Power distribution network topology graph generation method, device and equipment based on time-slice network

Abstract

This application relates to the fields of power distribution network technology and deep learning technology. It discloses a method, apparatus, and device for generating power distribution network topology diagrams based on temporally explicit networks. The method includes: a power distribution network automation system generating an adjacency list for the current time; dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the traversal start node for each region, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the traversal start node to obtain a connection tree for each region; merging the connection trees of each region to generate the connection tree of the power distribution network; converting the connection tree of the power distribution network into a topology matrix of the power distribution network; inputting the topology matrix of the power distribution network into a drawing tool; and having the drawing tool generate the topology diagram of the power distribution network at the current time. This application is beneficial for improving the efficiency of topology diagram generation.

Description

Technical Field

[0001] This application relates to the fields of power distribution network technology and deep learning technology, and in particular to a method, apparatus and equipment for generating power distribution network topology diagrams based on temporal clear networks. Background Technology

[0002] The power distribution network is a crucial link in the power system that directly faces end users, undertaking the core functions of power distribution and supply. Using medium or low voltage levels as the transmission carrier, the distribution network uses components such as overhead lines, cables, switchgear, and transformers to progressively reduce the high-voltage power transmitted from the transmission network before safely and reliably distributing it to various users such as factories, communities, and households.

[0003] With the continuous rise in energy demand, the number of various devices connected to the distribution network has also increased dramatically. This development has directly led to an increasingly complex topology of the distribution network. The originally relatively simple and clear line connection relationships have gradually evolved into a complex network form, with lines from different levels and regions intertwined. Against this backdrop, timely understanding of the current topology of the distribution network has become crucial. The current topology map of the distribution network can intuitively present the complex line connections and equipment distribution of the distribution network. Therefore, how to generate the current topology map of the distribution network has become a technical problem that the power industry urgently needs to solve. Summary of the Invention

[0004] This application provides a method, apparatus, and device for generating distribution network topology maps based on time-defined networks, in order to solve the aforementioned technical problem of how to generate a distribution network topology map at the current moment.

[0005] In a first aspect, embodiments of this application provide a method for generating a distribution network topology map based on a time-defined network, applied to electronic devices. The distribution network topology map generation method includes:

[0006] The device receives a connection request from the data acquisition equipment of the power distribution network. Based on the connection request, it establishes a 5G dual channel between the device and the data acquisition equipment of the power distribution network. It receives a first data packet from the data acquisition equipment through the first channel of the 5G dual channel and a second data packet from the data acquisition equipment through the second channel of the 5G dual channel.

[0007] Obtain the reference time of the time-clear network, obtain the first difference between the timestamp of the first data packet and the reference time, obtain the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as the valid data packet when the absolute value of the first difference is less than the absolute value of the second difference; select the second data packet as the valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference.

[0008] Obtain electrical quantity data of the distribution network at the current moment from the valid data packets, input the electrical quantity data of the distribution network at the current moment into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current moment.

[0009] Input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment;

[0010] The adjacency list is divided into multiple regions. The bus node of each region is selected as the starting node for traversal. A worker thread is created for each region. The worker thread performs a breadth-first search traversal operation on each region, starting from the starting node, to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, which generates the topology map of the distribution network at the current time.

[0011] In one possible implementation of the first aspect, before establishing a 5G dual-channel connection between the device and the data acquisition equipment of the power distribution network, and before receiving a first data packet sent by the data acquisition equipment through the first channel of the 5G dual-channel connection and a second data packet sent by the data acquisition equipment through the second channel of the 5G dual-channel connection, the power distribution network topology generation method includes:

[0012] Obtain electrical quantity data of the distribution network at a preset time, combine the electrical quantity data of the distribution network at the preset time and the switch status data of the distribution network at the preset time into a sample, and combine multiple different samples into a sample set;

[0013] The convolutional neural network model is trained based on the sample set. The current loss value of the convolutional neural network model on the sample set is obtained. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

[0014] In one possible implementation of the first aspect, obtaining the reference time of the time-clarified network, obtaining a first difference between the timestamp of the first data packet and the reference time, obtaining a second difference between the timestamp of the second data packet and the reference time, selecting the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and selecting the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference, includes:

[0015] Obtain message data from the time-clarified network and extract the reference time of the time-clarified network from the message data;

[0016] Obtain the first difference between the timestamp of the first data packet and the reference time, and obtain the second difference between the timestamp of the second data packet and the reference time. When the absolute value of the first difference is less than the absolute value of the second difference, select the first data packet as the valid data packet. When the absolute value of the first difference is greater than or equal to the absolute value of the second difference, select the second data packet as the valid data packet.

[0017] In one possible implementation of the first aspect, multiple nodes in the adjacency list are divided into multiple regions. The bus node of each region is selected as the starting node for traversal. A worker thread is created for each region. Using the worker thread for each region, a breadth-first search traversal operation is performed on each region, starting from the starting node, to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into a drawing tool, which generates the topology diagram of the distribution network at the current time, including:

[0018] Divide multiple nodes in the adjacency list into multiple regions, select the parent node of each region as the starting node for traversal of each region, obtain the creation instructions from the configuration file, and create the corresponding worker thread for each region by executing the creation instructions;

[0019] Starting from the traversal start node in each region, the worker thread corresponding to each region performs a breadth-first search traversal operation to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

[0020] In one possible implementation of the first aspect, the method for generating a distribution network topology map includes: dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the traversal start node of each region, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the traversal start node of each region to obtain the connection tree of each region. The connection trees of each region are then merged to generate the connection tree of the distribution network. The connection tree of the distribution network is then converted into the topology matrix of the distribution network. The topology matrix of the distribution network is then input into a drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

[0021] Create a display window to show the topology of the distribution network at the current moment.

[0022] In one possible implementation of the first aspect, the step of training a convolutional neural network model based on a sample set, obtaining the current loss value of the convolutional neural network model on the sample set, stopping training the convolutional neural network model when the current loss value is less than a preset loss value, and saving the trained convolutional neural network model includes:

[0023] The convolutional neural network model is trained based on a sample set. The current loss value of the convolutional neural network model on the sample set is obtained through a preset loss model. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

[0024] In one possible implementation of the first aspect, the loss model is defined as follows:

[0025] ;

[0026] in, Indicates the current loss value; This represents the total number of samples. Indicates the sample number. express The actual state of the switch in each sample. This indicates that the convolutional neural network model believes that the first... The probability value of a switch in a sample being in a closed state.

[0027] In one possible implementation of the first aspect, the first data packet carries the same acquisition data as the second data packet, but the channel identifier carried by the first data packet and the channel identifier carried by the second data packet are different, and the first channel and the second channel are two parallel data transmission channels.

[0028] Secondly, embodiments of this application provide a distribution network topology generation device based on time-clear networks, applied to electronic devices, including:

[0029] The receiving module is used to receive connection requests sent by the data acquisition equipment of the power distribution network. Based on the connection requests, it establishes a 5G dual channel between the device and the data acquisition equipment of the power distribution network. It receives the first data packet sent by the data acquisition equipment through the first channel of the 5G dual channel and the second data packet sent by the data acquisition equipment through the second channel of the 5G dual channel.

[0030] The first acquisition module is used to acquire the reference time of the time-clear network, acquire the first difference between the timestamp of the first data packet and the reference time, acquire the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and select the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference.

[0031] The second acquisition module is used to acquire electrical quantity data of the distribution network at the current time from the valid data packet, input the electrical quantity data of the distribution network at the current time into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current time.

[0032] The input module is used to input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment.

[0033] The generation module divides multiple nodes in the adjacency list into multiple regions, selects the bus node of each region as the traversal start node of each region, creates a corresponding worker thread for each region, and performs a breadth-first search traversal operation on each region starting from the traversal start node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network, and the connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

[0034] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power distribution network topology generation method described in the first aspect above.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the power distribution network topology generation method described in the first aspect above.

[0036] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the power distribution network topology generation method described in the first aspect.

[0037] The beneficial effects of the embodiments of this application are as follows:

[0038] Firstly, the multiple nodes in the adjacency list are divided into multiple regions. The bus node of each region is selected as the starting node for traversal of each region. A corresponding worker thread is created for each region. Through the worker thread corresponding to each region, a breadth-first search traversal operation is performed on each region starting from the starting node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool. The drawing tool generates the topology map of the distribution network at the current time, thus solving the technical problem of how to generate the topology map of the distribution network at the current time.

[0039] Secondly, since the topology map of the distribution network at the current moment is automatically generated without manual operation, it helps to reduce the generation time of the topology map of the distribution network at the current moment and improve the generation efficiency of the topology map of the distribution network at the current moment. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is an application scenario diagram of the power distribution network topology generation method provided in the embodiments of this application;

[0042] Figure 2 This is a schematic flowchart of the power distribution network topology generation method provided in the embodiments of this application;

[0043] Figure 3 A flowchart illustrating the implementation of S205 provided in this application embodiment;

[0044] Figure 4 A schematic block diagram of a power distribution network topology generation device provided in the embodiments of this application;

[0045] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0046] Figure 6 This is a sample diagram of the distribution network topology at the current moment. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0049] It should be understood that in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0050] The power distribution network topology generation method provided in this application can be applied to electronic devices, including but not limited to servers, mobile phones, tablets, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptops, and netbooks. This application does not impose any restrictions on the specific type of electronic device.

[0051] Please see Figure 1 , Figure 1 The application scenario diagram of the power distribution network topology diagram generation method provided in the embodiments of this application is described in detail below:

[0052] The electronic device receives a connection request sent by the data acquisition equipment of the power distribution network. Based on the connection request, it establishes a 5G dual channel between itself and the data acquisition equipment of the power distribution network. Through the first channel of the 5G dual channel, it receives the first data packet sent by the data acquisition equipment, and through the second channel of the 5G dual channel, it receives the second data packet sent by the data acquisition equipment.

[0053] In this embodiment of the application, the electronic device receives the first data packet sent by the acquisition device through the first channel of the 5G dual-channel and receives the second data packet sent by the acquisition device through the second channel of the 5G dual-channel, which can ensure the reliability and timeliness of data transmission.

[0054] Please see Figure 2 , Figure 2 This is a flowchart illustrating the distribution network topology generation method provided in this application embodiment, which can be applied to electronic devices.

[0055] like Figure 2 As shown in the figure, the method for generating a power distribution network topology map provided in this application includes the following steps, which are detailed below:

[0056] S201, Receive connection request sent by the data acquisition equipment of the power distribution network, establish 5G dual channel between this device and the data acquisition equipment of the power distribution network according to the connection request, receive the first data packet sent by the data acquisition equipment through the first channel of the 5G dual channel, and receive the second data packet sent by the data acquisition equipment through the second channel of the 5G dual channel;

[0057] Among them, 5G dual-channel is a dual-channel system that uses the 5th generation mobile communication protocol.

[0058] The first data packet carries the same collection data as the second data packet, but the channel identifier carried by the first data packet and the channel identifier carried by the second data packet are different. The first channel and the second channel are two parallel data transmission channels.

[0059] The acquired data refers to the data information collected by the acquisition equipment. The acquisition equipment includes feeder terminals and distribution terminals.

[0060] The method for generating the power distribution network topology map includes the following steps: Before establishing a 5G dual-channel connection between the device and the power distribution network acquisition equipment, and before receiving a first data packet sent by the acquisition equipment through the first channel and a second data packet sent by the acquisition equipment through the second channel, the power distribution network topology map generation method includes:

[0061] Obtain electrical quantity data of the distribution network at a preset time, combine the electrical quantity data of the distribution network at the preset time and the switch status data of the distribution network at the preset time into a sample, and combine multiple different samples into a sample set;

[0062] The convolutional neural network model is trained based on the sample set. The current loss value of the convolutional neural network model on the sample set is obtained. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

[0063] Convolutional neural networks are a type of deep learning model based on the core mechanism of convolution operations.

[0064] The step of training a convolutional neural network model based on a sample set, obtaining the current loss value of the convolutional neural network model on the sample set, stopping training the convolutional neural network model when the current loss value is less than a preset loss value, and saving the trained convolutional neural network model includes:

[0065] The convolutional neural network model is trained based on a sample set. The current loss value of the convolutional neural network model on the sample set is obtained through a preset loss model. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

[0066] The loss model is defined as follows:

[0067] ;

[0068] in, Indicates the current loss value; This represents the total number of samples. Indicates the sample number. express The actual state of the switch in each sample. This indicates that the convolutional neural network model believes that the first... The probability value of a switch in a sample being in a closed state.

[0069] Among them, 5G dual-channel technology significantly improves network performance by simultaneously utilizing two independent first and second channels for data transmission.

[0070] S202, obtain the reference time of the time-clear network, obtain the first difference between the timestamp of the first data packet and the reference time, obtain the second difference between the timestamp of the second data packet and the reference time, when the absolute value of the first difference is less than the absolute value of the second difference, select the first data packet as the valid data packet, when the absolute value of the first difference is greater than or equal to the absolute value of the second difference, select the second data packet as the valid data packet;

[0071] The process of obtaining a reference time from a time-clear network, obtaining a first difference between the timestamp of a first data packet and the reference time, obtaining a second difference between the timestamp of a second data packet and the reference time, and selecting the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and selecting the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference, includes:

[0072] Obtain message data from the time-clarified network and extract the reference time of the time-clarified network from the message data;

[0073] Obtain the first difference between the timestamp of the first data packet and the reference time, and obtain the second difference between the timestamp of the second data packet and the reference time. When the absolute value of the first difference is less than the absolute value of the second difference, select the first data packet as the valid data packet. When the absolute value of the first difference is greater than or equal to the absolute value of the second difference, select the second data packet as the valid data packet.

[0074] Specifically, when the absolute value of the first difference is less than the absolute value of the second difference, the first data packet is selected as the valid data packet; when the absolute value of the first difference is greater than or equal to the absolute value of the second difference, the second data packet is selected as the valid data packet; when the absolute value of the first difference is not less than the absolute value of the second difference, the second data packet is selected as the valid data packet; and when the absolute value of the first difference is equal to the absolute value of the second difference, either the first data packet or the second data packet is selected as the valid data packet.

[0075] Time Aware Networking (TAN) is an Ethernet architecture that uses high-precision time synchronization as its core to ensure deterministic data transmission and time-trackability.

[0076] In the topology analysis scenario of distribution networks, the reference time provided by the time-clear network can serve as a unified time standard.

[0077] Specifically, the first difference between the timestamp of the first data packet and the reference time is calculated. The smaller the absolute value of the first difference, the shorter the transmission delay of the first data packet, and the closer the collected data carried by the first data packet is to the real-time status of the acquisition device. In the application scenario of distribution network topology analysis, the collected data carried by the first data packet can more accurately reflect the electrical quantity data at the current moment. This avoids topology analysis errors caused by data delay and ensures the timeliness and transmission reliability of the collected data carried by the first data packet.

[0078] S203: Obtain the electrical quantity data of the distribution network at the current moment from the valid data packet, input the electrical quantity data of the distribution network at the current moment into the trained convolutional neural network model, and generate the switch state data of the distribution network at the current moment from the trained convolutional neural network model.

[0079] The trained convolutional neural network model, through deep learning mechanisms, possesses automatic recognition capabilities, thus generating switch status data of the distribution network at the current moment.

[0080] S204: Input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment.

[0081] The adjacency list at the current moment is a graph-based data structure used in distribution network topology analysis.

[0082] S205: Divide the multiple nodes in the adjacency list into multiple regions, select the bus node of each region as the traversal start node of each region, create a corresponding worker thread for each region, and perform a breadth-first search traversal operation on each region starting from the traversal start node of each region to obtain the connection tree of each region. Merge the connection trees of each region to generate the connection tree of the distribution network, convert the connection tree of the distribution network into the topology matrix of the distribution network, input the topology matrix of the distribution network into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

[0083] For ease of explanation, please refer to Figure 6 , Figure 6 Here is a sample diagram of the distribution network topology at the current moment, detailed below:

[0084] The purpose of a topology diagram is to visually represent the connection methods of all nodes in the distribution network, the installation locations of various equipment, and the path logic of power transmission. This supports power grid dispatching and analysis, and helps improve fault diagnosis efficiency and power supply optimization.

[0085] The topology diagram includes nodes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, sectionalizing switches, tie switches, and circuit breakers.

[0086] Section switches are used to divide line sections, isolate faulty sections during a fault, and ensure continuous power supply to non-faulty sections.

[0087] Interconnection switch: Enables communication between different sections, serves as a normal backup, and switches power supply during faults, thereby improving power supply reliability.

[0088] Circuit breaker: It has the function of cutting off and connecting load current and fault current, and can quickly trip to protect the line in case of fault.

[0089] Topology diagrams can improve troubleshooting efficiency and power supply optimization. Details are as follows:

[0090] Fault identification: The circuit breaker at node 3 trips, power supply to the branch lines from node 23 to node 25 is interrupted, and the system triggers an alarm.

[0091] Fault isolation: After confirming that the circuit breaker at node 3 is in the open state, isolate the branch lines from node 23 to node 25 to avoid affecting the main line from node 1 to node 18.

[0092] Power supply restoration: Check the connection switch between node 25 and node 26 and the status of the circuit breaker at node 26. After the closing conditions are met, close the connection switch between node 25 and node 26, and reverse the power supply from the branch line from node 26 to node 33, thereby restoring the power supply to the branch line from node 23 to node 25.

[0093] The method for generating a distribution network topology map includes the following steps: dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the starting node for traversal, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the starting node to obtain the connection tree of each region. The connection trees of each region are then merged to generate the connection tree of the distribution network. The connection tree of the distribution network is then converted into a topology matrix of the distribution network. The topology matrix of the distribution network is then input into a drawing tool, which generates the topology map of the distribution network at the current time.

[0094] Create a display window to show the topology of the distribution network at the current moment.

[0095] The beneficial effects of the embodiments of this application are as follows:

[0096] Firstly, the multiple nodes in the adjacency list are divided into multiple regions. The bus node of each region is selected as the starting node for traversal of each region. A corresponding worker thread is created for each region. Through the worker thread corresponding to each region, a breadth-first search traversal operation is performed on each region starting from the starting node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool. The drawing tool generates the topology map of the distribution network at the current time, thus solving the technical problem of how to generate the topology map of the distribution network at the current time.

[0097] Secondly, since the topology map of the distribution network at the current moment is automatically generated without manual operation, it helps to reduce the generation time of the topology map of the distribution network at the current moment and improve the generation efficiency of the topology map of the distribution network at the current moment.

[0098] Please see Figure 3 , Figure 3The implementation flowchart of S205 provided in the embodiments of this application is described in detail below:

[0099] S301: Divide multiple nodes in the adjacency list into multiple regions, select the parent node of each region as the traversal start node of each region, obtain the creation instruction from the configuration file, and create the worker thread corresponding to each region by executing the creation instruction;

[0100] S302: Using the working thread corresponding to each region, starting from the traversal start node in each region, perform a breadth-first search traversal operation on each region to obtain the connection tree of each region. Merge the connection trees of each region to generate the connection tree of the distribution network. Convert the connection tree of the distribution network into the topology matrix of the distribution network. Input the topology matrix of the distribution network into the drawing tool. The drawing tool generates the topology map of the distribution network at the current time.

[0101] In this embodiment of the application, since the topology map of the distribution network at the current moment is automatically generated, it is not affected by human intervention, which helps to improve the reliability of the topology map of the distribution network at the current moment.

[0102] For the distribution network topology generation method described in the above embodiments, please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic block diagram of the power distribution network topology generation device provided in the embodiments of this application. Figure 4 The power distribution network topology generation device 400 shown can be applied to, for example... Figure 1 The application scenario diagram shows electronic devices. The following section uses electronic devices as an example to illustrate this. Figure 4 The distribution network topology diagram generation device 400 shown will be described in detail. The distribution network topology diagram generation device 400 may include a receiving module 401, a first acquisition module 402, a second acquisition module 403, an input module 404, and a generation module 405.

[0103] The receiving module 401 is used to receive a connection request sent by the data acquisition device of the power distribution network, establish a 5G dual channel between the device and the data acquisition device of the power distribution network according to the connection request, receive a first data packet sent by the data acquisition device through the first channel of the 5G dual channel, and receive a second data packet sent by the data acquisition device through the second channel of the 5G dual channel.

[0104] The first acquisition module 402 is used to acquire the reference time of the time-clear network, acquire the first difference between the timestamp of the first data packet and the reference time, acquire the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and select the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference.

[0105] The second acquisition module 403 is used to acquire electrical quantity data of the distribution network at the current time from the valid data packet, input the electrical quantity data of the distribution network at the current time into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current time.

[0106] Input module 404 is used to input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current time.

[0107] The generation module 405 is used to divide multiple nodes in the adjacency list into multiple regions, select the bus node of each region as the traversal start node of each region, create a corresponding worker thread for each region, and perform a breadth-first search traversal operation on each region starting from the traversal start node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network, and the connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

[0108] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0109] The beneficial effects of the embodiments of this application are as follows:

[0110] Firstly, the multiple nodes in the adjacency list are divided into multiple regions. The bus node of each region is selected as the starting node for traversal of each region. A corresponding worker thread is created for each region. Through the worker thread corresponding to each region, a breadth-first search traversal operation is performed on each region starting from the starting node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool. The drawing tool generates the topology map of the distribution network at the current time, thus solving the technical problem of how to generate the topology map of the distribution network at the current time.

[0111] Secondly, since the topology map of the distribution network at the current moment is automatically generated without manual operation, it helps to reduce the generation time of the topology map of the distribution network at the current moment and improve the generation efficiency of the topology map of the distribution network at the current moment.

[0112] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0113] like Figure 5 As shown, Figure 5 The electronic device 2 includes: at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, wherein the processor 20 executes the computer program 22 to implement the steps in any of the above method embodiments.

[0114] The electronic device 2 may include, but is not limited to, a processor 20 and a memory 21. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 2 and does not constitute a limitation on electronic device 2. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0115] The processor 20 is used to run a computer program 22 stored in the memory 21, and performs the following steps when executing the computer program 22:

[0116] The device receives a connection request from the data acquisition equipment of the power distribution network. Based on the connection request, it establishes a 5G dual channel between the device and the data acquisition equipment of the power distribution network. It receives a first data packet from the data acquisition equipment through the first channel of the 5G dual channel and a second data packet from the data acquisition equipment through the second channel of the 5G dual channel.

[0117] Obtain the reference time of the time-clear network, obtain the first difference between the timestamp of the first data packet and the reference time, obtain the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as the valid data packet when the absolute value of the first difference is less than the absolute value of the second difference; select the second data packet as the valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference.

[0118] Obtain electrical quantity data of the distribution network at the current moment from the valid data packets, input the electrical quantity data of the distribution network at the current moment into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current moment.

[0119] Input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment;

[0120] The adjacency list is divided into multiple regions. The bus node of each region is selected as the starting node for traversal. A worker thread is created for each region. The worker thread performs a breadth-first search traversal operation on each region, starting from the starting node, to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, which generates the topology map of the distribution network at the current time.

[0121] The processor 20 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors, field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0122] In some embodiments, the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2. In other embodiments, the memory 21 may be an external storage device of the electronic device 2, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device 2.

[0123] Furthermore, the memory 21 may include both internal storage units and external storage devices of the electronic device 2. The memory 21 is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0124] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0125] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0126] The computer-readable storage medium may also be an external storage device of the power distribution network topology generation device or electronic device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, or non-transitory computer-readable storage medium equipped on the power distribution network topology generation device or electronic device.

[0127] Since the computer program stored in the computer-readable storage medium can execute any of the distribution network topology map generation methods based on time-clear networks provided in the embodiments of this application, the computer-readable storage medium can achieve the beneficial effects that any of the distribution network topology map generation methods based on time-clear networks provided in the embodiments of this application can achieve, as detailed in the preceding embodiments, and will not be repeated here.

[0128] This application provides a computer program product that, when run on an electronic device, causes the electronic device to execute the above-described power distribution network topology generation method.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0130] Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium includes: an entity or device for carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium.

[0131] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0132] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for generating a distribution network topology map based on a time-defined network, characterized in that, The power distribution network topology generation method, applied to electronic equipment, includes: The device receives a connection request from the data acquisition equipment of the power distribution network. Based on the connection request, it establishes a 5G dual channel between the device and the data acquisition equipment of the power distribution network. It receives a first data packet from the data acquisition equipment through the first channel of the 5G dual channel and a second data packet from the data acquisition equipment through the second channel of the 5G dual channel. Obtain the reference time of the time-clear network, obtain the first difference between the timestamp of the first data packet and the reference time, obtain the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as the valid data packet when the absolute value of the first difference is less than the absolute value of the second difference; select the second data packet as the valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference. Obtain electrical quantity data of the distribution network at the current moment from the valid data packets, input the electrical quantity data of the distribution network at the current moment into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current moment. Input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment; The adjacency list is divided into multiple regions. The bus node of each region is selected as the starting node for traversal. A worker thread is created for each region. The worker thread performs a breadth-first search traversal operation on each region, starting from the starting node, to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, which generates the topology map of the distribution network at the current time.

2. The method for generating a power distribution network topology map according to claim 1, characterized in that, Before establishing a 5G dual-channel connection between this device and the power distribution network acquisition device, and before receiving a first data packet sent by the acquisition device through the first channel of the 5G dual-channel connection and a second data packet sent by the acquisition device through the second channel of the 5G dual-channel connection, the power distribution network topology generation method includes: Obtain electrical quantity data of the distribution network at a preset time, combine the electrical quantity data of the distribution network at the preset time and the switch status data of the distribution network at the preset time into a sample, and combine multiple different samples into a sample set; The convolutional neural network model is trained based on the sample set. The current loss value of the convolutional neural network model on the sample set is obtained. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

3. The method for generating a power distribution network topology map according to claim 1, characterized in that, The process of obtaining a reference time from a time-clear network, obtaining a first difference between the timestamp of a first data packet and the reference time, obtaining a second difference between the timestamp of a second data packet and the reference time, and selecting the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and selecting the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference, includes: Obtain message data from the time-clarified network and extract the reference time of the time-clarified network from the message data; Obtain the first difference between the timestamp of the first data packet and the reference time, and obtain the second difference between the timestamp of the second data packet and the reference time. When the absolute value of the first difference is less than the absolute value of the second difference, select the first data packet as the valid data packet. When the absolute value of the first difference is greater than or equal to the absolute value of the second difference, select the second data packet as the valid data packet.

4. The method for generating a power distribution network topology map according to claim 1, characterized in that, The adjacency list is divided into multiple regions. The bus node of each region is selected as the starting node for traversal. A worker thread is created for each region. Starting from the starting node, a breadth-first search is performed on each region to obtain its connection tree. These connection trees are then merged to generate the distribution network's connection tree. This connection tree is converted into a topology matrix, which is then input into a plotting tool. The tool generates the current topology map of the distribution network, including: Divide multiple nodes in the adjacency list into multiple regions, select the parent node of each region as the starting node for traversal of each region, obtain the creation instructions from the configuration file, and create the corresponding worker thread for each region by executing the creation instructions; Starting from the traversal start node in each region, the worker thread corresponding to each region performs a breadth-first search traversal operation to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network. The connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

5. The method for generating a power distribution network topology map according to claim 1, characterized in that, The method for generating a distribution network topology map includes: dividing multiple nodes in the adjacency list into multiple regions, selecting the bus node of each region as the starting node for traversal, creating a corresponding worker thread for each region, and performing a breadth-first search traversal operation on each region starting from the starting node to obtain the connection tree of each region. The connection trees of each region are then merged to generate the connection tree of the distribution network. The connection tree of the distribution network is then converted into the topology matrix of the distribution network. The topology matrix of the distribution network is then input into a drawing tool, which generates the topology map of the distribution network at the current time. Create a display window to show the topology of the distribution network at the current moment.

6. The method for generating a power distribution network topology map according to claim 2, characterized in that, The process of training a convolutional neural network model based on a sample set, obtaining the current loss value of the convolutional neural network model on the sample set, stopping training the convolutional neural network model when the current loss value is less than a preset loss value, and saving the trained convolutional neural network model includes: The convolutional neural network model is trained based on a sample set. The current loss value of the convolutional neural network model on the sample set is obtained through a preset loss model. When the current loss value is less than the preset loss value, the training of the convolutional neural network model is stopped and the trained convolutional neural network model is saved.

7. The method for generating a power distribution network topology map according to claim 6, characterized in that, The loss model is defined as follows: ； in, Indicates the current loss value; This represents the total number of samples. Indicates the sample number. express The actual state of the switch in each sample. This indicates that the convolutional neural network model believes that the first... The probability value of a switch in a sample being in a closed state.

8. The method for generating a distribution network topology map according to claim 1, characterized in that, The first data packet carries the same collection data as the second data packet, but the channel identifiers carried by the first data packet and the second data packet are different. The first channel and the second channel are two parallel data transmission channels.

9. A distribution network topology generation device based on time-clear networks, characterized in that, Applied to electronic devices, including: The receiving module is used to receive connection requests sent by the data acquisition equipment of the power distribution network. Based on the connection requests, it establishes a 5G dual channel between the device and the data acquisition equipment of the power distribution network. It receives the first data packet sent by the data acquisition equipment through the first channel of the 5G dual channel and the second data packet sent by the data acquisition equipment through the second channel of the 5G dual channel. The first acquisition module is used to acquire the reference time of the time-clear network, acquire the first difference between the timestamp of the first data packet and the reference time, acquire the second difference between the timestamp of the second data packet and the reference time, and select the first data packet as a valid data packet when the absolute value of the first difference is less than the absolute value of the second difference, and select the second data packet as a valid data packet when the absolute value of the first difference is greater than or equal to the absolute value of the second difference. The second acquisition module is used to acquire electrical quantity data of the distribution network at the current time from the valid data packet, input the electrical quantity data of the distribution network at the current time into the trained convolutional neural network model, and the trained convolutional neural network model generates the switching state data of the distribution network at the current time. The input module is used to input the current switch status data of the distribution network into the distribution network automation system, and the distribution network automation system generates the adjacency list for the current moment. The generation module divides multiple nodes in the adjacency list into multiple regions, selects the bus node of each region as the traversal start node of each region, creates a corresponding worker thread for each region, and performs a breadth-first search traversal operation on each region starting from the traversal start node of each region to obtain the connection tree of each region. The connection trees of each region are merged to generate the connection tree of the distribution network, and the connection tree of the distribution network is converted into the topology matrix of the distribution network. The topology matrix of the distribution network is input into the drawing tool, and the drawing tool generates the topology map of the distribution network at the current time.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the power distribution network topology generation method as described in any one of claims 1 to 7.

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