留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于应急预案的煤矿应急救援辅助决策系统设计

高洪波

高洪波. 基于应急预案的煤矿应急救援辅助决策系统设计[J]. 工矿自动化,2024,50(2):147-152, 160.  doi: 10.13272/j.issn.1671-251x.2023090033
引用本文: 高洪波. 基于应急预案的煤矿应急救援辅助决策系统设计[J]. 工矿自动化,2024,50(2):147-152, 160.  doi: 10.13272/j.issn.1671-251x.2023090033
GAO Hongbo. Design of coal mine emergency rescue auxiliary decision system based on emergency plan[J]. Journal of Mine Automation,2024,50(2):147-152, 160.  doi: 10.13272/j.issn.1671-251x.2023090033
Citation: GAO Hongbo. Design of coal mine emergency rescue auxiliary decision system based on emergency plan[J]. Journal of Mine Automation,2024,50(2):147-152, 160.  doi: 10.13272/j.issn.1671-251x.2023090033

基于应急预案的煤矿应急救援辅助决策系统设计

doi: 10.13272/j.issn.1671-251x.2023090033
基金项目: 国家自然科学基金资助项目(52374165)。
详细信息
    作者简介:

    高洪波(1979—),男,北京人,工程师,硕士,现从事安全生产信息化及应急管理技术研究与应用方面的工作,E-mail:hongbo_g@hotmail.com

  • 中图分类号: TD774

Design of coal mine emergency rescue auxiliary decision system based on emergency plan

  • 摘要: 针对煤矿应急救援辅助决策系统中应急预案应用不足、应用效率低及系统生成的救援方案可执行性欠佳等问题,提出了一种基于应急预案的煤矿应急救援辅助决策系统设计方法。该方法采用基于大语言模型的信息抽取技术,从应急预案中提炼出关键任务要素,如任务名称、触发条件、执行部门和任务内容等,形成元任务,并构建根据事故类型和级别对元任务进行分类存储的元任务库;发生煤矿安全事故时,运用基于SBERT模型的语义匹配技术,根据现场收集的信息进行事故分类分级,并从元任务库中筛选出与当前应急需求相符合的元任务集;为提高任务的可执行性,将元任务与实时采集的现场数据结合,通过指令模板构建具体的行动指令,并利用任务规划技术对指令的优先级进行优化和调整,生成切实可行的现场救援方案。基于应急预案的煤矿应急救援辅助决策系统充分利用了应急预案的规范化内容,形成了与现场信息紧密结合、资源优化的救援方案,进一步提高了救援决策的准确性、科学性和智能化水平。

     

  • 图  1  煤矿应急救援辅助决策系统业务流程

    Figure  1.  Business flow of coal mine emergency rescue auxiliary decision system

    图  2  煤矿应急救援指令模板构成要素

    Figure  2.  Coal mine emergency rescue instruction template components

    图  3  煤矿应急救援辅助决策系统架构

    Figure  3.  Architecture of coal mine emergency rescue auxiliary decision system

    图  4  SBERT模型结构

    Figure  4.  SBERT model structure

  • [1] 刘常昊,郑万波,杨志全,等. 区域煤矿智慧应急管理信息平台的多层次数字预案信息系统[J]. 能源与环保,2020,42(12):124-129.

    LIU Changhao,ZHENG Wanbo,YANG Zhiquan,et al. Multi-level digital pre-plan information system of regional coal mine intelligent emergency management information platform[J]. China Energy and Environmental Protection,2020,42(12):124-129.
    [2] 杨梦,周恩波. 煤矿智能应急预案生成系统设计与关键技术[J]. 煤矿安全,2018,49(7):96-98.

    YANG Meng,ZHOU Enbo. Design and key technologies for coal mine intelligent emergency plan generation system[J]. Safety in Coal Mines,2018,49(7):96-98.
    [3] 陈波. 基于“六化”目标导向的煤矿安全应急预案管理系统构建[J]. 煤,2020,29(9):71-72,75.

    CHEN Bo. The construction of coal mine safety emergency plan management system based on "six" target-oriented[J]. Coal,2020,29(9):71-72,75.
    [4] 赖祥威,郑万波,吴燕清,等. 矿山事故应急救援数字预案的任务协同流程网络模型及时效分析[J]. 计算机科学,2021,48(增刊1):596-602.

    LAI Xiangwei,ZHENG Wanbo,WU Yanqing,et al. Task collaborative process network model and time analysis of mine accident emergency rescue digital plan[J]. Computer Science,2021,48(S1):596-602.
    [5] 杨梦,周恩波. 基于专家系统的煤矿事故现场处置方案自动生成系统研究[J]. 煤炭工程,2019,51(11):138-142.

    YANG Meng,ZHOU Enbo. Automatic generation system of coal mine accident disposal scheme based on expert system[J]. Coal Engineering,2019,51(11):138-142.
    [6] 赵红泽,张超力. 煤矿应急物资需求预测与虚拟演练系统研究[J]. 煤炭工程,2021,53(4):172-176.

    ZHAO Hongze,ZHANG Chaoli. Demand forecasting of coal mine emergency supplies and the virtual drill teaching system[J]. Coal Engineering,2021,53(4):172-176.
    [7] 林麟. 网络爬虫和案例推理技术在煤矿智能应急预案系统中的研究及应用[J]. 陕西煤炭,2021,40(2):38-42.

    LIN Lin. Research and application of web crawler and case reasoning technology in mine intelligent emergency plan system[J]. Shaanxi Coal,2021,40(2):38-42.
    [8] 王庆荣,马辰坤. 面向案例消耗推理的应急物资预测[J]. 计算机工程与应用,2021,57(22):281-287.

    WANG Qingrong,MA Chenkun. Forecast of emergency supplies for case consumption reasoning[J]. Computer Engineering and Applications,2021,57(22):281-287.
    [9] GB/T 29639—2020 生产经营单位生产安全事故应急预案编制导则[S].

    GB/T 29639-2020 Guidelines for enterprises to develop emergency response plan for work place accidents[S].
    [10] 魏涛,侯腊梅,张亚星,等. 一种面向任务的作战指令生成方法[J]. 火力与指挥控制,2020,45(8):114-118.

    WEI Tao,HOU Lamei,ZHANG Yaxing,et al. Method for generating task-oriented military instruction[J]. Fire Control & Command Control,2020,45(8):114-118.
    [11] CARBONE P,KATSIFODIMOS A,EWEN S,et al. Apache flink:stream and batch processing in a single engine[J]. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering,2015,36(4):28-38.
    [12] DU Zhengxiao,QIAN Yujie,LIU Xiao,et al. GLM:general language model pretraining with autoregressive blank infilling[C]. The 60th Annual Meeting of the Association for Computational Linguistics,Dublin,2022:320-335.
    [13] REIMERS N,GUREVYCH I. Sentence-BERT:sentence embeddings using siamese BERT-networks[C]. Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing,Hong Kong,2019:3980-3990.
    [14] 祝涛杰,卢记仓,周刚,等. 文档级关系抽取技术研究综述[J]. 计算机科学,2023,50(5):189-200.

    ZHU Taojie,LU Jicang,ZHOU Gang,et al. Review of document-level relation extraction techniques[J]. Computer Science,2023,50(5):189-200.
    [15] 朱艺娜,曹阳,钟靖越,等. 事件抽取技术研究综述[J]. 计算机科学,2022,49(12):264-273.

    ZHU Yina,CAO Yang,ZHONG Jingyue,et al. Survey on event extraction technology[J]. Computer Science,2022,49(12):264-273.
    [16] 梁建军,雷咸锐,吴斌,等. 基于规则模式的瓦斯爆炸事故信息抽取技术[J]. 煤矿安全,2023,54(2):239-245.

    LIANG Jianjun,LEI Xianrui,WU Bin,et al. Gas explosion accident information extraction technology based on regular model[J]. Safety in Coal Mines,2023,54(2):239-245.
    [17] RADFORD A,NARASIMHAN K,SALIMANS T,et al. Improving language understanding by generative pre-training[EB/OL]. [2023-08-21]. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
    [18] RADFORD A,WU J,CHILD R,et al. Language models are unsupervised multitask learners[EB/OL]. [2023-08-21]. https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf.
    [19] BROWN T B,MANN B,RYDER N,et al. Language models are few-shot learners[C]. The 34th International Conference on Neural Information Processing Systems,New York,2020:1877-1901.
    [20] DEVLIN J,CHANG Mingwei,LEE K,et al. BERT:pre-training of deep bidirectional transformers for language understanding[C]. Conference on the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Minneapolis,2019:4171-4186.
    [21] 邵天浩,张宏军,程恺,等. 层次任务网络中的重新规划研究综述[J]. 系统工程与电子技术,2020,42(12):2833-2846.

    SHAO Tianhao,ZHANG Hongjun,CHENG Kai,et al. Review of replanning in hierarchical task network[J]. System Engineering and Electronics,2020,42(12):2833-2846.
    [22] 易侃,张杰勇,焦志强,等. 基于层次任务网络的作战任务−系统功能映射方法[J]. 系统工程与电子技术,2023,45(10):3183-3191.

    YI Kan,ZHANG Jieyong,JIAO Zhiqiang,et al. Combat task-system function mapping method based on hierarchical task network[J]. Systems Engineering and Electronics,2023,45(10):3183-3191.
  • 加载中
图(4)
计量
  • 文章访问数:  81
  • HTML全文浏览量:  29
  • PDF下载量:  15
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-09-08
  • 修回日期:  2024-02-21
  • 网络出版日期:  2024-03-05

目录

    /

    返回文章
    返回