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Article(id=1149738624159232569, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738621005119786, articleNumber=1003-3033(2024)09-0001-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.09.0096, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1710000000000, receivedDateStr=2024-03-10, revisedDate=1718812800000, revisedDateStr=2024-06-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048649110, onlineDateStr=2025-07-09, pubDate=1727452800000, pubDateStr=2024-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048649110, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048649110, creator=13701087609, updateTime=1752048649110, updator=13701087609, issue=Issue{id=1149738621005119786, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='9', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048648358, creator=13701087609, updateTime=1757401551172, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1172190322751816581, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738621005119786, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1172190322751816582, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738621005119786, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1149738625971171906, articleId=1149738624159232569, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Situation awareness and behavior response model of crane drivers based on DEMATEL-AISM, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To have a deep understanding of the causal relationship between crane drivers' situation awareness information and behavior，the situation awareness and behavior response model of crane drivers was proposed by combining the ENDSLEY situation awareness model with the DEMATEL-AISM method. Firstly，the situation awareness theory was used to analyze the driver's behavioral response process and obtain information factors during the crane operation task. Secondly，the DEMATEL method was used to quantitatively analyze the association between the factors and determine a comprehensive influence matrix. Moreover，the attributes and characteristic values of the factors were analyzed to identify the key factors. Finally，a stable hierarchical structure of cause-effect attributes obtained by the AISM was used to propose the situation awareness and behavior response model of crane drivers. The results revealed that a five-layer information model consisted of 22 elements and influence relationships such as key elements of trajectory prediction and planning，and collision avoidance. Furthermore，the proposed model clarified the attribute characteristics，influence relationships，and influence degree among the information elements，offering a deep understanding of the crane driver's situation awareness and behavior response.

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为深入挖掘起重驾驶员态势感知信息-行为的因果关系，结合ENDSLEY态势感知模型与决策实验室分析(DEMATEL)-对抗解释结构模型(AISM)方法，构建起重驾驶员态势感知与行为响应模型。首先，以起重作业任务为研究对象，使用态势感知理论分析驾驶员行为响应过程，获取信息要素；其次，基于DEMATEL法，量化研究要素间的相互关系，得到综合影响矩阵，进而分析要素的属性及特征值，确定模型的关键要素；最后，采用AISM获得原因-结果属性稳定的层级结构，据此构建起重驾驶员态势感知与行为响应信息模型。结果表明： 得到包含轨迹预测与规划、避碰等关键要素的22个要素及影响关系构成的5层信息模型；而且，模型明确了信息要素属性特征、影响关系及影响程度，较好地解释了驾驶员态势感知及行为响应过程。

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晋良海 (1973—)，男，四川简阳人，博士，教授，主要从事水利水电建设项目运筹、安全工效学等方面的研究。E-mail：。

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晋良海 (1973—)，男，四川简阳人，博士，教授，主要从事水利水电建设项目运筹、安全工效学等方面的研究。E-mail：。

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晋良海 (1973—)，男，四川简阳人，博士，教授，主要从事水利水电建设项目运筹、安全工效学等方面的研究。E-mail：。

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Information elements and their interpretations

, figureFileSmall=null, figureFileBig=null, tableContent=
信息要素 释义
起重臂位置M1 观察起重机起重臂的具体位置
小车位置M2 观察小车(运载机构)的位置
吊钩高度M3 观察起重机吊钩的垂直高度
起重臂操纵杆控制M4 通过操纵杆对起重臂
进行的操控行为
小车操纵杆控制M5 通过操纵杆对起重机
上小车的操控行为
吊钩操纵杆控制M6 通过操纵杆对吊钩的
升降进行的操控行为
起重臂运动状态M7 观察起重臂的运动
情况(方向、速度等)
小车运动状态M8 观察起重机小车运动情况
吊钩运动状态M9 观察吊钩运动情况
负载运动状态M10 观察负载运动方向和姿态
风向风速M11 在起重作业环境中风的
方向和速度
障碍物识别M12 识别环境中的可能的障碍物
与就位点相对位置M13 判断负载相对于预定
放置位置的关系
发现负载M14 确认负载的起始位置，
标志任务开始
负载类型识别与处理M15 识别负载的类型并
采取相应的策略
轨迹预测与规划M16 负载移动轨迹的预测和规划
避碰M17 起重作业中采取措施避免
与障碍物碰撞
光线与视野管理M18 确保良好的视野和充足的照明
地面团队协同M19 作业过程中与信号工等地面
作业团队的沟通合作
生命维持系统M20 维持驾驶员生理状况良好的
基础工作条件
极端天气M21 需要停止作业的极端气象条件
故障预警M22 起重机械或系统出现故障警报
), ArticleFig(id=1167865350747070969, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738624159232569, language=CN, label=表1, caption=

信息要素及其释义

, figureFileSmall=null, figureFileBig=null, tableContent=
信息要素 释义
起重臂位置M1 观察起重机起重臂的具体位置
小车位置M2 观察小车(运载机构)的位置
吊钩高度M3 观察起重机吊钩的垂直高度
起重臂操纵杆控制M4 通过操纵杆对起重臂
进行的操控行为
小车操纵杆控制M5 通过操纵杆对起重机
上小车的操控行为
吊钩操纵杆控制M6 通过操纵杆对吊钩的
升降进行的操控行为
起重臂运动状态M7 观察起重臂的运动
情况(方向、速度等)
小车运动状态M8 观察起重机小车运动情况
吊钩运动状态M9 观察吊钩运动情况
负载运动状态M10 观察负载运动方向和姿态
风向风速M11 在起重作业环境中风的
方向和速度
障碍物识别M12 识别环境中的可能的障碍物
与就位点相对位置M13 判断负载相对于预定
放置位置的关系
发现负载M14 确认负载的起始位置，
标志任务开始
负载类型识别与处理M15 识别负载的类型并
采取相应的策略
轨迹预测与规划M16 负载移动轨迹的预测和规划
避碰M17 起重作业中采取措施避免
与障碍物碰撞
光线与视野管理M18 确保良好的视野和充足的照明
地面团队协同M19 作业过程中与信号工等地面
作业团队的沟通合作
生命维持系统M20 维持驾驶员生理状况良好的
基础工作条件
极端天气M21 需要停止作业的极端气象条件
故障预警M22 起重机械或系统出现故障警报
), ArticleFig(id=1167865350822568442, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738624159232569, language=EN, label=Table 2, caption=

Calculation results by DEMATEL for each element

, figureFileSmall=null, figureFileBig=null, tableContent=
要素 D C E F G 要素 D C E F G
M1 0.895 0.25 1.145 0.645 0.052 M12 0.347 0.2 0.547 0.147 0.025
M2 0.895 0.25 1.145 0.645 0.052 M13 0.347 0.2 0.547 0.147 0.025
M3 0.895 0.25 1.145 0.645 0.052 M14 1.569 0 1.569 1.569 0.071
M4 0.6 0.729 1.329 -0.129 0.06 M15 0.347 0.3 0.647 0.047 0.029
M5 0.45 0.729 1.179 -0.279 0.053 M16 1.316 1.425 2.741 -0.109 0.123
M6 0.3 0.729 1.029 -0.429 0.046 M17 1.316 1.438 2.753 -0.122 0.124
M7 0 0.503 0.503 -0.503 0.023 M18 0 0.9 0.9 -0.9 0.041
M8 0 0.762 0.762 -0.762 0.034 M19 1.484 0 1.484 1.484 0.067
M9 0 1.021 1.021 -1.021 0.046 M20 0 0 0 0 0
M10 0 1.021 1.021 -1.021 0.046 M21 0 0 0 0 0
M11 0.347 0.4 0.747 -0.053 0.034 M22 0 0 0 0 0
), ArticleFig(id=1167865350902260219, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738624159232569, language=CN, label=表2, caption=

各要素DEMATEL的指标计算结果

, figureFileSmall=null, figureFileBig=null, tableContent=
要素 D C E F G 要素 D C E F G
M1 0.895 0.25 1.145 0.645 0.052 M12 0.347 0.2 0.547 0.147 0.025
M2 0.895 0.25 1.145 0.645 0.052 M13 0.347 0.2 0.547 0.147 0.025
M3 0.895 0.25 1.145 0.645 0.052 M14 1.569 0 1.569 1.569 0.071
M4 0.6 0.729 1.329 -0.129 0.06 M15 0.347 0.3 0.647 0.047 0.029
M5 0.45 0.729 1.179 -0.279 0.053 M16 1.316 1.425 2.741 -0.109 0.123
M6 0.3 0.729 1.029 -0.429 0.046 M17 1.316 1.438 2.753 -0.122 0.124
M7 0 0.503 0.503 -0.503 0.023 M18 0 0.9 0.9 -0.9 0.041
M8 0 0.762 0.762 -0.762 0.034 M19 1.484 0 1.484 1.484 0.067
M9 0 1.021 1.021 -1.021 0.046 M20 0 0 0 0 0
M10 0 1.021 1.021 -1.021 0.046 M21 0 0 0 0 0
M11 0.347 0.4 0.747 -0.053 0.034 M22 0 0 0 0 0
), ArticleFig(id=1167865351028089340, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738624159232569, language=EN, label=Table 3, caption=

Reachable sets，antecedent sets，and common sets

, figureFileSmall=null, figureFileBig=null, tableContent=
要素 O P Q 要素 O P Q
M1 1，16，17，18 1，14 1 M12 12，17 12，19 12
M2 2，16，17，18 2，14 2 M13 13，16 13，19 13
M3 3，16，17，18 3，14 3 M14 1，2，3，11，14，15 14 14
M4 4，7，8，9，10 4，16，17 4 M15 15，16 14，15，19 15
M5 5，8，9，10 5，16，17 5 M16 4，5，6，16，17 1，2，3，13，15，16，17 16，17
M6 6，9，10 6，16，17 6 M17 4，5，6，16，17 1，2，3，11，12，16，17 16，17
M7 7 4，7 7 M18 18 1，2，3，18 18
M8 8 4，5，8 7 M19 11，12，13，15，19 19 19
M9 9 4，5，6，9 9 M20 20 20 20
M10 10 4，5，6，10 10 M21 21 21 21
M11 11，17 11，14，19 11 M22 22 22 22
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可达集合、先行集合及共同集合

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要素 O P Q 要素 O P Q
M1 1，16，17，18 1，14 1 M12 12，17 12，19 12
M2 2，16，17，18 2，14 2 M13 13，16 13，19 13
M3 3，16，17，18 3，14 3 M14 1，2，3，11，14，15 14 14
M4 4，7，8，9，10 4，16，17 4 M15 15，16 14，15，19 15
M5 5，8，9，10 5，16，17 5 M16 4，5，6，16，17 1，2，3，13，15，16，17 16，17
M6 6，9，10 6，16，17 6 M17 4，5，6，16，17 1，2，3，11，12，16，17 16，17
M7 7 4，7 7 M18 18 1，2，3，18 18
M8 8 4，5，8 7 M19 11，12，13，15，19 19 19
M9 9 4，5，6，9 9 M20 20 20 20
M10 10 4，5，6，10 10 M21 21 21 21
M11 11，17 11，14，19 11 M22 22 22 22
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Element confrontation rotation extraction hierarchy

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层级 结果-原因抽取方法 原因-结果抽取方法 综合要素层级
L5 M7，M8，M9，M10， M 18 ，M20，M21，M22 M7，M8，M9，M10，M18 M7，M8，M9，M10，M18
L4 M4，M5，M6 M4，M5，M6 M4，M5，M6
L3 M16，M17 M16，M17 M16，M17
L2 M1，M2，M3，M11，M12，M13，M15 M1，M2，M3，M11，M12，M13，M15 M1，M2，M3，M11，M12，M13，M15
L1 M14，M19 M14，M19M20，M21，M22 M11，M12
活动要素 M20，M21，M22
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要素对抗轮换抽取层级划分

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层级 结果-原因抽取方法 原因-结果抽取方法 综合要素层级
L5 M7，M8，M9，M10， M 18 ，M20，M21，M22 M7，M8，M9，M10，M18 M7，M8，M9，M10，M18
L4 M4，M5，M6 M4，M5，M6 M4，M5，M6
L3 M16，M17 M16，M17 M16，M17
L2 M1，M2，M3，M11，M12，M13，M15 M1，M2，M3，M11，M12，M13，M15 M1，M2，M3，M11，M12，M13，M15
L1 M14，M19 M14，M19M20，M21，M22 M11，M12
活动要素 M20，M21，M22
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基于DEMATEL-AISM的起重驾驶员态势感知与行为响应模型
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晋良海 1, 2, 3 , 刘皓 2 , 武帮杰 2 , 石辉 2 , 何诗语 2
中国安全科学学报 | 安全社会科学与安全管理 2024,34(9): 1-8
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中国安全科学学报 | 安全社会科学与安全管理 2024, 34(9): 1-8
基于DEMATEL-AISM的起重驾驶员态势感知与行为响应模型
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晋良海1, 2, 3 , 刘皓2, 武帮杰2, 石辉2, 何诗语2
作者信息
  • 1 三峡大学 水电工程施工与管理湖北省重点实验室，湖北 宜昌 443002
  • 2 三峡大学 水利与环境学院，湖北 宜昌 443002
  • 3 三峡大学 安全生产标准化评审中心，湖北 宜昌 443002

    • 晋良海 (1973—)，男，四川简阳人，博士，教授，主要从事水利水电建设项目运筹、安全工效学等方面的研究。E-mail：。

Situation awareness and behavior response model of crane drivers based on DEMATEL-AISM
Lianghai JIN1, 2, 3 , Hao LIU2, Bangjie WU2, Hui SHI2, Shiyu HE2
Affiliations
  • 1 Hubei Provincial Key Laboratory of Hydropower Engineering Construction and Management，Yichang Hubei 443002，China
  • 2 College of Water Conservancy and Environment，China Three Gorges University，Yichang Hubei 443002，China
  • 3 Safety Production Standardization Evaluation Center，China Three Gorges University，Yichang Hubei 443002，China
出版时间: 2024-09-28 doi: 10.16265/j.cnki.issn1003-3033.2024.09.0096
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摘要
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为深入挖掘起重驾驶员态势感知信息-行为的因果关系，结合ENDSLEY态势感知模型与决策实验室分析(DEMATEL)-对抗解释结构模型(AISM)方法，构建起重驾驶员态势感知与行为响应模型。首先，以起重作业任务为研究对象，使用态势感知理论分析驾驶员行为响应过程，获取信息要素；其次，基于DEMATEL法，量化研究要素间的相互关系，得到综合影响矩阵，进而分析要素的属性及特征值，确定模型的关键要素；最后，采用AISM获得原因-结果属性稳定的层级结构，据此构建起重驾驶员态势感知与行为响应信息模型。结果表明： 得到包含轨迹预测与规划、避碰等关键要素的22个要素及影响关系构成的5层信息模型；而且，模型明确了信息要素属性特征、影响关系及影响程度，较好地解释了驾驶员态势感知及行为响应过程。

决策实验室分析(DEMATEL)  /  对抗解释结构模型(AISM)  /  起重驾驶员  /  态势感知  /  行为响应

To have a deep understanding of the causal relationship between crane drivers' situation awareness information and behavior，the situation awareness and behavior response model of crane drivers was proposed by combining the ENDSLEY situation awareness model with the DEMATEL-AISM method. Firstly，the situation awareness theory was used to analyze the driver's behavioral response process and obtain information factors during the crane operation task. Secondly，the DEMATEL method was used to quantitatively analyze the association between the factors and determine a comprehensive influence matrix. Moreover，the attributes and characteristic values of the factors were analyzed to identify the key factors. Finally，a stable hierarchical structure of cause-effect attributes obtained by the AISM was used to propose the situation awareness and behavior response model of crane drivers. The results revealed that a five-layer information model consisted of 22 elements and influence relationships such as key elements of trajectory prediction and planning，and collision avoidance. Furthermore，the proposed model clarified the attribute characteristics，influence relationships，and influence degree among the information elements，offering a deep understanding of the crane driver's situation awareness and behavior response.

decision-making trial and evaluation laboratory (DEMATEL)  /  adversarial interpretive structure modeling (AISM)  /  crane driver  /  situation awareness  /  behavior response
晋良海, 刘皓, 武帮杰, 石辉, 何诗语. 基于DEMATEL-AISM的起重驾驶员态势感知与行为响应模型. 中国安全科学学报, 2024 , 34 (9) : 1 -8 . DOI: 10.16265/j.cnki.issn1003-3033.2024.09.0096
Lianghai JIN, Hao LIU, Bangjie WU, Hui SHI, Shiyu HE. Situation awareness and behavior response model of crane drivers based on DEMATEL-AISM[J]. China Safety Science Journal, 2024 , 34 (9) : 1 -8 . DOI: 10.16265/j.cnki.issn1003-3033.2024.09.0096
正文
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0 引言
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近年来，起重伤害事故频发，在全国房屋市政工程生产安全较大事故中占比39.13%，位列第一[1]。研究表明：不安全行为是造成起重伤害事故的主导因素[2]，而高达88%的不安全行为源于态势感知能力低下[3]，态势感知水平直接影响起重作业的安全、准度、精度和速度[4]。因此，研究起重驾驶员态势感知与行为响应过程，对于减少由不安全行为导致的起重伤害事故具有重要意义。
起重作业安全研究中，从驾驶员行为的角度考虑，主要有行为量化评价[5-6]、不安全行为监控[7⇓-9]、行为可靠性预测[10⇓-12]等几类。其中，王全伟等[5]通过建立岸桥司机基本动作序列和认知可靠性模型，提出了量化分析与评价机制等技术措施；DING Lieyun等[7]通过建立长短期记忆混合模型，自动识别不安全行为，有效提升了不安全行为监控的效率和准确性；王洪德等[12]针对人因失误的模糊性、随机性和不确定性等特点，构建基于径向基核函数的起重机操作岗位人因可靠性预测分析神经网络模型。以上研究在提升驾驶员行为安全管理方面取得了一定进展，但主要聚焦于对驾驶员外在行为表现的分析，未考虑到感知和决策过程中的脑力资源消耗而引发的判断失误问题，缺少对驾驶员内在认知过程的研究，对于态势感知的深入分析和理解仍然存在缺口，不利于安全管理措施的有效实施。目前关于态势感知的研究在其他如军事[13-14]、航空[15-16]等领域已得到广泛应用，但在起重作业方面的研究相对较少。
鉴于此，笔者将引入态势感知理论，分析起重驾驶员行为响应过程，结合决策实验室分析法(Decision-making Trial and Evaluation Laboratory，DEMATEL)和对抗解释结构模型(Adversarial Interpretive Structure Modeling，AISM)构建起重驾驶员态势感知与行为响应模型，通过模型分析要素权重、层级关系及影响路径，识别关键要素，以期为起重人机交互界面优化、人机交互行为风险辨识与防控提供理论支撑。
1 态势感知与行为响应模型构建
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态势感知是驾驶员行为响应的基础，本文基于ENDSLEY[17]提出的态势感知三级动态模型，结合文献调查法获得起重驾驶员态势感知与行为响应过程的信息要素。
在要素定性定量分析方法中，DEMATEL模型可以计算出每个要素对其他要素的影响度以及被影响度值，但无法有效识别要素层级结构。AISM则在解释结构模型的基础上，采用对抗轮换层级要素抽取方法筛选活动要素，但无法确定要素对系统的影响程度[18]。将2种方法结合，可以识别系统中关键要素并获取要素间相互影响程度，并构建要素的层级结构[19]。
因此，采用ENDSLEY[17]三级动态模型及DEMATEL-AISM模型研究起重驾驶员态势感知及行为响应过程，研究流程如图1所示。
1.1 态势感知模型及信息要素
在起重作业过程中，驾驶员通过视觉、听觉、触觉等系统感知作业环境信息，经过大脑认知处理后进行操作。基于ENDSLEY[17]态势感知三级动态模型，邀请6位驾驶员，采用德尔菲法适应性调整模型中的名词，获得起重驾驶员三级态势感知动态模型，如图2所示。
根据三级态势感知动态模型，驾驶员态势感知与行为响应过程主要分为4个层面：①驾驶员对驾驶环境的感知；②驾驶员对当前形势的理解；③驾驶员对未来情况的预测；④驾驶员操作行为。
以“起重作业”为主题词，在中国知网数据库中进行检索，限制核心期刊及以上得到307篇文献。以“lifting operation”为主题词，在Web of Science核心数据库进行检索得到115篇文献。整理、汇总检索到的422篇文献，围绕“驾驶员”“行为”“driver”“action”等关键词进一步筛选。以《起重机械安全技术规程》(TSG 51—2023)及筛选出的文献[9⇓⇓⇓⇓⇓⇓-16]为数据基础，按照三级态势动态模型对应的4个层面对起重作业进行分解，识别驾驶员态势感知与行为响应过程的信息要素，结果见表1。
1.2 基于DEMATEL的要素特征值计算
1.2.1 要素直接关系
邀请专家对要素间关系进行打分，分值为0~4，分别代表前要素对后要素的影响程度，分别为无影响(0)、弱影响(1)、一般影响(2)、强影响(3)、很强影响(4)[20]。以aij(i=1，2，…，n; j=1，2，…，n)表示要素ai对要素aj的影响程度。根据下式得到关系矩阵A:
A = ( a i j ) n × n
规范初始矩阵，采用最大值归一化，并根据下式得到规范直接影响矩阵B:
B i j = 1 m a x 0 ≤ x ≤ n ∑ 1 n   a i j
1.2.2 要素综合关系
通过下式得到综合影响矩阵T，获取要素综合关系。
T = B ( I - B ) - 1
式中I为单位矩阵。
1.2.3 要素属性计算
通过以下5式计算各要素属性特征值，包括影响度D、被影响度C、中心度E、原因度F、权重G。
D i = ∑ j = 1 n t i j ( i = 1，2 ， … ， n )
C i = ∑ i = 1 n t i j ( j = 1，2 ， … ， n )
E i = D i + C i
F i = D i - C i
G i = 1 ∑ 1 n   E i j
1.3 基于AISM的可达矩阵计算及模型构建
1.3.1 计算可达矩阵
在矩阵T的基础上，通过下式获取整体影响矩阵H:
H = I + T
选择合适的修正因子λ，去除要素间较弱的影响关系，简化矩阵。通过式(10)，获取可达矩阵K=(kij)n×n，其中：
k i j = 1 ， g i j ≥ λ   0 ， g i j λ   ( i ， j = 1，2 ， … ， n )
1.3.2 划分要素层级
确定要素的可达集和先行集。通过下式，求出要素的可达集O、先行集P;通过后面的公式求出共同集Q，共同集为可达集与先行集的交集：
O ( M i ) = { M j ∣ h i j ≠ 0 }
P ( M i ) = { M j ∣ h j i ≠ 0 }
Q ( M i ) = O ( M i ) ⋂ P ( M i )
通过下式，采用对抗轮换层级要素抽取方法来划分影响要素层级，分别使用结果-原因抽取方法以及原因-结果抽取方法得到不同的要素层级，通过两者结合得到综合要素层级：
l i = { M j ∈ M - l 1 - … - l i - 1 ， O ( M i ) = Q ( M i ) }
l i = { M j ∈ M - l 1 - … - l i - 1 ， P ( M i ) = Q ( M i ) }
式中li为第i次抽取得到的要素集合。
1.3.3 构建模型
根据综合要素层级及要素间影响关系，构建起重作业态势感知与行为响应模型。
2 基于DEMATEL-AISM的实例分析
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选取宜昌市某工地起重任务，以表1中的22个要素为基础，采用德尔菲法对要素间影响程度进行打分。由于已有文献并未明确采用德尔菲法对DEMATEL-AISM进行打分时所需的专家数量，本文的专家人数依据以往的成功案例确定[18]，邀请10名起重作业相关专家对要素间影响程度进行打分，每位专家的分数具有相同的权重，使分数能够科学地代表要素间的影响程度。考虑到本文主要研究驾驶员视角下的起重作业任务，偏向工程实践，邀请的专家均具有10年以上施工现场安全技能经验，其中5名为起重驾驶员，5名为从事安全管理方面研究的高校教授。
根据式(4)—式(7)，得到各要素的D、C、E、F、G，计算结果见表2。若原因度为正是原因要素，反之为结果要素。
根据式(9)计算整体影响矩阵H，选择合适的修正因子λ去除要素间较弱的影响关系。本文修正因子λ的取值由矩阵T统计获得[21]。按照李克特量级，使用SPSS软件对矩阵T中的数据进行K-means聚类分析，获得“小、较小、一般、较大、大”5个组别，对应的λ值分别为0.02、0.05、0.11、0.15、0.19。将5个值分别带入矩阵，根据λ的取值原则，选择λ=0.15。根据式(10)计算可达矩阵K=( h i j)22×22，求得可达矩阵结果。
根据式(11)—式(13)，求出信息要素的可达集O、先行集P、共同集Q，见表3。
根据式(14)、式(15)，采用对抗轮换层级要素抽取方法来划分影响要素层级，使用结果-原因抽取法以及原因-结果抽取法分别得到不同的要素层级，通过两者结合得到综合要素层级，抽取过程见表4。
根据表4，构建起重驾驶员态势感知与行为响应要素结构模型，如图3所示。图中椭圆形表示原因属性要素，矩形表示结果属性要素，菱形表示活动要素。
3 态势感知与行为响应结果分析
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根据研究得到的起重驾驶员态势感知与行为响应模型，结合态势感知理论对模型的结构特征以及要素致因关系进行分析并提出建议。
3.1 层级特征分析
通过对比图2、图3可以发现，由DEMATEL-AISM法得到的模型层级与驾驶员三级态势感知动态模型的要素分类相互对应，其中，L1、L2层的要素为原因属性，对应驾驶员对驾驶环境的感知和对驾驶环境的理解；L3层的要素为过渡属性，对应驾驶员对未来情况的预测；L4、L5层的要素为结果属性，对应驾驶员的操作行为以及行为结果对环境的反馈。模型自下而上，原因属性不断变弱，结果属性不断变强。
不同层级的要素具有明显的任务顺序特征：L1是作业态势感知的开始，其中，M14、M19分别对应发现负载及地面团队协同，表示驾驶员需要做好准备并听从地面指挥开始作业，是根本原因层；L2为任务开始前的外部信息，包括M1起重臂当前位置、M11风向风速、M12障碍物识别等，是直接原因层；L3是驾驶员依据外部信息进行行为响应的过程，M16轨迹轨迹预测与规划和M17避碰的影响形成闭环，代表行为响应过程不断调整修正。M16、M17是权重最大的要素，是介于致变、因变层级之间的过渡层，代表态势感知的结果与行为响应的开始；L4、L5是驾驶员的行为响应及其引起的状态变化，例如：M4起重臂操纵杆控制、M10负载运动状态及姿态情况，是结构模型的直接结果层及终端结果层。需要注意的是，M7~M10在DEMATEL-AISM模型中被单独划分为终端结果层，代表在实际任务中，驾驶员基于态势感知信息进行的响应行为，会引起环境改变。如M7起重臂运动状态的变化情况会导致L2中M1起重臂位置的变化。说明模型是动态循环的，驾驶员在行为响应后，需要进一步感知修正环境态势，与循环感知理论(图4)相对应，验证了DEMATEL-AISM模型结果与态势感知理论上的一致性。
在实际任务中，当原因要素的变化超出安全范围时，会从根本上改变任务。例如：由风向风速引起避碰等信息突然变化，就需要直接终止正在进行的任务，将保证安全作为最高目标。驾驶员对于原因要素的及时发现，可以避免因人为忽视导致的安全事故；结果要素是模型的结果信息，这类信息能够使驾驶员及时获得行为反馈，减少因操作不当引发的安全事故。
起重机人机交互界面可从致变-因变的角度分区设计，使驾驶员在任务中及时发现风险，迅速采取措施及时获得反馈，降低负荷，提升作业安全水平。
3.2 要素致因关系分析
以层级关系分析为基础，根据要素间致因关系及要素影响权重，分析重要要素致因性及致因关系。
M14发现负载、M19地面团队协同的影响度最大。M14代表着任务的开始，而M19的致因性在于地面指挥是驾驶员的另一双“眼睛”，在驾驶员视野外的区域，其态势感知信息主要来自于地面团队。作业开始前，驾驶员也必须同地面指挥人员确认。
要素M16、M17代表的轨迹预测与规划、避碰的中心度最大，是模型中最关键的要素。以致因路径M19地面团队协同→M12障碍物识别→M17避碰→M16轨迹预测与规划→…为例，当驾驶员通过地面团队的指挥信号发现障碍，需要进行避碰及轨迹预测与规划时，与M16、M17相关的信息要素(与其直接相连的信息要素)多达10个，表明驾驶员需要同时感知来自不同视角的多维度信息。若地面指挥发出声音信号，需要驾驶员眼、耳、手三者协同，容易产生较大的脑力负荷。若发出手势信号则需要频繁的眼动，使驾驶员在视觉上产生较大的负荷。针对这一问题，一方面应对驾驶员进行模拟训练，提升驾驶员在处理避碰问题时的安全态势感知能力，合理分配注意力资源。另一方面，人机交互界面应考虑运动轨迹预测与避碰的辅助功能，减轻驾驶员态势感知负荷，降低作业风险。
此外，将其他具有致因关系的要素连接可以得到相应的致因路径。致因路径可以体现出驾驶员安全态势感知和行为响应的相关性关系。当任意要素发生变化时，交互界面可以通过突出显示其致变及因变要素，帮助驾驶员对相关的信息进行关注，提升态势感知与行为响应的准确性。
3.3 活动要素分析
活动要素M20、M21、M22是作业中不可缺少但独立在正常作业流程之外的要素。例如：当生命维持系统正常、没有故障预警、不出现极端天气时，这3个要素不会对起重作业产生影响，然而一旦信息发生变化并超出阈值，会直接改变任务的优先级。其中，生命维持系统最容易被驾驶员忽略。针对该要素，人机交互界面应设计提示功能，例如：温度、氧气浓度、持续作业时长的显示并设置阈值提醒。故障预警、极端天气一旦出现，在生理和心理上容易对驾驶员造成巨大的瞬时负荷，影响驾驶员正常的决策水平。针对此类要素，交互界面应当设置辅助驾驶员进行快速判断的功能。
4 结论
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1) 起重驾驶员态势感知与行为响应信息要素被划分为5个层次，从上到下，要素原因属性逐渐增强，且层间因果关系显著。起重机驾驶舱人机交互界面设计应以此为切入点，从致变-因变的角度分区设置界面，降低驾驶员驾驶负荷。
2) “轨迹预测与规划”、“避碰”是驾驶员态势感知的关键要素。施工单位应对驾驶员进行“轨迹预测与规划”、“避碰”方面的针对性训练，提升驾驶员安全态势感知能力。
3) 将DEMATEL-AISM法得到的模型层级与驾驶员三级态势感知动态模型层级进行比较分析，结果显示，两者在结构层次上存在显著的对应关系与一致性，证明了DEMATEL-AISM法用于态势感知分析的合理性和有效性。
4) 建立的模型仅局限于理论层面，后续将在理论结果的基础上，开展眼动、脑电等试验研究，进一步提升模型的实用性和科学性。
基金
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  • 国家自然科学基金面上项目资助(52179136)
  • 教育部人文社科规划基金资助(21YJA630038)
  • 中国长江三峡集团有限公司企业科研项目(202103551)
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2024年第34卷第9期
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doi: 10.16265/j.cnki.issn1003-3033.2024.09.0096
  • 接收时间：2024-03-10
  • 首发时间：2025-07-09
  • 出版时间：2024-09-28
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  • 收稿日期：2024-03-10
  • 修回日期：2024-06-20
基金
国家自然科学基金面上项目资助(52179136)
教育部人文社科规划基金资助(21YJA630038)
中国长江三峡集团有限公司企业科研项目(202103551)
作者信息
    1 三峡大学 水电工程施工与管理湖北省重点实验室，湖北 宜昌 443002
    2 三峡大学 水利与环境学院，湖北 宜昌 443002
    3 三峡大学 安全生产标准化评审中心，湖北 宜昌 443002
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2024.09.0096
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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