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 |
[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.
|