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Article(id=1254362825632178842, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.14, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1723564800000, receivedDateStr=2024-08-14, revisedDate=1761840000000, revisedDateStr=2025-10-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993002564, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993002564, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993002564, creator=13701087609, updateTime=1776993002564, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=124, endPage=132, ext={EN=ArticleExt(id=1254362829100868268, articleId=1254362825632178842, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Dynamic Scheduling Algorithm and System for Ship Workshop Based on Cloud-Edge-Device Collaboration, columnId=1254362827888722754, journalTitle=Ship Engineering, columnName=Ship Materials, Manufacturing Processes and Management, runingTitle=null, highlight=null, articleAbstract=
[Purpose]

Aims to establish a cloud-edge-device collaborative intelligent management and control system to enhance production process controllability, shorten construction cycles, and strengthen decision support capabilities.

[Method]

Driven by production plans and guided by process flows, a three-level cloud-edge-device collaborative architecture is designed. By constructing a physical-information fusion environment in the ship block workshop, a "plan-resource-execution" linkage mechanism is established. An improved genetic algorithm (IGA) combined with simulated annealing is proposed for the dynamic scheduling model, alongside the development of a multi-source heterogeneous data fusion engine to achieve full-factor visual management and control.

[Result]

After system implementation, the ship block construction cycle is reduced by 19.7% compared to traditional models, production anomaly response time is shortened by 75%, and the equipment load balancing index is optimized by 28%.

[Conclusion]

The proposed cloud-edge-device collaborative management and control model effectively resolves the dynamic matching dilemma between planning and execution in ship block workshops. The established "perception-analysis-decision-execution" closed-loop system provides a reusable implementation framework for intelligent ship manufacturing, promoting the digital transformation of the shipbuilding industry.

, correspAuthors=Jianxun CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Ganlong WANG, Yanxia WU, Jianxun CHEN, Hao JIANG, Jichang WANG, Jifeng WANG), CN=ArticleExt(id=1254362831265129148, articleId=1254362825632178842, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=基于云-边-端的船舶车间动态排产算法及系统, columnId=1254362829709050707, journalTitle=船舶工程, columnName=船舶材料、制造工艺与管理, runingTitle=null, highlight=null, articleAbstract=
［目的］

旨在构建基于云-边-端协同的智能管控体系，提升生产过程可控性、缩短建造周期并强化决策支撑能力。

［方法］

以生产计划为驱动，工艺流程为牵引，设计云-边-端三级协同架构，通过构建船体分段车间物理信息融合环境，建立“计划、资源、执行”联动机制。提出混沌初始化种群与自适应遗传操作的改进遗传算法（IGA），融合模拟退火算法构建动态排产模型；开发多源异构数据融合引擎，实现设备状态、工序进度等实时数据驱动的排产优化。

［结果］

系统应用后，船舶分段建造周期较传统模式缩短19.7%，生产异常响应时间缩短75%，设备负荷均衡指数优化28%。

［结论］

提出的云-边-端协同管控模式有效破解船舶分段车间计划与执行的动态匹配难题，建立的“感知、分析、决策、执行”闭环体系为船舶智能制造提供可复用的实施框架，推动船舶工业数字化转型。

, correspAuthors=陈建勋, authorNote=null, correspAuthorsNote=
陈建勋（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。
, copyrightStatement=本刊已许可中国学术期刊（光盘版）电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文，本刊著作权使用费与本刊稿酬一并支付；本刊所刊登文章均无知识产权争议，不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明，文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=xMDJrRCeMmpMmwuk+H9WoA==, magXml=VgfRWpb32HrqEl9bMyeYeg==, pdfUrl=null, pdf=qI22feODlGhwn7wnycOkbQ==, pdfFileSize=2321699, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=YxIpTtT2VqlHfusBqpYBCg==, mapNumber=null, authorCompany=null, fund=null, authors=

王淦龙（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。

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王淦龙（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。

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王淦龙（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。

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基于云-边-端的船舶车间动态排产算法及系统
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王淦龙 1, 2 , 吴艳霞 1 , 陈建勋 2, * , 江浩 2 , 王吉昌 2 , 王继峰 2
船舶工程 | 船舶材料、制造工艺与管理 2026,48(3): 124-132
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船舶工程 | 船舶材料、制造工艺与管理 2026, 48(3): 124-132
基于云-边-端的船舶车间动态排产算法及系统
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王淦龙1, 2, 吴艳霞1, 陈建勋2, *, 江浩2, 王吉昌2, 王继峰2
作者信息
  • 1．哈尔滨工程大学计算机科学与技术学院，哈尔滨　150001
  • 2．中船数字信息技术有限公司，北京　100161

    • 王淦龙（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。

通讯作者:

陈建勋（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。
Dynamic Scheduling Algorithm and System for Ship Workshop Based on Cloud-Edge-Device Collaboration
Ganlong WANG1, 2, Yanxia WU1, Jianxun CHEN2, *, Hao JIANG2, Jichang WANG2, Jifeng WANG2
Affiliations
  • 1.College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
  • 2.China Shipbuilding Digital Information Technology Co., Ltd., Beijing 100161, China
出版时间: 2026-03-25 doi: 10.13788/j.cnki.cbgc.2026.03.14
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摘要
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［目的］

旨在构建基于云-边-端协同的智能管控体系，提升生产过程可控性、缩短建造周期并强化决策支撑能力。

［方法］

以生产计划为驱动，工艺流程为牵引，设计云-边-端三级协同架构，通过构建船体分段车间物理信息融合环境，建立“计划、资源、执行”联动机制。提出混沌初始化种群与自适应遗传操作的改进遗传算法（IGA），融合模拟退火算法构建动态排产模型；开发多源异构数据融合引擎，实现设备状态、工序进度等实时数据驱动的排产优化。

［结果］

系统应用后，船舶分段建造周期较传统模式缩短19.7%，生产异常响应时间缩短75%，设备负荷均衡指数优化28%。

［结论］

提出的云-边-端协同管控模式有效破解船舶分段车间计划与执行的动态匹配难题，建立的“感知、分析、决策、执行”闭环体系为船舶智能制造提供可复用的实施框架，推动船舶工业数字化转型。

船舶分段车间  /  云-边-端协同  /  动态排产算法  /  生产管控系统
[Purpose]

Aims to establish a cloud-edge-device collaborative intelligent management and control system to enhance production process controllability, shorten construction cycles, and strengthen decision support capabilities.

[Method]

Driven by production plans and guided by process flows, a three-level cloud-edge-device collaborative architecture is designed. By constructing a physical-information fusion environment in the ship block workshop, a "plan-resource-execution" linkage mechanism is established. An improved genetic algorithm (IGA) combined with simulated annealing is proposed for the dynamic scheduling model, alongside the development of a multi-source heterogeneous data fusion engine to achieve full-factor visual management and control.

[Result]

After system implementation, the ship block construction cycle is reduced by 19.7% compared to traditional models, production anomaly response time is shortened by 75%, and the equipment load balancing index is optimized by 28%.

[Conclusion]

The proposed cloud-edge-device collaborative management and control model effectively resolves the dynamic matching dilemma between planning and execution in ship block workshops. The established "perception-analysis-decision-execution" closed-loop system provides a reusable implementation framework for intelligent ship manufacturing, promoting the digital transformation of the shipbuilding industry.

ship block workshop  /  cloud-edge-device collaboration  /  dynamic scheduling algorithm  /  production management and control system
王淦龙, 吴艳霞, 陈建勋, 江浩, 王吉昌, 王继峰. 基于云-边-端的船舶车间动态排产算法及系统. 船舶工程, 2026 , 48 (3) : 124 -132 . DOI: 10.13788/j.cnki.cbgc.2026.03.14
Ganlong WANG, Yanxia WU, Jianxun CHEN, Hao JIANG, Jichang WANG, Jifeng WANG. Dynamic Scheduling Algorithm and System for Ship Workshop Based on Cloud-Edge-Device Collaboration[J]. Ship Engineering, 2026 , 48 (3) : 124 -132 . DOI: 10.13788/j.cnki.cbgc.2026.03.14
2026年第48卷第3期
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doi: 10.13788/j.cnki.cbgc.2026.03.14
  • 接收时间：2024-08-14
  • 首发时间：2026-04-24
  • 出版时间：2026-03-25
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  • 收稿日期：2024-08-14
  • 修回日期：2025-10-31
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    1．哈尔滨工程大学计算机科学与技术学院，哈尔滨　150001
    2．中船数字信息技术有限公司，北京　100161

通讯作者:

陈建勋（1988—），男，硕士、高级工程师。研究方向：船舶先进制造技术，智能制造。
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2种不同金属材料的力学参数
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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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