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Article(id=1148011758163325534, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, articleNumber=null, orderNo=null, doi=10.7654/j.issn.2097-1974.20240216, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1706198400000, receivedDateStr=2024-01-26, revisedDate=1708963200000, revisedDateStr=2024-02-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1751636932172, onlineDateStr=2025-07-04, pubDate=1713974400000, pubDateStr=2024-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751636932172, onlineIssueDateStr=2025-07-04, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=1751940413646, onlineFirstDateStr=2025-07-08, sourceXml=null, magXml=null, createTime=1751636932172, creator=13701087609, updateTime=1751636932172, updator=13701087609, issue=Issue{id=1149298831252079541, tenantId=1146029695717560320, journalId=1146119989267898375, year='2024', volume='47', issue='2', pageStart='1', pageEnd='106', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1751943794309, creator=13701087609, updateTime=1754895895552, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1161680841353552315, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1161680841353552316, tenantId=1146029695717560320, journalId=1146119989267898375, issueId=1149298831252079541, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=100, endPage=106, ext={EN=ArticleExt(id=1148011758335292013, articleId=1148011758163325534, tenantId=1146029695717560320, journalId=1146119989267898375, language=EN, title=Research on Intelligent Support Technology of Equipment, columnId=1154067056828863055, journalTitle=Missiles and Space Vehicles, columnName=Systems Engineering, runingTitle=null, highlight=null, articleAbstract=

The combat effectiveness of equipment strongly depends on the support of ground equipment. Under system combat conditions, how to achieve system empowerment, system enhancement and system collaboration through innovative ground equipment is a major issue of the overall ground profession. An equipment system is taken as the research object. Firstly, the capability requirements of the system operation on the weapon system are analyzed, and the combat mode, task flow and system composition of the weapon system are combined to form a condensed list of the support capability requirements. Secondly, aiming at improving the operational accompanying support management ability and improving the allocation of accompanying support resources, the technical approaches of applying intelligent technology to mobile operations support and enhancing the effectiveness of ground equipment are sorted out, and a technical scheme of intelligent support of equipment is formed. Based on the above research, the preliminary design of information and intelligent support vehicle scheme is completed, and its mobile combat support capability is evaluated.

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体系对抗中通过创新地面装备实现体系赋能、体系增强和体系协同,是地面总体专业面临的重大难题。以典型装备系统为研究对象,分析体系对抗对该武器系统的能力需求,结合武器系统的任务模式、任务流程和系统组成,梳理保障能力需求清单;以提升伴随保障支持管理能力、改善伴随保障资源配置为目标,梳理智能技术应用于机动伴随保障和增强地面装备效能的技术途径,形成装备智能保障技术方案。基于上述研究,完成信息化、智能化保障车方案概要设计,对其进行机动伴随保障能力评估。

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崔蕾(1971-),女,高级工程师,主要研究方向为地面总体技术。

迟学谦(1985—),男,高级工程师,主要研究方向为飞行器设计。

张家骏(1990—),男,工程师,主要研究方向为车辆总体设计。

雷雪媛(1990–),女,工程师,主要研究方向为特种车辆总体设计。

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崔蕾(1971-),女,高级工程师,主要研究方向为地面总体技术。

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崔蕾(1971-),女,高级工程师,主要研究方向为地面总体技术。

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迟学谦(1985—),男,高级工程师,主要研究方向为飞行器设计。

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迟学谦(1985—),男,高级工程师,主要研究方向为飞行器设计。

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张家骏(1990—),男,工程师,主要研究方向为车辆总体设计。

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张家骏(1990—),男,工程师,主要研究方向为车辆总体设计。

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雷雪媛(1990–),女,工程师,主要研究方向为特种车辆总体设计。

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雷雪媛(1990–),女,工程师,主要研究方向为特种车辆总体设计。

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Shenyang: Northeastern University, 2013., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1197274125589856700, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, xref=null, ext=[AuthorCompanyExt(id=1197274125602439613, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, companyId=1197274125589856700, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=China Academy of Launch Vehicle Technology,Beijing,100076), AuthorCompanyExt(id=1197274125610828222, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, companyId=1197274125589856700, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国运载火箭技术研究院,北京,100076)])], figs=[ArticleFig(id=1197274146381021680, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=EN, label=Fig. 1, caption=Accompany support task advance planning requirements, figureFileSmall=YOxVkTsnuiHbg+YXSC/+Fg==, figureFileBig=mYc7yrPpgAoBYbYY2/Cb8g==, tableContent=null), ArticleFig(id=1197274146695594481, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=CN, label=图1, caption=伴随保障任务预先规划需求, figureFileSmall=YOxVkTsnuiHbg+YXSC/+Fg==, figureFileBig=mYc7yrPpgAoBYbYY2/Cb8g==, tableContent=null), ArticleFig(id=1197274148570448370, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=EN, label=Fig. 2, caption=Efficient and accurate supervision requirements of SV support work, figureFileSmall=V2GRmWNyYYn3g1dpUHl6fw==, figureFileBig=MywMvi5tGM0WJ3VNz4yI4g==, tableContent=null), ArticleFig(id=1197274149266702835, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=CN, label=图2, caption=保障车保障工作高效精准监管需求, figureFileSmall=V2GRmWNyYYn3g1dpUHl6fw==, figureFileBig=MywMvi5tGM0WJ3VNz4yI4g==, tableContent=null), ArticleFig(id=1197274149837128180, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=EN, label=Fig. 3, caption=Vehicle-mounted mobile accompany support regulatory framework, figureFileSmall=JaA4wAi3d6MF9z5TNhDplw==, figureFileBig=4Q2VvASn+t11NgLIpxQUlw==, tableContent=null), ArticleFig(id=1197274150202032629, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=CN, label=图3, caption=车载机动伴随保障监管架构, figureFileSmall=JaA4wAi3d6MF9z5TNhDplw==, figureFileBig=4Q2VvASn+t11NgLIpxQUlw==, tableContent=null), ArticleFig(id=1197274150730514934, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=EN, label=Fig. 4, caption=Comparative evaluation of mobile accompany support capability, figureFileSmall=PumRrdta6bPruaf7tomFKA==, figureFileBig=AjxPgLeJlyXhpGN72swTdw==, tableContent=null), ArticleFig(id=1197274151166722551, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=CN, label=图4, caption=机动伴随保障能力对比评价情况, figureFileSmall=PumRrdta6bPruaf7tomFKA==, figureFileBig=AjxPgLeJlyXhpGN72swTdw==, tableContent=null), ArticleFig(id=1197274152680866296, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=EN, label=Tab. 1, caption=Valuate and analyze index weights, figureFileSmall=null, figureFileBig=null, tableContent=
IAO.1 Valuate and Analyze Micha Weights
层次指标权重
第1层保障车自身保障能力Ul0.200
保障车协同保障能力U20.200
伴随保障工作能力U30.200
伴随保障设备能力U40.200
伴随保障备件能力U50.200
第2层保障车机动反应能力U110.637
保障车健康管理能力U120.105
保障车系统可靠度U130.258
保障车保障任务预先规划能力U210.143
保障车保障任务现场规划能力U220.429
保障车现场协同能力U230.429
保障工作设计规划能力U310.500
保障工作监管能力U320.500
保障设备设计规划能力U410.500
保障设备监管能力U420.500
保障备件设计规划能力U510.500
保障备件监管能力U520.500
第3层最大机动速度U1110.564
带载续驶里程U1120.263
车载存储单元定位出入时间U1130.118
车载无人机舱展收时间U1140.055
系统设备检测率U1210.151
系统设备虚警率U1220.067
系统设备健康状态评估实用性U1230.391
系统设备变化趋势预测准确度U1240.391
底盘系统平均无故障时间U1310.398
车载定位设备平均无故障时间U1320.161
车载指挥通信设备平均无故障时间U1330.161
车载舱体供配电系统平均无故障时间U1340.161
车载无人机舱系统平均无故障时间U1350.059
车载出入存储单元系统平均无故障时间U1360.059
战场态势影响预测准确度U2110.055
保障任务预测分析准确度U2120.118
保障任务预先规划时间U2130.263
保障任务预先规划实用性U2140.564
现场保障信息交换精确度U2210.054
现场保障信息交换时间U2220.054
现场保障任务分析准确度U2230.123
现场保障任务规划时间U2240.259
现场保障任务规划实用性U2250.510
现场协同保障区域范围U2310.166
现场协同保障任务时间U2320.166
现场保障安全预警时间U2330.166
现场保障机动路线设计合理性U2340.062
现场保障任务技术支持能力U2350.441
维修保障工作设计规划精准度U3110.318
维修保障工作设计规划时间U3120.318
感知预警工作设计规划精准度U3130.129
感知预警工作设计规划时间U3140.129
伪装防御工作设计规划精准度U3150.052
伪装防御工作设计规划时间U3160.052
现场维修保障工作完成程度U3210.318
现场维修保障工作时间U3220.318
现场感知预警工作完成程度U3230.129
现场感知预警工作时间U3240.129
现场伪装防御工作完成程度U3250.052
现场伪装防御工作时间U3260.052
维修保障设备设计规划精准度U4110.318
维修保障设备设计规划时间U4120.318
感知预警设备设计规划精准度U4130.129
感知预警设备设计规划时间U4140.129
伪装防御设备设计规划精准度U4150.052
伪装防御设备设计规划时间U4160.052
现场维修保障设备利用率U4210.318
现场维修保障设备满足率U4220.318
现场感知预警设备利用率U4230.129
现场感知预警设备满足率U4240.129
现场伪装防御设备利用率U4250.052
现场伪装防御设备满足率U4260.052
维修保障备件设计规划精准度U5110.318
维修保障备件设计规划时间U5120.318
感知预警备件设计规划精准度U5130.129
感知预警备件设计规划时间U5140.129
伪装防御备件设计规划精准度U5150.052
伪装防御备件设计规划时间U5160.052
现场维修保障备件利用率U5210.318
现场维修保障备件满足率U5220.318
现场感知预警备件利用率U5230.129
现场感知预警备件满足率U5240.129
现场伪装防御备件利用率U5250.052
现场伪装防御备件满足率U5260.052
), ArticleFig(id=1197274153154822649, tenantId=1146029695717560320, journalId=1146119989267898375, articleId=1148011758163325534, language=CN, label=表1, caption=分析评估指标权重, figureFileSmall=null, figureFileBig=null, tableContent=
IAO.1 Valuate and Analyze Micha Weights
层次指标权重
第1层保障车自身保障能力Ul0.200
保障车协同保障能力U20.200
伴随保障工作能力U30.200
伴随保障设备能力U40.200
伴随保障备件能力U50.200
第2层保障车机动反应能力U110.637
保障车健康管理能力U120.105
保障车系统可靠度U130.258
保障车保障任务预先规划能力U210.143
保障车保障任务现场规划能力U220.429
保障车现场协同能力U230.429
保障工作设计规划能力U310.500
保障工作监管能力U320.500
保障设备设计规划能力U410.500
保障设备监管能力U420.500
保障备件设计规划能力U510.500
保障备件监管能力U520.500
第3层最大机动速度U1110.564
带载续驶里程U1120.263
车载存储单元定位出入时间U1130.118
车载无人机舱展收时间U1140.055
系统设备检测率U1210.151
系统设备虚警率U1220.067
系统设备健康状态评估实用性U1230.391
系统设备变化趋势预测准确度U1240.391
底盘系统平均无故障时间U1310.398
车载定位设备平均无故障时间U1320.161
车载指挥通信设备平均无故障时间U1330.161
车载舱体供配电系统平均无故障时间U1340.161
车载无人机舱系统平均无故障时间U1350.059
车载出入存储单元系统平均无故障时间U1360.059
战场态势影响预测准确度U2110.055
保障任务预测分析准确度U2120.118
保障任务预先规划时间U2130.263
保障任务预先规划实用性U2140.564
现场保障信息交换精确度U2210.054
现场保障信息交换时间U2220.054
现场保障任务分析准确度U2230.123
现场保障任务规划时间U2240.259
现场保障任务规划实用性U2250.510
现场协同保障区域范围U2310.166
现场协同保障任务时间U2320.166
现场保障安全预警时间U2330.166
现场保障机动路线设计合理性U2340.062
现场保障任务技术支持能力U2350.441
维修保障工作设计规划精准度U3110.318
维修保障工作设计规划时间U3120.318
感知预警工作设计规划精准度U3130.129
感知预警工作设计规划时间U3140.129
伪装防御工作设计规划精准度U3150.052
伪装防御工作设计规划时间U3160.052
现场维修保障工作完成程度U3210.318
现场维修保障工作时间U3220.318
现场感知预警工作完成程度U3230.129
现场感知预警工作时间U3240.129
现场伪装防御工作完成程度U3250.052
现场伪装防御工作时间U3260.052
维修保障设备设计规划精准度U4110.318
维修保障设备设计规划时间U4120.318
感知预警设备设计规划精准度U4130.129
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现场维修保障设备满足率U4220.318
现场感知预警设备利用率U4230.129
现场感知预警设备满足率U4240.129
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维修保障备件设计规划精准度U5110.318
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感知预警备件设计规划精准度U5130.129
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伪装防御备件设计规划精准度U5150.052
伪装防御备件设计规划时间U5160.052
现场维修保障备件利用率U5210.318
现场维修保障备件满足率U5220.318
现场感知预警备件利用率U5230.129
现场感知预警备件满足率U5240.129
现场伪装防御备件利用率U5250.052
现场伪装防御备件满足率U5260.052
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装备智能保障技术研究
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崔蕾 , 迟学谦 , 张家骏 , 雷雪媛
导弹与航天运载技术 | 航天系统工程 2024,47(2): 100-106
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导弹与航天运载技术 | 航天系统工程 2024, 47(2): 100-106
装备智能保障技术研究
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崔蕾, 迟学谦, 张家骏, 雷雪媛
作者信息
  • 中国运载火箭技术研究院,北京,100076

    • 崔蕾(1971-),女,高级工程师,主要研究方向为地面总体技术。

    迟学谦(1985—),男,高级工程师,主要研究方向为飞行器设计。

    张家骏(1990—),男,工程师,主要研究方向为车辆总体设计。

    雷雪媛(1990–),女,工程师,主要研究方向为特种车辆总体设计。

Research on Intelligent Support Technology of Equipment
Lei CUI, Xueqian CHI, Jiajun ZHANG, Xueyuan LEI
Affiliations
  • China Academy of Launch Vehicle Technology,Beijing,100076
出版时间: 2024-04-25 doi: 10.7654/j.issn.2097-1974.20240216
文章导航
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摘要
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体系对抗中通过创新地面装备实现体系赋能、体系增强和体系协同,是地面总体专业面临的重大难题。以典型装备系统为研究对象,分析体系对抗对该武器系统的能力需求,结合武器系统的任务模式、任务流程和系统组成,梳理保障能力需求清单;以提升伴随保障支持管理能力、改善伴随保障资源配置为目标,梳理智能技术应用于机动伴随保障和增强地面装备效能的技术途径,形成装备智能保障技术方案。基于上述研究,完成信息化、智能化保障车方案概要设计,对其进行机动伴随保障能力评估。

机动伴随保障  /  智能化保障车  /  体系对抗  /  需求分析  /  能力评估

The combat effectiveness of equipment strongly depends on the support of ground equipment. Under system combat conditions, how to achieve system empowerment, system enhancement and system collaboration through innovative ground equipment is a major issue of the overall ground profession. An equipment system is taken as the research object. Firstly, the capability requirements of the system operation on the weapon system are analyzed, and the combat mode, task flow and system composition of the weapon system are combined to form a condensed list of the support capability requirements. Secondly, aiming at improving the operational accompanying support management ability and improving the allocation of accompanying support resources, the technical approaches of applying intelligent technology to mobile operations support and enhancing the effectiveness of ground equipment are sorted out, and a technical scheme of intelligent support of equipment is formed. Based on the above research, the preliminary design of information and intelligent support vehicle scheme is completed, and its mobile combat support capability is evaluated.

mobile accompany support  /  intelligent support vehicle  /  system combat  /  requirements analysis  /  capability evaluation
崔蕾, 迟学谦, 张家骏, 雷雪媛. 装备智能保障技术研究. 导弹与航天运载技术, 2024 , 47 (2) : 100 -106 . DOI: 10.7654/j.issn.2097-1974.20240216
Lei CUI, Xueqian CHI, Jiajun ZHANG, Xueyuan LEI. Research on Intelligent Support Technology of Equipment[J]. Missiles and Space Vehicles, 2024 , 47 (2) : 100 -106 . DOI: 10.7654/j.issn.2097-1974.20240216
正文
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0 引言
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体系对抗是当前和未来战场的主要模式, 围绕体系目标[1],各领域武器装备共享战场态势、协同战场任务、展开同步行动。武器装备强烈依赖地面装备的支援保障以发挥效能, 体系对抗中如何通过创新地面装备实现体系赋能、体系增强和体系协同, 是地面总体专业的重大课题。随着信息智能化、战争复杂性不断提高[2-3],装备系统生存压力倍增,地面装备支援保障技术能够提升系统生存能力,满足任务需要[4], 支援保障对体系对抗能力的作用凸显,但现有保障能力与需求之间尚存较大差距[5-7],需要进一步研究支援保障技术以提升效能。
智能科技引领着新一轮科技革命和产业变革[8], 具有深度学习、群智开放、自主操控等新特征。相关学科的发展、理论建模的突破及软硬件的创新升级[9-10],突破了当前智能技术的瓶颈,将智能认知决策等技术融入到装备机动伴随保障体系,能够使机动伴随保障智能高效、精准灵活, 从而释放更大效能, 本文以典型装备系统为研究对象进行机动伴随智能保障技术探索研究。
1 体系对抗机动伴随保障能力需求分析
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随着体系对抗空间极大拓展、节奏不断加快, 传统保障方式暴露出响应速度慢、保障需求难以及时满足等问题。随着超前感知、智能引导等伴随保障思想和技术的提出, 前方自主保障成为研究重点。典型装备系统由多型车辆组成, 完成多波次任务。其中保障车主要承担武器系统机动伴随保障工作, 是战场前方自主保障的核心装备, 须在车载保障资源配置精准灵活、单车执行保障任务智能高效、多车协同保障工作自主灵敏等方面进行探索和创新, 保障能力需求的分析基于以下3个流程阶段。
1.1 准备阶段
准备阶段首先需要基于任务要求预测生成保障方案, 完成本次伴随保障任务预先规划, 统筹规划保障车车载能力和伴随保障任务需求, 生成保障车任务派遣数量、车载人员物资规划方案。其次,保障车需要高效精准监管车载物资,实现车载保障备件及设备等快速装车固定、存储统计。最后,保障车需要健康管理, 通过检修维护快速满足任务需求。
1.1.1 预测生成保障方案需求分析
体系对抗增加了武器装备战伤战损趋势, 仅依靠装备日常维修保养经验数据, 已不能满足保障方案预测需求, 需要开展战伤抢修预评估、战伤抢修性效能评估等技术研究。
a)战伤抢修预评估:基于任务要求,预测战伤情况,预先制定保障指南和方案[4,11-12]。
1)目标与威胁综合分析: 根据战场环境、武器装备及威胁的特征因素, 综合分析遭遇后的情况。其一,分析武器装备任务包线、机动及隐身特性等,确定威胁类型及任务包线。针对不具备自主寻的功能的攻击单元已有数十年研究积累[13],重点为自主寻的任务包线研究, 近年来研究应用神经网络等算法, 大幅提升精度[14]。其二,综合估计武器装备受攻击概率, 确定遭遇时双方的系统条件。国内外针对装备生存敏感性开展了很多研究[12,15],给出概率分析方法。 其三, 规划路线, 保证武器系统到达目标区域的最优可机动路径,增大生存概率[16-18],目前${\mathrm{A}}^{* }$及改进算法等已逐渐应用AI智能技术。
2)战伤模式与效应分析评估:武器系统需要自上而下开展FMECA分析, 总结系统装备战伤模式及原因, 综合评判战伤等级, 定位战伤高发系统及部位等。国内外开发了多套战伤抢修评估系统[19-21],可预测战伤状况、评估战伤抢修前后工作。
3)修复方法与资源分析: 基于抢修任务资源需求最大化武器装备抢修效益。存在资源消耗和时间限制难题[22-23]时,需要合理安排抢修资源及工作,主要采用混合粒子群算法等方法。
b)战伤抢修性效能评估:分析度量战伤武器装备修复后的有效性和完成任务的能力[4]。
1)分析战伤抢修效能影响因素:包括任务可靠性、生存力、安全性及装备系统功能等,建立影响因素评价指标和判据应考虑的非线性、实践性。
2)战伤抢修性效能评估分析是军事领域研究重点, 最早沿用维修领域试验统计法, 随着研究深入[24-25],形成了基于状态空间与任务关系的效能评估方法、模糊AHP与熵值法结合的效能评估模型等, 因其复杂性和不确定性, 分析方法和技术尚需完善。
1.1.2 伴随保障任务预先规划需求分析
伴随保障任务预先规划包括3个方面:维修任务、支援保障任务、保障车任务。任务规划技术是国内外军事领域研究热点, 伴随保障任务预先规划为其组成部分之一[26],需求见图1。
a)实现战场态势高效预测。快速自动分析保障车机动伴随保障能力、保障地区对支援保障工作的影响。
b)实现保障任务精确高效规划。尽可能缩短决策周期, 统筹资源匹配、任务分配, 使其符合整体机动伴随保障态势需求, 同时需要研究保障任务分解、 行动序列生成、任务冲突检测与消解等技术。
c)实现保障方案智能化推演评估。随着保障系统复杂性的不断增加, 需要构建大样本多策略仿真推演与评估能力, 全方位分析体系对抗环境下的综合保障情况, 科学预判保障工作发展趋势。研究离线与在线学习相结合的智能推演技术、AI模型构建方法、整体与局部评估相结合的保障效能智能评估方法。
1.1.3 保障车保障工作高效精准监管需求分析
保障车高效精准监管需求贯穿于保障工作的3个阶段, 见图2。
基于武器系统不断提升机动伴随保障能力的需求, 通过高效精准的保障监管技术研究, 探索系统装备战备完好性和功能性能设计间的最佳匹配关系,完善机动伴随保障资源配置, 不断提升机动伴随保障工作的性价比。将保障车升级为武器系统机动伴随保障监管移动终端平台,支持武器系统指挥决策,能够适应机动伴随保障工作和所需要的保障备件、设备不断动态调整优化的要求, 能够针对保障工作信息数据进行系统收集、统计监管和健康预测,为武器系统全寿命周期优化提供支持, 根据需求可增加不同拓展功能配置, 实现多辆保障车协同完成任务的目标。
随着智能技术的发展, 语音图像智能识别、深度学习等AI智能技术能够提升保障车高效精准监管技术, 研发新的高效保障设备。
1.1.4 保障车自身健康管理需求分析
21世纪以来, 装备诊断预测与健康管理(Diagnostics, Prognostics and Health Management, DPHM)作为装备综合保障的关键技术, 得到许多国家的高度重视[27]。DPHM利用先进传感器、智能算法模型, 预测、诊断和监管装备状态, 提升装备使用与维护安全和经济性。保障车寿命周期内复杂多变的环境及极端工况会诱发重要部件缺陷损伤, 从而引发事故, 其DPHM系统应具备故障检测隔离、健康管理和寿命追踪等功能, 实现不同层级的诊断预测和健康管理, 研究融入智能技术和更为准确的实用预测模型等, 提升保障车自身的保障能力。
1.2 机动伴随保障阶段
在机动伴随保障阶段, 首先, 需要通过大数据量信息快速通信, 保障车接受上级命令, 共享战场信息, 交换车辆状态信息及保障需求, 综合分析并生成现场伴随保障任务方案及规划; 其次, 通过保障车高效精准监管保障工作, 快速规划现场所需要的保障工作、备件及设备等,生成现场车载人员和物资策划方案；最后,保障车机动至保障工作地点,快速开展现场保障工作。
1.2.1 保障车大数据量信息快速通信需求分析
体系对抗环境中, 通信系统面临频谱资源短缺和战场电磁环境复杂等问题[10],近年来兴起的智能认知无线电技术, 可提高战时频谱管理和通信网络抗干扰能力, 有良好的保密及网络鲁棒性, 成为国内外军用无线通信研究的关键技术, 其理论和模型研究尚需解决频谱感知重构、高速跳频及网络同步等关键问题。
1.2.2 保障车生成现场保障方案和规划需求分析
现场保障方案和规划生成快速现场评估技术,包括快速战伤抢修和支援保障等现场评估。为了保证现场评估的快速高效性, 主要采用经验评估和智能评估法[4]。
经验评估通过现场查看系统装备战伤状况快速评定战伤等级与剩余性能, 随着武器装备出现缺乏判断更复杂毁伤特征的经验、短时间内多个战伤装备需要抢修等问题, 传统评估技术难以满足要求, 智能评估成为现场评估的新选择, 如采用信息网络化手段实现战伤程度快速评估, 研究AI评估决策系统模型库和知识库建立方法、可检索战伤评估案例及处理办法的专家系统等。
1.2.3 保障车快速机动开展现场保障需求分析
a)保障车需要优化质量结构与动力总成匹配等技术以满足高机动性需要, 可融入无人驾驶等智能技术来提升生存和快速反应能力。无人移动平台技术日益成熟, 解决了人员防护问题, 搭载特定装备即可用于侦察打击、保障救援等,具有成本低、机动性高、 生存能力强等特点。保障车需要开发基于无人机配套的预警防护装备,提升其机动伴随保障能力。
b)保障车需要研究快速智能出入存储单元技术, 实现快速录入、删除保障备件及设备信息,适应其数量和种类的调整, 实现保障备件及设备与存储单元位置快速绑定定位,适应存储空间位置和数量调整。
c)研究车载无人机快速起降等技术, 提升车载智能保障装备快速反应能力。
1.3 任务结束归库阶段
在任务结束归库阶段, 需要统计任务中的保障工作、备件及设备情况, 总结保障车车载能力及自身状态, 补充完善相关数据库, 为武器系统机动伴随保障相关模型的建立、评估数据的完善、智能预测及健康管理等提供精准的研究基础。
2 智能保障技术方案设计评估
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2.1 方案设计概要
本文基于典型保障车设计概要升级智能保障车方案。
2.1.1 完善典型保障车功能
a)提供地面车辆及武器系统基层级技术保障, 对其战伤战损或故障装备进行必要紧急抢修, 保证地面车辆恢复行车状态。
b)为运载基层级保障所需要的维修人员、设备与备件提供必要的工作环境和条件。
2.1.2 增加智能保障功能
a)完善车载智能保障设备:增加车载便携式电子维修手册。以智能技术构建多层级模型, 优化操作维修信息, 精确交互保障所需信息, 加速实施保障工作。
b)增加健康管理功能:增加车载保障车健康管理系统, 实现保障车系统运行状态数据采集、存储与处理, 重要参数监测及超限报警, 部分关键设备和系统级健康状态评估等目标。
c)增加保障工作高效精准监管功能:增加车载机动伴随保障监管系统, 见图3。设计分层级监管流程、参数模型及监管方法等。
d)增加车载支援保障功能:增加可拓展功能模块配置功能,支持保障车系列化发展。
1)增加以快速自动起降无人机为组成单元的无人机系统, 主要针对特定区域地面目标侦察预警;
2)增加以探测雷达为组成单元的无人机反制系统, 主要针对特定区域低慢小空中目标预警反击;
3)可根据保障需要,将无人机系统、无人机反制系统等, 以功能模块形式布置于2辆或2辆以上保障车上,协同完成支援保障任务。
e)增加快速智能出入存储单元功能:增加车载出入存储单元系统,提升快速开展保障工作的能力。
f)增加自动无人机快速起降功能:增加车载无人机舱系统,增强快速开展保障工作的能力。
2.2 方案比较评估
基于优化保障车的功能配置、提高智能保障能力的目的选取评估参数, 包括反映顶层特性和系统内部资源特性的参数, 构建的评估指标体系分为3层。第1层为5个一级指标, 第2层为12个二级指标, 第3层为64个三级指标, 主要依据技术发展情况和装备系统现状, 通过相关统计分析、公式计算、专家咨询及主观判断等途径获得信息, 采用多层次模糊综合评价法[28]得到总评价结果。求得的指标体系各层指标权重系数见表1。
对升级智能保障车方案前后的机动伴随保障能力进行分析评价, 评价情况见图4, 其中评价等级1为一等(好)、2为二等(较好)、3为三等(一般)、4为四等(较差)、5为五等(差)。按最大隶属度法则和基于隶属度打分的原则[28],设定一等(好) 90分、 二等(较好)75分、三等(一般)60分、四等(较差)45分、五等(差)30分。
考虑智能技术现状及发展趋势, 升级智能保障车方案前UII综合评价为54.47分,属于三等(一般); 升级智能保障车方案后UI综合评价为79.01分, 属于二等(较好), 相对于智能升级前方案, 智能升级后方案的综合评价分数有较大程度的提高。智能升级后,保障车升级为武器系统智能保障监管移动终端平台, 能够适应保障工作和所需要的保障备件、设备不断动态调整优化的要求; 能够基于保障信息统计监管和健康预测, 为武器系统全寿命周期优化提供支持; 可以初步完成伴随保障任务预先规划和现场规划,实现保障车任务协同,因而新型智能保障技术能够满足体系对抗条件下装备系统机动伴随保障需求。
3 结束语
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本文以典型装备系统为研究对象, 分析了体系对抗对该武器系统的能力需求, 梳理了智能技术应用于机动伴随保障和增强地面装备效能的技术途径。基于提高智能保障方案实用性、精准度及可靠性的目标, 分析了应用智能技术尚需进一步解决的关键技术, 设计了智能保障车方案, 并对其机动伴随保障能力进行了评估。
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2024年第47卷第2期
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doi: 10.7654/j.issn.2097-1974.20240216
  • 接收时间：2024-01-26
  • 首发时间：2025-07-04
  • 出版时间：2024-04-25
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  • 收稿日期：2024-01-26
  • 修回日期：2024-02-27
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    中国运载火箭技术研究院,北京,100076
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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