面向战场条件的无人机集群分布式存储方法

秦潜聪; 吴冠霖; 高原; 王双双; 李朋

doi:10.3969/j.issn.0258-2724.20230521

面向战场条件的无人机集群分布式存储方法

doi: 10.3969/j.issn.0258-2724.20230521

秦潜聪^{1, 2,},
吴冠霖^1, ,,
高原¹,
王双双¹,
李朋¹

1.
军事科学院战争研究院，北京 100091
2.
军事科学院研究生院，北京 100091

基金项目: 国家自然科学基金（62222121，62341110）

详细信息

作者简介:
秦潜聪（1997—），男，博士研究生，研究方向为指挥控制和体系工程，E-mail：570209535@qq.com

通讯作者:
吴冠霖（1993—），男，助理研究员，博士，研究方向为无人机、云计算，E-mail：wuguanlin16@nudt.edu.cn

中图分类号: V279；E91；TP333
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- 文章访问数: 357
- HTML全文浏览量: 97
- PDF下载量: 81
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 修回日期: 2024-01-08
- 网络出版日期: 2024-05-24
- 刊出日期: 2024-01-18

Distributed Storage Methods for Unmanned Aerial Vehicle Clusters in Battlefield

1.
Institute of War Studies, Academy of Military Science, Beijing 100091, China
2.
School of Graduate, Academy of Military Science, Beijing 100091, China

摘要

摘要:
无人机是世界军事强国执行战术行动的重要武器装备，利用无人机实现战场数据存储是保障作战的重要手段. 为更好地满足无人机集群在恶劣战场条件下数据存储需求，在分析无人机数据存储军事应用背景基础上，总结战场无人机数据资源特征和数据存储技术军事需求，并提出适用于我军无人机战术行动的数据存储设计方法流程；结合学术和产业界的成果，梳理数据存储技术的分类和发展历程，并总结数据存储的主要类型；提出面向无人集群战术行动场景的分布式数据存储关键技术方法和存储系统；介绍国内外学者在分布式存储海量数据处理、数据实时传输和数据可靠性等相关算法模型的研究现状；展望未来战术行动中无人机数据运用方法的研究趋势，强调在战场条件下无人机集群数据存储是未来重要的研究领域.
- 无人机 /
- 无人机集群 /
- 战场条件 /
- 战术行动 /
- 数据存储 /
- 分布式存储
Abstract:
Unmanned aerial vehicles (UAVs) are important weapons and equipment for military powers in the world to carry out tactical operations. Applying UAVs in the battlefield to achieve data storage is an important way to ensure the operation. In order to better meet the data storage requirements of UAV clusters under harsh battlefield conditions, the military application background of UAV data storage was analyzed, and the data resources characteristics of UAVs in the battlefield and military requirements of data storage technologies were summarized. The design process of data storage method suitable for tactical operations of UAVs from Chinese armed forces was put forward. Then, on the basis of the achievements of data storage in the field of academia and industry, the classification and development process of data storage technologies were reviewed, and the main types of data storage were summarized. The key technologies and storage systems of distributed data storage for tactical operations of UAV clusters were presented. Moreover, the research status of the algorithm models of distributed mass data storage and processing, real-time data transmission, and data reliability in China and abroad was introduced. The research direction for the data application method of UAVs in tactical operations was proposed, and it was pointed out that data storage of UAV clusters in battlefields is an important research field in the future.
- UAV /
- UAV cluster /
- battlefield condition /
- tactical operations /
- data storage /
- distributed storage

HTML全文

图 1 无人机战术行动数据存储设计方法流程

Figure 1. Design process of data storage method for tactical operations of UAVs

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图 2 3种数据存储方式

Figure 2. Three data storage methods

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图 3 集中式存储模型

Figure 3. Centralized storage model

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图 4 分布式存储模型

Figure 4. Distributed storage model

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图 5 云存储服务模式

Figure 5. Cloud storage service pattern

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表 1 美军无人机数据链

Table 1. UAV data link of US army

无人机类型	数据链路类型	工作波段	数据传输率/（Mbit·s⁻¹）	存储量/GB
捕食者无人机	通用数据链（CDL）	X、Ku	10.71～274.00	≥1024
猎人、E-8、先锋无人机	战术通用数据链（TCDL）	Ku	10.71～200.00	≥1024
战术无人机	战术数字数据链（TDDL）	S、C、X、Ku	10.71～274.00	≥2 048
火力侦察无人机	高完整性数据链路（HIDL）	UHF	4.00～20.00	≥1024

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表 2 分布式存储系统总结

Table 2. Summary of distributed storage systems

类型	来源	适用场景	优势	劣势
GFS	文献[39-41]	大规模数据存储	容错机制、可部署于廉价机器、自动负载均衡	特定于大数据场景、单点失效问题
Ceph	文献[42-47]	统一存储、云储存	开源系统，功能强大、高可扩展性、高可用性	底层结构数据为对象，性能上限不高；运维成本高
GlusterFS	文献[48-51]	大文件存储	开源系统、节点对等结构、具备强大横向扩展能力、支持运行任何标准IP网络	遍历目录下文件耗时、海量小文件存储能力弱、配置信息变化需要时间同步
Amaz S3	文献[52-57]	对象存储	采用对象存储、提供了统一的接口 REST/SOAP 来统一访问任何数据	亚马逊公司非开源产品，战场特殊数据存储场景无法完全适用
MooseFS	文献[58-61]	中小规模轻量型应用	高可扩展性、灵活性高和易于部署配置、适用于中小规模轻量型应用、支持多机冗余备份	受主服务器的性能限制、主服务器内存的需求量、元数据复制时间较长
OneFS	文献[62-63]	大数据存储、非结构化数据存储	支持IP地址文件控制、灵活简单、支持多线程不同文件并发写入	外国戴尔公司收费服务产品，战场特殊数据存储场景无法完全适用
IPFS	文献[64-67]	文件存储	数据架构简单；避免文件内容相同重复存储；类似区块链的不可变数据存储，持续性更强	保持文件可用性易受到节点影响，用户无法主动删除文件

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表 3 分布式存储海量数据研究文献总结

Table 3. Review of research on distributed storage of massive data

技术手段	来源	研究对象	主要方法
负载均衡	文献[68]	地理分布式存储系统	哈希算法
	文献[69]	存储系统	哈希算法
	文献[70]	分布式文件系统	随机算法
	文献[71]	城市视频存储系统	动态负载均衡算法
	文献[72]	分布式文件系统	哈希算法
	文献[73]	计算机存储器	ISP 架构
	文献[74]	企业云平台存储服务	SkyMax 算法
	文献[75]	报文分类存储	SDLBA 算法
	文献[76]	云计算	蜂群负载算法
	文献[77-79]	算法模型	循环、蚁群、禁忌搜索算法
数据压缩	文献[80]	分布式文件系统	动态选择算法
	文献[81]	文本数据	CBC 算法
	文献[82]	表格数据	语义压缩算法
	文献[83]	图像数据	高效图形压缩算法
	文献[84]	不规则序列	数据压缩算法
	文献[85-87]	算法模型	时间序列、检测、多分辨率压缩算法

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表 4 分布式存储实时数据研究文献总结

Table 4. Review of research on distributed storage of real-time data

技术手段	文献	研究对象	主要方法
数据流处理	[88]	分布式数据流	GALOIS 边缘数据流处理架构
	[89]	边缘计算数据流	优化处理框架
	[90]	分布式数据流	数据分配算法
	[91]	数据流异常值检测	局部离群因子算法
	[92]	异构资源数据流	自主分层伸缩策略
	[93]	动态数据流聚类	在线聚类算法
	[94]	数据流动态变化	OVRMAXGB 算法
实时数据库	[96]	实时数据推送	基于集合的实时推送方法
	[97]	网络视频监控数据存储	本地数据库缓存模型
	[98]	电网数据实时存储	分布式NoSQL 数据库
	[99]	船舶控制系统实时数据	分布式实时数据库同步技术方案

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表 5 分布式存储数据抗毁研究文献总结

Table 5. Review of research on distributed storage data survivability

技术手段	来源	研究对象	主要方法
数据备份	文献[101-102]	地理分布式数据中心	ILP 模型数据备份方案
	文献[103]	云平台数据	类型区分的数据复制技术
	文献[104]	分布式备份存储系统	数据放置算法
	文献[105]	磁盘备份和恢复	数据合并算法
	文献[106]	云平台数据	备份服务器数据访问框架
	文献[107]	云平台数据	三角洲压缩算法
数据容灾	文献[108]	分布式数据存储容灾	Slogger 数据灾备架构
	文献[109]	网络突变数据容灾	DELTA 压缩算法
	文献[110]	渐进式网络恢复	启发式算法
	文献[111]	云平台数据	区块链分布式多副本数据存储
	文献[112]	医疗数据	远程复制技术

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