基于改进的局部保持投影的战时备件分类

doi:10.3969/j.issn.1001-506X.2020.01.18

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (1): 133-140.doi: 10.3969/j.issn.1001-506X.2020.01.18

基于改进的局部保持投影的战时备件分类

王强^1,²(), 贾希胜¹(), 程中华^1,*(), 王双川¹(), 马云飞¹()

1. 陆军工程大学石家庄校区装备指挥与管理系, 河北石家庄 050003
2. 陆军军事交通学院汽车指挥系, 天津 300161

收稿日期:2019-03-31 出版日期:2020-01-01 发布日期:2019-12-23
通讯作者: 程中华 E-mail:511091065@qq.com;Xs_jia@hotmail.com;qw_up@foxmail.com;834631895@qq.com;fcz1992@sina.com
作者简介:王强(1987-),男,讲师,博士研究生,主要研究方向为装备保障理论与应用、维修工程。E-mail:511091065@qq.com|贾希胜(1964-),男,教授,博士研究生导师,博士,主要研究方向为装备保障理论与应用、维修工程。E-mail:Xs_jia@hotmail.com|王双川(1992-),男,博士研究生,主要研究方向为维修工程。E-mail:834631895@qq.com|马云飞(1992-),男,博士研究生,主要研究方向为维修工程。E-mail:fcz1992@sina.com
基金资助:
国家自然科学基金(71871219);军队预研项目(KYSZJWK1742);国家社会科学基金(16GJ003-069)

Classification of spare parts based on improved local preserving projection in wartime

Qiang WANG^1,²(), Xisheng JIA¹(), Zhonghua CHENG^1,*(), Shuangchuan WANG¹(), Yunfei MA¹()

1. Equipment Command and Management Department, Shijiazhuang Campus of Army Engineering University, Shijiazhuang 050003, China
2. Vehicle Command Department, Army Military Transportation University, Tianjin 300161, China

Received:2019-03-31 Online:2020-01-01 Published:2019-12-23
Contact: Zhonghua CHENG E-mail:511091065@qq.com;Xs_jia@hotmail.com;qw_up@foxmail.com;834631895@qq.com;fcz1992@sina.com
Supported by:
国家自然科学基金(71871219);军队预研项目(KYSZJWK1742);国家社会科学基金(16GJ003-069)

摘要/Abstract

摘要：

为提升战时合成部队备件保障效能，需对其进行有效分类，以便开展备件的预储预置。针对备件种类多、时效性强、影响分类因素复杂的现实问题，提出了基于改进的局部保持投影的备件分类方法。首先，根据战时备件分类储备的影响因素，作为备件分类的特征指标，其次，利用改进的局部保持投影的降维方法对备件原始特征数据进行特征降维，得到低维特征向量。再利用支持向量机（support vector machine，SVM）的分类器对低维数据进行分类。并通过量子粒子群对SVM的核函数参数进行寻优，提升备件分类精度，得到满足备件分类准确率最优时的降维维数和分类器参数。最后，通过对演习装备备件分类的实例分析，验证了模型的可行性和合理性，并对比分析了其他分类方法，表明该方法能够较好地解决战时备件分类的问题。

关键词: 局部保持投影算法, 量子粒子群优化支持向量机, 战时, 备件分类

Abstract:

In order to improve the support efficiency of combined army spare parts in wartime, it is necessary to classify them effectively in order to carry out the pre-storage and pre-configuration of spare parts. Aiming at the practical problems of many kinds of spare parts, strong timeliness and complex factors affecting the classification, a method of spare parts classification based on improved local preservation projection is proposed. Firstly, according to the influencing factors of the classified reserve of wartime spare parts, it is taken as the characteristic index of the spare parts classification. Secondly, the dimension reduction method of improved local preserving projection is used to reduce the dimension of the original feature data of spare parts, and low-dimensional feature vectors are obtained. Then the classifier of the support vector machine (SVM) is used to classify the low dimensional data. The kernel function parameters of the SVM are optimized by quantum particle swarm optimization to improve the accuracy of spare parts classification. The dimension reduction and classifier parameters are obtained when the spare parts classification accuracy is optimized. Finally, the feasibility and rationality of the model are verified by an example of the classification of spare parts of military manoeuvre equipment. By comparing and analyzing other classification methods, it shows that the method can better solve the problem of spare parts classification in wartime.

Key words: local preserving projection algorithm, quantum particle swarm optimization-support vector machines (QSPO-SVM), wartime, spare parts classification

中图分类号:

王强, 贾希胜, 程中华, 王双川, 马云飞. 基于改进的局部保持投影的战时备件分类[J]. 系统工程与电子技术, 2020, 42(1): 133-140.

Qiang WANG, Xisheng JIA, Zhonghua CHENG, Shuangchuan WANG, Yunfei MA. Classification of spare parts based on improved local preserving projection in wartime[J]. Systems Engineering and Electronics, 2020, 42(1): 133-140.

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备件分类类别	库存策略
不储备备件	——
基于状态的储备备件	(S-1, S)
部件级储备备件	经济订货批量 (economic order quantity, EOQ)
系统级储备备件	综合装备可用度要求进行库存补充

序号	备件分类指标	偏好	数据类型	备注
1	备件故障模式严酷程度	Gain	Ordinal	通过装备的损伤模式及影响(damage mode and effect analysis，DMEA)分析,可根据单元损伤模式得出的严酷程度进行确定,由高到低分为:1-4级
2	备件满足率	Gain	Continuous	根据战时装备保障任务的要求,得到备件供应量与需求量的比值
3	备件可替换性	Gain	Ordinal	指备件是否能替代使用,不能用“0”进行标记,能用“1”进行标记
4	仓库数量	Cost	Continuous	指后方仓库中,备件储备的仓库数量
5	备件周转时间/h	Gain	Continuous	指备件由后方仓库运输到装备使用分队的平均时间
6	备件消耗数量/个	Gain	Continuous	预测一次战役中,备件消耗的数量
7	备件的体积/m³	Cost	Continuous	备件的体积
8	备件储备环境要求	Cost	Ordinal	指备件在仓库储备时的环境要求,如温度、湿度等,根据要求等级由高到低为:1-4级
9	限时可更换性	Gain	Ordinal	指备件是否能在本级限定的小修时间内,完成更换维修任务,能进行更换用“1”进行标记,否则记为“0”
10	备件储备费用/元	Cost	Continuous	指备件在仓库储备的总费用,包括购置、管理、储存等费用之和
11	备件维修能力	Gain	Ordinal	指备件是否能在本级进行维修,能在本级维修单元进行维修,用“1”标记,否则用“0”标记
12	易损伤程度	Gain	Ordinal	指战时备件损伤的难易程度,根据装备使用分队演习等历史数据确定其难易程度,由高到低分为1-4级

序号	指标1	指标2	指标3	指标4	指标5	指标6	指标7	指标8	指标9	指标10	指标11	指标12	种类
1	1	0.95	0	1	38	2	2.3	1	1	5 000	1	1	C
2	1	0.92	0	1	30	5	1.5	1	1	18 000	1	1	C
3	2	0.68	0	7	26	136	0.9	4	1	600	0	3	B
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮

序号	降维方法	降维的维数	分类准确率	算法计算时间/s
1	PCA	4	0.82	348.442
2	LPP	8	0.88	421.533
3	KDE-LPP	6	0.92	458.288

序号	分类方法	备件分类查全率(P)				备件分类查准率(R)				备件分类准确率
序号	分类方法	N	C	B	X	N	C	B	X	备件分类准确率
1	DT	0.902 6	0.923 3	0.693 6	0.714 3	0.889 6	0.723 6	0.583 9	0.630 2	0.783 6
2	KNN	0.925 3	0.875 6	0.816 2	0.793 5	0.835 6	0.902 3	0.756 2	0.864 3	0.837 4
3	SVM	0.965 3	0.935 6	0.834 6	0.812 7	0.957 8	0.899 6	0.838 8	0.809 3	0.895 9
4	QPSO-SVM	0.954 1	0.965 7	0.884 8	0.892 6	0.964 3	0.895 1	0.895 9	0.905 6	0.920 2

基于改进的局部保持投影的战时备件分类

Classification of spare parts based on improved local preserving projection in wartime

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