Article(id=1254362828589171528, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.04, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749830400000, receivedDateStr=2025-06-14, revisedDate=1759248000000, revisedDateStr=2025-10-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993003270, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993003270, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993003270, creator=13701087609, updateTime=1776993003270, 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=32, endPage=38, ext={EN=ArticleExt(id=1254362830447248222, articleId=1254362828589171528, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Improved Intelligent Algorithm Load Forecasting Design Based on Tugboat Operation Requirements, columnId=1254362824956895894, journalTitle=Ship Engineering, columnName=Special Topic: Intelligent Ship, runingTitle=null, highlight=null, articleAbstract=
[Purpose] In order to improve the efficiency of tugboat operation scheduling and energy utilization,
[Method] an improved intelligent load forecasting algorithm integrating dynamic data preprocessing and online learning is proposed. Based on a hybrid LSTM-Adaboost architecture, the algorithm addresses the issues of temporal feature degradation and multimodal data fusion through the integration of differentiated LSTM weak predictors and dynamic weight allocation (error sensitivity penalty mechanism), and designs an online learning trigger mechanism (automatic retraining based on prediction error threshold) to achieve dynamic model updating. Additionally, an environmental data collaborative optimization module, including tidal information, is introduced to enhance the adaptability of load forecasting to port conditions. The algorithm is compared with conventional LSTM-Adaboost to validate its effectiveness.
[Result] The results indicate that after iterative optimization, the mean squared error of the improved algorithm is reduced by 40.8% compared to the conventional LSTM-Adaboost algorithm, demonstrating higher prediction accuracy and environmental adaptability.
[Conclusion] The research results can provide a reference for tugboat energy optimization, safety management, and intelligent scheduling in ports.
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［方法］ 提出一种融合动态数据预处理与在线学习的改进智能负荷预测算法。基于LSTM与Adaboost混合架构，通过差异化LSTM弱预测器集成与动态权重分配（误差敏感度惩罚机制），解决时序特征退化和多模态数据融合问题，设计在线学习触发机制（基于预测偏差阈值的自动再训练），实现模型动态更新；引入潮汐等环境数据协同优化模块，提升负荷预测对港口环境的适应性。将该算法与常规LSTM-Adaboost算法相对比，验证该算法的有效性。
［结果］ 结果表明，经迭代优化之后，改进算法的均方误差相比常规LSTM-Adaboost算法降低40.8%，具有较高的预测精度和环境适应性。
［结论］ 研究成果可供港口拖船能耗优化、安全保障和智能化调度参考。
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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=y/nAx52uLQcCzD++H3F5YA==, magXml=YgOungBt9l+khsQVo6u/9g==, pdfUrl=null, pdf=pf3ibjKhgw9e2x/ENt2cwQ==, pdfFileSize=1399375, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=J1xwUZEHv5BXayqUvT4sGA==, mapNumber=null, authorCompany=null, fund=null, authors=
付文秀（1989—），女，硕士、高级工程师。研究方向：船舶电力系统及智能化。
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