灾难医学救援分类理论与应用研究

发布时间：2018-06-23 05:45

本文选题：粗糙集 + 属性约减　；参考：《北京交通大学》2017年博士论文

【摘要】：灾难医学救援以最大限度地减轻自然灾难或人为灾难对人类生命造成的危害为目标,在灾难救援过程中发挥着举足轻重的作用。然而,当前的灾难医学救援行动多数基于经验指导层面,仅凭借主观评估难以快速准确的锁定救援需要的各类资源的数量和品类,无法满足大规模灾难对医学救援资源的需求。本文的主要内容是以灾难医学救援作为研究对象,基于粗糙集理论建立医学救援分类相关的理论体系,通过对各类灾难的分类指导,建立医学救援系统的分类因素与措施适应集合,可为实际灾难救援活动中实施有效管理,优化资源分配提供决策支持,从而提高医学救援的及时性、准确性、科学性、有效性。从灾难应对角度,可以根据灾难医学救援特征对灾难进行重新组合分类。论文对传统的粗糙集模型进行了拓展,设计了适应于灾难医学救援综合特征的多决策属性粗糙集模型,结合灾难救援实际决策特点设计并计算了"灾难因素依赖度"、"灾难粗糙隶属度",将遗传算法与粗糙集条件属性约简算法进行有机结合,对灾难医学救援知识系统进行了约简计算,有效提取了灾难医学救援决策表中的规则,为灾难分类提供更加准确的数据及模型基础。进一步的,由于传统粗糙集处理连续信息的能力有限,论文将模糊理论与粗糙集理论进行了有机结合,针对灾难中的伤情程度和救援方案,设计引入隶属函数,构建出具有多决策属性的模糊粗糙集模型,将遗传算法和模糊粗糙集条件属性约简算法进行有机结合,对知识系统进行了约简计算,有效提取了模糊灾难决策表中的决策规则,对模型进行优化,更加贴近于现实。以上述模型为基础,论文带入汶川、玉树、芦山地震救援案例和实际数据,进行了计算分析。最后基于上述结果梳理出了我国基于灾难医学救援视角的灾难分类建议和地震救援的医学特征分类建议,并系统分析提出了涉及我国灾难医学救援管理的相关措施和建议。
[Abstract]:The aim of disaster medical rescue is to minimize the harm to human life caused by natural disaster or man-made disaster, which plays an important role in the disaster rescue process. However, most of the current disaster medical rescue operations are based on the experience guidance level, relying solely on subjective evaluation to quickly and accurately lock the number and category of various kinds of resources needed for rescue, which can not meet the needs of large-scale disasters for medical rescue resources. The main content of this paper is to take the disaster medical rescue as the research object, based on the rough set theory to establish the medical rescue classification related theory system, through the classification of all kinds of disasters guidance, The establishment of classification factors and measures for medical rescue system can provide decision support for effective management and optimization of resource allocation in actual disaster relief activities, thus improving the timeliness, accuracy, science and effectiveness of medical rescue. From the point of view of disaster response, the disaster can be recombined and classified according to the characteristics of disaster medical rescue. In this paper, the traditional rough set model is extended, and a multi-decision attribute rough set model is designed, which adapts to the comprehensive characteristics of disaster medical rescue. Combining the characteristics of disaster rescue decision making, the paper designs and calculates the "disaster factor dependence degree" and "disaster rough membership degree". The genetic algorithm and the rough set conditional attribute reduction algorithm are combined organically. The disaster medical rescue knowledge system is reduced and the rules in the disaster medical rescue decision table are extracted effectively to provide more accurate data and model basis for disaster classification. Furthermore, due to the limited ability of traditional rough set to deal with continuous information, the fuzzy theory and rough set theory are combined organically. According to the degree of injury and rescue scheme in disaster, the membership function is designed. The fuzzy rough set model with multiple decision attributes is constructed. The genetic algorithm and the fuzzy rough set conditional attribute reduction algorithm are combined organically. The knowledge system is reduced and the decision rules in the fuzzy disaster decision table are extracted effectively. The model is optimized to get closer to reality. Based on the above model, the paper carries on the calculation and analysis of Wenchuan, Yushu, Lushan earthquake rescue cases and actual data. Finally, based on the above results, the suggestions of disaster classification based on disaster medical rescue and medical characteristics of earthquake rescue in China are sorted out, and the relevant measures and suggestions related to disaster medical rescue management in China are put forward systematically.
【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：R129

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