基于层次话题模型的煤矿安全事故致因要素研究

为促进安全发展,强化煤矿安全管理的科技支撑,在事故致因理论基础上,利用文本挖掘中的话题模型和创新性构建的层次致因要素话题模型,对我国2000—2015年发生的386起重特大煤矿事故调查报告进行了深入地挖掘、分析和研究。发现事故致因隐含的规律及各类事故之间的关联与共性,并进一步研究发现不同致因要素随时间的演化规律及致灾倾向,为煤矿安全管理找出重点,指导煤矿安全生产管理实践。     

基于危化品事故案例的决策规则提取算法研究*

为充分解析文本形式的危化品事故案例,探讨事故类型与大量事故致因间的因果关联,从化工行业入手,通过扎根理论对175起危化品事故案例进行因素提取,共确定5个子系统、19个因素、80个子因素,提出基于事故生命周期的事故致因框架;在此基础上,提出改进的Gra Apriori算法,以解决经典的Apriori无法考虑属性类别关系的问题,最终获得14个以事故类型为决策属性的规则。结果表明:这些规则从不同事故阶段细致地剖析了危化品事故所遵循的规律,重现事故的演变过程,为危化品事故风险管理提供准确可靠的预防策略和决策支持。     

基于APRIORI-TAN的交通事故伤害分析与预测*

为探究道路交通事故因素和事故伤害的相关性,以2 467起涉及人员伤亡的交通事故为数据集,运用Apriori算法分别挖掘事故伤害关联规则,并结合社会网络分析的可视化和核心-边缘分析构建受伤事故和死亡事故的关联规则网络。结果表明:事故伤害程度与事故时间、道路条件和交通环境等因素关系紧密,尤其死亡事故与碰撞固定物、人行横道事故、高速公路、高速道路、非市区、酒驾和超速存在高相关性。基于树型贝叶斯网络（TAN）构建事故伤害程度的预测模型,预测结果准确率可达87.56%。     

基于文本挖掘和复杂网络的事故致因重要度评估方法——以房屋市政较大以上事故为例

为充分挖掘事故调查报告中的有效信息,明确安全管理工作的内容.首先,利用文本挖掘分析事故调查报告,采用最小词频阈值文档频改进信息增益评估函数对分词结果降噪,通过回溯特征项在报告中的具体表述,提取事故致因,再构建同义词词库.然后,引入复杂网络以改进TF-IDF,综合事故致因因素的关联特征评估其重要度.最后,以房屋市政较大及...     

易燃易爆场所静电事故链路及断链减灾模式

为了探究易燃易爆场所静电事故形成过程及防护机理,基于事故链式理论,以及静电点燃源形成过程和爆炸性环境形成过程这2条并行支链,绘制了易燃易爆场所静电事故链路;通过事故案例说明了静电事故链路的实用性;研究了静电荷、易燃/可燃物质在外界扰动作用下的特征形态。研究结果表明:静电事故断链减灾模式包括静电点燃源断链减灾模式、爆炸性环境断链减灾模式以及降低事故影响的事故后果断链减灾模式;针对静电事故的各类断链减灾模式,提出了相应的防护措施,可为易燃易爆场所静电事故防护提供系统性的指导方案。     

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.     

An algorithm for assessing the risk of traffic accident

INTRODUCTION: This study is aimed at developing an algorithm to estimate the number of traffic accidents and assess the risk of traffic accidents in a study area. METHOD: The algorithm involves a combination of mapping technique (Geographical Information System (GIS) techniques) and statistical methods (cluster analysis and regression analysis). Geographical Information System is used to locate accidents on a digital map and realize their distribution. Cluster analysis is used to group the homogeneous data together. Regression analysis is performed to realize the relation between the number of accident events and the potential causal factors. Negative binomial regression model is found to be an appropriate mathematical form to mimic this relation. Accident risk of the area, derived from historical accident records and causal factors, is also determined in the algorithm. The risk is computed using the Empirical Bayes (EB) approach. A case study of Hong Kong is presented to illustrate the effectiveness of the proposed algorithm. RESULTS: The results show that the algorithm improves accident risk estimation when comparing to the estimated risk based on only the historical accident records. The algorithm is found to be more efficient, especially in the case of fatality and pedestrian-related accident analysis. IMPACT ON INDUSTRY: The output of the proposed algorithm can help authorities effectively identify areas with high accident risk. In addition, it can serve as a reference for town planners considering road safety.     

基于贝叶斯网络的煤矿事故人因推理

煤矿事故的不可重现性决定了事故原因的调查具有很强的不确定性,如何通过事故发生后的相关信息提高事故深层次原因调查的准确性是非常重要的。将HFACS与贝叶斯网络相结合,以煤矿事故HFACS分析结果为样本,通过卡方检验和让步比分析建立人因的贝叶斯网络因果图,进一步利用最大似然估计算法确定了煤矿事故人因的贝叶斯网络参数。最后,以双柳煤业顶板事故的调查信息为证据推理导致煤矿事故发生的深层次原因,提高事故原因调查的准确性,从而验证模型的有效性。     

Off-road truck-related accidents in U.S. mines

IntroductionOff-road trucks are one of the major sources of equipment-related accidents in the U.S. mining industries. A systematic analysis of all off-road truck-related accidents, injuries, and illnesses, which are reported and published by the Mine Safety and Health Administration (MSHA), is expected to provide practical insights for identifying the accident patterns and trends in the available raw database. Therefore, appropriate safety management measures can be administered and implemented based on these accident patterns/trends.MethodsA hybrid clustering-classification methodology using K-means clustering and gene expression programming (GEP) is proposed for the analysis of severe and non-severe off-road truck-related injuries at U.S. mines. Using the GEP sub-model, a small subset of the 36 recorded attributes was found to be correlated to the severity level.ResultsGiven the set of specified attributes, the clustering sub-model was able to cluster the accident records into 5 distinct groups. For instance, the first cluster contained accidents related to minerals processing mills and coal preparation plants (91%). More than two-thirds of the victims in this cluster had less than 5 years of job experience. This cluster was associated with the highest percentage of severe injuries (22 severe accidents, 3.4%). Almost 50% of all accidents in this cluster occurred at stone operations. Similarly, the other four clusters were characterized to highlight important patterns that can be used to determine areas of focus for safety initiatives.ConclusionsThe identified clusters of accidents may play a vital role in the prevention of severe injuries in mining. Further research into the cluster attributes and identified patterns will be necessary to determine how these factors can be mitigated to reduce the risk of severe injuries.Practical applicationAnalyzing injury data using data mining techniques provides some insight into attributes that are associated with high accuracies for predicting injury severity.     

基于FDA事故致因模型的铁路行车安全管理研究

为了从安全信息视角深入分析铁路行车事故原因,基于信息学、管理科学和行为科学等相关理论知识,结合铁路行车事故致因原理,构建铁路行车FDA(Forecast-Decision-Action)事故致因模型.该模型由行车人员、铁路运输企业和国家铁路局3条事故致因子链和1条事故致因主链构成,进一步归纳总结出3条子事故域、1条总事...     

一种改进的事故再现蒙特卡罗优化算法

为使事故再现结果更符合实际情况,对事故再现中的计算过程进行优化。基于蒙特卡罗方法和随机加权方法,提出一种改进的事故再现蒙特卡罗优化算法。该算法以二维碰撞模型和车辆轨迹模型为计算模型,选择碰撞点位置、碰撞前速度、法向恢复系数为优化参数,以实际车辆碰撞后运动轨迹离差最小为优化目标。分别用所提出的改进算法和Pc-Crash中的优化方法对一算例进行优化。结果表明,改进算法在准确度和稳定性等方面优于Pc-Crash中的方法。利用改进的事故再现蒙特卡罗优化算法,不仅能获得最优的事故再现结果,还能获得再现结果落在任意区间的概率。     

STAMP-based analysis on the railway accident and accident spreading: Taking the China–Jiaoji railway accident for example

Each hazard analysis technique is based on a model of accident causation. Most accident models regard accidents as resulting from a chain or sequence of events, such models are fit for accidents caused by failures of physical components and for relatively simple systems, but suffer from serious deficiencies when they are applied to software-intensive, complex engineering systems. Recently, a new accident model called System-Theoretic Accident Models and Process (STAMP) for system safety has been proposed, it is based on control theory and enforces constraints on hazards and thereby prevent accidents. In this paper, taking the China–Jiaoji railway accident happened on April 28, 2008 as an example, the STAMP approach has been used to analyze the railway accident and some improvement measures have been proposed. As the occurrence of one accident can cause many other accidents happen, based on the STAMP-based analysis, the accident spreading processes have also been discussed and modeled, which will be helpful to analyze accidents spreading in a broad sense and establish effective emergent measures for accident response management.     

基于ISODATA算法的水上交通事故黑点识别

针对水上交通安全问题,基于ISODATA算法和水上交通事故等级综合加权平均法,构建水上交通事故黑点识别模型。结合道路交通黑点及现有水上交通多发区的研究方法,定义水上交通事故黑点;采用ISODATA算法分析水上交通事故空间分布特征,实现对水上交通事故空间的构建;对水上交通事故等级梯度赋值,以量化事故的严重程度,利用等级综合加权平均法确定黑点阈值。并以深圳西部港区水上交通事故为例进行模型应用,共识别出10处事故黑点及其边界,表明水上交通黑点识别模型能有效识别水上交通黑点的空间分布及特征,为水上交通安全状态分析提供了一种度量方法。     

基于复杂网络的内河船舶碰撞事故致因分析*

为了探明内河船舶碰撞事故致因内在联系,基于船舶碰撞事故调查报告,从人-机-环-管视角构建事故致因复杂网络模型。运用“2-4”模型从人-机-环-管视角识别和提取事故致因,采用事故树方法分析调查报告中碰撞事故过程,提取内河船舶碰撞事故致因链,利用复杂网络理论融合多事故致因链,构建包括36个节点、123条边在内的事故致因网络,计算致因网络拓扑特征参数,定量分析内河船舶碰撞事故致因之间相互作用。研究结果表明:疏忽瞭望、错误估计碰撞危险、安全管理不到位、船员不适任、船与船通信信息不足、未及时行动是内河船舶碰撞事故的关键致因。同时,内河船舶碰撞致因网络是1个无标度网络,且具有小世界特性,表明事故致因之间连锁效应明显,管控上述关键致因可有效预防碰撞事故。研究结果可为预防内河船舶碰撞事故、提高内河航运管理水平提供参考。     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性，基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3），RF方法均能达到较高的准确率及稳定性，具有较优的预测性能。根据特征重要度（FI）排序，明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3），FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据，同时为企业安全工作的规划设计提供思路。     

我国现行生产安全事故统计制度和模式分析及完善建议

为进一步完善我国生产安全事故统计工作，对以2016年新版《生产安全事故统计管理办法》和《生产安全事故统计报表制度》为核心、以12个相关行业领域的规章和报表制度为补充的我国现行生产安全事故统计制度体系进行系统梳理，阐明我国现行生产安全事故统计制度主要特点；从事故统计的协同性、事故范围、填报主体、报表的指导作用、统计结果的发布内容和模式5个方面深入分析我国现行生产安全事故统计工作存在的不足，并提出针对性的完善建议，以期为我国生产安全事故统计工作完善提供参考借鉴。     

基于强化学习的城镇燃气事故信息抽取方法

为解决因城镇燃气事故调查报告标注样本缺乏,从而影响命名实体识别性能这一问题,提出基于BiLSTM-CRF+强化学习的燃气事故领域命名实体识别方法。首先在数据预处理阶段,采用基于文本结构的主旨段落抽取方法,识别事故调查报告的关键段落;其次在模型训练阶段,采用BiLSTM-CRF+强化学习模型,实现城镇燃气事故命名实体识别模型训练;最后利用城镇燃气事故调查报告作为试验数据进行验证。研究结果表明:经由强化学习模型降噪后,实体识别模型的综合评价指标提高5.76%,主旨段落识别方法相比Word2vec特征表示方法,使模型的综合评价指标提升7.17%。     

基于知识图谱的建筑工程施工事故智能分析*

为预防和减少建筑工程施工事故,应用人工智能领域知识图谱技术,对建筑工程施工事故进行分析。通过定义领域知识图谱概念体系结构,从建筑工程施工事故数据中提取关键知识要素,构建建筑工程施工事故知识图谱,将其储存在Neo4j图数据库中,并提出基于知识图谱的建筑工程施工事故分析流程,针对事故相关信息开展查询、统计分析以及关联路径分析等智能分析。研究结果表明:基于知识图谱技术,将建筑工程施工事故知识以可视化图形或表格等进行展示,将事故信息以知识形式结构化存储及表达,可有效提高事故分析工作效率,为事故预防以及安全管理提供决策支持。     

Development of an algorithm for an EEG-based driver fatigue countermeasure

PROBLEM: Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability. METHOD: Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue. RESULTS: The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01). DISCUSSION: This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system. IMPACT ON INDUSTRY: The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.     

Analysis of work related accidents in the Spanish mining sector from 1982-2006

Introduction

The rate for work related accidents in the Spanish mining sector is notably higher than in other countries such as the United States. It produces a very negative impact on the mining industry. This paper is the report of a study on serious and fatal accidents in Spanish mining from 1982-2006. It is based on the reports of 212 accidents (serious or fatal) carried out by the General Management of Energy and Mining of Catalonia (Spain). Method: The high work-related accident rate in the Spanish mining sector makes it necessary to carry out an analysis and research that can shed light on the causes of this high rate; this is the only way that a solution can be found. The study is based on Feyer and Williamson's analysis of accident causes, as they apply to 212 accidents. The types and causes of the accidents are coded according to the coding system used by the Spanish National Institute for Safety and Hygiene in the Workplace, which allows us to identify a series of direct causes and contributing factors in different accidents. Results If all the causes and factors that are present in the accidents are known, we are able to look for appropriate solutions to reduce them as much as possible. In short, we are able to come up with a series of conclusions that expose the weak links in the management of accident prevention in companies. This is helpful in the struggle to reduce work injuries in the Spanish mining sector.