技术领域Technical Field
本发明涉及数据交互技术领域，尤其涉及一种人机终端画面数据交互方法及系统。The present invention relates to the technical field of data interaction, and in particular to a method and system for human-machine terminal screen data interaction.
背景技术Background technique
电力系统中的设备故障可能对整个系统造成严重影响，需要快速、准确地进行故障诊断和预测。通过人机终端画面数据交互，运维人员可以利用机器学习和数据分析技术，实现对电力设备故障的早期诊断和预测Equipment failure in the power system may have a serious impact on the entire system, and requires rapid and accurate fault diagnosis and prediction. Through human-machine terminal screen data interaction, operation and maintenance personnel can use machine learning and data analysis technology to achieve early diagnosis and prediction of power equipment failures.
现有技术中，CN113726856A公开了一种基于微服务的调控画面综合数据轻量化交互方法及系统，方法包括如下步骤：对部署在客户端的单体应用程序下的功能进行分类，分成必要功能和非必要功能两类；将单体应用程序下的非必要功能按照功能特点划分为多个功能单一的微服务；对多个功能单一的微服务进行部署，部署到服务器端的多台服务器上，组成服务器集群；接收客户端发送的调用请求，并根据服务器集群的运行状态，选择运行请求服务的服务器；将服务器运行服务的结果返回到客户端。In the prior art, CN113726856A discloses a lightweight interactive method and system for comprehensive data of a control screen based on microservices, the method comprising the following steps: classifying the functions under a single application deployed on a client into two categories: necessary functions and non-essential functions; dividing the non-essential functions under the single application into a plurality of microservices with a single function according to their functional characteristics; deploying the plurality of microservices with a single function to a plurality of servers on the server side to form a server cluster; receiving a call request sent by a client, and selecting a server to run the requested service according to the running status of the server cluster; and returning the result of the server running the service to the client.
综上，现有技术虽然能够实现人机终端画面数据的交互，但仅能实现预设的简单需求，无法对电网系统中的信息进行实时交互，因此需要一种方案解决现有技术中存在的问题。In summary, although the existing technology can realize the interaction of human-machine terminal screen data, it can only realize simple preset requirements and cannot realize real-time interaction of information in the power grid system. Therefore, a solution is needed to solve the problems existing in the existing technology.
发明内容Summary of the invention
本发明实施例提供一种人机终端画面数据交互方法及系统，用于基于电网数据对电网故障进行诊断并通过人机终端画面数据实现交互。The embodiment of the present invention provides a method and system for human-machine terminal screen data interaction, which are used to diagnose power grid faults based on power grid data and realize interaction through human-machine terminal screen data.
本发明实施例的第一方面，提供一种人机终端画面数据交互方法，包括：According to a first aspect of an embodiment of the present invention, a method for interacting with screen data of a human-machine terminal is provided, comprising:
安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域；Install a traveling wave sensor, obtain a fault electromagnetic wave according to the traveling wave sensor, determine a timestamp corresponding to the fault electromagnetic wave and a traveling wave propagation speed of the fault electromagnetic wave, and determine a fault area through a fault location algorithm according to the timestamp and the traveling wave propagation speed;
根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征，根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量；Acquire the traveling wave signal data of the fault electromagnetic wave according to the fault area, and simultaneously acquire the power grid topology data, extract the traveling wave features in the traveling wave signal data and the topological features in the power grid topology data, determine the structural features according to the topological features, identify the shortest path of the nodes in the power grid topology diagram, perform feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient, and obtain a comprehensive topology vector;
基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述初始节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征，根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置。The comprehensive topological vector is encoded based on a preset graph neural network, and the node features of the traveling wave feature and the encoded comprehensive topological vector are fused to generate initial node features. According to the initial node features and in combination with the pre-introduced attention mechanism, the initial node features are updated through the graph neural network to generate input node features. According to the input node features and in combination with the traveling wave features, fault location is performed through the random forest module in the graph neural network to determine the fault location.
在一种可选的实施方式中，In an optional embodiment,
所述安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域包括：The step of installing a traveling wave sensor, acquiring a fault electromagnetic wave according to the traveling wave sensor, determining a timestamp corresponding to the fault electromagnetic wave and a traveling wave propagation speed of the fault electromagnetic wave, and determining a fault area according to the timestamp and the traveling wave propagation speed by a fault location algorithm includes:
在电力线路中安装行波传感器，根据所述行波传感器获取因故障引起的故障电磁波，并根据所述行波传感器获取所述故障电磁波的时间为行波检测数据添加时间戳，确定所述电力线路中的线路长度和导线类型，确定所述故障电磁波的行波传播速度；Installing a traveling wave sensor in the power line, acquiring a fault electromagnetic wave caused by a fault according to the traveling wave sensor, adding a timestamp to the traveling wave detection data according to the time when the traveling wave sensor acquires the fault electromagnetic wave, determining the line length and the type of conductor in the power line, and determining the traveling wave propagation speed of the fault electromagnetic wave;
根据所述时间戳和所述行波传播速度，判断故障发生区域中行波传感器的数量，若有两台及以上行波传感器，则通过比较所述故障电磁波的波形在不同行波传感器的到达时间，确定故障区域，若只有一台行波传感器，则通过反射行波的信息确定故障区域。According to the timestamp and the traveling wave propagation speed, the number of traveling wave sensors in the fault occurrence area is determined. If there are two or more traveling wave sensors, the fault area is determined by comparing the arrival time of the waveform of the fault electromagnetic wave at different traveling wave sensors. If there is only one traveling wave sensor, the fault area is determined by the information of the reflected traveling wave.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征包括：The step of acquiring the traveling wave signal data of the fault electromagnetic wave according to the fault area, acquiring the power grid topology data at the same time, and extracting the traveling wave features in the traveling wave signal data and the topology features in the power grid topology data comprises:
根据所述故障区域，获取所述故障电磁波的幅值、频率、能量分布和相位角，记为行波数据信号，获取电网拓扑数据，包括节点信息、连接关系和线路参数；According to the fault area, the amplitude, frequency, energy distribution and phase angle of the fault electromagnetic wave are obtained, recorded as a traveling wave data signal, and the grid topology data is obtained, including node information, connection relationship and line parameters;
对所述行波数据信号进行预处理并提取所述行波数据信号中的幅值、能量和持续时间，并通过傅里叶变换分析所述行波数据信号的频率成分，并将提取得到的数据组合成行波特征；Preprocessing the traveling wave data signal and extracting the amplitude, energy and duration of the traveling wave data signal, analyzing the frequency components of the traveling wave data signal by Fourier transform, and combining the extracted data into traveling wave features;
识别电网中的每个节点及节点对应的属性，根据节点的连接关系，确定连接类型和连接参数，将所述连接关系、所述连接类型和所述连接参数组合成拓扑特征。Identify each node in the power grid and the attributes corresponding to the node, determine the connection type and connection parameters according to the connection relationship of the nodes, and combine the connection relationship, the connection type and the connection parameters into a topological feature.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量包括：Determining the structural features according to the topological features, identifying the shortest path of nodes in the power grid topology diagram, and performing feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient to obtain a comprehensive topological vector includes:
根据所述拓扑特征，对于电网中的每个节点，计算与当前节点直接相连的节点数量，记为当前节点的发散度，根据所述发散度，提取与当前节点直接相连的节点信息，并将与当前节点直接相连的节点记为当前节点的邻居节点；According to the topological features, for each node in the power grid, the number of nodes directly connected to the current node is calculated, recorded as the divergence of the current node, and according to the divergence, the node information directly connected to the current node is extracted, and the nodes directly connected to the current node are recorded as neighbor nodes of the current node;
根据所述邻居节点，确定所述邻居节点与所述当前节点间的实际连接数，并计算所述邻居节点与所述当前节点间的聚类系数，遍历所述电网，计算所述电网中每个节点的聚类系数和电网的平均聚类系数，并通过预设的局部聚类算法计算当前节点的局部聚类系数，根据所述局部聚类系数将所述发散度，所述邻居节点的特征信息和节点间的路径信息进行融合，得到综合拓扑向量。According to the neighbor nodes, the actual number of connections between the neighbor nodes and the current node is determined, and the clustering coefficient between the neighbor nodes and the current node is calculated, the power grid is traversed, the clustering coefficient of each node in the power grid and the average clustering coefficient of the power grid are calculated, and the local clustering coefficient of the current node is calculated by a preset local clustering algorithm. According to the local clustering coefficient, the divergence, the characteristic information of the neighbor nodes and the path information between the nodes are integrated to obtain a comprehensive topology vector.
在一种可选的实施方式中，In an optional embodiment,
所述通过预设的局部聚类算法计算当前节点的局部聚类系数如下公式所示：The local clustering coefficient of the current node is calculated by the preset local clustering algorithm as shown in the following formula:
； ;
其中，Cwi表示节点i的局部聚类系数，△ijk表示节点i，j和k之间的三角关系，wij表示边ij的权重，wik表示边ik的权重，wjk表示边jk的权重，fi表示节点i的发散度。Among them,Cwi represents the local clustering coefficient of nodei ,△ijk represents the triangular relationship between nodesi , j and k,wij represents the weight of edgeij, wik represents the weight of edge ik, wjkrepresentsthe weight of edge jk,andfirepresents the divergence of node i.
在一种可选的实施方式中，In an optional embodiment,
所述基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征包括：The preset graph neural network is used to encode the comprehensive topological vector, and the traveling wave feature and the encoded comprehensive topological vector are subjected to node feature fusion to generate initial node features. According to the node features, the initial node features are updated through the graph neural network in combination with the pre-introduced attention mechanism to generate input node features, including:
获取所述综合拓扑向量，根据所述综合拓扑向量和所述行波特征初始化节点特征并构建当前节点的邻接矩阵并确定当前节点的邻居节点，根据所述邻居节点更新当前节点，记为初始节点特征，对于每个初始节点特征，收集所述邻居节点的节点信息，并根据预先引入的多头注意力机制计算每个邻居节点对当前节点的重要性，即注意力系数；Obtain the comprehensive topological vector, initialize the node features according to the comprehensive topological vector and the traveling wave features, construct the adjacency matrix of the current node, determine the neighbor nodes of the current node, update the current node according to the neighbor nodes, record them as initial node features, collect node information of the neighbor nodes for each initial node feature, and calculate the importance of each neighbor node to the current node according to the pre-introduced multi-head attention mechanism, that is, the attention coefficient;
将每个邻居节点的节点信息进行合并，结合每个邻居节点对应的注意力系数通过激活函数加权计算邻居节点的综合特征，根据所述综合特征，更新当前节点的特征表示，生成输入节点特征。The node information of each neighbor node is merged, and the comprehensive features of the neighbor nodes are weighted by the activation function in combination with the attention coefficient corresponding to each neighbor node. According to the comprehensive features, the feature representation of the current node is updated to generate the input node features.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置包括：The fault location is determined by performing fault location based on the input node characteristics and the traveling wave characteristics through the random forest module in the graph neural network, and the fault location includes:
根据所述行波特征初始化所述图神经网络中的随机森林模块，设置所述随机森林模块的超参数，即树的数量和深度，将所述输入节点特征作为输入信息添加至所述随机森林模块中；Initialize the random forest module in the graph neural network according to the traveling wave feature, set the hyperparameters of the random forest module, namely the number and depth of trees, and add the input node feature as input information to the random forest module;
对于所述随机森林模块中的每个树，随机生成训练集，根据所述训练集交叉验证不同树的数量下所述随机森林模块的性能，选择性能最佳时树的数量，对于每个树，增加最大深度，并根据所述训练集设置所述树中每个节点进行拆分的最小样本数，同时设置叶节点的最小样本数，在调整超参数的过程中观察所述随机森林模块的性能，当性能不再提升时，根据对应的超参数调整所述随机森林模块中树的深度和树的数量；For each tree in the random forest module, a training set is randomly generated, and the performance of the random forest module under different numbers of trees is cross-validated according to the training set, and the number of trees with the best performance is selected. For each tree, the maximum depth is increased, and the minimum number of samples for splitting each node in the tree is set according to the training set, and the minimum number of samples for leaf nodes is set. The performance of the random forest module is observed during the process of adjusting the hyperparameters. When the performance is no longer improved, the depth and number of trees in the random forest module are adjusted according to the corresponding hyperparameters;
所述随机森林模块将所述输入节点特征发送至模块中的每个树中，所述树生成每个节点对应的预测故障概率，结合节点对应的行波信息判断所述预测故障概率的置信度，若所述置信度大于预设的置信度阈值，则认为当前节点存在故障，获取故障位置并返回。The random forest module sends the input node features to each tree in the module. The tree generates a predicted fault probability corresponding to each node, and determines the confidence of the predicted fault probability in combination with the traveling wave information corresponding to the node. If the confidence is greater than a preset confidence threshold, it is considered that the current node has a fault, and the fault location is obtained and returned.
本发明实施例的第二方面，提供一种人机终端画面数据交互系统，包括：A second aspect of an embodiment of the present invention provides a human-machine terminal screen data interaction system, comprising:
第一单元，用于安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域；The first unit is used to install a traveling wave sensor, obtain a fault electromagnetic wave according to the traveling wave sensor, determine a timestamp corresponding to the fault electromagnetic wave and a traveling wave propagation speed of the fault electromagnetic wave, and determine a fault area through a fault location algorithm according to the timestamp and the traveling wave propagation speed;
第二单元，用于根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征，根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量；The second unit is used to obtain the traveling wave signal data of the fault electromagnetic wave according to the fault area, and simultaneously obtain the power grid topology data, extract the traveling wave features in the traveling wave signal data and the topological features in the power grid topology data, determine the structural features according to the topological features, identify the shortest path of the nodes in the power grid topology diagram, perform feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient, and obtain a comprehensive topology vector;
第三单元，用于基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述初始节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征，根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置。The third unit is used to encode the comprehensive topological vector based on a preset graph neural network, and perform node feature fusion on the traveling wave feature and the encoded comprehensive topological vector to generate initial node features, and based on the initial node features and in combination with a pre-introduced attention mechanism, update the initial node features through the graph neural network to generate input node features, and based on the input node features and in combination with the traveling wave features, perform fault location through the random forest module in the graph neural network to determine the fault location.
本发明实施例的第三方面，According to a third aspect of the embodiments of the present invention,
提供一种电子设备，包括：An electronic device is provided, comprising:
处理器；processor;
用于存储处理器可执行指令的存储器；a memory for storing processor-executable instructions;
其中，所述处理器被配置为调用所述存储器存储的指令，以执行前述所述的方法。The processor is configured to call the instructions stored in the memory to execute the aforementioned method.
本发明实施例的第四方面，According to a fourth aspect of the embodiments of the present invention,
提供一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现前述所述的方法。A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.
本发明中，通过行波传感器获取故障电磁波的时间戳和行波传播速度，结合故障定位算法，能够精确定位故障发生的区域，提高了故障定位的准确性，利用电网拓扑数据提取结构特征，包括电网中节点的最短路径和聚类系数有助于更好地捕捉电力系统的结构信息，提供更全面的系统认知，利用图神经网络对行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征提高了模型对节点状态的综合把握，为后续的故障定位提供更准确的输入，引入注意力机制对初始节点特征进行更新，根据不同节点的重要性进行加权有助于提高模型对电网中不同节点的关注程度，更有针对性地进行故障定位，综上，本发明实现了高效、准确的电力系统故障定位并实现了用户和系统间的数据交互。In the present invention, the timestamp and traveling wave propagation speed of the fault electromagnetic wave are obtained by a traveling wave sensor. Combined with the fault location algorithm, the area where the fault occurs can be accurately located, thereby improving the accuracy of fault location. The structural features extracted by the power grid topology data, including the shortest path and clustering coefficient of the nodes in the power grid, help to better capture the structural information of the power system and provide a more comprehensive system cognition. The traveling wave features and the encoded comprehensive topology vector are fused with node features by using a graph neural network to generate initial node features, which improves the model's comprehensive grasp of the node state and provides more accurate input for subsequent fault location. The attention mechanism is introduced to update the initial node features. Weighting according to the importance of different nodes helps to improve the model's attention to different nodes in the power grid and perform fault location in a more targeted manner. In summary, the present invention realizes efficient and accurate power system fault location and realizes data interaction between users and systems.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明实施例人机终端画面数据交互方法的流程示意图；FIG1 is a schematic diagram of a flow chart of a method for interacting with screen data of a human-machine terminal according to an embodiment of the present invention;
图2为本发明实施例人机终端画面数据交互系统的结构示意图。FIG. 2 is a schematic diagram of the structure of a human-machine terminal screen data interaction system according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
下面以具体地实施例对本发明的技术方案进行详细说明。下面这几个具体的实施例可以相互结合，对于相同或相似的概念或过程可能在某些实施例不再赘述。The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
图1为本发明实施例人机终端画面数据交互方法的流程示意图，如图1所示，所述方法包括：FIG. 1 is a flow chart of a method for interacting with screen data of a human-machine terminal according to an embodiment of the present invention. As shown in FIG. 1 , the method includes:
S1.安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域；S1. Install a traveling wave sensor, obtain a fault electromagnetic wave according to the traveling wave sensor, determine the timestamp corresponding to the fault electromagnetic wave and the traveling wave propagation speed of the fault electromagnetic wave, and determine the fault area through a fault location algorithm according to the timestamp and the traveling wave propagation speed;
所述行波传感器是一种用于检测电力系统中的故障的传感器，当电力系统中发生故障时，故障点处会产生电磁波，行波传感器可以探测这些电磁波并提供关于故障的信息，所述故障电磁波是在电力系统中发生故障时产生的电磁辐射，所述行波传播速度是故障电磁波在电力系统中传播的速度，所述故障定位算法用于根据行波传感器测量到的电磁波信息，确定电力系统中的故障位置。The traveling wave sensor is a sensor used to detect faults in an electric power system. When a fault occurs in an electric power system, electromagnetic waves are generated at the fault point. The traveling wave sensor can detect these electromagnetic waves and provide information about the fault. The fault electromagnetic wave is the electromagnetic radiation generated when a fault occurs in the electric power system. The traveling wave propagation speed is the speed at which the fault electromagnetic wave propagates in the electric power system. The fault location algorithm is used to determine the fault location in the electric power system based on the electromagnetic wave information measured by the traveling wave sensor.
在一种可选的实施方式中，In an optional embodiment,
所述安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域包括：The step of installing a traveling wave sensor, acquiring a fault electromagnetic wave according to the traveling wave sensor, determining a timestamp corresponding to the fault electromagnetic wave and a traveling wave propagation speed of the fault electromagnetic wave, and determining a fault area according to the timestamp and the traveling wave propagation speed by a fault location algorithm includes:
在电力线路中安装行波传感器，根据所述行波传感器获取因故障引起的故障电磁波，并根据所述行波传感器获取所述故障电磁波的时间为行波检测数据添加时间戳，确定所述电力线路中的线路长度和导线类型，确定所述故障电磁波的行波传播速度；Installing a traveling wave sensor in the power line, acquiring a fault electromagnetic wave caused by a fault according to the traveling wave sensor, adding a timestamp to the traveling wave detection data according to the time when the traveling wave sensor acquires the fault electromagnetic wave, determining the line length and the type of conductor in the power line, and determining the traveling wave propagation speed of the fault electromagnetic wave;
根据所述时间戳和所述行波传播速度，判断故障发生区域中行波传感器的数量，若有两台及以上行波传感器，则通过比较所述故障电磁波的波形在不同行波传感器的到达时间，确定故障区域，若只有一台行波传感器，则通过反射行波的信息确定故障区域。According to the timestamp and the traveling wave propagation speed, the number of traveling wave sensors in the fault occurrence area is determined. If there are two or more traveling wave sensors, the fault area is determined by comparing the arrival time of the waveform of the fault electromagnetic wave at different traveling wave sensors. If there is only one traveling wave sensor, the fault area is determined by the information of the reflected traveling wave.
安装行波传感器并确保其能够准确捕捉故障电磁波，通过行波传感器获取故障电磁波，并根据所述故障电磁波到达所述行波传感器的时间为每个数据点添加时间戳，根据电力线路的实际情况，测量线路的长度和确定导线的类型，使用已知的线路长度和导线类型，计算故障电磁波在电力线路中的行波传播速度；Install a traveling wave sensor and ensure that it can accurately capture the fault electromagnetic wave, obtain the fault electromagnetic wave through the traveling wave sensor, and add a timestamp to each data point according to the time when the fault electromagnetic wave reaches the traveling wave sensor, measure the length of the line and determine the type of wire according to the actual situation of the power line, and use the known line length and wire type to calculate the traveling wave propagation speed of the fault electromagnetic wave in the power line;
根据传感器的布局和电力线路的特性，判断故障发生区域内需要安装或已经安装的行波传感器的数量和位置，如果有两台及以上行波传感器，通过比较故障电磁波在不同传感器上的到达时间，使用时差定位法计算故障发生的位置，如果只有一台行波传感器，通过对反射波形的分析和处理确定故障区域。According to the layout of the sensors and the characteristics of the power lines, determine the number and location of the traveling wave sensors that need to be installed or have been installed in the fault area. If there are two or more traveling wave sensors, the location of the fault is calculated using the time difference positioning method by comparing the arrival time of the fault electromagnetic wave at different sensors. If there is only one traveling wave sensor, the fault area is determined by analyzing and processing the reflected waveform.
本实施例中，实时监测每个行波传感器捕获到的故障电磁波数据，使得系统能够迅速响应故障事件，提高电力系统的可靠性，利用人机终端的图形界面，将电力线路的布局、传感器位置、故障定位结果等以图形化的方式展示，有助于用户直观地理解故障发生的位置，减少用户的认知负担，用户可以与系统进行交互式分析，如点击某个传感器点来查看该点的详细信息，包括故障电磁波波形、时间戳，使用户能够更深入地了解故障定位的过程，综上，本实施例可以提高用户对电力系统状态的实时监测和分析能力，实现对故障的迅速定位和处理。In this embodiment, the fault electromagnetic wave data captured by each traveling wave sensor is monitored in real time, so that the system can respond quickly to fault events and improve the reliability of the power system. The layout of the power line, sensor location, fault location results, etc. are displayed in a graphical manner using the graphical interface of the human-machine terminal, which helps users to intuitively understand the location of the fault and reduces the user's cognitive burden. Users can conduct interactive analysis with the system, such as clicking on a sensor point to view detailed information about the point, including the fault electromagnetic wave waveform and timestamp, so that users can have a deeper understanding of the fault location process. In summary, this embodiment can improve the user's real-time monitoring and analysis capabilities of the power system status and achieve rapid location and processing of faults.
S2.根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征，根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量；S2. Acquire the traveling wave signal data of the fault electromagnetic wave according to the fault area, and simultaneously acquire the power grid topology data, extract the traveling wave features in the traveling wave signal data and the topological features in the power grid topology data, determine the structural features according to the topological features, identify the shortest path of the nodes in the power grid topology diagram, perform feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient, and obtain a comprehensive topology vector;
所述电网拓扑数据是指描述电力系统中各元件（如发电机、变电站、输电线路等）之间连接关系的信息，包括节点、支路、线路的连接方式和拓扑结构，以及它们之间的电气连接关系，所述行波特征是指在电力系统中传播的电磁波的性质和特点，所述拓扑特征描述了电力系统的结构连接关系，通常涉及节点和支路之间的连接关系，所述结构特征在电力系统中通常指描述元件（例如变电站、发电机等）之间的物理结构、连接方式和电气参数等特性，所述聚类系数是一种网络分析中的度量，用于描述图中节点之间的紧密程度，在电力系统中，节点的聚类系数可以表示节点与其邻居节点之间的连接密度，所述综合拓扑向量是一种综合考虑电力系统拓扑结构的特征向量，通常包括节点度、支路的连接方式、聚类系数等多个拓扑特征的组合。The power grid topology data refers to information describing the connection relationship between various components in the power system (such as generators, substations, transmission lines, etc.), including the connection method and topological structure of nodes, branches, and lines, and the electrical connection relationship between them. The traveling wave characteristics refer to the properties and characteristics of electromagnetic waves propagating in the power system. The topological characteristics describe the structural connection relationship of the power system, usually involving the connection relationship between nodes and branches. The structural characteristics in the power system usually refer to the characteristics such as the physical structure, connection method and electrical parameters between components (such as substations, generators, etc.). The clustering coefficient is a metric in network analysis, which is used to describe the closeness between nodes in the graph. In the power system, the clustering coefficient of a node can represent the connection density between the node and its neighboring nodes. The comprehensive topology vector is a feature vector that comprehensively considers the topological structure of the power system, usually including a combination of multiple topological characteristics such as node degree, branch connection method, and clustering coefficient.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征包括：The step of acquiring the traveling wave signal data of the fault electromagnetic wave according to the fault area, acquiring the power grid topology data at the same time, and extracting the traveling wave features in the traveling wave signal data and the topology features in the power grid topology data comprises:
根据所述故障区域，获取所述故障电磁波的幅值、频率、能量分布和相位角，记为行波数据信号，获取电网拓扑数据，包括节点信息、连接关系和线路参数；According to the fault area, the amplitude, frequency, energy distribution and phase angle of the fault electromagnetic wave are obtained, recorded as a traveling wave data signal, and the grid topology data is obtained, including node information, connection relationship and line parameters;
对所述行波数据信号进行预处理并提取所述行波数据信号中的幅值、能量和持续时间，并通过傅里叶变换分析所述行波数据信号的频率成分，并将提取得到的数据组合成行波特征；Preprocessing the traveling wave data signal and extracting the amplitude, energy and duration of the traveling wave data signal, analyzing the frequency components of the traveling wave data signal by Fourier transform, and combining the extracted data into traveling wave features;
识别电网中的每个节点及节点对应的属性，根据节点的连接关系，确定连接类型和连接参数，将所述连接关系、所述连接类型和所述连接参数组合成拓扑特征。Identify each node in the power grid and the attributes corresponding to the node, determine the connection type and connection parameters according to the connection relationship of the nodes, and combine the connection relationship, the connection type and the connection parameters into a topological feature.
根据已确定的故障区域，使用传感器获取故障电磁波的信号，包括波形、电压、电流等信息，收集电网拓扑数据，包括节点信息（节点编号、类型、坐标等）、连接关系（支路连接、节点连接关系）和线路参数（阻抗、长度等）；According to the determined fault area, use sensors to obtain the signal of the fault electromagnetic wave, including waveform, voltage, current and other information, and collect grid topology data, including node information (node number, type, coordinates, etc.), connection relationship (branch connection, node connection relationship) and line parameters (impedance, length, etc.);
对故障电磁波的行波数据信号进行预处理，包括滤波、降噪、去趋势，从行波数据信号中提取幅值、能量、持续时间等特征，使用傅里叶变换分析频率成分，得到频率、频谱分布等信息，将提取得到的各种特征组合成行波特征向量，包括幅值、频率、能量分布、相位角等多个特征参数；Preprocess the traveling wave data signal of the fault electromagnetic wave, including filtering, noise reduction, and trend removal, extract the amplitude, energy, duration and other features from the traveling wave data signal, use Fourier transform to analyze the frequency component, obtain the frequency, spectrum distribution and other information, and combine the various extracted features into a traveling wave feature vector, including multiple feature parameters such as amplitude, frequency, energy distribution, and phase angle;
根据电网拓扑数据，识别每个节点及其对应的属性信息，根据节点之间的连接关系，确定连接的类型（支路、变压器、开关等）和连接参数（阻抗、导纳等），将所得到的连接关系、连接类型和连接参数组合成拓扑特征向量，其中，所述拓扑特征向量描述了电力系统的拓扑结构。According to the power grid topology data, each node and its corresponding attribute information are identified, and according to the connection relationship between the nodes, the connection type (branch, transformer, switch, etc.) and connection parameters (impedance, admittance, etc.) are determined, and the obtained connection relationship, connection type and connection parameters are combined into a topological feature vector, wherein the topological feature vector describes the topological structure of the power system.
本实施例中，节点之间的连接关系、支路和线路参数等信息通过图形界面呈现，让用户能够清晰地了解电力系统的拓扑结构，通过傅里叶变换对行波数据信号进行频谱分析，将频率成分可视化呈现在人机终端上，用户可以直观地了解电磁波信号的频谱分布情况，幅值、频率、能量分布和相位角等故障特征以图形化方式展示在人机终端画面上，用户可以清晰地看到这些特征随时间的变化趋势，有助于快速识别和定位故障，综上，本实施例提高了系统的可操作性和用户体验，使用户可以在图形化界面上方便地获取、分析和理解电力系统的状态和故障信息。In this embodiment, the connection relationship between nodes, branch and line parameters and other information are presented through a graphical interface, so that the user can clearly understand the topological structure of the power system. The traveling wave data signal is subjected to spectrum analysis through Fourier transform, and the frequency components are visualized on the human-machine terminal. The user can intuitively understand the spectrum distribution of the electromagnetic wave signal, and the fault characteristics such as amplitude, frequency, energy distribution and phase angle are displayed in a graphical manner on the human-machine terminal screen. The user can clearly see the changing trend of these characteristics over time, which helps to quickly identify and locate the fault. In summary, this embodiment improves the operability of the system and the user experience, so that the user can easily obtain, analyze and understand the status and fault information of the power system on a graphical interface.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量包括：Determining the structural features according to the topological features, identifying the shortest path of nodes in the power grid topology diagram, and performing feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient to obtain a comprehensive topological vector includes:
根据所述拓扑特征，对于电网中的每个节点，计算与当前节点直接相连的节点数量，记为当前节点的发散度，根据所述发散度，提取与当前节点直接相连的节点信息，并将与当前节点直接相连的节点记为当前节点的邻居节点；According to the topological features, for each node in the power grid, the number of nodes directly connected to the current node is calculated, recorded as the divergence of the current node, and according to the divergence, the node information directly connected to the current node is extracted, and the nodes directly connected to the current node are recorded as neighbor nodes of the current node;
根据所述邻居节点，确定所述邻居节点与所述当前节点间的实际连接数，并计算所述邻居节点与所述当前节点间的聚类系数，遍历所述电网，计算所述电网中每个节点的聚类系数和电网的平均聚类系数，并通过预设的局部聚类算法计算当前节点的局部聚类系数，根据所述局部聚类系数将所述发散度，所述邻居节点的特征信息和节点间的路径信息进行融合，得到综合拓扑向量。According to the neighbor nodes, the actual number of connections between the neighbor nodes and the current node is determined, and the clustering coefficient between the neighbor nodes and the current node is calculated, the power grid is traversed, the clustering coefficient of each node in the power grid and the average clustering coefficient of the power grid are calculated, and the local clustering coefficient of the current node is calculated by a preset local clustering algorithm. According to the local clustering coefficient, the divergence, the characteristic information of the neighbor nodes and the path information between the nodes are integrated to obtain a comprehensive topology vector.
所述发散度指与该节点相连的边的数量，表示了节点的连接程度，是节点的一个基本拓扑特征，所述邻居节点是指与该节点直接连接的其他节点，所述平均聚类系数是图中所有节点的局部聚类系数的平均值，衡量了一个节点的邻居之间相互连接的程度，所述局部聚类系数是指该节点的邻居之间实际连接数与可能的最大连接数之比，反映了节点周围子图的紧密程度。The divergence refers to the number of edges connected to the node, which indicates the degree of connection of the node and is a basic topological feature of the node. The neighbor nodes refer to other nodes directly connected to the node. The average clustering coefficient is the average value of the local clustering coefficients of all nodes in the graph, which measures the degree of interconnection between neighbors of a node. The local clustering coefficient refers to the ratio of the actual number of connections between neighbors of the node to the maximum possible number of connections, which reflects the compactness of the subgraph around the node.
对于电网中的每个节点，计算与当前节点直接相连的节点数量，记为当前节点的发散度，根据发散度，提取与当前节点直接相连的节点信息，将其记为当前节点的邻居节点；For each node in the power grid, calculate the number of nodes directly connected to the current node, record it as the divergence of the current node, and extract the information of nodes directly connected to the current node based on the divergence, and record it as the neighbor nodes of the current node;
遍历每个节点的邻居节点，确定邻居节点与当前节点间的实际连接数，通过计算邻居节点相互连接的边数与可能的最大连接数的比例确定邻居节点与当前节点间的聚类系数，遍历整个电网，计算每个节点的聚类系数，将这些聚类系数求平均，得到电网的平均聚类系数，使用预设的局部聚类算法，计算每个节点的局部聚类系数，其中，所述局部聚类系数是一个与节点直接相连的邻居节点的连接密度相关的指标，根据局部聚类系数将发散度、邻居节点的特征信息和节点间的路径信息进行融合，得到每个节点的综合拓扑向量。Traverse the neighbor nodes of each node to determine the actual number of connections between the neighbor nodes and the current node, determine the clustering coefficient between the neighbor nodes and the current node by calculating the ratio of the number of edges connecting the neighbor nodes to each other and the possible maximum number of connections, traverse the entire power grid, calculate the clustering coefficient of each node, average these clustering coefficients to obtain the average clustering coefficient of the power grid, use a preset local clustering algorithm to calculate the local clustering coefficient of each node, wherein the local clustering coefficient is an indicator related to the connection density of the neighbor nodes directly connected to the node, and according to the local clustering coefficient, the divergence, the characteristic information of the neighbor nodes and the path information between the nodes are integrated to obtain the comprehensive topological vector of each node.
本实施例中，针对每个节点，人机终端展示其直接相连的邻居节点信息，用户可以快速了解节点的周围连接情况，对每个节点的邻居节点进行遍历，计算实际连接数和聚类系数，将计算结果以图形化方式呈现在人机终端上，用户能够看到电网中每个节点的聚类情况，将发散度、邻居节点的特征信息和节点间的路径信息融合，形成综合拓扑向量，并在人机终端上以图形方式展示使用户能够全面了解节点的拓扑特征，综上，本实施例使用户能够方便地监控、分析和理解电网的拓扑特征，实现对网络结构的实时可视化和交互式分析，提高了用户对电力系统拓扑特性的感知和管理效率。In this embodiment, for each node, the human-machine terminal displays the information of its directly connected neighboring nodes. The user can quickly understand the surrounding connections of the node, traverse the neighboring nodes of each node, calculate the actual number of connections and the clustering coefficient, and present the calculation results in a graphical manner on the human-machine terminal. The user can see the clustering situation of each node in the power grid, integrate the divergence, the characteristic information of the neighboring nodes and the path information between the nodes to form a comprehensive topology vector, and display it in a graphical manner on the human-machine terminal so that the user can fully understand the topological characteristics of the node. In summary, this embodiment enables users to easily monitor, analyze and understand the topological characteristics of the power grid, realize real-time visualization and interactive analysis of the network structure, and improve the user's perception and management efficiency of the topological characteristics of the power system.
在一种可选的实施方式中，In an optional embodiment,
所述通过预设的局部聚类算法计算当前节点的局部聚类系数如下公式所示：The local clustering coefficient of the current node is calculated by the preset local clustering algorithm as shown in the following formula:
； ;
其中，Cwi表示节点i的局部聚类系数，△ijk表示节点i，j和k之间的三角关系，wij表示边ij的权重，wik表示边ik的权重，wjk表示边jk的权重，fi表示节点i的发散度。Among them,Cwi represents the local clustering coefficient of nodei ,△ijk represents the triangular relationship between nodesi , j and k,wij represents the weight of edgeij, wik represents the weight of edge ik, wjkrepresentsthe weight of edge jk,andfirepresents the divergence of node i.
本函数中，通过在人机终端上实时应用上述局部聚类系数计算公式，系统可以动态计算每个节点的局部聚类系数，实现了对电网拓扑动态特性的实时监测，考虑了边的权重使得局部聚类系数的计算更为精准，能够更好地反映节点与邻居节点之间的实际连接强度，通过综合考虑三角关系，可以更全面地评估节点的局部连接紧密性，有助于理解节点在网络中的角色，将发散度引入局部聚类系数的计算中，使得计算结果不仅考虑了连接的紧密性，还考虑了节点在整个网络中的多样性，综上，本函数使用户可以深入了解电网中每个节点的局部连接特性，从而更好地进行网络拓扑分析和故障诊断。In this function, by applying the above local clustering coefficient calculation formula in real time on the human-machine terminal, the system can dynamically calculate the local clustering coefficient of each node, realize real-time monitoring of the dynamic characteristics of the power grid topology, and consider the weight of the edge to make the calculation of the local clustering coefficient more accurate, which can better reflect the actual connection strength between the node and the neighboring nodes. By comprehensively considering the triangular relationship, the local connection tightness of the node can be more comprehensively evaluated, which is helpful to understand the role of the node in the network. The divergence is introduced into the calculation of the local clustering coefficient, so that the calculation result not only considers the tightness of the connection, but also considers the diversity of the node in the entire network. In summary, this function enables users to have an in-depth understanding of the local connection characteristics of each node in the power grid, so as to better perform network topology analysis and fault diagnosis.
S3.基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述初始节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征，根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置。S3. Encode the comprehensive topological vector based on the preset graph neural network, and perform node feature fusion on the traveling wave feature and the encoded comprehensive topological vector to generate initial node features. According to the initial node features and in combination with the pre-introduced attention mechanism, update the initial node features through the graph neural network to generate input node features. According to the input node features and in combination with the traveling wave features, perform fault location through the random forest module in the graph neural network to determine the fault location.
所述图神经网络是一类用于处理图数据的神经网络，目标是学习图结构中节点之间的复杂关系，从而能够对图中节点的特征进行有效的表示学习，所述初始节点特征是指图中每个节点最初具有的特征，所述注意力机制是一种模型学习不同部分之间关注度的方法，在图神经网络中，注意力机制可以用于调整节点之间信息传递的权重，使得网络更加关注重要的节点，所述输入节点特征是指图神经网络中每一轮迭代时，节点更新所依据的特征，所述随机森林是一种集成学习方法，通常由多个决策树组成。The graph neural network is a type of neural network used to process graph data. Its goal is to learn the complex relationships between nodes in the graph structure, so as to effectively represent the features of the nodes in the graph. The initial node features refer to the features that each node in the graph initially has. The attention mechanism is a method for a model to learn the attention between different parts. In the graph neural network, the attention mechanism can be used to adjust the weight of information transmission between nodes so that the network pays more attention to important nodes. The input node features refer to the features on which the nodes are updated in each round of iteration in the graph neural network. The random forest is an integrated learning method, usually composed of multiple decision trees.
在一种可选的实施方式中，In an optional embodiment,
所述基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述初始节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征包括：The preset graph neural network is used to encode the comprehensive topological vector, and the traveling wave feature and the encoded comprehensive topological vector are subjected to node feature fusion to generate initial node features. According to the initial node features, the graph neural network is used to update the initial node features in combination with the pre-introduced attention mechanism to generate input node features, including:
获取所述综合拓扑向量，根据所述综合拓扑向量和所述行波特征初始化节点特征并构建当前节点的邻接矩阵并确定当前节点的邻居节点，根据所述邻居节点更新当前节点，记为初始节点特征，对于每个初始节点特征，收集所述邻居节点的节点信息，并根据预先引入的多头注意力机制计算每个邻居节点对当前节点的重要性，即注意力系数；Obtain the comprehensive topological vector, initialize the node features according to the comprehensive topological vector and the traveling wave features, construct the adjacency matrix of the current node, and determine the neighbor nodes of the current node, update the current node according to the neighbor nodes, record them as initial node features, collect node information of the neighbor nodes for each initial node feature, and calculate the importance of each neighbor node to the current node according to the pre-introduced multi-head attention mechanism, that is, the attention coefficient;
将每个邻居节点的节点信息进行合并，结合每个邻居节点对应的注意力系数通过激活函数加权计算邻居节点的综合特征，根据所述综合特征，更新当前节点的特征表示，生成输入节点特征。The node information of each neighbor node is merged, and the comprehensive features of the neighbor nodes are weighted by the activation function in combination with the attention coefficient corresponding to each neighbor node. According to the comprehensive features, the feature representation of the current node is updated to generate the input node features.
从之前的步骤中获取计算得到的综合拓扑向量和行波特征，使用综合拓扑向量和行波特征初始化每个节点的特征表示，构建当前节点的邻接矩阵，确定当前节点的邻居节点，对于每个初始节点特征，收集其邻居节点的节点信息，包括邻居节点的当前特征表示、行波特征，引入预先定义的多头注意力机制，通过计算注意力得分、应用softmax函数确定每个邻居节点对当前节点的重要性，即注意力系数；Obtain the calculated comprehensive topological vector and traveling wave features from the previous step, use the comprehensive topological vector and traveling wave features to initialize the feature representation of each node, build the adjacency matrix of the current node, determine the neighbor nodes of the current node, and for each initial node feature, collect the node information of its neighbor nodes, including the current feature representation and traveling wave features of the neighbor nodes, introduce a predefined multi-head attention mechanism, and determine the importance of each neighbor node to the current node, i.e., the attention coefficient, by calculating the attention score and applying the softmax function;
将每个邻居节点的节点信息进行合并，结合每个邻居节点对应的注意力系数，通过激活函数加权计算邻居节点的综合特征，根据综合特征，通过将综合特征与当前节点的原始特征表示进行融合，例如使用残差连接，更新当前节点的特征表示，生成输入节点特征。The node information of each neighbor node is merged, combined with the attention coefficient corresponding to each neighbor node, and the comprehensive features of the neighbor nodes are weighted by the activation function. According to the comprehensive features, the comprehensive features are fused with the original feature representation of the current node, such as using residual connection, to update the feature representation of the current node and generate the input node features.
本实施例中，在人机终端上通过图形化展示多头注意力机制的运作，包括计算注意力系数的过程，用户可以通过交互方式了解每个邻居节点对当前节点的重要性，以及注意力系数的变化情况，通过图形界面呈现邻居节点信息的收集和综合特征的计算过程，用户可以直观地了解邻居节点信息是如何被整合到当前节点的特征表示中的，以及每个邻居节点对当前节点的贡献程度，综上，本实施例有助于用户更全面地理解图结构中的信息传递和节点表示学习过程。In this embodiment, the operation of the multi-head attention mechanism is graphically displayed on the human-machine terminal, including the process of calculating the attention coefficient. The user can interactively understand the importance of each neighbor node to the current node and the changes in the attention coefficient. The collection of neighbor node information and the calculation process of comprehensive features are presented through a graphical interface. The user can intuitively understand how the neighbor node information is integrated into the feature representation of the current node and the contribution of each neighbor node to the current node. In summary, this embodiment helps users to more comprehensively understand the information transmission and node representation learning process in the graph structure.
在一种可选的实施方式中，In an optional embodiment,
所述根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置包括：The fault location is determined by performing fault location based on the input node characteristics and the traveling wave characteristics through the random forest module in the graph neural network, and the fault location includes:
根据所述行波特征初始化所述图神经网络中的随机森林模块，设置所述随机森林模块的超参数，即树的数量和深度，将所述输入节点特征作为输入信息添加至所述随机森林模块中；Initialize the random forest module in the graph neural network according to the traveling wave feature, set the hyperparameters of the random forest module, namely the number and depth of trees, and add the input node feature as input information to the random forest module;
对于所述随机森林模块中的每个树，随机生成训练集，根据所述训练集交叉验证不同树的数量下所述随机森林模块的性能，选择性能最佳时树的数量，对于每个树，增加最大深度，并根据所述训练集设置所述树中每个节点进行拆分的最小样本数，同时设置叶节点的最小样本数，在调整超参数的过程中观察所述随机森林模块的性能，当性能不再提升时，根据对应的超参数调整所述随机森林模块中树的深度和树的数量；For each tree in the random forest module, a training set is randomly generated, and the performance of the random forest module under different numbers of trees is cross-validated according to the training set, and the number of trees with the best performance is selected. For each tree, the maximum depth is increased, and the minimum number of samples for splitting each node in the tree is set according to the training set, and the minimum number of samples for leaf nodes is set. The performance of the random forest module is observed during the process of adjusting the hyperparameters. When the performance is no longer improved, the depth and number of trees in the random forest module are adjusted according to the corresponding hyperparameters;
所述随机森林模块将所述输入节点特征发送至模块中的每个树中，所述树生成每个节点对应的预测故障概率，结合节点对应的行波信息判断所述预测故障概率的置信度，若所述置信度大于预设的置信度阈值，则认为当前节点存在故障，获取故障位置并返回。The random forest module sends the input node features to each tree in the module. The tree generates a predicted fault probability corresponding to each node, and determines the confidence of the predicted fault probability in combination with the traveling wave information corresponding to the node. If the confidence is greater than a preset confidence threshold, it is considered that the current node has a fault, and the fault location is obtained and returned.
所述超参数是指在模型训练之前设置的参数，所述置信度是指模型对于某个预测结果的信心程度或置信水平，通常，置信度是以概率的形式表示，所述置信度阈值是一个在二分类或多分类问题中的阈值，用于决定模型输出的预测结果是否被接受，如果模型输出的置信度大于等于阈值，则将其判定为正类别；反之，则判定为负类别。The hyperparameter refers to the parameter set before model training. The confidence refers to the model's confidence or confidence level for a certain prediction result. Usually, the confidence is expressed in the form of probability. The confidence threshold is a threshold in a binary or multi-classification problem, which is used to determine whether the prediction result output by the model is accepted. If the confidence of the model output is greater than or equal to the threshold, it is judged as a positive category; otherwise, it is judged as a negative category.
使用行波特征作为输入，初始化图神经网络中的随机森林模块。设置随机森林模块的超参数，包括树的数量和深度，将输入节点特征作为信息添加到随机森林模块中；Initialize the random forest module in the graph neural network using the traveling wave features as input. Set the hyperparameters of the random forest module, including the number and depth of trees, and add the input node features as information to the random forest module.
对于随机森林模块中的每个树，随机生成训练集，并在不同树的数量下进行交叉验证，以选择性能最佳的树的数量，对于每个树，逐步增加最大深度，并根据训练集设置树中每个节点进行拆分的最小样本数，同时设置叶节点的最小样本数，在调整超参数的过程中观察随机森林模块的性能，当性能不再提升时，根据对应的超参数调整随机森林模块中树的深度和树的数量；For each tree in the random forest module, randomly generate a training set and perform cross-validation with different numbers of trees to select the number of trees with the best performance. For each tree, gradually increase the maximum depth and set the minimum number of samples for splitting each node in the tree according to the training set, and set the minimum number of samples for leaf nodes. Observe the performance of the random forest module during the process of adjusting the hyperparameters. When the performance no longer improves, adjust the depth and number of trees in the random forest module according to the corresponding hyperparameters.
随机森林模块将输入节点特征发送至每个树中，每个树生成每个节点对应的预测故障概率，结合节点对应的行波信息，判断预测故障概率的置信度。如果置信度大于预设的置信度阈值，则认为当前节点存在故障，当判断存在故障时，根据随机森林模块中的树的预测结果，获取故障位置，并将结果返回。The random forest module sends the input node features to each tree. Each tree generates the predicted fault probability corresponding to each node, and combines the traveling wave information corresponding to the node to determine the confidence of the predicted fault probability. If the confidence is greater than the preset confidence threshold, it is considered that the current node has a fault. When it is judged that there is a fault, the fault location is obtained according to the prediction results of the trees in the random forest module, and the results are returned.
综上，本实施例在界面上实时显示随机森林模块的性能指标，例如准确度、精确度、召回率等，随着超参数的调整，用户可以即时观察到性能的变化，帮助其做出更明智的调参决策，通过图表或曲线展示超参数调优的过程，显示不同树的数量和深度下模型性能的变化趋势，有助于用户更好地理解超参数选择对模型的影响，在界面上实时显示每个节点的预测故障概率以及相应的置信度，通过热图、图表等方式展示，帮助用户直观地了解哪些节点可能存在故障以及置信度的大小，综上，本实施例提高了用户对系统运行状态和故障预测过程的理解，同时也增加了用户对系统的操作和调参的参与感。In summary, this embodiment displays the performance indicators of the random forest module, such as accuracy, precision, recall rate, etc., in real time on the interface. As the hyperparameters are adjusted, the user can immediately observe the changes in performance, helping them to make more informed parameter adjustment decisions. The hyperparameter tuning process is displayed through charts or curves, and the changing trend of model performance under different numbers and depths of trees is displayed, which helps users better understand the impact of hyperparameter selection on the model. The predicted failure probability and corresponding confidence level of each node are displayed in real time on the interface, and displayed through heat maps, charts, etc., to help users intuitively understand which nodes may have failures and the size of the confidence level. In summary, this embodiment improves the user's understanding of the system operation status and fault prediction process, and also increases the user's sense of participation in the operation of the system and parameter adjustment.
图2为本发明实施例人机终端画面数据交互系统的结构示意图，如图2所示，所述系统包括：FIG. 2 is a schematic diagram of the structure of a human-machine terminal screen data interaction system according to an embodiment of the present invention. As shown in FIG. 2 , the system includes:
第一单元，用于安装行波传感器，根据所述行波传感器获取故障电磁波，确定所述故障电磁波对应的时间戳和所述故障电磁波的行波传播速度，根据所述时间戳和所述行波传播速度，通过故障定位算法确定故障区域；The first unit is used to install a traveling wave sensor, obtain a fault electromagnetic wave according to the traveling wave sensor, determine a timestamp corresponding to the fault electromagnetic wave and a traveling wave propagation speed of the fault electromagnetic wave, and determine a fault area through a fault location algorithm according to the timestamp and the traveling wave propagation speed;
第二单元，用于根据所述故障区域获取所述故障电磁波的行波信号数据，同时获取电网拓扑数据，提取所述行波信号数据中的行波特征和所述电网拓扑数据中的拓扑特征，根据所述拓扑特征确定结构特征，识别电网拓扑图中节点最短路径，根据所述节点最短路径结合预先引入的聚类系数进行特征融合，得到综合拓扑向量；The second unit is used to obtain the traveling wave signal data of the fault electromagnetic wave according to the fault area, and simultaneously obtain the power grid topology data, extract the traveling wave features in the traveling wave signal data and the topological features in the power grid topology data, determine the structural features according to the topological features, identify the shortest path of the nodes in the power grid topology diagram, perform feature fusion according to the shortest path of the nodes combined with the pre-introduced clustering coefficient, and obtain a comprehensive topology vector;
第三单元，用于基于预设的图神经网络对所述综合拓扑向量进行编码，并对所述行波特征和编码后的综合拓扑向量进行节点特征融合，生成初始节点特征，根据所述初始节点特征，结合预先引入的注意力机制，通过所述图神经网络更新所述初始节点特征，生成输入节点特征，根据所述输入节点特征，结合所述行波特征，通过所述图神经网络中的随机森林模块进行故障定位，确定故障位置。The third unit is used to encode the comprehensive topological vector based on a preset graph neural network, and perform node feature fusion on the traveling wave feature and the encoded comprehensive topological vector to generate initial node features, and based on the initial node features and in combination with a pre-introduced attention mechanism, update the initial node features through the graph neural network to generate input node features, and based on the input node features and in combination with the traveling wave features, perform fault location through the random forest module in the graph neural network to determine the fault location.
本发明实施例的第三方面，According to a third aspect of the embodiments of the present invention,
提供一种电子设备，包括：An electronic device is provided, comprising:
处理器；processor;
用于存储处理器可执行指令的存储器；a memory for storing processor-executable instructions;
其中，所述处理器被配置为调用所述存储器存储的指令，以执行前述所述的方法。The processor is configured to call the instructions stored in the memory to execute the aforementioned method.
本发明实施例的第四方面，According to a fourth aspect of the embodiments of the present invention,
提供一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现前述所述的方法。A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.
本发明可以是方法、装置、系统和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于执行本发明的各个方面的计算机可读程序指令。The present invention may be a method, an apparatus, a system and/or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.
最后应说明的是：以上各实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述各实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.
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