Article(id=1271501777754071715, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, articleNumber=PA20260121_gqmfofWL, orderNo=null, doi=10.19666/j.rlfd.202505109, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747670400000, receivedDateStr=2025-05-20, revisedDate=1752336000000, revisedDateStr=2025-07-13, acceptedDate=1752595200000, acceptedDateStr=2025-07-16, onlineDate=1761235200000, onlineDateStr=2025-10-24, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1781079247177, onlineIssueDateStr=2026-06-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1781079247177, creator=admin, updateTime=1781079247177, updator=admin, issue=Issue{id=1271501633826530070, tenantId=1146029695717560320, journalId=1210938733613449225, year='2026', volume='55', issue='1', pageStart='1', pageEnd='186', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=1, specialIssue=null, createTime=1781079212860, creator='ztmeta', updateTime=1781079304307, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1271502017525657824, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1271502017529852129, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1271501633826530070, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=177, endPage=186, ext={EN=ArticleExt(id=1271501779482124966, articleId=1271501777754071715, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Design of a multi-domain collaborative redundant computing engine for the district heating network, columnId=null, journalTitle=Thermal Power Generation, columnName=null, runingTitle=null, highlight=null, articleAbstract=With the continuous expansion of the scale of urban heating network systems and the sustained growth of intelligent demands, the conventional centralized control systems based on the single-controller mode have gradually exposed technical bottlenecks in terms of computing power support, system fault tolerance, equipment compatibility, and deployment costs, and have been unable to meet the application requirements of multi-domain collaboration and intelligent optimization control. To this end, a new type of redundant computing engine for multi-domain collaborative control of the heat network is proposed. A hierarchical architecture is adopted and the computing engine is decoupled into two major modules: the management program and the kernel program, achieving the separation of overall management and core computing functions. Through the task hierarchical management and coordination scheduling mechanism, the efficient collaboration of periodic and aperiodic mixed computing tasks has been achieved, improving the operational efficiency and real-time response capability of the system. Adding a redundancy mechanism and proposing a hot standby redundancy synchronization scheme have improved the reliability and stability of the system in high-load scenarios. A unified system supporting the flexible access of multi-language heterogeneous intelligent algorithms has been constructed. Through the dynamic loading and interface mapping mechanism, the two-way interaction between graphical configuration and the underlying algorithm code has been achieved. This computing engine effectively enhances the deployment flexibility and execution efficiency of intelligent algorithms, improves the stability and reliability of system operation. In actual deployment, the loading success rate of intelligent algorithms is 100%, the primary and backup switching time is ≤200 ms, and at the same time the conventional hardware deployment cost is reduced, providing a high-performance and low-cost solution for the intelligent transformation of industrial control systems. Furthermore, it has the potential to be promoted in multiple fields such as energy and transportation., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, 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, fund=null), CN=ArticleExt(id=1271501779394044581, articleId=1271501777754071715, tenantId=1146029695717560320, journalId=1210938733613449225, language=CN, title=面向热网的多域协同冗余计算引擎设计, columnId=null, journalTitle=热力发电, columnName=null, runingTitle=null, highlight=null, articleAbstract=随着城市热网系统规模的不断扩大和智能化需求的持续增长，传统基于单控制器模式的集控系统在算力支持、系统容错性、设备兼容性以及部署成本等方面逐渐暴露出技术瓶颈，已难以满足多域协同与智能优化控制的应用需求。为此，提出一种面向热网多域协同控制的新型冗余计算引擎，采用分层架构，将计算引擎解耦为管理程序与内核程序两大模块，实现了统筹管理与核心运算功能的分离；通过任务分级管理与协调调度机制，实现了周期与非周期混合运算任务的高效协同，提高了系统的运行效率与实时响应能力；增添冗余机制，提出一种热备式冗余同步方案，提高了系统在高负载场景下的可靠性与稳定性；构建了支持多语言异构智能算法灵活接入的统一体系，通过动态加载与接口映射机制，实现了图形化组态与底层算法代码的双向联动。该计算引擎有效提高了智能算法的部署灵活性与执行效率，提高了系统运行的稳定性与可靠性，在实际部署中智能算法加载成功率100%，主备切换时间不大于200 ms，同时降低了传统的硬件部署成本，为工业控制系统的智能化转型提供了高性能、低成本的解决方案，并具备向能源、交通等多领域推广的潜力。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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