Article(id=1223201260069835502, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20222582, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1670860800000, receivedDateStr=2022-12-13, revisedDate=1675180800000, revisedDateStr=2023-02-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563506381, onlineDateStr=2026-01-28, pubDate=1690214400000, pubDateStr=2023-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563506381, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563506381, creator=13701087609, updateTime=1769563506381, updator=13701087609, issue=Issue{id=1223201250133524577, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='7', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769563504012, creator=13701087609, updateTime=1769563583713, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223201584469885927, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223201584469885928, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223201250133524577, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=80, endPage=83, ext={EN=ArticleExt(id=1223201260824810302, articleId=1223201260069835502, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Knowledge Graph Construction for Scheduling Regulation of Reservoirs Based on DataG, columnId=1222925283431272938, journalTitle=Water Resources and Power, columnName=HYDROLOGICAL FORECAST AND OPTIMAL SCHEDULING, runingTitle=null, highlight=null, articleAbstract=

As a large number of water conservancy projects have been put into use in recent years, water conservancy departments have accumulated a large amount of information about reservoir scheduling regulations. This paper analyzed the existing reservoir scheduling regulations, and proposed a construction method of reservoir scheduling regulations knowledge graph based on DataG. In the design of the ontology model, the top-down method of disassembling the ontology twice was adopted on the reservoir scheduling mode. The differentiated knowledge extraction was performed on the structured and unstructured data. In the knowledge reasoning, the reasoning path of preconditions, scheduling measures and scheduling objectives were analyzed and defined. Finally, the visualization of the reservoir scheduling regulation knowledge graph was introduced for the Three Gorges Reservoir. The knowledge graph of reservoir scheduling regulation meets the needs of spatiotemporal dimension, solves the problem of multi-source heterogeneous data extraction, and realizes intelligent retrieval and recommendation in the field of reservoir operation.

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随着近年来以水库为主的大量水利工程投入使用，水利部门积累了大量的水库调度规程信息。通过分析现有水库调度规程，引入知识图谱，提出了一种基于DataG的水库调度规程知识图谱构建方法，即在本体模型设计中，水库调度方式采用自顶向下的二次拆解法。在知识抽取时，对结构化和非结构化数据进行差异化知识抽取。在知识推理中，对前置条件、调度措施、调度目标的推理流程进行推理路径分析和定义。最后，以三峡水库为例介绍了水库调度规程知识图谱可视化展示。结果表明，水库调度规程知识图谱满足调度方式时空维度化的需求，解决了水库调度领域中多源异构数据的抽取问题，实现了水库调度方式的智能检索与推理。

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顾丽（1990-），女，工程师，研究方向为水文信息化，E-mail：1393116725@qq.com

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顾丽（1990-），女，工程师，研究方向为水文信息化，E-mail：1393116725@qq.com

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