【摘要】：眾所周知,能源問題關(guān)乎國民經(jīng)濟(jì)命脈。國家"十三五"規(guī)劃指出打造綠色煤炭能源勢在必行。然而,據(jù)《中國煤炭工業(yè)年鑒》數(shù)據(jù)顯示,近二十年的煤礦事故占全國工礦企業(yè)事故的25%左右,死亡人數(shù)占40%左右。不言而喻,造成這些事故的原因不能僅僅從單一層面加以斷定,地方政府監(jiān)督不嚴(yán)、企業(yè)經(jīng)濟(jì)利益驅(qū)使、礦工安全意識低下、安全技能欠缺、作業(yè)環(huán)境差、安全管理混亂等因素均在特定水平上導(dǎo)致礦難發(fā)生,但歸根結(jié)底源于員工的行為。因此,針對復(fù)雜環(huán)境下的煤礦員工安全行為影響因素進(jìn)行有效識別和預(yù)控成為政府及企業(yè)決策者亟需解決的難題。本文以煤礦一線員工安全行為為研究對象,基于煤礦安全生產(chǎn)的復(fù)雜性和系統(tǒng)性,研讀國內(nèi)外文獻(xiàn),剖析2001年至2016年典型煤礦事故案例,歸納整合員工安全行為影響因素,借助典型事故分析、實地調(diào)研以及問卷調(diào)查、行為事件訪談等,驗證所提取影響因子的可靠性和科學(xué)性。在甄別煤礦員工安全行為影響指標(biāo)的基礎(chǔ)上,對指標(biāo)進(jìn)行優(yōu)選及分析,量化指標(biāo)層級結(jié)構(gòu),進(jìn)而構(gòu)建切實有效的煤礦員工安全行為評價指標(biāo)體系。采用信息熵法,辨析計算煤礦員工安全行為各指標(biāo)權(quán)重。接著借助5P神經(jīng)網(wǎng)絡(luò)的自學(xué)習(xí)、自適應(yīng)能力,通過對淮南礦業(yè)集團(tuán)、河南平煤礦業(yè)集團(tuán)下轄的10個已知樣本的學(xué)習(xí),獲取專家思維,采用訓(xùn)練好的網(wǎng)絡(luò)仿真尚未測度的樣本,有效縮減了人因在安全評價中影響程度;此外,通過訓(xùn)練好的網(wǎng)絡(luò)還可以求得各指標(biāo)相應(yīng)的權(quán)重大小,進(jìn)而根據(jù)權(quán)重值明晰指標(biāo)對煤礦員工安全行為的影響程度。在此基礎(chǔ)上,進(jìn)一步明確煤礦員工安全行為預(yù)警機(jī)制,即:預(yù)警指標(biāo)選取、預(yù)警體系構(gòu)成、單一指標(biāo)預(yù)警區(qū)間確定以及綜合指標(biāo)預(yù)警區(qū)間確定等。以此為基礎(chǔ),選用5P神經(jīng)網(wǎng)絡(luò)同遺傳算法改進(jìn)后的5P神經(jīng)網(wǎng)絡(luò)進(jìn)行對比分析,結(jié)果表明:GA-5P的收斂速度與測算精度更加準(zhǔn)確、有效。最后,依據(jù)預(yù)警分析與規(guī)避對策,以期實現(xiàn)煤礦安全預(yù)警管理模式的良好運(yùn)行及員工安全行為的有效綜合管控。
[Abstract]:As we all know, the energy problem concerns the lifeblood of the national economy. National "13 th five-year plan points out to build green coal energy is imperative." However, according to the data of China Coal Industry Yearbook, coal mine accidents account for about 25% of the accidents in China's industrial and mining enterprises in the past 20 years, and the death toll accounts for about 40%. It goes without saying that the causes of these accidents cannot be determined from a single level. Local governments are not strictly supervised, enterprises are driven by economic interests, miners have low awareness of safety, lack of safety skills, and poor working environment. Confusion in safety management and other factors all lead to mine accidents at certain level, but ultimately result from the behavior of employees. Therefore, it is a difficult problem for government and enterprise decision-makers to effectively identify and control the influencing factors of coal mine employees' safety behavior in complex environment. Based on the complexity and systematization of coal mine safety, this paper analyzes the typical coal mine accident cases from 2001 to 2016, and summarizes the influencing factors of integrating the safety behavior of the workers, based on the complexity and systematization of coal mine safety production. By means of typical accident analysis, field investigation, questionnaire investigation and behavior event interview, the reliability and scientificity of the factors extracted were verified. On the basis of discriminating the influence index of coal mine employee safety behavior, the index is selected and analyzed, and the index hierarchy structure is quantified, and an effective evaluation index system of coal mine employee safety behavior is constructed. The information entropy method is used to analyze and calculate the weights of coal mine employees' safety behavior. Then, with the help of self-learning and adaptive ability of 5p neural network, through the learning of 10 known samples under Huainan Mining Group and Henan Pingmei Mining Group, the expert thinking is obtained, and the trained network is used to simulate the unmeasured samples. The influence degree of human factor in safety evaluation is reduced effectively, in addition, the corresponding weight of each index can be obtained by the trained network, and then the influence degree of safety behavior of coal mine employees can be determined according to the weight value. On this basis, it is further clear that the early warning mechanism of coal mine employees' safety behavior, namely: early warning index selection, early warning system composition, single index early warning interval determination and comprehensive index early warning interval determination and so on. On this basis, 5p neural network is compared with the improved 5P neural network based on genetic algorithm. The results show that the convergence rate and the accuracy of the calculation are more accurate and effective. Finally, according to the early warning analysis and the circumvention countermeasure, the author hopes to realize the good operation of the coal mine safety early warning management mode and the effective comprehensive control of the safety behavior of the staff.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD79;F426.21

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