An electrical node aggregation method and apparatus based on graph theory

Abstract

The application discloses an electrical node aggregation method and device based on graph theory, comprising: acquiring operation data information of an electrical system, and constructing a graph theory model of the electrical system; performing first aggregation on electrical nodes in the graph theory model to obtain a plurality of central electrical nodes, and initializing each central electrical node; traversing each initialized central electrical node, and performing second aggregation on each central electrical node, so that when the second aggregation is performed on each central electrical node, the connection weight of the central electrical node and all neighbor nodes of the central electrical node is calculated according to the electrical transmission power, the electrical output voltage or the electrical output loss corresponding to the central electrical node and the neighbor nodes, and the central electrical node and the neighbor nodes are aggregated; until a preset termination condition is reached, a central electrical node set after the second aggregation is output, and electrical information of the central electrical node set is displayed, so that the aggregation of the electrical nodes is completed.

Description

Technical Field

[0001] This invention relates to the field of electrical system technology, and in particular to a graph theory-based method and apparatus for aggregating electrical nodes. Background Technology

[0002] An electrical system is a system composed of low-voltage power supply components, also known as a low-voltage power distribution system or low-voltage power distribution line.

[0003] Currently, the backbone network of power communication is basically a composite network structure combining tree and star topologies. As a dedicated communication network for the power system, the shortest path between electrical nodes in the electrical system describes the distance between nodes. The average shortest distance of the network reflects the tightness between network nodes and can be used to measure the overall reliability of the network connection. However, most current power communication network models only represent nodes and links. The tightness between electrical nodes is described by the shortest path between them, which makes the accuracy of the aggregated information of electrical nodes not high and makes it difficult to display the aggregated information of electrical nodes.

[0004] Therefore, there is an urgent need for a method that can improve the accuracy and convenience of displaying aggregated information of electrical nodes. Summary of the Invention

[0005] This invention provides a graph theory-based method and apparatus for aggregating electrical nodes, which solves the technical problem that the accuracy of the aggregation information display of electrical nodes is not high in the prior art, and it is difficult to display the aggregation information of electrical nodes.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a graph-based method for electrical node aggregation, comprising:

[0007] Obtain operational data information of the electrical system, and construct a graph theory model of the electrical system based on the operational data information;

[0008] The electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes, and each central electrical node is set as an independent aggregation unit;

[0009] Traverse each central electrical node and perform a second aggregation on each central electrical node so that when performing the second aggregation on each central electrical node, obtain the neighboring nodes of the central electrical node, and calculate the connection weight of the central electrical node and all its neighboring nodes based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes. Then, aggregate the central electrical node and its neighboring nodes based on the obtained connection weights.

[0010] Until the preset termination condition is reached, the second aggregated set of central electrical nodes is output, and the electrical information of the set of central electrical nodes is displayed, thereby completing the aggregation of electrical nodes.

[0011] As a preferred embodiment, the step of acquiring the operating data information of the electrical system and constructing a graph theory model of the electrical system based on the operating data information specifically involves:

[0012] The system acquires electrical node equipment information, voltage information, and power information, and constructs the node positions, edges, and degree distribution of the electrical system's graph theory model based on the electrical node equipment information, voltage information, power information, and loss information, thereby constructing the graph theory model of the electrical system.

[0013] As a preferred embodiment, the first aggregation of electrical nodes in the graph theory model to obtain several central electrical nodes, and the initialization of each central electrical node, specifically involves:

[0014] By using K-means clustering, agglomerative clustering, or DBSCA algorithms, and taking power substations, feeders, and other electrical unit information as clustering targets, the electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes; wherein, the other electrical unit information refers to the electrical unit node information other than power substations and feeders.

[0015] Each of the obtained central electrical nodes is set as an independent aggregation unit, thereby completing the initialization of the central electrical nodes.

[0016] As a preferred embodiment, the step of obtaining the neighboring nodes of the central electrical node and calculating the connection weight between the central electrical node and all its neighboring nodes based on the electrical transmission power corresponding to the central electrical node and its neighboring nodes is specifically as follows:

[0017] The nodes directly connected to the central electrical node are designated as neighboring nodes, and the weight attributes of the edges between the central electrical node and its connected neighboring nodes are determined as electrical transmission power attribute, electrical output voltage attribute, or electrical output loss attribute.

[0018] The connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage, or electrical output loss of the central electrical node and its neighboring nodes.

[0019] As a preferred embodiment, the aggregation of the central electrical node and its neighboring nodes based on the obtained connection weights specifically involves:

[0020] Based on the obtained connection weights, select the neighboring node with the smallest connection weight to the central electrical node, and aggregate it with the central electrical node so that the edge between the central electrical node and the aggregated neighboring node is updated to the edge between the new central electrical node and the new central electrical node's neighboring nodes.

[0021] As a preferred embodiment, the preset termination conditions include: when all central electrical nodes are aggregated into one aggregation unit, or when the number of aggregation units into which all central electrical nodes are aggregated is less than a preset value.

[0022] As a preferred option, the operational data information is obtained through real-time monitoring, historical records, or simulation of the electrical system.

[0023] Accordingly, the present invention also provides a graph theory-based electrical node aggregation device, comprising: an acquisition module, a first aggregation module, a second aggregation module, and an output module;

[0024] The acquisition module is used to acquire the operating data information of the electrical system and construct a graph theory model of the electrical system based on the operating data information.

[0025] The first aggregation module is used to perform a first aggregation on the electrical nodes in the graph theory model to obtain a number of central electrical nodes, and set each central electrical node as an independent aggregation unit;

[0026] The second aggregation module is used to traverse each central electrical node and perform a second aggregation on each central electrical node, so that when performing the second aggregation on each central electrical node, the neighboring nodes of the central electrical node are obtained, and the connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes, and then the central electrical node and its neighboring nodes are aggregated based on the obtained connection weights.

[0027] The output module is used to output a second aggregated set of central electrical nodes until a preset termination condition is met, and to display the electrical information of the set of central electrical nodes, thereby completing the aggregation of electrical nodes.

[0028] Accordingly, the present invention also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the graph theory-based electrical node aggregation method as described above.

[0029] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the graph theory-based electrical node aggregation method as described above.

[0030] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0031] The technical solution of this invention acquires the operational data of an electrical system to construct a graph theory model of the electrical system. Then, it performs a first aggregation on the electrical nodes in the graph theory model to obtain several central electrical nodes. By traversing each central electrical node, a second aggregation is performed on each central electrical node. This aggregation is achieved by calculating the connection weights between the central electrical node and all its neighboring nodes based on the electrical transmission power, electrical output voltage, or electrical output loss corresponding to the central electrical node and its neighboring nodes. This ensures the accuracy of the aggregation information of the electrical nodes, resulting in a set of central electrical nodes after the second aggregation. By using different edge attributes as weights, the electrical graph connections can be aggregated according to different electrical conditions, thus achieving different aggregation situations. This facilitates the display of electrical information based on graph theory information and improves the convenience of aggregation. Finally, the set of central electrical nodes is used to display the electrical information, achieving accurate aggregation information display of the electrical nodes. Attached Figure Description

[0032] Figure 1 : A flowchart illustrating the steps of a graph theory-based electrical node aggregation method provided in an embodiment of the present invention;

[0033] Figure 2 : This is a structural diagram of an electrical node aggregation device based on graph theory provided in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1

[0036] Please refer to Figure 1 The present invention provides a graph-based method for aggregating electrical nodes, comprising the following steps S101-S104:

[0037] Step S101: Obtain the operating data information of the electrical system, and construct a graph theory model of the electrical system based on the operating data information.

[0038] As a preferred embodiment, the operational data information is obtained through real-time monitoring, historical records, or simulation of the electrical system.

[0039] As a preferred embodiment, the step of acquiring the operating data information of the electrical system and constructing a graph theory model of the electrical system based on the operating data information specifically involves:

[0040] The system acquires electrical node equipment information, voltage information, and power information, and constructs the node positions, edges, and degree distribution of the electrical system's graph theory model based on the electrical node equipment information, voltage information, power information, and loss information, thereby constructing the graph theory model of the electrical system.

[0041] In this embodiment, the information that the graph theory model of the electrical system can describe includes, but is not limited to, one or more of the following attributes: node location, edge and degree distribution, namely voltage, power information and loss information, etc. The operating data information is obtained through real-time monitoring, historical records and simulation.

[0042] Step S102: Perform a first aggregation on the electrical nodes in the graph theory model to obtain several central electrical nodes, and set each central electrical node as an independent aggregation unit.

[0043] As a preferred embodiment of the present invention, the first aggregation of electrical nodes in the graph theory model to obtain a plurality of central electrical nodes, and the initialization of each central electrical node, specifically involves:

[0044] By using K-means clustering, agglomerative clustering, or DBSCA algorithms, and taking power substations, feeders, and other electrical unit information as clustering targets, the electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes; wherein, the other electrical unit information refers to the electrical unit node information other than power substations and feeders; each of the obtained central electrical nodes is set as an independent aggregation unit, thereby completing the initialization of the central electrical nodes.

[0045] In this embodiment, the aggregation method includes, but is not limited to, one or more of K-means clustering, agglomerative clustering, and DBSCA. The selection of an appropriate clustering algorithm depends on the characteristics of the dataset and the aggregation objective. K-means clustering is a commonly used clustering algorithm used to divide the samples in the dataset into K different clusters. The core idea of ​​this algorithm is to determine the cluster to which a sample point belongs by calculating the distance between the sample point and the cluster center, and then update the cluster center, iterating continuously until the algorithm converges.

[0046] It should be noted that K-means clustering initially selects K initial cluster centers. For each electrical node, it calculates its distance to each cluster center and classifies it into the cluster with the closest distance. The cluster center is then updated to be the average value of electrical nodes within each cluster. The above steps are repeated until the cluster centers no longer change or the predetermined number of iterations is reached.

[0047] It should be noted that agglomerative clustering is a hierarchical clustering algorithm, also known as bottom-up clustering. It first treats each electrical node as an initial single cluster; calculates the similarity or distance between clusters, and merges the two clusters with the smallest distance into a new cluster; updates the similarity matrix or distance matrix; and repeats the above steps until all electrical nodes are merged into one cluster or the predetermined number of clusters is reached.

[0048] It should be noted that DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. This algorithm clusters electrical nodes by grouping them into different core objects, boundary objects, and noise objects. The clustering of electrical nodes depends on the density of the surrounding electrical nodes. Unlike K-means and agglomerative clustering, DBSCAN can discover clusters of arbitrary shapes.

[0049] Step S103: Traverse each central electrical node and perform a second aggregation on each central electrical node so that when performing the second aggregation on each central electrical node, the neighboring nodes of the central electrical node are obtained, and the connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes. Then, the central electrical node and its neighboring nodes are aggregated based on the obtained connection weights.

[0050] As a preferred embodiment, the step of obtaining the neighboring nodes of the central electrical node and calculating the connection weight between the central electrical node and all its neighboring nodes based on the electrical transmission power corresponding to the central electrical node and its neighboring nodes specifically involves:

[0051] The nodes directly connected to the central electrical node are designated as neighboring nodes, and the weight attributes of the edges between the central electrical node and its connected neighboring nodes are determined as electrical transmission power attribute, electrical output voltage attribute, or electrical output loss attribute. Based on the electrical transmission power, electrical output voltage, or electrical output loss corresponding to the central electrical node and its neighboring nodes, the connection weights between the central electrical node and all its neighboring nodes are calculated.

[0052] In this embodiment, the neighboring nodes of the current central electrical node are obtained, i.e., directly connected nodes. The connection weight of a neighboring node refers to the relative importance or strength of the connection between central electrical nodes in a graph or network. These connection weights can be used to represent a certain relationship or similarity between central electrical nodes. Typically, connection weights can be determined in the following ways: Distance or proximity: The connection weight between nodes can be determined by calculating the spatial distance or similarity between nodes. For example, the closer the distance or similarity between two objects, the higher the connection weight. Relevance or correlation: The connection weight between nodes can be determined by calculating their relevance or correlation. For example, in a social network, the connection between nodes can be determined by shared interests, friendships, or jointly participated activities. Strength or influence: The connection weight between nodes can be determined based on the intensity or influence of their interaction. For example, in an information dissemination network, the connection between nodes can be determined based on the frequency or magnitude of message dissemination. It should be noted that the specific calculation method for connection weights can be determined according to the application scenario and specific needs. Mathematical models, machine learning algorithms, or rules of thumb can be used to determine the connection weights between nodes, or they can be determined manually.

[0053] Furthermore, the connection weights between the current node and its neighboring nodes are calculated, using the electrical transmission power attribute of the edges as the weight attribute for weight calculation. The electrical transmission power attribute information is used to determine the neighboring nodes and connection weights, thus enabling node aggregation in power graph theory based on the electrical transmission power attribute.

[0054] In a preferred embodiment, the aggregation of the central electrical node and its neighboring nodes based on the obtained connection weights specifically involves:

[0055] Based on the obtained connection weights, select the neighboring node with the smallest connection weight to the central electrical node, and aggregate it with the central electrical node so that the edge between the central electrical node and the aggregated neighboring node is updated to the edge between the new central electrical node and the new central electrical node's neighboring nodes.

[0056] In this embodiment, the neighbor node with the smallest connection weight to the current central electrical node is selected and aggregated with it, thereby updating the edge between the current central electrical node and the aggregated neighbor node to the edge between the new central electrical node and its neighbor node.

[0057] Understandably, the method of traversing the central electrical node involves obtaining the neighboring nodes of the current central electrical node (i.e., directly connected central electrical nodes), calculating the connection weight between the current node and its neighbors, using the edge attributes as weights, selecting the neighbor with the smallest connection weight to the current node, and aggregating it. The edge between the current node and the aggregated neighbor is then updated to reflect the new node's connection to its neighbors. This allows for the aggregation of nodes in the electrical graph theory, improving the ease of aggregation. Furthermore, by using different edge attributes as weights, the electrical graph connections can be aggregated based on different electrical conditions, resulting in different aggregation scenarios and facilitating the display of electrical information based on graph theory data.

[0058] Step S104: Until the preset termination condition is reached, the second aggregated set of central electrical nodes is output, and the electrical information of the set of central electrical nodes is displayed, thereby completing the aggregation of electrical nodes.

[0059] As a preferred embodiment, the preset termination conditions include: when all central electrical nodes are aggregated into one aggregation unit, or when the number of aggregation units into which all central electrical nodes are aggregated is less than a preset value.

[0060] In this embodiment, the termination condition is when all central electrical nodes are aggregated into one aggregation unit, or when all nodes are aggregated into fewer than a preset number of aggregation units, i.e., the number of aggregation units obtained is less than a preset value. Aggregation can be stopped when either of the above conditions is met. The preset number of aggregation units can be set to the number of electrical graph theory node aggregation units, so that electrical graph theory node information can be aggregated according to the desired information and the number of aggregation units.

[0061] It is understood that, by utilizing the different attributes of edges as weights, the electrical graph connections can be aggregated according to different electrical conditions, thereby achieving different aggregation states. This facilitates the display of electrical information based on graph theory information. The vertices of the graph are called nodes, where nodes represent entities in the network, edges represent the connections between nodes, and aggregation in the network refers to the collection of various information. Therefore, by using the different attributes of edges as weights, the electrical graph connections can be aggregated according to different electrical conditions, thereby achieving different aggregation states. This facilitates the display of electrical information based on graph theory information.

[0062] Implementing the above embodiments has the following effects:

[0063] The technical solution of this invention acquires the operational data of an electrical system to construct a graph theory model of the electrical system. Then, it performs a first aggregation on the electrical nodes in the graph theory model to obtain several central electrical nodes. By traversing each central electrical node, a second aggregation is performed on each central electrical node. This aggregation is achieved by calculating the connection weights between the central electrical node and all its neighboring nodes based on the electrical transmission power, electrical output voltage, or electrical output loss corresponding to the central electrical node and its neighboring nodes. This ensures the accuracy of the aggregation information of the electrical nodes, resulting in a set of central electrical nodes after the second aggregation. By using different edge attributes as weights, the electrical graph connections can be aggregated according to different electrical conditions, thus achieving different aggregation situations. This facilitates the display of electrical information based on graph theory information and improves the convenience of aggregation. Finally, the set of central electrical nodes is used to display the electrical information, achieving accurate aggregation information display of the electrical nodes.

[0064] Example 2

[0065] Please see Figure 2 The present invention provides a graph theory-based electrical node aggregation device, comprising: an acquisition module 201, a first aggregation module 202, a second aggregation module 203, and an output module 204.

[0066] The acquisition module 201 is used to acquire the operating data information of the electrical system and construct a graph theory model of the electrical system based on the operating data information.

[0067] The first aggregation module 202 is used to perform a first aggregation on the electrical nodes in the graph theory model to obtain a number of central electrical nodes, and set each central electrical node as an independent aggregation unit.

[0068] The second aggregation module 203 is used to traverse each central electrical node and perform a second aggregation on each central electrical node, so that when performing the second aggregation on each central electrical node, the neighboring nodes of the central electrical node are obtained, and the connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes, and then the central electrical node and its neighboring nodes are aggregated based on the obtained connection weights.

[0069] The output module 204 is used to output a second aggregated set of central electrical nodes until a preset termination condition is met, and to display the electrical information of the set of central electrical nodes, thereby completing the aggregation of electrical nodes.

[0070] As a preferred embodiment, the step of acquiring the operating data information of the electrical system and constructing a graph theory model of the electrical system based on the operating data information specifically involves:

[0071] The system acquires electrical node equipment information, voltage information, and power information, and constructs the node positions, edges, and degree distribution of the electrical system's graph theory model based on the electrical node equipment information, voltage information, power information, and loss information, thereby constructing the graph theory model of the electrical system.

[0072] As a preferred embodiment, the first aggregation of electrical nodes in the graph theory model to obtain several central electrical nodes, and the initialization of each central electrical node, specifically involves:

[0073] By using K-means clustering, agglomerative clustering, or DBSCA algorithms, and taking power substations, feeders, and other electrical unit information as clustering targets, the electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes; wherein, the other electrical unit information refers to the electrical unit node information other than power substations and feeders.

[0074] Each of the obtained central electrical nodes is set as an independent aggregation unit, thereby completing the initialization of the central electrical nodes.

[0075] As a preferred embodiment, the step of obtaining the neighboring nodes of the central electrical node and calculating the connection weight between the central electrical node and all its neighboring nodes based on the electrical transmission power corresponding to the central electrical node and its neighboring nodes is specifically as follows:

[0076] The nodes directly connected to the central electrical node are designated as neighboring nodes, and the weight attributes of the edges between the central electrical node and its connected neighboring nodes are determined as electrical transmission power attribute, electrical output voltage attribute, or electrical output loss attribute.

[0077] The connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage, or electrical output loss of the central electrical node and its neighboring nodes.

[0078] As a preferred embodiment, the aggregation of the central electrical node and its neighboring nodes based on the obtained connection weights specifically involves:

[0079] Based on the obtained connection weights, select the neighboring node with the smallest connection weight to the central electrical node, and aggregate it with the central electrical node so that the edge between the central electrical node and the aggregated neighboring node is updated to the edge between the new central electrical node and the new central electrical node's neighboring nodes.

[0080] As a preferred embodiment, the preset termination conditions include: when all central electrical nodes are aggregated into one aggregation unit, or when the number of aggregation units into which all central electrical nodes are aggregated is less than a preset value.

[0081] As a preferred option, the operational data information is obtained through real-time monitoring, historical records, or simulation of the electrical system.

[0082] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0083] Implementing the above embodiments has the following effects:

[0084] The technical solution of this invention acquires the operational data of an electrical system to construct a graph theory model of the electrical system. Then, it performs a first aggregation on the electrical nodes in the graph theory model to obtain several central electrical nodes. By traversing each central electrical node, a second aggregation is performed on each central electrical node. This aggregation is achieved by calculating the connection weights between the central electrical node and all its neighboring nodes based on the electrical transmission power, electrical output voltage, or electrical output loss corresponding to the central electrical node and its neighboring nodes. This ensures the accuracy of the aggregation information of the electrical nodes, resulting in a set of central electrical nodes after the second aggregation. By using different edge attributes as weights, the electrical graph connections can be aggregated according to different electrical conditions, thus achieving different aggregation situations. This facilitates the display of electrical information based on graph theory information and improves the convenience of aggregation. Finally, the set of central electrical nodes is used to display the electrical information, achieving accurate aggregation information display of the electrical nodes.

[0085] Example 3

[0086] Accordingly, the present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the graph theory-based electrical node aggregation method as described in any of the above embodiments.

[0087] The terminal device in this embodiment includes a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps described in Embodiment 1 above, for example... Figure 1 The steps S101 to S104 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiment, such as the first aggregation module 202.

[0088] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device. For example, the first aggregation module 202 is used to perform a first aggregation of electrical nodes in the graph theory model to obtain several central electrical nodes, and to set each central electrical node as an independent aggregation unit.

[0089] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0090] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0091] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0092] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0093] Example 4

[0094] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the graph theory-based electrical node aggregation method as described in any of the above embodiments.

[0095] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A graph theory-based method for electrical node aggregation, characterized in that, include: Obtain operational data information of the electrical system, and construct a graph theory model of the electrical system based on the operational data information. The operational data information includes equipment information, voltage information, power information, and device information of electrical nodes. The electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes, and each central electrical node is initialized. The initialization process is designed to set each central electrical node as an independent aggregation unit. Traverse each initialized central electrical node and perform a second aggregation on each central electrical node so that when performing the second aggregation on each central electrical node, obtain the neighboring nodes of the central electrical node, and calculate the connection weight of the central electrical node and all its neighboring nodes based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes. Then, aggregate the central electrical node and its neighboring nodes based on the obtained connection weights. Until the preset termination condition is reached, the second aggregated set of central electrical nodes is output, and the electrical information of the set of central electrical nodes is displayed, thereby completing the aggregation of electrical nodes.

2. The graph-based electrical node aggregation method as described in claim 1, characterized in that, The process of acquiring operational data information of the electrical system and constructing a graph theory model of the electrical system based on the operational data information specifically involves: The system acquires electrical node equipment information, voltage information, and power information, and constructs the node positions, edges, and degree distribution of the electrical system's graph theory model based on the electrical node equipment information, voltage information, power information, and loss information, thereby constructing the graph theory model of the electrical system.

3. The graph-based electrical node aggregation method as described in claim 2, characterized in that, The first aggregation of electrical nodes in the graph theory model to obtain several central electrical nodes, and the initialization of each central electrical node, specifically involves: By using K-means clustering, agglomerative clustering, or DBSCA algorithms, and taking power substations, feeders, and other electrical unit information as clustering targets, the electrical nodes in the graph theory model are first aggregated to obtain several central electrical nodes; wherein, the other electrical unit information refers to the electrical unit node information other than power substations and feeders. Each of the obtained central electrical nodes is set as an independent aggregation unit, thereby completing the initialization of the central electrical nodes.

4. The graph-based electrical node aggregation method as described in claim 1, characterized in that, The process of obtaining the neighboring nodes of the central electrical node and calculating the connection weights of the central electrical node and all its neighboring nodes based on the electrical transmission power corresponding to the central electrical node and its neighboring nodes is as follows: The nodes directly connected to the central electrical node are designated as neighboring nodes, and the weight attributes of the edges between the central electrical node and its connected neighboring nodes are determined as electrical transmission power attribute, electrical output voltage attribute, or electrical output loss attribute. The connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage, or electrical output loss of the central electrical node and its neighboring nodes.

5. The graph-based electrical node aggregation method as described in claim 4, characterized in that, The aggregation of the central electrical node and its neighboring nodes based on the obtained connection weights is specifically as follows: Based on the obtained connection weights, select the neighboring node with the smallest connection weight to the central electrical node, and aggregate it with the central electrical node so that the edge between the central electrical node and the aggregated neighboring node is updated to the edge between the new central electrical node and the new central electrical node's neighboring nodes.

6. A graph-based electrical node aggregation method as described in any one of claims 1-5, characterized in that, The preset termination conditions include: when all central electrical nodes are aggregated into one aggregation unit, or when the number of aggregation units into which all central electrical nodes are aggregated is less than a preset value.

7. A graph-based electrical node aggregation method as described in any one of claims 1-5, characterized in that, The operational data information is obtained through real-time monitoring, historical records, or simulation of the electrical system.

8. A graph-theory-based electrical node aggregation device, characterized in that, include: The module consists of an acquisition module, a first aggregation module, a second aggregation module, and an output module. The acquisition module is used to acquire the operating data information of the electrical system and construct a graph theory model of the electrical system based on the operating data information. The operating data information includes the device information, voltage information, power information and equipment information of the electrical nodes. The first aggregation module is used to perform a first aggregation on the electrical nodes in the graph theory model to obtain a number of central electrical nodes, and set each central electrical node as an independent aggregation unit; The second aggregation module is used to traverse each central electrical node and perform a second aggregation on each central electrical node, so that when performing the second aggregation on each central electrical node, the neighboring nodes of the central electrical node are obtained, and the connection weights of the central electrical node and all its neighboring nodes are calculated based on the electrical transmission power, electrical output voltage or electrical output loss corresponding to the central electrical node and its neighboring nodes, and then the central electrical node and its neighboring nodes are aggregated based on the obtained connection weights. The output module is used to output a second aggregated set of central electrical nodes until a preset termination condition is met, and to display the electrical information of the set of central electrical nodes, thereby completing the aggregation of electrical nodes.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the graph theory-based electrical node aggregation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the graph theory-based electrical node aggregation method as described in any one of claims 1 to 7.

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