[1]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.[doi:10.3969/j.issn.1001-7461.2022.01.31]
　LI Dan-yi,WANG Wu-kui*,JING Hui,et al. Intelligent Decisional Gorithm of Forest Tending Based on Case Reasoning[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2022,37(1):211-216.[doi:10.3969/j.issn.1001-7461.2022.01.31]
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 基于案例推理的森林抚育智能决策算法研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第37卷
期数:
2022年第1期
页码:
211-216
栏目:
出版日期:
2022-01-31

文章信息/Info

Title:
 Intelligent Decisional Gorithm of Forest Tending Based on Case Reasoning
文章编号:
1001-7461(2022)01-0211-06
作者:
 李丹一; 王武魁*; 井晖; 樊坤
 （北京林业大学 经济管理学院，北京 100083）
Author(s):
 LI Dan-yi; WANG Wu-kui*; JING Hui; FAN Kun
 (College of Economics and Management,Beijing Forestry University,Beijing100083,China)
关键词:
 ; 经理小班选择; 抚育方式确定; 智能决策; 林业大数据
Keywords:
 ; subcompartment selection;  determination of tending methods;  intelligent decision;  forestry big data
分类号:
S753
DOI:
10.3969/j.issn.1001-7461.2022.01.31
文献标志码:
A
摘要:
 为帮助林业基层单位管理者摆脱在决策过程中对技术人员经验的过度依赖，实现森林抚育经理小班智能选取以及抚育方式的智能决策，使森林抚育决策过程更具科学性。以黑龙江省森工总局林口林业局为对象，收集2015-2018年度森林抚育作业设计数据为试验数据构建案例库，结合注水分配算法(water-filling assignment,WFA)对案例中各属性进行权重分配；结合基于“原点距”的KML-CB自组织机制优化推理效率，实现森林抚育经理小班的快速选取；采用归一化欧式距离来计算源案例和目标案例之间的相似度，参考“fish-and-shrink”算法思想进行案例推理。进而寻找与目标案例相似的源案例，以源案例的解作为目标案例的满意解，从而实现抚育方式的辅助决策。案例库中共有4 045条案例数据，从中随机抽取80%进行训练，20%的案例数据进行测试。经多次试验，算法的预测精度达到97.16%。研究提出的算法对传统的相似度推理算法进行了改进，提高了推理的精度和速度。与传统算法相比，计算复杂度从O(M×N)降低到O(M+N+4)，在降低计算复杂度、优化推理效率方面有一定的意义。
Abstract:
 The objectives of this study were to help forestry grassroot unit managers get rid of the excessive dependence on the experience of technicians in the decision-making process,to realize the intelligent selection of forest tending subcompartments and intelligent decision-making of their tending methods,and to make the forest tending decision-making process more scientific and effective.Taking the administrative area of Linkou Forestry Bureau of Longjiang Forest Industry as the research object,the forest tending operation design data during 2015-2018 were collected to construct a case database.Combined with the water-filling assignment algorithm (WFA),the weight of each attribute in the case was assigned.The reasoning efficiency was optimized by using the KML-CB self-organization mechanism that was based on "origin distance" to realize the quick selection of forest tending subcompartments.The similarity between the source case and the target case was calculated by using normalized Euclidean distance,and to conduct case reasoning by referring to the "fish-and-shrink" algorithm idea.After finding out the source case that was similar to the target case,the solution of the source case was considered as the satisfactory solution of the target case,so as to realize the auxiliary decision-making of the tending method.Totally,4 045 cases were collected in the case database,from which 80% of the case data were randomly selected for training,and 20% for testing.After many experiments,the prediction accuracy of the algorithm reached 97.16%.The algorithm proposed in this research improves the traditional similarity inference algorithm,promotes the accuracy and speed of the inference.The algorithm reduces the computational complexity from traditional O(M×N) to O(M+N+4),which has some significance in reducing computational complexity and optimizing reasoning efficiency,compared with the one of classical similarity reasoning.

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备注/Memo

备注/Memo:
 收稿日期：2021-03-05修回日期：2021-05-07
基金项目：国家自然科学基金（71502015）；国有林区林业局智慧管理决策信息化（200-661502149）。
第一作者：李丹一。研究方向：林业信息化。E-mail:lidanyi@bjfu.edu.cn
*通信作者：王武魁，教授，博士生导师。研究方向：信息管理与信息系统，林业信息化，数据挖掘。E-mail：wangwukui@bjfu.edu.cn
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