基于ANP-VIKOR的青藏高原金属矿山智慧应急能力综合评价

doi:10.16265/j.cnki.issn1003-3033.2025.08.1530

摘要/Abstract

摘要： 为提升青藏高原金属矿山的应急管理能力,基于智能技术的应用,构建智慧应急能力综合评价模型;通过分析青藏高原地区的特殊地理条件与复杂的矿山作业环境,提出包含智能技术、行为安全、资源配置、制度机制及社群关系5个维度的评价指标体系;运用网络层次分析法(ANP)方法确定指标权重,使用多准则妥协解排序法(VIKOR)系统评估青藏高原3座典型金属矿山(矿山Y、矿山V、矿山J)的智慧应急能力。结果表明:基于ANP-VIKOR的智慧应急能力综合评价体系涵盖“智能技术、行为安全、资源配置、制度机制、社群关系”5大维度共20项评价指标,充分融合高原地区复杂地理环境、极端气候条件与矿山运营特征,有助于弥补高原地区智慧应急能力量化研究的不足。实例中,矿山J的智慧应急管理能力表现最优,尤其在智能技术和应急响应方面具有显著优势;矿山Y在技术应用上表现出中等水平,后期应急管理能力有所提升;矿山V在资源配置与应急响应速度上存在改进空间,三者在智慧应急能力方面差异明显,反映出高原矿区智慧管理水平的不均衡性,验证了所构建模型的有效性与适应性。

关键词: 网络层次分析法(ANP), 多准则妥协解排序法(VIKOR), 青藏高原, 金属矿山, 智慧应急能力, 综合评价

Abstract:

In order to enhance the emergency management capabilities of metal mines on Qinghai-Tibet Plateau, a comprehensive intelligent emergency capability evaluation model was developed based on the application of intelligent technology. By analyzing the region's unique geographical conditions and complex mining operations, a five-dimensional evaluation index system was proposed, encompassing intelligent technology, behavioral safety, resource allocation, institutional mechanisms, and community relations. This study employed ANP method to determine indicator weights and applied the VIKOR multi-criteria decision-making method to systematically assess the intelligent emergency capabilities of three representative metal mines (Mines Y, V, and J) on the Qinghai-Tibet Plateau. The findings indicate that the comprehensive evaluation system of smart emergency capability based on ANP-VIKOR covers 5 dimensions, including "intelligent technology, behavior safety, resource allocation, mechanism, and community relationship", with a total of 20 evaluation indicators. It fully integrates the complex geographical environment, extreme climatic conditions, and mining operation characteristics of the region. It is helpful to make up for the lack of quantitative research on smart emergency capability in plateau areas. In the case, the mine J exhibits the strongest intelligent emergency management capabilities, particularly excelling in intelligent technology and emergency response. Mine Y demonstrates a moderate level of technological application with notable improvements in emergency management over time, while Mine V shows room for improvement in resource allocation and emergency response speed. The significant differences in emergency capabilities reflect the uneven development of intelligent emergency systems in plateau mining areas. The effectiveness and adaptability of the constructed model are verified.

Key words: analytic network process(ANP), vlsekriterijumska optimizacija kompromisno resenje(VIKOR), Qinghai-Tibet plateau, metal mines, intelligent emergency capability

中图分类号:

X913.4安全系统工程

王国栋, 胡家瑜. 基于ANP-VIKOR的青藏高原金属矿山智慧应急能力综合评价[J]. 中国安全科学学报, 2025, 35(8): 253-262.

WANG Guodong, HU Jiayu. Comprehensive evaluation of intelligent emergency capability of metal mines on Qinghai-Tibet plateau based on ANP-VIKOR[J]. China Safety Science Journal, 2025, 35(8): 253-262.

图/表 7

图1

表1

表2

表3

表4

表5

图2

参考文献 19

[1]	李光明, 张林奎, 张志, 等. 青藏高原南部的主要战略性矿产: 勘查进展, 资源潜力与找矿方向[J]. 沉积与特提斯地质, 2021, 41(2): 351-360.
	LI Guangming, ZHANG Linkui, ZHANG Zhi, et al. New exploration progresses, resource potentials and prospecting targets of strategic minerals in the southern Qinghai-Tibet Plateau[J]. Sedimentary Geology and Tethyan Geology, 2021, 41(2): 351-360.
[2]	VERA V, WEVER N, BÜHLER Y, et al. Modelling wet snow avalanche runout to assess road safety at a high-altitude mine in the central Andes[J]. Natural Hazards and Earth System Sciences, 2016, 16(11): 2303-2323.
[3]	CACCIUTTOLO C, CANO D. Environmental impact assessment of mine tailings spill considering metallurgical processes of gold and copper mining: case studies in the Andean countries of Chile and Peru[J]. Water, 2022, 14(19): DOI: 10.3390/w14193057.
[4]	SALAS C G. Mining and the living materiality of mountains in Andean societies[J]. Journal of Material Culture, 2017, 22(2): 133-150.
[5]	HALABI R, KUMRAL M. Addressing specific safety and occupational health challenges for the Canadian mines located in remote areas where extreme weather conditions dominate[J]. Journal of Sustainable Mining, 2022, 21(3): 180-190.
[6]	崔鹏, 贾洋, 苏凤环, 等. 青藏高原自然灾害发育现状与未来关注的科学问题[J]. 中国科学院院刊, 2017, 32(9): 985-992.
	CUI Peng, JIA Yang, SU Fenghuan, et al. Natural hazards in Tibetan Plateau and key Issue for feature research[J]. Bulletin of Chinese Academy of Sciences, 2017, 32(9): 985-992.
[7]	张永双, 郭长宝, 姚鑫, 等. 青藏高原东缘活动断裂地质灾害效应研究[J]. 地球学报, 2016, 37(3): 277-286.
	ZHANG Yongshuang, GUO Changbao, YAO Xin, et al. Research on the geohazard effect of active fault on the eastern margin of the Tibetan Plateau[J]. Acta Geoscientica Sinica, 2016, 37(3): 277-286.
[8]	黄知恩, 李明, 廖国礼, 等. 基于 HFACS 的高原矿山作业疲劳与人因失误率浅析[J]. 黄金科学技术, 2020, 28(4): 610-618. doi: 10.11872/j.issn.1005-2518.2020.04.181
	HUANG Zhien, LI Ming, LIAO Guoli, et al. Analysis of human fatigue and error rates in plateau mines based on HFACS[J]. Gold Science and Technology, 2020, 28(4): 610-618. doi: 10.11872/j.issn.1005-2518.2020.04.181
[9]	丁文清, 丁林. 岩矿高光谱遥感及其在青藏高原的应用前景[J]. 地质科学, 2022, 57(3): 924-944.
	DING Wenqing, DING Lin. Hyperspectral remote sensing of rock and mineral and its application prospects on the Tibetan Plateau[J]. Chinese Journal of Geology, 2022, 57(3): 924-944.
[10]	孙华丽, 项美康, 薛耀锋. 不确定信息下应急设施选址-路径鲁棒优化[J]. 系统管理学报, 2019, 28(6): 1126-1133.
	SUN Huali, XIANG Meikang, XUE Yaofeng. Robust optimization for emergency location-routing problem with uncertainty[J]. Journal of Systems & Management, 2019, 28(6): 1126-1133.
[11]	杨继星, 房玉东, 边路, 等. 应急救援数字化战场体系研究与应用探索[J]. 中国安全科学学报, 2023, 33(10): 240-246. doi: 10.16265/j.cnki.issn1003-3033.2023.10.2199
	YANG Jixing, FANG Yudong, BIAN Lu, et al. Research and application exploration of digital battlefield system for emergency rescue[J]. China Safety Science Journal, 2023, 33(10): 240-246. doi: 10.16265/j.cnki.issn1003-3033.2023.10.2199
[12]	KYRKOU C, KOLIOS P, THEOCHARIDES T, et al. Machine learning for emergency management: a survey and future outlook[J]. Proceedings of the IEEE, 2022, 111(1): 19-41.
[13]	KROGH A H, LO C. Robust emergency management: the role of institutional trust in organized volunteers[J]. Public Administration, 2023, 101(1): 142-157.
[14]	杨山亮, 王鹏, 李革, 等. 基于直觉模糊 VIKOR 方法的装备优选群决策模型[J]. 系统仿真学报, 2015, 27(9): 2169-2175.
	YANG Shanliang, WANG Peng, LI Ge, et al. Group decision making model for weapon selection using extended VIKOR method under intuitionistic fuzzy environment[J]. Journal of System Simulation, 2015, 27(9): 2169-2175.
[15]	李烨. GIS 技术在矿山应急管理中的应用现状[J]. 中国矿业, 2024, 33(6): 143-147.
	LI Ye. Application of GIS technology in mining emergency management[J]. China Mining Magazine, 2024, 33(6): 143-147.
[16]	王丽, 陈文涛, 关文玲, 等. 面向国家需求的应急技术与管理专业人才培养体系研究[J]. 中国安全科学学报, 2024, 34(5): 9-16. doi: 10.16265/j.cnki.issn1003-3033.2024.05.0128
	WANG Li, CHEN Wentao, GUAN Wenling, et al. Research on talent development systems for emergency technology and management majors to meet national needs[J]. China Safety Science Journal, 2024, 34(5): 9-16. doi: 10.16265/j.cnki.issn1003-3033.2024.05.0128
[17]	李晟, 丰景春, 吴凯丽, 等. 政企联合储备应急物资的合作策略研究[J]. 中国管理科学, 2024, 32(11): 222-232.
	LI Sheng, FENG Jingchun, WU Kaili, et al. Study on cooperative strategy of government-enterprise joint reserve of emergency supplies[J]. Chinese Journal of Management Science, 2024, 32(11): 222-232.
[18]	邱芹军, 吴亮, 马凯, 等. 面向灾害应急响应的地质灾害链知识图谱构建方法[J]. 地球科学, 2023, 48(5): 1875-1891.
	QIU Qinjun, WU Liang, MA Kai, et al. A knowledge graph construction method for geohazard chain for disaster emergency response[J]. Earth Science, 2023, 48(5): 1875-1891.
[19]	梁爽, 贺山峰, 吴晓涛, 等. “微时代”网络突发舆情下政府应急管理研究:基于“长春长生疫苗事件”的再分析[J]. 河南理工大学学报:社会科学版, 2022, 23(2): 53-60.
	LIANG Shuang, HE Shanfeng, WU Xiaotao, et al. On government emergency management under network emergency public opinion in the "micro-era":reanalysis of Changchun Changsheng vaccine event[J]. Journal of Henan Polytechnic University:Social Sciences, 2022, 23(2): 53-60.

指标	B₁	B₂	B₃	B₄	B₅	B₆	B₇	B₈	B₉	B₁₀	B₁₁	B₁₂	B₁₃	B₁₄	B₁₅	B₁₆	B₁₇	B₁₈	B₁₉	B₂₀
B₁		1	0	1	0	1	1	1	1	1	0	1	1	0	0	1	0	1	1	1
B₂	0		0	1	1	1	0	1	0	1	1	0	0	0	0	1	1	1	1	1
B₃	0	0		1	1	1	1	1	0	0	0	0	0	0	0	1	0	1	1	1
B₄	0	0	0		1	0	1	1	0	0	0	1	1	1	0	1	0	1	1	1
B₅	0	1	1	1		1	1	1	0	0	0	0	0	0	0	0	0	1	1	1
B₆	1	1	1	0	1		1	1	1	0	0	0	0	1	1	1	1	0	0	0
B₇	1	0	1	1	1	0		0	0	0	1	1	0	0	0	0	0	1	1	1
B₈	1	1	1	1	1	1	1		0	0	0	0	0	0	0	0	0	1	1	1
B₉	1	0	0	0	0	1	0	0		1	1	1	1	0	0	1	1	0	0	1
B₁₀	1	1	0	0	0	0	0	0	0		0	0	0	1	0	1	0	0	1	0
B₁₁	0	1	0	0	0	0	1	0	0	0		0	0	1	0	1	0	0	0	1
B₁₂	1	0	0	1	0	0	1	0	0	0	0		0	1	1	1	0	1	1	1
B₁₃	1	0	0	1	0	0	0	0	0	0	0	0		0	1	0	1	0	1	0
B₁₄	0	0	0	1	0	1	0	0	0	1	1	1	0		1	1	1	1	1	1
B₁₅	0	0	0	0	0	1	0	0	0	0	0	1	1	0		0	0	1	1	1
B₁₆	1	1	1	1	0	1	0	0	1	1	1	1	0	1	1		0	1	1	1
B₁₇	0	1	0	0	0	1	0	0	1	0	0	0	1	0	1	0		1	1	1
B₁₈	1	1	1	1	1	0	1	1	0	0	0	1	0	1	1	1	1		1	1
B₁₉	1	1	1	1	1	0	1	1	0	1	0	1	1	1	1	1	1	0		0
B₂₀	1	1	1	1	1	0	1	1	1	0	1	1	0	1	1	1	1	1	1

编号	所在地区	海拔/ m	资源特征	应急管理做法	主要问题或不足
矿山Y	西藏昌都市	>4 500	铜、钼,伴生多种有价金属,品位较高	建立智能监控系统、应急指挥中心及现场指挥机制,实现数据集成与实时监控	在资源配置方面相对薄弱,物流响应时间和库存周转效率有待提升
矿山V	西藏拉萨市	>4 000	铜为主,伴生钼、铅、锌等资源	引入应急监控系统和智能化应急指挥平台,建立数据驱动的安全管理模式	在资源配置与应急响应速度上仍有提升空间
矿山J	西藏拉萨市	>4 000	铜、金、银、铅、锌等金属资源	部署实时监控设备与数据集成平台,建立高效指挥机制,配置尾矿库监控系统	系统集成程度仍有提升空间,协同演练频次可加强

一级指标	权重/%	二级指标	数据类型	方向	量化方法	权重/%
智能技术	37.53	高原实时监测系统完善度	占比	正向	1-故障时间/运行时间	11.85
		极端环境数据分析和预测能力	占比	正向	正确预测次数/总预测次数	7.59
		智能预警系统响应速度	时间/min	逆向	响应总时间/预警总次数	11.68
		紧急避险系统可靠性	等级评分(1-5)	正向	根据系统表现评定	6.41
行为安全	26.67	高原矿区应急演练频度	占比	正向	实际演练次数/计划次数	7.30
		团队协作效率	占比	正向	成功任务数/任务总数	5.82
		安全教育覆盖率	占比	正向	参训总人次/计划人次	2.90
		安全检查频度	占比	正向	实际检查次数/计划次数	10.65
资源配置	13.85	物资储备充足性	占比	正向	可用物资数/物资需求数	2.46
		物流响应时间	时间/min	逆向	响应总时间/响应次数	2.65
		库存周转效率	比率	正向	实际使用库存/平均库存	2.20
		人员和应急需求匹配度	占比	正向	有资质人员数/人员总需求	4.68
		应急资金和预算匹配度	占比	适度	实际使用应急资金/预算	1.86
制度机制	9.33	高原矿区分级响应机制有效性	等级评分(1~5)	正向	根据响应案例评定	2.84
		分级救援机制有效性	等级评分(1~5)	正向	根据救援案例评定	1.64
		内控整改机制有效性	等级评分(1~5)	正向	根据整改案例评定	3.52
		事故恢复机制有效性	等级评分(1~5)	正向	根据恢复案例评定	1.33
社群关系	12.62	高原矿区社区应急参与度	占比	正向	参与人数/社区总人数	3.53
		信息共享效率	占比	正向	共享信息数/应共享信息数	5.41
		舆情监测和危机处理能力	占比	正向	成功处置事件次数/检测次数	3.68

评估数据	B₁	B₂	B₃	B₄	B₅	B₆	B₇	B₈	B₉	B₁₀
V₂₀₁₉	90.2	94.1	5.0	97.7	91.2	89.4	97.1	99.1	96.8	48.0
V₂₀₂₀	92.9	91.6	5.0	91.2	91.1	93.1	99.9	97.3	91.4	41.0
V₂₀₂₁	90.4	90.8	5.0	97.1	90.6	99.5	99.9	99.9	94.5	30.0
V₂₀₂₂	91.6	94.4	5.0	98.3	90.7	91.2	97.3	94.8	93.4	29.0
V₂₀₂₃	97.1	85.7	4.0	99.9	94.7	86.2	97.6	92.8	95.9	26.0
Y₂₀₁₉	98.8	96.9	5.0	96.2	92.4	93.8	96.6	97.5	99.9	20.0
Y₂₀₂₀	96.7	94.4	4.0	92.3	98.4	92.0	97.7	91.0	95.6	1.0
Y₂₀₂₁	99.7	86.5	5.0	90.9	91.2	94.4	98.5	95.9	99.5	48.0
Y₂₀₂₂	93.8	96.4	3.0	96.5	97.9	88.2	98.6	92.0	98.5	30.0
Y₂₀₂₃	99.7	87.6	2.0	92.0	97.1	87.4	96.1	92.4	90.2	2.0
J₂₀₁₉	92.2	93.6	1.0	90.6	98.1	99.4	97.9	96.9	98.4	32.0
J₂₀₂₀	93.4	93.7	2.0	93.5	93.8	88.4	99.8	99.5	90.6	8.0
J₂₀₂₁	97.3	89.4	3.0	98.3	93.3	89.7	96.6	95.0	96.3	23.0
J₂₀₂₂	95.4	98.2	3.0	97.7	96.0	92.7	97.6	95.1	91.2	9.0
J₂₀₂₃	95.7	97.7	2.0	93.7	95.8	94.4	99.1	98.4	96.7	15.0
评估数据	B₁₁	B₁₂	B₁₃	B₁₄	B₁₅	B₁₆	B₁₇	B₁₈	B₁₉	B₂₀
V₂₀₁₉	85.1	92.4	95.7	5.0	3.0	4.0	5.0	81.0	97.6	95.2
V₂₀₂₀	95.7	92.0	96.7	3.0	5.0	5.0	5.0	76.2	90.2	89.1
V₂₀₂₁	89.5	97.0	98.1	4.0	4.0	4.0	4.0	91.7	91.3	96.1
V₂₀₂₂	97.9	96.8	97.3	5.0	4.0	5.0	4.0	94.9	94.3	92.8
V₂₀₂₃	94.1	92.7	95.2	4.0	3.0	5.0	4.0	86.1	90.2	97.0
Y₂₀₁₉	99.8	94.1	97.3	3.0	3.0	3.0	4.0	91.3	91.9	95.7
Y₂₀₂₀	96.7	98.2	95.4	3.0	5.0	4.0	5.0	86.4	94.6	92.0
Y₂₀₂₁	95.5	98.7	96.6	5.0	3.0	3.0	5.0	88.9	99.9	97.6
Y₂₀₂₂	87.0	95.7	99.8	3.0	3.0	4.0	4.0	91.5	94.0	93.8
Y₂₀₂₃	86.5	99.7	99.3	4.0	4.0	3.0	5.0	77.4	98.1	93.6
J₂₀₁₉	96.8	97.7	97.2	3.0	5.0	4.0	3.0	82.1	91.9	95.2
J₂₀₂₀	94.3	93.5	95.3	5.0	3.0	3.0	5.0	88.1	92.4	91.2
J₂₀₂₁	98.5	97.5	95.2	5.0	5.0	4.0	3.0	81.3	91.2	93.1
J₂₀₂₂	98.5	98.4	97.7	3.0	4.0	3.0	5.0	93.5	90.9	99.7
J₂₀₂₃	97.6	96.0	96.3	5.0	4.0	3.0	4.0	94.2	92.6	88.0

数据组合	群体效用值	个体遗憾值	折中值	排序
V_{201 9}	0.689 8	0.116 8	0.987 6	15
V_{202 0}	0.622 4	0.118 5	0.891 1	14
V_{202 1}	0.571 8	0.116 8	0.796 9	13
V_{202 2}	0.601 6	0.087 6	0.632 3	9
V_{202 3}	0.498 8	0.089 7	0.481 8	6
Y_{201 9}	0.550 8	0.116 8	0.762 9	12
Y_{202 0}	0.520 6	0.116 8	0.714 2	11
Y_{202 1}	0.515 2	0.094 5	0.543 2	7
Y_{202 2}	0.430 7	0.093 6	0.399 4	4
Y_{202 3}	0.508 5	0.058 6	0.270 8	3
J_{201 9}	0.501 9	0.116 8	0.684	10
J_{202 0}	0.514 9	0.106 5	0.629 8	8
J_{202 1}	0.501	0.078 6	0.404	5
J_{202 2}	0.424	0.058 4	0.132 4	2
J_{202 3}	0.380 4	0.049 9	0	1