东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (5): 756-760.DOI: 10.12068/j.issn.1005-3026.2016.05.031

• 管理科学 • 上一篇    

基于FCM和CBR-GRA双重检索的应急救援物质需求预测

段在鹏1,2， 钱新明1， 夏登友1,3， 多英全4   

  1. (1. 北京理工大学 爆炸科学与技术国家重点实验室， 北京100081； 2. 福州大学 环境与资源学院， 福建 福州350116； 3. 中国人民武装警察部队学院 消防指挥系， 河北 廊坊065000； 4. 中国安全生产科学研究院， 北京100012)
  • 收稿日期:2014-08-10 修回日期:2014-08-10 出版日期:2016-05-15 发布日期:2016-05-13
  • 通讯作者: 段在鹏
  • 作者简介:段在鹏(1985-)，男，山东潍坊人，北京理工大学博士研究生，福州大学讲师； 钱新明(1967-)，男，浙江湖州人，北京理工大学教授，博士生导师.
  • 基金资助:
    “十二五”国家科技支撑计划项目(2012BAK13B01).

Emergency Resources Demand Forecast Based on FCM and CBR-GRA Dual Search

DUAN Zai-peng1,2， QIAN Xin-ming1， XIA Deng-you1,3， DUO Ying-quan4   

  1. 1. State Key Laboratory of Explosion Science and Technology， Beijing Institute of Technology， Beijing 100081， China; 2. College of Environment and Resources， Fuzhou University， Fuzhou 350116， China; 3. Department of Fire Command， Chinese People’s Armed Police Force Academy， Langfang 065000， China; 4. China Academy of Safety Science and Technology， Beijing 100012， China.
  • Received:2014-08-10 Revised:2014-08-10 Online:2016-05-15 Published:2016-05-13
  • Contact: QIAN Xin-ming
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摘要： 运用多项数据分析及推理技术提高物资需求预测速度及可靠性.首先利用历史案例信息求救援案例指标权重；之后建立模糊聚类(FCM)及案例检索相结合的算法，案例检索采用CBR-GRA双重检索技术，在得到相似度向量与灰色关联度向量之后，再次应用灰色关联分析求取案例相似-关联度向量，从而保证可靠案例检索；最后建立救援物质需求模型.经实例验证可知:案例聚类实现数据初步筛选，提升了检索速度，2种检索方法融合，提升了检索可靠性.

关键词: 应急救援, 需求预测, 案例推理, 灰色关联分析, 模糊C均值聚类, 主客观综合权重

Abstract: Multi-data analysis and reasoning techniques were adopted to improve the forecasting speed and reliability of emergency resources demand. Firstly， based on the historical case information， the rescue case index weights were calculated. Then an algorithm combining fuzzy C-means clustering with case retrieval was established to increase the efficiency of case retrieval， which was performed by CBR (casebased reason) similarity and GRA (grey relational analysis) correlation. After the CBR similarity vector and GRA correlation vector were obtained， the grey relational analysis was used to calculate the similarity-correlation vector so as to ensure that similar cases are retrieved efficiently. Finally， a resources demand model was built up. The results confirmed that case clustering to achieve preliminary data filtering can enhance retrieval speed and combining two retrieval methods can improve the reliability of retrieval.

Key words: emergency rescue, demand forecast, casebased reason (CBR), grey relational analysis (GRA), fuzzy C-means clustering, subjective and objective comprehensive weight

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