基于深度学习的施工安全隐患整改智能推荐系统

doi:10.11959/j.issn.2096-0271.2023076

摘要/Abstract

摘要：

水利工程施工安全隐患治理正向信息化与智能化转型，为了高效地从大量非结构化的施工安全隐患数据中挖掘出有价值的潜在信息，提出了基于深度学习的施工安全隐患整改智能推荐系统。该算法基于词频逆向文档频率算法，提取施工安全隐患的特征词，构建安全隐患关联桑基图，展示施工标段、隐患特征、隐患类型之间的信息流动特征；基于FP-Growth算法挖掘历史数据中的关联规则；结合序列相似度匹配（sequence similarity matching，SSM）算法和Doc2Vec模型，优化案例检索推荐的过程。该算法利用珠江三角洲水资源配置工程2019—2023年记录的80 953条施工安全隐患信息作为数据源。实例验证表明，该算法能够为当前的施工安全隐患匹配出较为准确的整改措施，可有效辅助施工安全管理者排查治理隐患问题。

关键词: 施工安全, 关联分析, 深度学习, 智能推荐

Abstract:

The management of safety hazards in water conservancy engineering construction is transitioning towards informatization and intelligence.In order to efficiently mine valuable potential information from a large amount of unstructured construction safety hazard data, an intelligent recommendation system for construction safety hazard rectification based on deep learning is proposed.This paper is based on the TF-IDF algorithm to extract feature words of hidden danger, construct a safety hazard association Sankey diagram and display the information flow characteristics among construction sections, hazard features and hazard types.Then, this paper mines association rules in historical data based on the FP-Growth algorithm.In addition, the process of case retrieval recommendation is optimized by combining the sequence similarity matching algorithm and the Doc2Vec model.This paper uses 80 953 construction safety hazard information as the data source, which is recorded in the water resources allocation project of Pearl River Delta from 2019 to 2023.Example verification shows that the proposed method can match accurate rectification measures for current construction safety hazards, effectively assisting construction safety managers to identify and address hidden danger.

Key words: construction safety, correlation analysis, deep learning, intelligent recommendation

中图分类号:

TP183

刘震, 赵嵩, 杨涛, 蔡太伟. 基于深度学习的施工安全隐患整改智能推荐系统[J]. 大数据, 2023, 9(6): 124-136.

Zhen LIU, Song ZHAO, Tao YANG, Taiwei CAI. Intelligent recommendation system for rectification of construction safety hazards based on deep learning[J]. Big Data Research, 2023, 9(6): 124-136.

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参考文献 24

[1]	陈利 . 对水利工程建设管理信息化、智能化的几点思考[J]. 农业科技与信息, 2022,19(21): 126-128.
	CHEN L . Thoughts on informatization and intelligentization of water conservancy project construction management[J]. Information of Agricultural Science and Technology, 2022,19(21): 126-128.
[2]	钟登华, 时梦楠, 崔博 ,等. 大坝智能建设研究进展[J]. 水利学报, 2019,50(1): 38-52,61.
	ZHONG D H , SHI M N , CUI B ,et al. Research progress of the intelligent construction of dams[J]. Journal of Hydraulic Engineering, 2019,50(1): 38-52,61.
[3]	刘梅, 许林宇, 廖彬超 ,等. 基于数据驱动的施工安全隐患特征网络分析与预警策略[J]. 清华大学学报(自然科学版), 2023,63(2): 191-200.
	LIU M , XU L Y , LIAO B C ,et al. Datadriven network analysis of construction hazard characteristics and warning strategy[J]. Journal of Tsinghua University (Science and Technology), 2023,63(2): 191-200.
[4]	谭章禄, 王泽, 陈晓 . 基于LDA的煤矿安全隐患主题发现研究[J]. 中国安全科学学报, 2016,26(6): 123-128.
	TAN Z L , WANG Z , CHEN X . Research on topic extraction for coal mine hidden danger based on LDA[J]. China Safety Science Journal, 2016,26(6): 123-128.
[5]	陈述, 刘文濯, 邵波 ,等. 水电工程施工安全隐患时空分布特征研究[J]. 中国安全科学学报, 2022,32(11): 90-96.
	CHEN S , LIU W Z , SHAO B ,et al. Research on spatial and temporal distribution characteristics of safety nazards in hydropower projects[J]. China Safety Science Journal, 2022,32(11): 90-96.
[6]	林旭杰 . 基于数据挖掘算法的煤矿安全隐患预警研究及应用分析[J]. 现代信息科技, 2022,6(4): 141-144.
	LIN X J . Research and application analysis of coal mine hidden danger warning based on data mining algorithm[J]. Modern Informationn Technology, 2022,6(4): 141-144.
[7]	LE T , DAVID J H . NLP-based approach to semantic classification of heterogeneous transportation asset data terminology[J]. Journal of Computing in Civil Engineering, 2017,31(6): 04017057.
[8]	JATNIKA D , BIJAKSANA M A , SURYANI A A . Word2Vec model analysis for semantic similarities in English words[J]. Procedia Computer Science, 2019,157: 160-167.
[9]	KUMAR H S , KRISHNAMOORTHY C S . A framework for case-based reasoning in engineering design[J]. Artificial Intelligence for Engineering Design,Analysis and Manufacturing, 1995,9(3): 161-182.
[10]	郑霞忠, 汪珂, 陈云 ,等. 水电工程施工安全隐患文本智能类推研究[J]. 安全与环境学报,doi:10.13637/j.issn.1009-6094.2022. 2138.
	ZHENG X Z , WANG K , CHEN Y ,et al. Study on Intelligent Analogy of Safety Hazards in Construction of Hydropower Projects[J]. Journal of Safety and Environment,doi:10.13637/j.issn.1009-6094.2022. 2138.
[11]	原江涛, 张瑞新, 赵红泽 ,等. 煤矿案例推理隐患排查治理信息系统开发及应用[J]. 中国安全科学学报, 2018,28(8): 135-141.
	YUAN J T , ZHANG R X , ZHAO H Z ,et al. Development and application of casebased reasoning based information system for identifying and controlling hidden danger in coal mine[J]. China Safety Science Journal, 2018,28(8): 135-141.
[12]	夏登友, 李丞曜, 朱毅 ,等. 基于情景元的案例推理及其在应急决策中的应用[J]. 安全与环境学报, 2020,20(3): 1028-1033.
	XIA D Y , LI C Y , ZHU Y ,et al. Casebased reasoning method based on the scenario element and its application in the emergency decision-making[J]. Journal of Safety and Environment, 2020,20(3): 1028-1033.
[13]	LIU B , ZHANG L . A survey of opinion mining and sentiment analysis[M]// Mining text data. boston: springer, 2012: 415-463.
[14]	MA G F , WU M . A big data and FMEAbased construction quality risk evaluation model considering project schedule for Shanghai apartment projects[J]. International Journal of Quality ＆ Reliability Management, 2020,37(1): 18-33.
[15]	HAN J W , PEI J , YIN Y W ,et al. Mining frequent patterns without candidate generation[J]. ACM Sigmod Record, 2000,29(2): 1-12.
[16]	PAL A , GILLAM L . Set-based similarity measurement and ranking model to identify cases of journalistic text reuse[C]// Proceedings of FIRE’13 Forum for Information Retrieval Evaluation.[S.l.:s.n.], 2013.
[17]	TAKABATAKE Y , NAKASHIMA K , KUBOYAMA T ,et al. siEDM:an efficient string index and search algorithm for edit distance with moves[J]. Algorithms, 2016,9(2): 26.
[18]	WAHYUNINGSIH T , HENDERI H , WINARNO W . Text mining an automatic short answer grading (ASAG),comparison of three methods of cosine similarity,Jaccard similarity and Dice’s coefficient[J]. JADS, 2021,2(2): 45-54.
[19]	LE Q V , MIKOLOV T . Distributed representations of sentences and documents[EB]. arXiv preprint,2014,arXiv:1405. 4053.
[20]	MIKOLOV T , CHEN K , CORRADO G ,et al. Efficient estimation of word representations in vector space[EB]. arXiv preprint, 2013,arXiv:1301.3781.
[21]	VIEHWEGER A , KRAUTWURST S , K?NIG B , ,et al. 1 Distributed representations of protein domains and genomes 2 and their compositionality[J]. bioRxiv, 2019:524280.
[22]	ABDELALIM A , O’BRIEN W . Visualization of building performance using sankey diagrams to enhance the decision-making process[C]// Proceedings of the Symposium on Simulation for Architecture and Urban Design. San Diego:Society for Computer Simulation International, 2017: 1-8.
[23]	李丹一, 王武魁, 井晖 ,等. 基于案例推理的森林抚育智能决策算法研究[J]. 西北林学院学报, 2022,37(1): 211-216.
	LI D Y , WANG W K , JING H ,et al. Intelligent decisional gorithm of forest tending based on case reasoning[J]. Journal of Northwest Forestry University, 2022,37(1): 211-216.
[24]	盛姝, 黄奇, 郭进京 ,等. 基于知识库与案例库的在线健康社区诊疗解决方案自动推理模型研究[J]. 情报科学, 2022,40(5): 161-172.
	SHENG S , HUANG Q , GUO J J ,et al. Research on automatic reasoning model of online health community diagnosis and treatment solution based on knowledge base and case base[J]. Information Science, 2022,40(5): 161-172.

标段	隐患描述	隐患类型	整改措施	检查日期
A1	沉淀池无标识牌，无安全防护	环境隐患	悬挂标识牌，做好防护	2019-09-01
B1	现场人员存在吸烟现象	人的不安全行为	进行现场安全教育	2021-01-21
C1	现场多处电缆损坏且拖地	管理隐患	对电缆进行更换	2022-10-05

序号	条件	结果	置信度	支持度
1	配电箱	不规范、灭火器、接线	0.75	0.0031
2	区域、人员	警示、设置、安全帽	0.66	0.0023
3	电缆线	拖地、破损	0.67	0.0024
4	施工、警示	设置、临时	0.50	0.0024
5	杂物	堆放、灭火器	0.53	0.0027
6	标识牌	记录、拖地	1.00	0.0025
7	电缆、防护栏杆	防护、基坑	0.55	0.0030
8	高处作业	安全带、佩戴、安全帽	0.76	0.0032
9	气瓶	防倾倒、措施	0.82	0.0045
10	危险源	公示牌、警示	0.92	0.0026
…	…	…	…	…

编号	当前隐患描述	历史案例数据库	相似度
1	左线一电箱未及时贴标识牌	配电箱未张贴标识牌	0.903
2	高压线安全距离防护措施不足	220 kV高压线安全距离防护措施不足	0.891
3	仓库氧气瓶未专库存放	氧气瓶未使用时专库存放	0.869
4	现场安全警示牌排序未按标准进行挂设	工作井井底施工作业架安全警示牌排序未按标准进行挂设	0.782
5	入库输水箱涵处木工盘锯无防护罩	木工盘锯无防护罩	0.879
6	北三角营地宿舍私拉乱接，违规使用插线板	西南角营地宿舍内私拉乱接，违规使用插线板	0.891
7	灭火器无巡查记录	仓库灭火器无巡查记录	0.888
8	现场交叉道路无减速带	工区现场大门车道缺少减速带	0.785
9	工作井道路积土严重、扬尘较多	工作井场地积水、污泥严重	0.783
10	工作井泥浆池临边防护不到位	工作井临边防护不到位	0.880
…	…	…	…
综合准确率		0.869

编号	相似案例推荐整改措施	安全管理者制定整改措施	相似度
1	加强现场人员教育，正确佩戴安全帽	对现场不正确佩戴安全帽人员进行教育	0.934
2	及时修复损坏警示标志牌	及时加设安全提示标语有安全标志牌	0.869
3	加设相应灭火器	及时加配灭火器	0.897
…	…	…	…
综合准确率		0.914

输入	模型	输出
焊工棚二氧化碳气瓶无防护棚、	SSM	焊工棚二氧化碳气瓶无防倾倒措施
无防倾倒措施
		二氧化碳气瓶无防倾倒措施
		工作井内二氧化碳气瓶无防倾倒措施
		工作井二氧化碳气瓶无防倾倒措施、无防震圈、无防撞帽
		钢筋加工场所二氧化碳气瓶无防倾倒措施
	Doc2Vec	颜屋工区料仓防护棚揽风绳花篮螺栓无保险扣
		洞门墙上的2级配电箱需要加防护棚
		检修交通涧工区拌和站氧气、乙炔瓶无防护棚和防倾倒措施
		焊工棚二氧化碳气瓶无防倾倒措施
		配电箱无防护棚
	SSM+Doc2Vec	焊工棚气瓶无防倾倒措施
		焊工棚气瓶未做防倾倒
		焊工棚氧气瓶无防倾倒
		焊工棚氧气瓶无防倾倒
		焊工棚二氧化碳气管开裂较多