作平臺選取最優(yōu)深度學(xué)習(xí)結(jié)構(gòu),進行模型交互分析。結(jié)果表明,用該模型能實現(xiàn)對采煤面空頂作業(yè)、噴漿機故障仍然加料、主要通風(fēng)機異常響動未停機檢查等不安全因素的識別和交互分析,完成不安全行為的描述以及風(fēng)險分級、行為痕跡的分類。
[Abstract]:In order to solve the problem of identification and interaction analysis of unsafe factors in the field of behavioral security, a model of identification and interaction analysis of unsafe factors based on in-depth learning is constructed. Firstly, the identification layer of unsafe factors is constructed from the three aspects of "man-machine-ring". Different depth learning structures are used to identify the unsafe factors of operator behavior attributes, work environment scene and operating equipment working state, and then, through the factor interaction layer, The interaction analysis of unsafe factors is accomplished by using association and regression multi-valued algorithms. Finally, the analysis results are represented by output display layer. Taking the video and audio data of three production activity categories of a coal mine as an example, such as fully mechanized mining, tunneling and ventilation, The optimal depth learning structure is selected through Matlab operating platform, and the model interaction analysis is carried out. The results show that the model can be used to realize the work of coal mining surface with empty roof, and the ejector fault can still be fed. The identification and interactive analysis of unsafe factors such as abnormal noise and unshutdown check of main ventilator are carried out to describe unsafe behavior and to classify risk classification and behavior trace.
【作者單位】： 中國礦業(yè)大學(xué)(北京)資源與安全工程學(xué)院;
【基金】：國家自然科學(xué)基金資助(51674268)
【分類號】：TD771
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本文編號：1534288