基于知识图谱的大坝安全智能监控预警方法

Intelligent Monitoring and Early Warning Method for Dam Safety Based on Knowledge Graph

大坝监测点数量众多、分布无序,传统监控方法难以描述监测点之间的空间关系和结构机理关联特性。针对此问题,本文提出一种基于知识图谱的大坝安全智能监控预警方法。首先,参照知识图谱要素特征,明确大坝监测点命名规则、拟定监测点空间相邻关系和数据序列相似关系的判定规则、提取监测点的内在属性特征,提出大坝安全监控知识体系;其次,以Neo4j图数据库为基底,采用网状图结构描述监测点的复杂关系和内部特征,构建大坝安全监控知识图谱;然后,基于图数据库知识引导和多级关系查询能力,建立监测点的关联响应机制,将异常监测点识别出来并划分为不同异常测点组,明确各异常测点组之间的结构机理关联特性;进一步,提出多测点聚集度概念并构建异常测点组的空间影响区域算法,并以坝体的异常影响区域为核心计算大坝运行性态分数,从大坝整体结构层面进行综合监控预警;最后,以一座混凝土重力坝的部分坝段为例,验证所提出方法的可行性。研究表明:该方法充分利用知识图谱在信息融合、类脑推理方面的优势,将大坝监测点整合成数据网络进行统一分析,提出异常监测点的结构影响区域概念,并以此为依据对大坝安全进行评判,在综合考虑监测点数量、空间分布、结构机理关联特性情况下实现多测点大坝安全监控预警。

and fuzzy.Moreover,the dam has numerous monitoring points with disordered distri-bution,making it difficult for traditional monitoring methods to describe the complex relationships among monitoring points,such as spatial rela-tionships and structural mechanism correlations.Additionally,there is a lack of suitable platforms to systematically summarize and infer these monitoring points and their various complex relationships,thereby enabling comprehensive monitoring and early warning of dam safety from a holistic structural perspective.In response to these issue,a dam safety intelligent monitoring and early warning method based on knowledge graph was proposed.Methods Firstly,according to the regulations in“Drawing standard for hydraulic structure of hydropower and water conservancy project”,monit-oring points were named and distinguished in a combination of letters and numbers,forming distinguishable and independent entities.Referring to the manual determination of adjacent monitoring points,two adjacent conditions determining rules were proposed based on spatial mathematical models to form an automatic determination method for monitoring point spatial adjacency relationships.Addressing the common issues of vary-ing lengths and time-step misalignment in data sequences of monitoring points,a rule for determining data sequence similarity based on dynamic time warping distance was proposed,achieving automatic determination of spatial adjacency relationships and data sequence similarity relation-ships between monitoring points.By analyzing the internal characteristics of monitoring points,the monitored physical quantity,single monitor-ing point warning level,and affiliated components were designated as object properties,while the three-dimensional coordinates,current monitor-ing data,and historical monitoring data were designated as data properties,forming intrinsic attribute information of monitoring points.Through the above arrangement,the characteristics of entities,relationships,and attributes centered around dam monitoring points were obtained,forming a knowledge system for dam safety monitoring.Secondly,after comparing the advantages and disadvantages of various graph databases,Neo4j database,known for constructing complex relationships,was selected as the base to describe the complex relationships and internal characteristics of monitoring points using a mesh-like graph structure.Based on the py2neo toolkit to batch process the fixed instructions for building the graph database,the first dam safety monitoring knowledge graph centered around monitoring points was constructed,providing a new approach for effi-cient organization and relationship representation of monitoring points.Subsequently,based on the knowledge guidance and multi-level relation-ship query capabilities of the graph database,the associated response mechanism for spatial distribution characteristics and data sequence similar-ity relationships of monitoring points was established.The method can automatically identify abnormal monitoring points and divide them into different groups based on the intrinsic relationships of monitoring points,achieving interconnection and communication of monitoring points,thereby addressing the difficulty of traditional methods in dealing with complex relationships among dam monitoring points.Furthermore,consid-ering comprehensively the quantity of anomalous monitoring points,the alert level,spatial clustering degree,and similarity of data sequences,a method combining knowledge graph and mathematical models was proposed to introduce the concept of clustering degree for multiple monitor-ing points and to construct a spatial impact region algorithm for anomalous point groups.It ingeniously transforms the dam safety evaluation problem into a three-dimensional spatial calculation problem of anomalous monitoring point impact regions,achieving the quantification of dam safety monitoring and early warning.Then,taking the impact region of anomalous monitoring points of the dam as the core,considering the ab-normal level and the data sequences similarity relationship of monitoring points,the safety operational state score of the dam was calculated,and the function of Tanh was utilized to convert the operational state score into a standardized range.Finally,referring to the safety level standards for dam failure risk in the“Guide for safety assessment of large dams for hydropower station in operation”,the operational state of the dam was di-vided into four levels,facilitating dam safety managers to differentiate the risk level and enabling comprehensive monitoring and management from the perspective of the overall dam structure.Results and Discussions To validate the feasibility of the proposed dam monitoring and early warning method,a section of a concrete gravity dam was taken as an example,analyzing 155 deformation and seepage monitoring points.The result was that the calculated operational state score of the dam is 76.99,indicating a second-level warning state,requiring timely measures to be taken.Conclusions The research indicates that the proposed method fully leverages the advantages of knowledge graphs in information fusion and brain-like reasoning.It integrates dam monitoring points into a data network for unified analysis,and proposes for the first time the concepts of cluster-ing degree for anomalous monitoring points and structural impact regions.The method realizes multi-point dam safety monitoring and early warn-ing by comprehensively considering the quantity of monitoring points,spatial distribution,and structural mechanism association characteristics,thereby addressing the problem of numerous and independent dam monitoring points.