Power distribution network edge side topology identification method, device and equipment and readable storage medium

Abstract

The invention relates to a power distribution network edge side topology identification method, device and equipment and a readable storage medium. The method comprises the following steps: establishing a voltage mutual information graph based on first voltage mutual information between devices in a target edge computing node, second voltage mutual information between the device in the target edge computing node and a device of an adjacent edge computing node, and each device in the target edge computing node; processing the voltage mutual information graph by using a distributed maximum spanning tree algorithm, and establishing a spanning tree; and under the condition that the spanning tree comprises all devices in the voltage mutual information graph, obtaining a topology identification result based on the spanning tree and the voltage mutual information graph. By adopting the method, the topology identification efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method, device, equipment and readable storage medium for identifying topology at the edge of a distribution network. Background Art

[0002] In recent years, with the advancement of distribution network modernization, the level of digitalization has increased rapidly, which not only significantly improved the reliability and operating efficiency of the distribution system, but also promoted the efficient access of renewable energy and the intelligent progress of load management.

[0003] However, as the structure of distribution networks becomes increasingly complex, traditional topology identification methods have the problem of low topology identification efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network edge side topology identification method, device, equipment and readable storage medium that can improve the topology identification efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for identifying topology at the edge of a distribution network, comprising:

[0006] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0007] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0008] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0009] In one embodiment, the above-mentioned use of the distributed maximum spanning tree algorithm to process the voltage mutual information graph to establish a spanning tree includes:

[0010] For each device, the voltage mutual information that meets the preset conditions is selected from the voltage mutual information graph as the edge to be selected;

[0011] When two devices connected to the edge to be selected belong to the same target edge computing node, the edge to be selected is added to a preset edge list to be selected, and a spanning tree is established using the edge list to be selected;

[0012] When the two devices connected to the edge to be selected do not belong to the same target edge computing node, the interactive information between the target edge computing node and the adjacent edge computing nodes is used to coordinate and update the spanning tree.

[0013] In one embodiment, the step of selecting voltage mutual information satisfying a preset condition as an edge to be selected from the voltage mutual information graph includes:

[0014] When selecting the edge to be selected for the first time, the voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the edge to be selected;

[0015] In the case of non-first selection of the candidate edge, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0016] In one embodiment, the topology identification result obtained based on the spanning tree and the voltage mutual information graph includes:

[0017] In the case where the spanning tree is radial, the spanning tree is determined as a topology identification result;

[0018] When the spanning tree is not radial, a topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0019] In one embodiment, the generating of the topology identification result based on the conditional mutual information between the spanning tree and the adjacent devices in the spanning tree includes:

[0020] Compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information;

[0021] Broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the devices corresponding to the returned comparison value.

[0022] Generate topology identification results based on spanning tree and ring topology.

[0023] In one embodiment, the process of determining the second voltage mutual information includes:

[0024] Obtaining first voltage time series information of each device in the target edge computing node;

[0025] Obtaining second voltage time series information of each device in an adjacent edge computing node;

[0026] Based on the first voltage time series information and the second voltage time series information, second voltage mutual information is determined.

[0027] In a second aspect, the present application also provides a topology identification device at the edge of a distribution network, comprising:

[0028] A mutual information graph establishing module, configured to establish a voltage mutual information graph based on first voltage mutual information between each device in a target edge computing node, second voltage mutual information between a device in the target edge computing node and a device in an adjacent edge computing node, and each device in the target edge computing node;

[0029] A spanning tree establishment module is used to process the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0030] The result determination module is used to obtain a topology identification result based on the spanning tree and the voltage mutual information graph when the spanning tree contains all devices in the voltage mutual information graph.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0033] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0034] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0036] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0037] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0038] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0039] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0040] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0041] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0042] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0043] The above-mentioned topology identification method, device, equipment and readable storage medium at the edge of the distribution network can, first of all, systematically capture the voltage relationship between devices by establishing a voltage mutual information graph. Secondly, the distributed maximum spanning tree algorithm is used to process the voltage mutual information graph, which not only speeds up the construction process of the spanning tree, but also supports parallel computing, so that topology identification can be completed quickly in complex distribution networks. Finally, the topology identification results obtained based on the spanning tree and the voltage mutual information graph have high accuracy and can reflect the network status in a timely manner, thereby improving the efficiency of topology identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A diagram of an application environment of a method for identifying a topology at an edge of a distribution network in an embodiment;

[0046] Figure 2 A schematic diagram of a flow chart of a method for identifying a topology at an edge of a distribution network in an embodiment;

[0047] Figure 3 A schematic diagram of a flow chart of a method for identifying topology at the edge of a distribution network in another embodiment;

[0048] Figure 4 A reference topology identification result that meets the radial characteristics in one embodiment;

[0049] Figure 5 An actual topology identification result that meets radial characteristics in one embodiment;

[0050] Figure 6 is a reference topology identification result that meets the non-radial characteristics in one embodiment;

[0051] Figure 7 is an actual topology identification result that meets the non-radial characteristics in one embodiment;

[0052] Figure 8 A schematic diagram of a flow chart of a method for identifying topology at the edge of a distribution network in another embodiment;

[0053] Fig. 9 A schematic diagram of a flow chart of a method for identifying topology at the edge of a distribution network in another embodiment;

[0054] Fig.10 Based on Figure 4 The calculated second voltage interaction information between the No. 24 branch box and other transformers, branch boxes, and users;

[0055] Fig.11 It is a structural block diagram of a topology identification device at the edge of a distribution network in one embodiment;

[0056] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] The distribution network edge side topology identification method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in the figure, the target edge computing node 01 realizes data sharing and collaborative computing by communicating with the adjacent edge computing node 02 and other edge computing nodes 03.

[0059] An edge computing node is a computing device or system located at the edge of a network, close to a data source or device. Its main function is to process and analyze data locally, thereby reducing latency, reducing network bandwidth usage, and improving data processing efficiency. Compared with traditional cloud computing models, edge computing nodes can perform calculations near the source of data generation, reducing the need to transmit data to remote data centers. Its advantages include:

[0060] (1) Low latency: Since computing is done near the data source, edge computing reduces the time it takes to send data to remote servers, thereby reducing latency.

[0061] (2) Bandwidth saving: Edge computing nodes can process large amounts of data locally, reducing the amount of raw data uploaded to the cloud.

[0062] (3) Enhanced privacy and security: Sensitive data can be processed locally, reducing the risk of data leakage during transmission.

[0063] (4) Reliability: When the network connection is unstable or interrupted, the edge computing node can continue to work without relying on the remote server.

[0064] In an exemplary embodiment, Figure 2 As shown in FIG, a method for identifying the topology of the edge side of a distribution network is provided. The method is applied to Figure 1 The target edge computing node in is taken as an example to illustrate, including the following steps 201 to 203. Among them:

[0065] Step 201, based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node, a voltage mutual information graph is established.

[0066] In the embodiment of the present application, first, the area that each edge computing node is responsible for is determined according to the overall layout of the distribution network and the distribution of equipment. The jurisdiction of each edge computing node includes specific equipment, such as transformers, branch boxes, and user meters. The principle of regional division is usually based on the geographical location of the equipment, communication capabilities, and load distribution of the computing node to ensure that each edge node can efficiently manage the equipment it is responsible for.

[0067] Next, all device information in each divided area is input into the corresponding edge computing node. Then, the first voltage mutual information is calculated for the devices in the area governed by the target edge computing node. The first voltage mutual information represents the voltage correlation between the devices under the same edge computing node. By analyzing the mutual information of voltage changes between devices, their voltage dependence is evaluated.

[0068] After calculating the voltage mutual information within the device, the second voltage mutual information is calculated for the devices between the target edge computing node and the adjacent edge computing nodes. The second voltage mutual information is used to measure the voltage relationship between the device in the target edge node and other adjacent node devices, so as to identify the connection and dependency between cross-region devices in the topology.

[0069] Finally, the target edge computing node generates a voltage mutual information graph by integrating the voltage information of the devices in the area under its jurisdiction. In the voltage mutual information graph, the vertices represent the devices, and the weights of the edges correspond to the voltage mutual information values ​​between the devices. The first voltage mutual information is used to describe the connection between internal devices, while the second voltage mutual information covers the voltage correlation between devices across regions.

[0070] In some embodiments, the target edge computing node uses the first voltage mutual information and the second voltage mutual information as edge weights, and uses the equipment in the area under its jurisdiction, such as transformers, branch boxes, and user meters, as vertices to establish and store a voltage mutual information subgraph, and the voltage mutual information graph formed by merging the voltage mutual information subgraphs obtained by all edge computing nodes is recorded as G V ; Among them, the voltage mutual information graph G V Distributed storage on each edge computing node:

[0071]

[0072] ε V =ε V (1)∪ε V (2)∪…∪ε V (N E )

[0073]

[0074] In the formula, Represents a set of equipment consisting of transformers, branch boxes, and user meters. represents the set of devices in the area under the jurisdiction of the i-th edge computing node; ε V express The mutual information value calculated from the voltage timing data between each two devices, ε V (i)(i=1,2,…,N E ) represents the voltage mutual information between the devices in the area under the jurisdiction of the i-th edge computing node and the area under the jurisdiction of the adjacent edge computing node; G V (i) is the voltage mutual information subgraph stored on the i-th edge computing node; N E Indicates the number of edge computing nodes.

[0075] Step 202: Process the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0076] In an embodiment of the present application, in a distributed maximum spanning tree algorithm, each edge computing node is responsible for processing the mutual information graph of device voltages in its area, and collaborates with adjacent edge nodes through parallel processing. Each edge computing node initializes the device vertices in its area. Assign a unique identification (ID) to each device vertex, and set attributes such as "level" to 0. The search state of all edges is initially marked as the initial state, indicating that they have not been processed. Subsequently, the edge computing node starts from each vertex, searches for the edge with the largest voltage mutual information among adjacent edges, and adds it to the list of candidate edges for the spanning tree.

[0077] During the construction of the spanning tree, each edge computing node continuously communicates with adjacent nodes to obtain necessary topological information. For example, when two vertices of an edge belong to different edge computing nodes, the nodes will collaborate through message communication to determine whether the edge should be added to the spanning tree. If both nodes select the edge and it meets the maximum mutual information standard, it will be added to the spanning tree and the spanning tree structures of both parties will be updated.

[0078] After that, each edge computing node continues to search for other edges to be selected along the edges marked as having been added to the spanning tree. When each node searches, the edges that meet specific conditions will be added to the spanning tree first.

[0079] When all edge computing nodes complete the construction of the spanning tree, a maximum spanning tree containing all devices in the voltage mutual information graph is obtained. In this spanning tree, each edge represents the optimal voltage mutual information connection between two devices.

[0080] Step 203 : when the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0081] In the embodiment of the present application, first, the integrity of the spanning tree is verified. In the previous step, a spanning tree covering all devices in the voltage mutual information graph has been constructed by the distributed maximum spanning tree algorithm. At this time, it is necessary to confirm whether the spanning tree has included all device nodes and ensure that there are no missing or unprocessed devices.

[0082] Next, analyze the voltage mutual information of each edge in the spanning tree. Each edge in the spanning tree represents the voltage association between two devices. The voltage mutual information of each edge is obtained through the previous calculation. At this point, the voltage mutual information of all devices in the voltage mutual information graph can be combined to further analyze the strength of the association between each device.

[0083] Then, the topology is constructed based on the spanning tree. The connection relationships of all devices in the spanning tree have been determined, and the topology of the distribution network can be constructed based on these connection relationships. The edges in the spanning tree reflect the strongest voltage correlation between devices and ensure that there are no loops. The target edge computing node converts the connection information of all devices and edges in the spanning tree into the actual topology of the distribution network, thereby forming a logical connection diagram between devices.

[0084] In the process of topology construction, the voltage mutual information graph can be combined for adjustment. Although the spanning tree provides the core device connection relationship, in some special cases, it is also necessary to refer to the information in the voltage mutual information graph that is not included in the spanning tree, such as some auxiliary connections or backup paths.

[0085] Finally, through the comprehensive analysis of the spanning tree and the voltage mutual information graph, the topology identification result of the distribution network is finally obtained. The generated topology structure will serve as the basis for the operation and maintenance of the distribution network and provide support for subsequent network optimization, fault diagnosis and power dispatching.

[0086] In the above-mentioned topology identification method at the edge of the distribution network, firstly, by establishing a voltage mutual information graph, the voltage relationship between devices can be systematically captured. Secondly, the distributed maximum spanning tree algorithm is used to process the voltage mutual information graph, which not only speeds up the construction process of the spanning tree, but also supports parallel computing, so that topology identification can be completed quickly in complex distribution networks. Finally, the topology identification results obtained based on the spanning tree and the voltage mutual information graph have high accuracy and can reflect the network status in a timely manner, thereby improving the efficiency of topology identification.

[0087] In an exemplary embodiment, Figure 3 As shown, the voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree, including steps 301 to 303. Among them:

[0088] Step 301 : for each device, select voltage mutual information that meets preset conditions from the voltage mutual information graph as a candidate edge.

[0089] The preset condition may be that the mutual information value is greater than a certain threshold or the correlation is strong. The candidate edges represent potential connections between two devices, and these candidate edges will be used for subsequent construction of the spanning tree.

[0090] In the embodiment of the present application, for each device node, it is necessary to obtain the voltage mutual information between the device and other devices from the voltage mutual information graph, and then screen according to preset conditions to select the voltage mutual information edges that meet the conditions.

[0091] Step 302: When two devices connected to the edge to be selected belong to the same target edge computing node, the edge to be selected is added to a preset edge to be selected list, and a spanning tree is established using the edge to be selected list.

[0092] In an embodiment of the present application, classification processing is performed according to the edge computing node to which the two devices connected to the edge to be selected belong. If the two devices connected to the edge to be selected belong to the jurisdiction of the same edge computing node, this edge can be directly regarded as a candidate edge for local connection and added to the list of edges to be selected of the edge computing node. The purpose of this is to minimize cross-node coordination operations and give priority to completing the construction of the spanning tree within each edge computing node.

[0093] Then, inside the edge computing node, the spanning tree is gradually constructed using the edges in the candidate edge list. The construction process follows the principle of the distributed maximum spanning tree algorithm, that is, the edges with the largest voltage mutual information value are preferentially selected, and the spanning tree is ensured not to form a loop. As the candidate edges are gradually added to the spanning tree, the edge computing node can obtain a local spanning tree covering all devices within its jurisdiction. This process is performed independently inside each edge computing node, ensuring the validity of the local topology.

[0094] In some embodiments, each edge computing node traverses the spanning tree candidate edge list stored in itself, and for each edge in the candidate edge list, if both vertices s and t of the two ends of the edge are selected as the spanning tree candidate edge, the edge is added to the spanning tree G. V(T) In the search state of the edge, set the edge to the edge that has been added to the spanning tree.

[0095] In some embodiments, according to the newly added to the spanning tree G V(T) The edge computing node to which one of the vertices k belongs starts searching along the edge that has been added to the spanning tree, visiting each vertex in turn; when visiting vertex s, if vertex s is not in In the example, a startup message containing ID(k) and Level(k) is sent to the edge computing node to which s belongs. After receiving the message, the edge computing node to which s belongs executes the maximum mutual information edge search process within the jurisdiction, and includes the maximum mutual information edge that it can search in the report message and returns it to the master node; if vertex s is in In the process, the maximum mutual information edge search process is performed in the jurisdiction to obtain the maximum mutual information edge that can be searched in the jurisdiction; after the master node completes the maximum mutual information edge search process in the jurisdiction and receives the report message returned by all slave nodes, it selects the edge with the largest mutual information among all edges as the spanning tree candidate edge and adds it to the spanning tree candidate edge list. It represents the set of devices in the area under the jurisdiction of the i-th edge computing node, ID(k) represents the identity information of the device k vertex, and Level(k) represents the level information of the device k vertex.

[0096] Step 303: When the two devices connected to the edge to be selected do not belong to the same target edge computing node, the spanning tree is updated by using the interaction information between the target edge computing node and the adjacent edge computing nodes.

[0097] In the embodiments of the present application, for the case where the candidate edge involves cross-node connection, that is, when the two devices connected by the candidate edge belong to different target edge computing node jurisdictions respectively, cross-node coordinated update is required. At this time, the candidate edge connecting these two devices cannot be directly added to the spanning tree of a certain target edge computing node, but is collaboratively processed through information interaction between the target edge computing node and adjacent edge computing nodes. The target edge computing node will send an interaction message containing candidate edge information to the adjacent edge computing node, and the two parties will coordinate whether to add this edge to the spanning tree. If both nodes confirm that the candidate edge meets the conditions, the candidate edge will be added to the spanning tree, and the local spanning tree structures of both parties will be updated.

[0098] Finally, a complete spanning tree is generated and coordinated for update. As the local spanning trees are constructed inside each edge computing node and cross-node collaborative processing is carried out, the spanning tree of the entire distribution network is gradually formed. Each edge computing node communicates and collaborates, and finally aggregates all local spanning trees and the edges connecting cross-nodes to form a complete spanning tree covering all global devices. In this process, the update of the spanning tree is dynamic, and as new candidate edges are added, the topological structure in the distribution network will be continuously optimized and adjusted.

[0099] In some embodiments, edge computing node i (abbreviated as "node i") traverses the list of candidate edges for the spanning tree stored by itself. For the two vertices connected by each edge, the vertex belonging to itself is denoted as s, and the vertex belonging to another edge computing node j (abbreviated as "node j") is denoted as t. Node i sends a connection message containing ID(s) and Level(s) to node j and waits for a response; when node j receives the connection message, it selects different processing methods according to the content of the connection message.

[0100] If t in its list of candidate edges for the spanning tree does not select this edge or Level(s) < level(t), then this message is not responded to temporarily, and this message is placed in the message queue.

[0101] In an exemplary embodiment, selecting voltage mutual information that meets preset conditions from the voltage mutual information graph as candidate edges includes:

[0102] Case 1, in the case of first selecting candidate edges, select the voltage mutual information with the largest voltage mutual information from the voltage mutual information graph as the candidate edge.

[0103] In the embodiments of the present application, when first selecting candidate edges, the edge with the largest voltage mutual information value can be directly selected from the voltage mutual information graph as the candidate edge. The basis for this selection is that the voltage mutual information value reflects the strong correlation between two devices. Prioritizing the selection of the edge with the largest mutual information value can ensure that the initial construction of the spanning tree is based on the most reliable connection, thereby enhancing the effectiveness of subsequent topology recognition.

[0104] In some embodiments, the search process for the maximum voltage mutual information may include the following steps:

[0105] 1) When the edge computing node i searches from vertex k along the edges with the search status of having been added to the spanning tree until it reaches s, update ID(s) = ID(k) and Level(s) = Level(k).

[0106] 2) Select an edge with the maximum voltage mutual information from the edges connected to s whose search status is set to the initial state, make a conditional judgment, and perform corresponding operations:

[0107] If ID(s) = ID(t), then mark this edge as rejected, select the edge with the maximum voltage mutual information from the edges connected to s whose search status is set to the initial state, and repeat step 2).

[0108] If ID(s) ≠ ID(t) and level(t) ≥ level(s), then select this edge as the edge to be determined for the maximum mutual information edge.

[0109] If ID(s) ≠ ID(t), level(t) < level(s) and t takes this edge as an edge to be selected for the spanning tree, then update the search status of this edge to an edge that has been added to the spanning tree. In the subsequent search by the edge computing node i, it should also search along this edge. Select the edge with the maximum voltage mutual information from the edges connected to s whose search status is set to the initial state and repeat 2).

[0110] If ID(s) ≠ ID(t), level(t) < level(s) and t does not take this edge as an edge to be selected for the spanning tree, then wait until level(t) is updated to be not less than level(s) or t takes this edge as an edge to be selected for the spanning tree and then repeat 2).

[0111] 3) Compare the mutual information values of the edges selected as the edges to be determined for the maximum mutual information for each vertex, and select the edge with the maximum mutual information as the search result.

[0112] 4) If the edge computing node i executes this search process after receiving the start message from the edge computing node j, then send a report message containing the search result to the edge computing node j. Here, Level(s) represents the hierarchical information of vertex s of the device, Level(t) represents the hierarchical information of vertex t of the device, ID(s) represents the identity information of vertex s of the device, and ID(t) represents the identity information of vertex t of the device.

[0113] In the second case, when the candidate edge is not selected for the first time, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, after the candidate edge is added, the spanning tree is still a tree graph, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0114] In the embodiment of the present application, when selecting the candidate edges for non-initial selection, first, all the candidate edges that have not been included in the current spanning tree are identified from the voltage mutual information graph. These candidate edges can become potential candidate edges.

[0115] Next, for each potential candidate edge, check whether it meets the preset cutoff condition:

[0116] (1) The edge must not be in the current spanning tree to ensure that the newly added edge is independent.

[0117] (2) After adding this edge, the spanning tree must still maintain the structure of the tree graph and cannot form a loop.

[0118] (3) The voltage mutual information value of the edge to be selected must be the largest among all the edges in the voltage mutual information graph, ensuring that the edge with the highest correlation is selected.

[0119] If a candidate edge that meets all the cutoff conditions is found, it is added to the spanning tree. Through this process, the spanning tree can be gradually expanded to cover more devices and maintain the highest voltage mutual information connection in the entire topology.

[0120] In some embodiments, each edge computing node executes a distributed maximum spanning tree algorithm on the voltage mutual information graph G V The spanning tree G that maximizes the sum of voltage mutual information V(T) ;in,

[0121] The distributed maximum spanning tree algorithm includes the following steps:

[0122] 1) The i-th edge computing node is based on the device information. Each vertex in the is initialized with a unique ID, the level attribute is set to 0, and the search state of all edges is set to the initial state;

[0123] 2) The i-th edge computing node will Each vertex in is set to the found state, according to For each vertex, select the edge with the largest voltage mutual information among the adjacent edges and add it to the spanning tree candidate edge list. In order to add the spanning tree candidate edge to the spanning tree, if the two vertices connected by an edge in the list belong to the same Then the autonomous update process of the spanning tree in the jurisdiction is executed, and the edges to be selected are added to the spanning tree G only in the current edge computing nodeV(T) Otherwise, the spanning tree collaborative update process between jurisdictions is executed, and the two edge computing nodes coordinate to add the candidate edge to the spanning tree G. V(T) ;

[0124] 3) The i-th edge computing node is added to G according to 2). V(T) Starting from one of the two vertices connected by the edge, visit each vertex in turn along the edge whose search state has been added to the spanning tree, perform the maximum mutual information edge collaborative search, and search G V The edges that meet the following three conditions are added to the spanning tree candidate edge list:

[0125] This edge is not on G V(T) middle;

[0126] After adding this edge G V(T) Still in tree form;

[0127] The voltage mutual information search state of this edge is the largest among the edges in the initial state;

[0128] 4) The edge computing node obtains a list of candidate edges of the spanning tree based on the search in 3). If the two vertices connected by an edge in the list belong to the same edge computing node, the autonomous update process of the spanning tree within the jurisdiction is executed; otherwise, the collaborative update process of the spanning tree between jurisdictions is executed;

[0129] 5) Return to 3). If no spanning tree edge that meets the requirements of 3) can be found, the algorithm stops. The edge that has been added to the spanning tree in each edge computing node is searched to represent the connection relationship between the devices in the area under its jurisdiction. If a spanning tree edge that meets the requirements of 3) can be found, execute 4).

[0130] In an exemplary embodiment, a topology identification result is obtained based on a spanning tree and a voltage mutual information graph, including:

[0131] In case 1, when the spanning tree is radial, the spanning tree is determined as a topology identification result.

[0132] In an embodiment of the present application, when the target edge computing node receives an input instruction from the user indicating that the spanning tree is radial, the target edge computing node will parse the instruction input by the user to confirm the validity of the instruction content and format. After ensuring that the instruction accurately conveys the user's intention, the target edge computing node will begin to analyze the structure of the current spanning tree. At this point, the target edge computing node will check the connection relationship of the spanning tree to confirm whether all devices form a radial layout around a central node.

[0133] If the spanning tree meets the radial characteristics, the target edge computing node will determine the spanning tree as the topology identification result.

[0134] like Figure 4 and Figure 5 As shown, Figure 4 The reference topology identification result that meets the radial characteristics is Figure 5 To meet the actual topological identification results of the radial characteristics, Figure 4 and Figure 5 It can be seen that the actual topology identification result obtained by the embodiment of the present application is substantially the same as the reference topology identification result.

[0135] In case 2, when the spanning tree is not radial, a topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0136] In the embodiment of the present application, when the target edge computing node receives the user's input instruction indicating that the spanning tree is non-radial, the target edge computing node will parse the user's input instruction to confirm the validity of the instruction content and format. After ensuring that the instruction accurately conveys the user's intention, the conditional mutual information between adjacent devices in the spanning tree is first analyzed to quantify the information flow and association strength between these devices.

[0137] Next, the calculated conditional mutual information value is used to evaluate which connections between devices can be optimized to reduce the complexity and potential redundant connections in the spanning tree. Finally, based on the evaluation results, the structure of the spanning tree is adjusted to retain connections with higher mutual information and remove connections with lower mutual information or redundant connections, thereby generating a new topology identification result.

[0138] Finally, after these adjustments, the target edge computing node will generate a new topology identification result and save it.

[0139] In some embodiments, for each pair of V(T) There are no edge connecting devices i and j in G. V(T) The conditional mutual information between the adjacent device voltage and device j is Compute device j in known G V(T) The conditional mutual information between the voltage of the adjacent device and device i If both are greater than γ, then edge ij is counted into the loop subgraph G V(O) Conditional mutual information and The calculation formula is as follows:

[0140]

[0141] Where V i 、V j Respectively represent G V(T) The voltage of devices i and j in N T (i) and N T(j) Device i and device j are in G V(T) The set of neighboring devices in Represents the adjacent device voltage sets of devices i and j respectively, They represent the joint distribution of the voltage of device i and device j with the voltage of their respective adjacent devices, denote the joint distribution of the voltages of the adjacent devices of device i and device j, respectively. represents the joint distribution of the voltage of device i, the voltage of device j, and the voltage of the adjacent devices of device i. represents the joint distribution of the voltage of device i, the voltage of device j, and the voltage of the adjacent devices of device i. represents the joint distribution of the voltage of device i, the voltage of device j, and the voltage of the adjacent devices of device j. is the intermediate parameter, and log2 represents the logarithm with base 2.

[0142] When devices i and j do not belong to the same edge computing node, the edge computing nodes to which they belong can collaboratively complete the calculation of conditional mutual information simply by exchanging the voltage time series data of devices i and j.

[0143] like Figure 6 and Figure 7 As shown, Figure 6 To meet the reference topology identification results of non-radial features, Figure 7 To meet the actual topological identification results of non-radial features, Figure 6 and Figure 7 It can be seen that the actual topology identification result obtained by the embodiment of the present application is substantially the same as the reference topology identification result.

[0144] In an exemplary embodiment, Figure 8 As shown, the voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree, including steps 401 to 403. Among them:

[0145] Step 401 : compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information.

[0146] Among them, the minimum conditional mutual information reflects the minimum limit of the information correlation between devices.

[0147] In an embodiment of the present application, first, the target edge computing node can collect conditional mutual information related to the adjacent relationship between each device in the spanning tree to form a list containing all conditional mutual information. Then, the target edge computing node traverses the list and compares each conditional mutual information one by one to determine the minimum conditional mutual information.

[0148] Step 402, broadcast the minimum conditional mutual information, and receive the comparison value returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the device corresponding to the returned comparison value.

[0149] In an embodiment of the present application, the target edge computing node first broadcasts the determined minimum conditional mutual information to all other edge computing nodes.

[0150] Next, after receiving the broadcast, each other edge computing node will compare the conditional mutual information stored in its internal storage. If the conditional mutual information value of another edge computing node is greater than the minimum value broadcast by the target node, the other edge computing node will return the value and the corresponding device information to the target node.

[0151] After receiving the comparison value from other nodes, the target edge computing node will conduct further analysis. If the comparison value is greater than the minimum conditional mutual information, it indicates that the connection between these devices has a low mutual information correlation, and there may be redundant or unnecessary connections; only when the comparison value is greater than the minimum conditional mutual information, there may be a link between the two devices. Based on this information, the target node will select these devices to generate a ring topology to optimize the network structure and reduce unnecessary complexity.

[0152] Ultimately, by combining the generated ring topology with the existing spanning tree, the target edge computing node will be able to generate more accurate topology identification results.

[0153] Step 403: Generate a topology identification result based on the spanning tree and the ring topology.

[0154] In an embodiment of the present application, first, the target edge computing node analyzes the connection relationship between devices in the spanning tree and the redundant connections shown in the ring topology. By comparing the two, the target edge computing node can identify which connections between devices are necessary and which may be redundant. On this basis, the target edge computing node will make structural adjustments to ensure that the necessary connections are maintained while removing redundant or low mutual information connections to optimize the overall topology. Ultimately, the target edge computing node will integrate the spanning tree and the ring topology to form a comprehensive topology identification result. The topology identification result not only reflects the actual connection status between devices in the distribution network, but also effectively reflects the information flow and interconnection between devices, thereby providing an important basis for subsequent power system management and optimization.

[0155] In an exemplary embodiment, Fig. 9 As shown, the voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree, including steps 501 to 502. Among them:

[0156] Step 501: Obtain first voltage time series information of each device in a target edge computing node.

[0157] In an embodiment of the present application, the target edge computing node monitors the voltage of each device, records the voltage value and its corresponding timestamp, and forms a first voltage time series signal.

[0158] Step 502: Obtain second voltage time series information of each device in an adjacent edge computing node.

[0159] In an embodiment of the present application, first, the target edge computing node establishes a connection with the adjacent edge computing node through a communication protocol for data exchange. Then, the target edge computing node sends a request to the adjacent edge computing node to obtain the first voltage time series information of each device within its jurisdiction. The first voltage time series information usually contains the voltage value of the device and the corresponding timestamp to ensure the consistency of the time series. After receiving the request, the adjacent edge computing node organizes the required data and sends it back to the target edge computing node.

[0160] Step 503: Determine second voltage mutual information based on the first voltage time series information and the second voltage time series information.

[0161] In an embodiment of the present application, the target edge computing node obtains the first voltage time series information from the devices in the area under its jurisdiction, and simultaneously obtains the second voltage time series information provided by the adjacent edge computing node. Next, the target node preprocesses the two sets of time series data, including denoising, standardization, and alignment, to ensure the consistency of the data on the time axis. Based on the first voltage time series information and the second voltage time series information, the second voltage mutual information is determined.

[0162] In some embodiments, according to the selected distribution network, the area under the jurisdiction of each edge computing node is divided and the equipment in the area under the jurisdiction is input, including transformers, branch boxes, and user meters; the first voltage time series information of each device is input;

[0163] Each edge computing node calculates the second voltage mutual information from the transformer to the branch box, between the branch boxes, and from the branch box to the user's meter in its own and adjacent areas based on the second voltage time series information of the equipment in its own area and the area under the jurisdiction of the adjacent edge computing node; wherein the second voltage mutual information from the transformer to the branch box, between the branch boxes, and from the branch box to the user's meter is expressed as:

[0164]

[0165] When calculating the voltage mutual information from transformer to branch box, M(V i ; V j) represents the mutual information between the i-th transformer and the j-th branch box, p(v i ,v j ) represents the voltage joint distribution of the i-th transformer and the j-th branch box, p(v i )、p(v j ) represent the voltage distribution of the i-th transformer and the j-th branch box respectively;

[0166] When calculating the voltage mutual information between each branch box, M(V i ; V j ) represents the mutual information between the i-th branch box and the j-th branch box, p(v i ,v j ) represents the joint voltage distribution of the i-th branch box and the j-th branch box, p(v i )、p(v j ) represent the voltage distribution of the i-th branch box and the j-th branch box respectively;

[0167] When calculating the distance from the branch box to the user's meter, M(V i ; V j ) represents the mutual information between the i-th branch box and the j-th user meter, p(v i ,v j ) represents the joint voltage distribution of the i-th branch box and the j-th user meter, p(v i )、p(v j ) represent the voltage distribution of the i-th branch box and the j-th user's meter respectively.

[0168] like Fig.10 As shown, Fig.10 Based on Figure 4 The calculated second voltage interaction information between the 24th branch box and other transformers, branch boxes, and users. Fig.10 In FIG. 1 , the mutual information in the vertical axis is the second voltage mutual information in the embodiment of the present application.

[0169] A detailed embodiment is given below to illustrate the process of the distribution network edge side topology identification method in this application. Based on the above embodiment, the implementation process of the method may include the following contents:

[0170] Step 1: Obtain the first voltage time series information of each device in the target edge computing node.

[0171] Step 2: Obtain the second voltage time series information of each device in the adjacent edge computing node.

[0172] Step 3: Determine second voltage mutual information based on the first voltage time series information and the second voltage time series information.

[0173] Step 4: Establish a voltage mutual information graph based on the first voltage mutual information between each device in the target edge computing node, the second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node.

[0174] Step 5: For each device, select the voltage mutual information that meets the preset conditions from the voltage mutual information graph as the edge to be selected.

[0175] In some embodiments, when selecting a candidate edge for the first time, a voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the candidate edge.

[0176] In some embodiments, when the candidate edge is not selected for the first time, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, after the candidate edge is added, the spanning tree is still a tree diagram, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0177] Step 6: If the two devices connected to the edge to be selected are within the jurisdiction of the same target edge computing node, the edge to be selected is added to the preset edge list, and the spanning tree is established using the edge list. If the two devices connected to the edge to be selected are not within the jurisdiction of the same target edge computing node, the spanning tree is updated by coordination using the interactive information between the target edge computing node and the adjacent edge computing node.

[0178] Step 7: When the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0179] Step 8: When the spanning tree is radial, determine that the spanning tree is a topology identification result.

[0180] Step 9: When the spanning tree is not radial, compare the conditional mutual information and determine the minimum conditional mutual information from the conditional mutual information.

[0181] Step 10: broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the device corresponding to the returned comparison value.

[0182] Step 11: Generate a topology identification result based on the spanning tree and the ring topology.

[0183] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0184] Based on the same inventive concept, the embodiment of the present application also provides a distribution network edge side topology identification device for implementing the distribution network edge side topology identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network edge side topology identification device embodiments provided below can refer to the limitations of the distribution network edge side topology identification method above, and will not be repeated here.

[0185] In an exemplary embodiment, Fig.11 As shown, a distribution network edge side topology identification device is provided, including: a mutual information graph establishment module 601, a spanning tree establishment module 602 and a result determination module 603, wherein:

[0186] A mutual information graph establishing module 601, configured to establish a voltage mutual information graph based on first voltage mutual information between each device in a target edge computing node, second voltage mutual information between a device in the target edge computing node and a device in an adjacent edge computing node, and each device in the target edge computing node;

[0187] A spanning tree establishment module 602 is used to process the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree;

[0188] The result determination module 603 is used to obtain a topology identification result based on the spanning tree and the voltage mutual information graph when the spanning tree contains all the devices in the voltage mutual information graph.

[0189] In some embodiments, the above-mentioned spanning tree establishment module 602 is specifically used to select, for each device, voltage mutual information that meets preset conditions from the voltage mutual information graph as a candidate edge; when the two devices connected to the candidate edge belong to the same target edge computing node, the candidate edge is added to the preset candidate edge list, and the spanning tree is established using the candidate edge list; when the two devices connected to the candidate edge do not belong to the same target edge computing node, the interactive information between the target edge computing node and the adjacent edge computing node is used to coordinate and update the spanning tree.

[0190] In some embodiments, the above-mentioned spanning tree establishment module 602 is specifically used to select the voltage mutual information with the largest voltage mutual information as the candidate edge from the voltage mutual information graph when the candidate edge is selected for the first time; when the candidate edge is not selected for the first time, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff condition from the voltage mutual information graph; the cutoff condition includes: the candidate edge is not in the spanning tree, after the candidate edge is added, the spanning tree is still a tree diagram, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0191] In some embodiments, the above-mentioned result determination module 603 is specifically used to determine that the spanning tree is a topology identification result when the spanning tree is radial; when the spanning tree is not radial, generate a topology identification result based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0192] In some embodiments, the result determination module 603 is specifically used to compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information;

[0193] Broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the devices corresponding to the returned comparison value.

[0194] Generate topology identification results based on spanning tree and ring topology.

[0195] In some embodiments, the above-mentioned mutual information graph establishing module 601 is specifically used to obtain the first voltage time series information of each device in the target edge computing node; obtain the second voltage time series information of each device in the adjacent edge computing node; and determine the second voltage mutual information based on the first voltage time series information and the second voltage time series information.

[0196] Each module in the above-mentioned distribution network edge side topology identification device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0197] In an exemplary embodiment, a computer device is provided, which may be a target edge computing node, and its internal structure diagram may be as follows: Fig.12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data in the process of topology identification on the edge side of the distribution network. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for topology identification on the edge side of the distribution network is implemented.

[0198] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0199] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0200] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0201] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0202] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0203] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0204] For each device, the voltage mutual information that meets the preset conditions is selected from the voltage mutual information graph as the edge to be selected;

[0205] When two devices connected to the edge to be selected belong to the same target edge computing node, the edge to be selected is added to a preset edge list to be selected, and a spanning tree is established using the edge list to be selected;

[0206] When the two devices connected to the edge to be selected do not belong to the same target edge computing node, the interactive information between the target edge computing node and the adjacent edge computing nodes is used to coordinate and update the spanning tree.

[0207] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0208] When selecting the edge to be selected for the first time, the voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the edge to be selected;

[0209] In the case of non-first selection of the candidate edge, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0210] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0211] In the case where the spanning tree is radial, the spanning tree is determined as a topology identification result;

[0212] When the spanning tree is not radial, a topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0213] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0214] Compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information;

[0215] Broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the devices corresponding to the returned comparison value.

[0216] Generate topology identification results based on spanning tree and ring topology.

[0217] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0218] Obtaining first voltage time series information of each device in the target edge computing node;

[0219] Obtaining second voltage time series information of each device in an adjacent edge computing node;

[0220] Based on the first voltage time series information and the second voltage time series information, second voltage mutual information is determined.

[0221] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0222] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0223] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0224] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0225] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0226] For each device, the voltage mutual information that meets the preset conditions is selected from the voltage mutual information graph as the edge to be selected;

[0227] When two devices connected to the edge to be selected belong to the same target edge computing node, the edge to be selected is added to a preset edge list to be selected, and a spanning tree is established using the edge list to be selected;

[0228] When the two devices connected to the edge to be selected do not belong to the same target edge computing node, the interactive information between the target edge computing node and the adjacent edge computing nodes is used to coordinate and update the spanning tree.

[0229] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0230] When selecting the edge to be selected for the first time, the voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the edge to be selected;

[0231] In the case of non-first selection of the candidate edge, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0232] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0233] In the case where the spanning tree is radial, the spanning tree is determined as a topology identification result;

[0234] When the spanning tree is not radial, a topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0235] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0236] Compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information;

[0237] Broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the devices corresponding to the returned comparison value.

[0238] Generate topology identification results based on spanning tree and ring topology.

[0239] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0240] Obtaining first voltage time series information of each device in the target edge computing node;

[0241] Obtaining second voltage time series information of each device in an adjacent edge computing node;

[0242] Based on the first voltage time series information and the second voltage time series information, second voltage mutual information is determined.

[0243] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0244] Establishing a voltage mutual information graph based on first voltage mutual information between each device in the target edge computing node, second voltage mutual information between the device in the target edge computing node and the device of the adjacent edge computing node, and each device in the target edge computing node;

[0245] The voltage mutual information graph is processed using a distributed maximum spanning tree algorithm to establish a spanning tree.

[0246] In the case where the spanning tree includes all devices in the voltage mutual information graph, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

[0247] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0248] For each device, the voltage mutual information that meets the preset conditions is selected from the voltage mutual information graph as the edge to be selected;

[0249] When two devices connected to the edge to be selected belong to the same target edge computing node, the edge to be selected is added to a preset edge list to be selected, and a spanning tree is established using the edge list to be selected;

[0250] When the two devices connected to the edge to be selected do not belong to the same target edge computing node, the interactive information between the target edge computing node and the adjacent edge computing nodes is used to coordinate and update the spanning tree.

[0251] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0252] When selecting the edge to be selected for the first time, the voltage mutual information with the largest voltage mutual information is selected from the voltage mutual information graph as the edge to be selected;

[0253] In the case of non-first selection of the candidate edge, the maximum mutual information edge collaborative search algorithm is used to select the candidate edge that meets the cutoff conditions from the voltage mutual information graph; the cutoff conditions include: the candidate edge is not in the spanning tree, the spanning tree is still a tree graph after the candidate edge is added, and the candidate edge has the largest voltage mutual information in the voltage mutual information graph.

[0254] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0255] In the case where the spanning tree is radial, the spanning tree is determined as a topology identification result;

[0256] When the spanning tree is not radial, a topology identification result is generated based on the conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

[0257] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0258] Compare and process each conditional mutual information, and determine the minimum conditional mutual information from the conditional mutual information;

[0259] Broadcast the minimum conditional mutual information and receive the comparison values ​​returned by other edge computing nodes. When the comparison value is greater than the minimum conditional mutual information, generate a ring topology based on the devices corresponding to the returned comparison value.

[0260] Generate topology identification results based on spanning tree and ring topology.

[0261] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0262] Obtaining first voltage time series information of each device in the target edge computing node;

[0263] Obtaining second voltage time series information of each device in an adjacent edge computing node;

[0264] Based on the first voltage time series information and the second voltage time series information, second voltage mutual information is determined.

[0265] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0266] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantitative calculations, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0267] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0268] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying topology at the edge of a distribution network, characterized in that: The method comprises: Establishing a voltage mutual information graph based on first voltage mutual information between each device in a target edge computing node, second voltage mutual information between a device in the target edge computing node and a device in an adjacent edge computing node, and each of the devices in the target edge computing node; Processing the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree; In the case where all devices in the voltage mutual information graph are included in the spanning tree, a topology identification result is obtained based on the spanning tree and the voltage mutual information graph.

2. The method according to claim 1, characterized in that The method of processing the voltage mutual information graph by using a distributed maximum spanning tree algorithm to establish a spanning tree includes: For each of the devices, selecting voltage mutual information that meets preset conditions from the voltage mutual information graph as an edge to be selected; In the case where two devices connected to the edge to be selected belong to the same range of the target edge computing node, adding the edge to be selected to a preset edge list to be selected, and establishing the spanning tree using the edge list to be selected; In the case that the two devices connected to the to-be-selected edge do not belong to the same jurisdiction of the target edge computing node, the spanning tree is updated in a coordinated manner by utilizing the interaction information between the target edge computing node and the adjacent edge computing node.

3. The method according to claim 2, characterized in that The step of selecting voltage mutual information satisfying a preset condition from the voltage mutual information graph as an edge to be selected includes: When the edge to be selected is selected for the first time, selecting the voltage mutual information with the largest voltage mutual information from the voltage mutual information graph as the edge to be selected; In the case where the edge to be selected is not selected for the first time, a maximum mutual information edge collaborative search algorithm is used to select an edge to be selected that meets a cutoff condition from the voltage mutual information graph; the cutoff condition includes: the edge to be selected is not in the spanning tree, after the edge to be selected is added, the spanning tree is still a tree diagram, and the edge to be selected has the largest voltage mutual information in the voltage mutual information graph.

4. The method according to claim 1, characterized in that The obtaining of a topology identification result based on the spanning tree and the voltage mutual information graph includes: In the case where the spanning tree is radial, determining that the spanning tree is the topology identification result; In the case that the spanning tree is not radial, the topology identification result is generated based on conditional mutual information between the spanning tree and adjacent devices in the spanning tree.

5. The method according to claim 4, characterized in that The generating the topology identification result based on the conditional mutual information between the spanning tree and the adjacent devices in the spanning tree includes: Comparing the conditional mutual information, and determining the minimum conditional mutual information from the conditional mutual information; Broadcasting the minimum conditional mutual information, and receiving comparison values ​​returned by other edge computing nodes, and generating a ring topology based on devices corresponding to the returned comparison values ​​when the comparison values ​​are greater than the minimum conditional mutual information; The topology identification result is generated based on the spanning tree and the ring topology.

6. The method according to claim 1, characterized in that The process of determining the second voltage mutual information includes: Obtaining first voltage time series information of each device in the target edge computing node; Obtaining second voltage time series information of each device in an adjacent edge computing node; The second voltage mutual information is determined based on the first voltage time series information and the second voltage time series information.

7. A topology identification device at the edge of a distribution network, characterized in that: The device comprises: A mutual information graph establishing module, configured to establish a voltage mutual information graph based on first voltage mutual information between each device in a target edge computing node, second voltage mutual information between a device in the target edge computing node and a device in an adjacent edge computing node, and each of the devices in the target edge computing node; A spanning tree establishment module, used to process the voltage mutual information graph using a distributed maximum spanning tree algorithm to establish a spanning tree; A result determination module is used to obtain a topology identification result based on the spanning tree and the voltage mutual information graph when all devices in the voltage mutual information graph are included in the spanning tree.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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