Power grid topology map determination method and apparatus, and electronic device

Abstract

The application discloses a kind of power grid topology graph determination method, device and electronic equipment.Specific scheme is: receiving topology graph construction request, and the demand information carried by topology graph construction request is analyzed and processed, determine the multiple nodes to be used and at least one line to be used corresponding in target area;At least one source system is obtained under the data to be processed, wherein, at least include: the node data of node to be used, line data of line to be used and equipment operation data of power equipment corresponding to node to be used;According to multiple nodes to be used, at least one line to be used and data to be processed, determine the target power grid topology graph corresponding to target area using G6 visualization engine, and the target power grid topology graph is displayed in target terminal.The application generates and presents the target power grid topology graph of target area by G6 visualization engine, improves the drawing efficiency and accuracy of topology graph.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, and electronic device for determining a power grid topology. Background Technology

[0002] Power grid planning, also known as transmission system planning, is based on load forecasting and power equipment planning. It aims to determine when and where to build what type of transmission lines or power equipment to meet local power transmission needs.

[0003] Power planning typically utilizes power grid topology diagrams. However, in current technology, these diagrams are primarily drawn manually using CAD technology. This manual method not only consumes significant manpower and time, resulting in low efficiency, but also easily leads to inaccuracies, thus impacting subsequent applications. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining power grid topology maps. By using the G6 visualization engine, it realizes the generation and presentation of target power grid topology maps of target areas, thereby improving the efficiency and accuracy of topology map drawing.

[0005] According to one aspect of the present invention, a method for determining a power grid topology is provided, the method comprising:

[0006] Receive a topology map construction request, parse and process the requirement information carried in the topology map construction request, and determine multiple nodes to be used and at least one line to be used in the target area. The nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area.

[0007] Acquire at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used;

[0008] Based on multiple nodes to be used, at least one line to be used, and data to be processed, the target power grid topology map corresponding to the target area is determined using the G6 visualization engine, and the target power grid topology map is displayed on the target terminal.

[0009] According to another aspect of the present invention, a power grid topology determination apparatus is provided, the apparatus comprising:

[0010] The information parsing module is used to receive the topology map construction request, and to parse and process the requirement information carried in the topology map construction request to determine multiple nodes to be used and at least one line to be used in the target area. The nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area.

[0011] The data acquisition module is used to acquire at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used;

[0012] The topology display module is used to determine the target power grid topology corresponding to the target area based on multiple nodes to be used, at least one line to be used, and data to be processed, using the G6 visualization engine, and then display the target power grid topology on the target terminal.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the power grid topology determination method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the power grid topology determination method of any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, characterized in that the computer program, when executed by a processor, implements a power grid topology determination method as described in any embodiment of the present invention.

[0019] The technical solution of this invention receives a topology map construction request and parses the requirement information carried in the request to determine multiple nodes to be used and at least one line to be used within the target area. It also acquires data to be processed from at least one source system. Based on the multiple nodes to be used, at least one line to be used, and the data to be processed, the G6 visualization engine is used to determine the target power grid topology map corresponding to the target area, and the target power grid topology map is displayed on the target terminal. This invention solves the problems of low drawing efficiency and inaccuracy caused by manual topology map drawing in the prior art. By using the G6 visualization engine, it realizes the generation and presentation of the target power grid topology map of the target area, improving the drawing efficiency and accuracy of the topology map, thereby achieving intuitive display and efficient management of power grid planning, and improving the efficiency and accuracy of planning work.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining a power grid topology according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining a power grid topology according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a power grid topology determination device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the power grid topology determination method of this invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a power grid topology determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where a target power grid topology map corresponding to a target area is generated using the G6 visualization engine. This method can be executed by a power grid topology determination device, which can be implemented in hardware and / or software. This power grid topology determination device can be configured in electronic devices such as mobile phones, computers, or servers. Figure 1 As shown, the method includes:

[0030] S110. Receive the topology map construction request, and parse the requirement information carried in the topology map construction request to determine the multiple nodes to be used and at least one line to be used in the target area.

[0031] Among them, the nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area.

[0032] In a power system, to demonstrate the connectivity of power equipment and transmission lines within a target area, a topology map construction request is initiated. This allows the receiving device or system to construct a topology map based on the power equipment and transmission lines within the target area. Optionally, the topology map can be a ring network topology diagram of the target area. The requirement information carried in the topology map construction request may include multiple nodes to be displayed in the target area and at least one line to be displayed. The target area can be understood as the region where the connectivity between its power equipment and transmission lines needs to be demonstrated.

[0033] The nodes to be used correspond to power equipment within the target area. Optionally, the nodes to be used may include key nodes at the power grid level of the target area and points of special concern. Key nodes at the power grid level may be equipment performing important functions, such as substations and transmission towers. Points of special concern can be understood as power equipment or transmission lines that require special attention during the operation and maintenance of the power grid in the target area, such as transmission lines prone to failure or power equipment in environmentally sensitive areas. At least one line to be used corresponds to a transmission line within the target area used to connect various power equipment. Optionally, the lines to be used can be of various types. For example, the lines to be used may be high-voltage transmission lines, low-voltage distribution lines, etc. It should be noted that the lines to be used can be classified into different types according to their voltage level, material, and purpose.

[0034] Specifically, upon receiving a topology graph construction request, the requirement information carried in the request is parsed and processed to determine multiple nodes to be used and at least one line to be used within the target area, so as to determine the edges and nodes required to construct the topology graph based on these nodes and lines to be used.

[0035] Optionally, based on multiple nodes to be used and at least one line to be used, a suitable data structure and format for displaying the topology diagram can be determined. The data structure typically includes the nodes to be used, the transmission lines, and the corresponding equipment operating data. For example, equipment operating data could be equipment voltage levels, equipment current capacity, etc. The data format can include JSON, XML, etc., which can be parsed by the G6 visualization engine and used to construct the topology diagram.

[0036] S120. Obtain at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used.

[0037] At least one source system can be a data source system related to the data type required to construct the topology map. For example, source systems can include internal and external data source systems. Internal data source systems can include Supervisory Control and Data Acquisition (SCADA) systems, asset management systems, and systems containing historical operational data. External data source systems can include systems containing meteorological data, Geographic Information Systems (GIS), and systems containing market transaction data.

[0038] The data to be processed may include node data of the nodes to be used, line data of the lines to be used, and equipment operation data of the power equipment corresponding to the nodes to be used. Node data may include the node location data, voltage level, and connection relationships between the nodes to be used. Line data may include the line location data, connection relationships, and line length data of the lines to be used. Equipment operation data may include fault data, load data, and historical operation data of the power equipment. For example, equipment operation data may include the type and model of the power equipment, as well as equipment operating status parameters such as current, voltage, and temperature.

[0039] Specifically, based on the data types required to construct the topology map, at least one source system is identified, and node data of the nodes to be used, line data of the lines to be used, and equipment operation data of the power equipment corresponding to the nodes to be used under at least one source system are obtained, so as to construct the topology map corresponding to the target area through the data to be processed, the nodes to be used, and the lines to be used.

[0040] In this embodiment of the invention, the method for obtaining the data to be processed may be as follows: obtaining raw data from at least one source system according to at least one required data type, wherein the raw data includes at least power grid topology data, load data of power equipment, and fault record data of power equipment; performing data preprocessing on the power grid topology data to obtain node data of the node to be used and line data of the line to be used, and performing data preprocessing on the load data and fault record data to obtain equipment operation data of the power equipment corresponding to the node to be used; using the node data, line data, and equipment operation data as the data to be processed; wherein the data preprocessing includes at least one of data verification processing, data cleaning processing, and data format conversion processing.

[0041] Here, data type can be understood as the type of raw data. Raw data can include data of multiple data types. Optionally, raw data may include at least power grid topology data, load data of power equipment, and fault record data of power equipment. Power grid topology data may include node location data corresponding to each power device in the power grid, line location data of transmission lines, and connection relationship data. Load data of power equipment can be used to characterize the load conditions borne by the power equipment. Optionally, load data may include the load size, load type, and load change rate of the power equipment. Fault record data of power equipment can be understood as various faults that occur in the operation of power equipment and related information. Optionally, fault record data may include the fault time, fault type, fault parameters, and fault location of the power equipment.

[0042] Data preprocessing can include one or more of the following operations: data validation, data cleaning, and data format conversion. Data validation can be used to check the integrity, consistency, and rationality of the raw data. Data cleaning can be used to remove duplicate data, correct erroneous data, and fill in missing data. Data format conversion can be used to integrate raw data from at least one source system into a unified format supported by the G6 visualization engine for subsequent topology map construction.

[0043] Specifically, based on at least one data type required for constructing the topology map, and considering the level of detail and update needs of the topology map, the required data precision and frequency are determined. Based on at least one data type, data precision, and data frequency, at least one source system is identified. Raw data, such as power grid topology data, load data of power equipment, and fault record data of power equipment, are obtained from at least one source system. It should be noted that data precision includes the precision of location data and voltage levels within the required data. Data frequency can be understood as the frequency at which the data to be processed is obtained for constructing the topology map.

[0044] The integrity, consistency, and rationality of the power grid topology data are checked. Data cleaning processes are performed on the power grid topology data with problems found during the inspection, such as removing duplicate data, correcting erroneous data, and filling in missing data. The cleaned power grid topology data is then integrated into a unified data format to obtain the node data of the nodes to be used and the line data of the lines to be used.

[0045] Preliminary verification of load and fault record data from power equipment is performed to check their completeness, consistency, and rationality. Data cleaning processes are then initiated for problematic load and fault record data, including removing duplicates, correcting errors, and filling in missing data. The cleaned load and fault record data are then integrated into a unified data format to obtain equipment operation data. Node data, line data, and equipment operation data are used as the data to be processed, and a topology map corresponding to the target area is constructed based on this data.

[0046] For example, a ring network topology diagram, i.e., a ring group diagram, is used as an example for illustration. Determine the data type of the data to be processed for the ring network diagram corresponding to the target area. For example, the data to be processed may include, but is not limited to, power grid topology data, load data, fault record data, meteorological data, and geospatial data. Determine the required data precision and frequency based on the required level of detail and update needs of the ring network diagram to be constructed. Select at least one source system based on the data type, precision, and frequency. The source system can include internal and external source systems. External source systems can be corresponding databases, log systems, or systems to which maintenance records belong. Establish a data acquisition mechanism, using a combination of online and offline methods to obtain raw data from at least one source system. Online raw data acquisition can be automated through Application Programming Interfaces (APIs), data scraping tools, etc. For raw data that cannot be automatically collected, manually collect the corresponding raw data according to pre-defined data acquisition procedures and specifications.

[0047] To ensure the accuracy and completeness of the raw data, data verification and cleaning processes are performed. Specifically, during the raw data collection process, preliminary verification is conducted to check the completeness, consistency, and reasonableness of the original data. Then, data cleaning processes are performed on the raw data identified during verification, including removing duplicate data, correcting erroneous data, and filling in missing data.

[0048] After data cleaning, the raw data from at least one source system is unified into a data format suitable for the G6 visualization engine. For example, a suitable data format for the G6 visualization engine could be JSON. Specifically, this can be achieved by associating raw data of different data types based on their logical relationships, forming a power grid dataset. ETL (Extract, Transform, Load) tools are then used to unify units and scale the data, transforming it into a format supported by the G6 visualization engine. For instance, taking power grid geospatial data as an example, geospatial data from a geographic information system (GIS), such as coordinates of power equipment and topography of the target area, is converted into a format supported by the G6 visualization engine, such as SVG, Canvas, or WebGL. If the geospatial data is stored in Shapefile format, it may need to be converted into JSON format using GIS software or specialized conversion tools, including coordinate information, so that the G6 visualization engine can correctly render the location of power equipment or transmission lines.

[0049] Optionally, after obtaining the data to be processed, filtering conditions can be set according to the presentation requirements of the ring network diagram to filter the data. For example, filtering conditions based on region and voltage level can be used to filter out power equipment or transmission lines within a specific voltage level and region. Optionally, the data to be processed can also be summarized for a more intuitive display in the ring network diagram. This summarization can be performed according to dimensions such as region or time.

[0050] Optionally, to improve the efficiency and accuracy of topology map generation, the data to be processed can be compressed to reduce storage space and transmission time. Data optimization processes, such as reducing redundant data and optimizing data structures, can improve the rendering speed and interactive functionality of subsequent ring network diagrams. For example, for large amounts of node data, line data, and equipment operation data in the data to be processed, compression algorithms can be used to reduce the volume of the data, and an index can be created in the G6 visualization engine to quickly locate and render the data at specific positions.

[0051] Optionally, the method also includes periodically backing up both the raw data and the data to be processed to prevent data loss or corruption. Appropriate storage media and storage strategies are selected to ensure the reliability and accessibility of data acquisition and storage.

[0052] S130. Based on multiple nodes to be used, at least one line to be used, and data to be processed, use the G6 visualization engine to determine the target power grid topology map corresponding to the target area, and display the target power grid topology map on the target terminal.

[0053] The G6 visualization engine can be understood as a graph visualization engine used to construct the target area. The target power grid topology map can be used to represent the topology of the connections between power equipment and transmission lines within the target area. Optionally, the target power grid topology map can be a ring network (path) topology diagram, presenting the overall structure of the target area's power grid in a ring layout, which helps improve the power grid's reliability and stability, and facilitates intuitive analysis and management of the target area's power grid's operating status and potential problems. The target terminal can be a pre-set electronic device used to display the target power grid topology map. For example, the target terminal can be a mobile phone, computer, or other electronic device.

[0054] Specifically, before using the G6 visualization engine to determine the target power grid topology, the G6 visualization engine can be configured first. This is done by including the G6 JavaScript library in the corresponding HTML file and defining an `<script>` element in the HTML file, which serves as the drawing area for the G6 visualization engine. For example, add the following to the HTML file: <div id＝"graphcontainer"> The process involves initializing a G6 instance in JavaScript and specifying this container as the mount point for the target power grid topology. Based on actual needs, the G6 visualization engine is configured with chart types, layout algorithms, and interaction modes adapted to the target power grid topology. Custom styles for nodes and lines to be used are also defined to differentiate between different types of nodes and lines.

[0055] Multiple nodes to be used, at least one line to be used, and data to be processed are imported into a pre-configured G6 visualization engine. The pre-configured layout algorithm within the G6 visualization engine then processes the nodes and lines to be used, and combined with the data to be processed, yields a target power grid topology map for the target area. This target power grid topology map is displayed on the target terminal, enabling real-time monitoring and analysis of the target area's topology, equipment status, and power flow. This facilitates operations such as power grid planning, fault diagnosis, load management, and optimization of power resource allocation.

[0056] For example, a ring network diagram of the target power grid topology is used as an example. The G6 library is imported into the system to create an HTML container. Based on actual needs, suitable chart types, layout algorithms, and interaction modes are configured for the ring network diagram. A circular layout is selected as the basic layout method to highlight the characteristics of the ring network structure. Custom styles for the nodes and lines to be used are defined to differentiate between different types of nodes and lines, enhancing visual appeal. At least one automatic layout algorithm corresponding to the basic layout method is configured to automatically adjust the positions of the nodes and lines based on their relationships, making the ring network diagram more natural and easier to understand. Optionally, the automatic layout algorithm may include at least one of force-directed layout, circular layout, and tree-structured layout algorithms. Based on the above, the G6 visualization engine is configured, improving its basic visualization capabilities, including drawing, layout, analysis, interaction, and animation. The nodes, lines, and data to be processed are imported into the pre-configured G6 visualization engine to obtain the ring network diagram corresponding to the target area.

[0057] Optionally, after determining the target power grid topology map corresponding to the target area, the method further includes: evaluating the target power grid topology map based on multiple evaluation dimensions to obtain evaluation attributes under each evaluation dimension, wherein the evaluation dimensions include at least: topology map loading dimension, topology map rendering frame rate dimension, and topology map memory usage dimension; determining the target evaluation dimension based on the evaluation attributes under each evaluation dimension, and determining at least one test case to be used corresponding to the target evaluation dimension; performing test processing on the target power grid topology map according to at least one test case to be used to obtain test results, updating the target power grid topology map based on the test results, and displaying the updated target power grid topology map on the target terminal.

[0058] The evaluation dimensions can be pre-defined and used to assess the performance of the target power grid topology. These dimensions include at least: topology loading, topology rendering frame rate, and topology memory usage. The topology loading dimension assesses the loading time of the target power grid topology. The topology rendering frame rate assesses the rendering frame rate of the target power grid topology. The topology memory usage dimension assesses the memory usage of the target power grid topology. Evaluation attributes can be understood as the evaluation values ​​corresponding to each evaluation dimension. Target evaluation dimensions are those whose evaluation values ​​exceed a preset evaluation threshold. Test cases are test cases used to process the target power grid topology. Each target evaluation dimension can correspond to at least one test case. Test results are the results obtained by processing the target power grid topology using the test cases. The test results can identify the key factors affecting the performance of the target power grid topology.

[0059] Specifically, after obtaining the target power grid topology map corresponding to the target region, the target power grid topology map can be evaluated based on multiple evaluation dimensions, such as topology map loading dimension, topology map rendering frame rate dimension, and topology map memory usage dimension, to obtain evaluation attributes corresponding to each evaluation dimension. Evaluation dimensions whose evaluation attributes exceed a preset evaluation threshold are designated as target evaluation dimensions. At least one test case corresponding to the target evaluation dimension is determined, and the target power grid topology map is tested based on at least one test case to obtain test results, thereby identifying key factors affecting the performance of the target power grid topology map. The target power grid topology map is updated based on the test results, resulting in an updated target power grid topology map. The updated target power grid topology map is then displayed on the target terminal.

[0060] For example, a ring network diagram is used as an example to illustrate the target power grid topology. Before performing performance testing on the ring network diagram according to the test cases to be used, compatibility testing can be performed first. This involves testing the ring network diagram from at least one compatibility dimension, such as browser compatibility, operating system compatibility, and device compatibility. For example, the compatibility of the ring network diagram on different versions of mainstream browsers and mobile browsers is tested. The display effect and interactive experience of the ring network diagram on electronic devices such as desktop computers, tablet computers, and mobile phones are also tested.

[0061] Use browser developer tools or other performance development tools to evaluate key performance indicators such as loading time, rendering frame rate, and memory usage of the ring network diagram, and obtain evaluation results. Based on the evaluation results, write test cases to be used. Test the basic functions of the ring network diagram using the test cases to obtain test results. For example, use the test cases to perform performance tests on the node display, edge connection, layout display, and interactive functions of the ring network diagram. For example, test cases can be written to simulate the user's click of specific buttons to verify whether the ring network diagram can be loaded and displayed correctly, and to check whether the power equipment appears in the preset positions of the ring network diagram. Optionally, the ring network diagram can be tested using a combination of automation and manual methods, that is, using automated testing tools to execute the test cases to improve testing efficiency and accuracy. For scenarios that are difficult to cover by automated testing, such as user experience testing, manual testing can be performed. Record in detail the problems found on each browser, operating system, and device, including problem description, screenshots, and reproduction steps. Based on the problem record, fix the problems one by one, and perform regression testing to ensure that the problems have been resolved. Write a detailed test report, including the test scope, test cases, test results, problem records and fixes, for future reference.

[0062] Based on the test results, the key factors affecting the performance of the ring network diagram were identified, such as the complex connection relationships between nodes and lines, a large amount of data to be processed, and unreasonable layout algorithms. The configuration of the G6 visualization engine or the data to be processed was changed by addressing these key factors to obtain an updated ring network diagram.

[0063] For example, for complex connections between nodes and lines, data structures can be optimized by reducing unnecessary nodes or lines, thus reducing memory usage. For large amounts of data to be processed, asynchronous loading can be used to load data from the visible or preset areas first, followed by loading data from other locations or areas within the target region based on user actions. For unsuitable layout algorithms, appropriate algorithms can be selected based on the characteristics of the ring network diagram to reduce overlap between nodes and lines, improving layout efficiency. Unnecessary DOM (Document Object Model) operations can be reduced by customizing the rendering logic between nodes and lines, optimizing rendering performance. Optionally, Canvas rendering can be used for high-performance rendering of more complex graphics to optimize the rendering of the ring network diagram. Previous rendering results can be reused directly when the positions of nodes or lines remain unchanged. For data requiring complex calculations, calculation results can be cached to avoid redundant calculations. Optimizing the editing code, using local variables, and reducing the use of global variables can avoid potential naming conflicts and performance issues, while also reducing DOM operations.

[0064] Optionally, the preset explanatory text information corresponding to each power device in the target area, the chart information corresponding to the data to be processed, and the target power grid topology map are displayed on the target terminal in a preset format.

[0065] The preset explanatory text information can be understood as textual descriptions of the power equipment within the target area. The chart information can be visual charts constructed based on the data to be processed. The preset format can be a format set according to actual needs. For example, for a single target area, a combined text and image display format can be created, with one booklet per site.

[0066] Specifically, the preset explanatory text information corresponding to each power device in the target area, the chart information corresponding to the data to be processed, and the target power grid topology map are displayed on the target terminal in a preset format.

[0067] For example, using the target power grid topology as a ring network diagram as an example, the ring network diagram, preset explanatory text information, and chart information corresponding to the data to be processed are displayed on the corresponding interface of the target terminal in a combined text and image format for each station. Furthermore, based on the pre-set table data for each station, the network diagram after planning new outgoing lines and line modifications can be automatically generated, and the future network effect can be achieved.

[0068] In this embodiment of the invention, after obtaining the target power grid topology map, the method further includes: performing load detection processing on the power equipment in the target area according to the target power grid topology map, obtaining detection results, and adjusting the power equipment based on the detection results.

[0069] The test results can be obtained after load testing of the power equipment.

[0070] Specifically, load detection is performed on electrical equipment within the target area based on the target power grid topology map to obtain the detection results. These results are then presented in a graphical format to allow for adjustments to the electrical equipment within the target area.

[0071] In this embodiment of the invention, after obtaining the target power grid topology map, the method further includes: performing fault simulation detection on power equipment in the target area based on the target power grid topology map, obtaining simulation results, so as to perform maintenance on the power equipment according to the simulation results when a power fault is detected in the power equipment.

[0072] The simulation results can be the handling results corresponding to the fault encountered by the simulated power equipment.

[0073] Specifically, based on the target power grid topology map, fault simulation detection is performed on the power equipment within the target area to determine the corresponding solutions for the power equipment faults, thus obtaining the simulation results. Based on this, when power equipment encounters the same fault, maintenance can be carried out according to the simulation results.

[0074] The technical solution of this embodiment receives a topology map construction request and parses the requirement information carried in the request to determine multiple nodes to be used and at least one line to be used within the target area. It also acquires data to be processed from at least one source system. Based on the multiple nodes to be used, at least one line to be used, and the data to be processed, the G6 visualization engine is used to determine the target power grid topology map corresponding to the target area, and the target power grid topology map is displayed on the target terminal. This invention solves the problems of low drawing efficiency and inaccuracy caused by manual topology map drawing in the prior art. By using the G6 visualization engine, it realizes the generation and presentation of the target power grid topology map of the target area, improving the drawing efficiency and accuracy of the topology map, thereby achieving intuitive display and efficient management of power grid planning, and improving the efficiency and accuracy of planning work.

[0075] Example 2

[0076] Figure 2 This is a flowchart of a method for determining a power grid topology map according to Embodiment 2 of the present invention. This embodiment is a refinement of the step "determining the target power grid topology map corresponding to the target area using the G6 visualization engine based on multiple nodes to be used, at least one line to be used, and data to be processed" in the above embodiment. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiment will not be repeated here. Figure 2 As shown, the method includes:

[0077] S210. Receive the topology map construction request, and parse the requirement information carried in the topology map construction request to determine the multiple nodes to be used and at least one line to be used in the target area.

[0078] Among them, the nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area.

[0079] S220. Obtain at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used.

[0080] S230. Input the data to be processed into a pre-trained target classification model to obtain classification results, wherein the classification results contain data to be processed in at least one category.

[0081] The target classification model can be a model used to classify the data to be processed. Optionally, the target classification model can be a classification decision tree. The classification result can be the data to be processed, which can be classified into at least one category.

[0082] Specifically, the data to be processed is input into a pre-trained target classification model, which then classifies the data according to preset classification criteria. This ensures that each node's data, line data, and equipment operation data are correctly classified, resulting in at least one category of data to be processed. Classifying the data using the target classification model and then constructing a target power grid topology map based on the classified data not only produces a clear and rationally laid-out ring network diagram but also improves accuracy and readability. Furthermore, it helps identify potential problems in the data to be processed, improving the overall efficiency of power grid planning and management.

[0083] In this embodiment of the invention, before processing the data to be processed using the target classification model, the method further includes: determining the feature attributes associated with the power grid topology based on the data to be processed; constructing a classification model to be trained based on the feature attributes and a preset classification criterion; acquiring multiple training sample data, wherein the training sample data includes sample node data, sample line data, and sample equipment operation data; and training the classification model to be trained based on the training sample data to obtain a trained target classification model.

[0084] Among these, feature attributes can be used to characterize the features of the data to be processed in a certain aspect. Preset classification criteria can be pre-selected, serving as a standard for dividing the data to be processed. Optionally, the preset classification criteria can be an information gain classification criterion. The classification model to be trained can be a classification model determined based on the preset classification criteria and feature attributes, used to classify the data. Training sample data can be sample data used to train the classification model to be trained. Sample node data can include node location data, etc. Sample line data can include line location data, line connection relationships, and line length data, etc. Sample equipment operation data can include fault data, load data, and historical operation data of the sample power equipment, etc.

[0085] Specifically, based on the data to be processed, the characteristic attributes associated with the power grid topology are determined. Based on these characteristic attributes and preset classification criteria, a classification model to be trained is constructed. Multiple training sample data are acquired to train the classification model until its classification accuracy reaches a preset accuracy threshold, at which point a well-trained target classification model is obtained.

[0086] For example, a classification decision tree is illustrated using a classification model to be trained and a target classification model. The feature attributes of the data to be processed are determined to prepare for the subsequent construction of the classification decision tree. The preset classification criterion used to construct the classification decision tree is set as the information gain classification criterion. The information gain classification criterion is used as the attribute selection metric for the classification decision tree. The classification decision tree is constructed based on the information gain classification criterion and the feature attributes. The classification decision tree is then trained using training sample data. That is, the training sample data is divided into at least one class according to the given class label training principle. The feature attribute with the highest information gain is selected as the splitting attribute of the current classification decision tree node. Based on the value of the splitting attribute, the training sample data is divided into at least one class to reflect the minimum randomness of the current partition. Based on this, the expected number of tests required for a given tuple can be minimized, thereby ensuring the acquisition of the target classification decision tree.

[0087] It's important to note that during the classification of training sample data, it's crucial to ensure that the relationships between the classified data do not conflict with the inherent relationships between the training sample data. In other words, it's necessary to ensure that each node, line, and equipment operation data in the training sample data can be correctly classified to reduce overfitting. Since there are no outliers or errors in the training sample data, the target classification decision tree does not suffer from overfitting.

[0088] The following two formulas are needed to determine the information gain in order to accurately divide the training sample data and the subsequent data to be processed.

[0089] Taking multiple training sample data as an example, the expected information required for classifying the tuples (training sample data) in the training sample set can be determined by the following expectation algorithm function.

[0090]

[0091] Where, p i This indicates that any tuple i in the training sample set D belongs to class C. i The probability, p i Through class C i It is estimated by the ratio between the number of occurrences of tuples in the training sample set D and the total number of tuples in the training sample set, i.e. m represents the number of tuples. Info(D) represents the average amount of information required to identify the class label of a tuple in D, which is also known as entropy.

[0092] Suppose we need to partition the training sample set D into tuples according to feature attribute A, where, based on observations of the training sample data, feature attribute A has v distinct values ​​{a1, a2, a3, ..., a...}. v Since feature attribute A has discrete values, the v values ​​of feature attribute A correspond to v output results. The training sample set D is divided into v subsets {D1, D2, D3, ..., D...} using feature attribute A. v}. Among them, D j A tuple containing the training sample set D, having a value a on feature attribute A. j These partitions will correspond to the branches generated by the classification decision tree node N. These partitions may contain tuples from different classes rather than from a single class, thus the partitions are not pure.

[0093] Based on the above, in order to obtain an accurate classification, we also need to utilize the classification information content algorithm function. The specific formula is expressed as follows:

[0094]

[0095] in, Let |D| represent the weight of the j-th partition. j | represents a subset D of the training sample set. j The number of tuples in the training sample set D, where |D| represents the total number of tuples in D. A (D) represents the expected amount of information required to classify tuples in the training sample set D according to feature attribute A, where Info A The smaller (D) is, the higher the purity of the classification, that is, the more accurate the classification.

[0096] Based on the above formula, information gain can be defined as Gain(A) = Info(D) - InfoA (D). The above quantitative information gain describes how much information is obtained through the splitting of feature attribute A. That is, by selecting feature attribute A with the highest information gain Gain(A) as the splitting attribute of the classification decision tree node N, it is equivalent to minimizing Info. A (D)

[0097] Optionally, if the training sample data is Common Information Model (CIM) model data, in the power grid geographic information system platform and distribution automation system application integration module, the incremental update of the system diagram CIM model is generated using a large feeder cascading method. However, due to the large size of the generated incremental data of the system diagram CIM model, the efficiency of generating, transmitting, and importing CIM model data is low. Therefore, it is necessary to optimize the original system diagram CIM model incremental update scheme.

[0098] In addition, CIM data parsing mainly uses ETL data warehouse technology to flexibly configure and schedule the CIM model data files obtained from the Supervisory Control and Data Acquisition (SCADA) system, as well as extract, convert, and load data into a temporary interface library. Then, the data is extracted from the interface library into the formal model library to provide data support for the automatic generation of graphics. Therefore, on this basis, the ring network grouping of the target area can be better determined.

[0099] S240. Import the classification results into the G6 visualization engine, and generate the power grid topology map to be rendered for the target area based on the pre-configured ring layout method and the corresponding preset layout algorithm in the G6 visualization engine.

[0100] The ring layout method can be understood as constructing a ring network topology using the nodes and lines to be used. The preset layout algorithm is a pre-configured algorithm within the G6 visualization engine used for the automated placement of the nodes and lines. This preset algorithm determines the position of each node and line within the rendered power grid topology. The rendered power grid topology can be a ring network topology constructed based on the ring layout method and the preset layout algorithm.

[0101] Specifically, at least one type of data to be processed is imported into the G6 visualization engine. Using the pre-configured ring layout method and corresponding preset layout algorithm in the G6 visualization engine, the nodes to be used, the lines to be used, and the data to be processed are laid out to obtain the power grid topology map to be rendered for the target area.

[0102] For example, at least one category of data to be processed is imported into the G6 visualization engine. This data is then organized and structured to clarify the relationships between nodes to be used, lines to be used, and equipment operation data within the target area. The method for mapping equipment operation data onto visual elements is determined. For instance, different shapes and colors of topology map nodes correspond to power equipment in different operating states, and different styles of topology map edges correspond to lines to be used at different voltage levels. A data interaction mechanism is established, such as allowing users to zoom, drag, and query equipment operation data for a specific node in the topology map.

[0103] The G6 visualization engine utilizes its ring layout method and preset layout algorithms to automatically lay out the nodes, lines, and data to be processed, optimally displaying the connections between power equipment and transmission lines. It's important to note that different preset layout algorithms generate rendering topology maps with varying visual effects and information delivery efficiency. For example, the tree layout algorithm is suitable for data with clear hierarchical structures, intuitively showing hierarchical relationships. The force-directed layout algorithm simulates physical attraction and repulsion, allowing nodes to automatically avoid each other, making it suitable for data with complex relationships. The ring layout algorithm arranges the nodes to be used in a ring, highlighting the cyclical and closed relationships between power equipment.

[0104] For example, taking the power grid topology diagram to be rendered as a ring network diagram, if the ring network diagram is built in offline mode, the line XML file can be parsed to incorporate the planning requirements of one station, one book. By using algorithms such as traversal, recursion, and search tree, the ring network diagram can be automatically generated. The tool defines a one station, one book planning table template. Data is filled into the table and directly imported into the tool to realize the association and fusion of the data to be processed. Furthermore, from a planning perspective, a tree layout algorithm is used to automatically build the ring network diagram.

[0105] Optionally, during the construction of the power grid topology map to be rendered, data mining and map coordinate scatter plot restoration operations can be performed on the line XML file to extract detailed information about power equipment or transmission lines in the target area. This detailed information can then be further processed and analyzed to apply it to the construction of the power grid topology map. For example, querying the XML file... <node>The elements are extracted, and their identifiers and coordinate attributes are extracted. This information is then used to draw the power grid topology diagram to be rendered.

[0106] S250. Based on at least one power grid planning stage and corresponding scene information corresponding to the target area, render the power grid topology map to be rendered to obtain the target power grid topology map, and display the target power grid topology map on the target terminal.

[0107] The power grid planning phase can be understood as the phase of planning for the target area. Scenario information can be the planning scenario corresponding to the power grid planning phase.

[0108] Specifically, based on at least one power grid planning stage and corresponding scenario information for the target area, detailed information about the equipment or lines related to the power grid planning is determined. The power grid topology map to be rendered is then processed using this detailed information to obtain the target power grid topology map, which is then displayed on the target terminal.

[0109] For example, using a ring network diagram as an example of the power grid topology to be rendered, the ring network diagram is dynamically loaded and rendered according to different stages and scenarios of power grid planning. In the initial stage of power grid planning, the ring network diagram only shows the main nodes and lines. As power grid planning progresses, detailed information is gradually added to the ring network diagram. Furthermore, user interaction functions are added, such as dragging nodes, zooming the ring network diagram, and highlighting key lines. Through interactive operations, users can better understand the changes and potential problems in the power grid structure of the target area. In addition, the nodes and edges corresponding to the lines to be used in the ring network diagram can be customized according to user needs, such as customizing node colors, edge widths, and node label styles.

[0110] The technical solution of this embodiment receives a topology map construction request and parses the requirement information carried in the request to determine multiple nodes to be used and at least one line to be used. It acquires data to be processed from at least one source system. The data to be processed is input into a pre-trained target classification model to obtain classification results. The classification results are imported into the G6 visualization engine, and a grid topology map to be rendered for the target area is generated based on the pre-configured ring layout and corresponding preset layout algorithm in the G6 visualization engine. Classifying the data to be processed using the target classification model and then constructing the target grid topology map based on the classified data not only makes the generated ring network map structure clear and reasonable in layout, but also has higher accuracy and readability. At the same time, it also helps to discover potential problems in the data to be processed, improving the overall efficiency of grid planning and management. Based on at least one grid planning stage and corresponding scenario information corresponding to the target area, the grid topology map to be rendered is rendered to obtain the target grid topology map, which is then displayed on the target terminal. This invention solves the problems of low drawing efficiency and inaccuracy caused by manual drawing of topology maps in the prior art. It realizes the generation and presentation of target power grid topology maps of target areas through the G6 visualization engine, which improves the drawing efficiency and accuracy of topology maps, thereby realizing intuitive display and efficient management of power grid planning and improving the efficiency and accuracy of planning work.

[0111] Example 3

[0112] Figure 3 This is a schematic diagram of a power grid topology determination device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an information parsing module 310, a data acquisition module 320, and a topology map display module 330.

[0113] The information parsing module 310 is used to receive a topology map construction request and parse the requirement information carried in the topology map construction request to determine multiple nodes to be used and at least one line to be used within the target area. The nodes to be used correspond to the power equipment within the target area, and the lines to be used correspond to the transmission lines connecting the power equipment within the target area. The data acquisition module 320 is used to acquire at least one data to be processed from a source system. The data to be processed includes at least: node data of the nodes to be used, line data of the lines to be used, and equipment operation data of the power equipment corresponding to the nodes to be used. The topology map display module 330 is used to determine the target power grid topology map corresponding to the target area using the G6 visualization engine based on the multiple nodes to be used, at least one line to be used, and the data to be processed, and to display the target power grid topology map on the target terminal.

[0114] The technical solution of this embodiment receives a topology map construction request and parses the requirement information carried in the request to determine multiple nodes to be used and at least one line to be used within the target area. It also acquires data to be processed from at least one source system. Based on the multiple nodes to be used, at least one line to be used, and the data to be processed, the G6 visualization engine is used to determine the target power grid topology map corresponding to the target area, and the target power grid topology map is displayed on the target terminal. This invention solves the problems of low drawing efficiency and inaccuracy caused by manual topology map drawing in the prior art. By using the G6 visualization engine, it realizes the generation and presentation of the target power grid topology map of the target area, improving the drawing efficiency and accuracy of the topology map, thereby achieving intuitive display and efficient management of power grid planning, and improving the efficiency and accuracy of planning work.

[0115] Based on the above embodiments, optionally, a data acquisition module is used to acquire raw data from at least one source system according to at least one required data type, wherein the raw data includes at least power grid topology data, load data of power equipment, and fault record data of power equipment; perform data preprocessing on the power grid topology data to obtain node data of the node to be used and line data of the line to be used, and perform data preprocessing on the load data and fault record data to obtain equipment operation data of the power equipment corresponding to the node to be used; and use the node data, line data, and equipment operation data as data to be processed; wherein the data preprocessing includes at least one of data verification processing, data cleaning processing, and data format conversion processing.

[0116] Optionally, the topology display module includes: a data classification unit, used to input the data to be processed into a pre-trained target classification model to obtain classification results, wherein the classification results contain at least one category of data to be processed; a data layout unit, used to import the classification results into the G6 visualization engine, and generate a power grid topology map to be rendered corresponding to the target area according to the pre-configured ring layout method and the corresponding preset layout algorithm in the G6 visualization engine; and a topology rendering unit, used to render the power grid topology map to be rendered according to at least one power grid planning stage and corresponding scene information corresponding to the target area to obtain the target power grid topology map.

[0117] Optionally, the device further includes: a classification model determination module, used to determine the characteristic attributes associated with the power grid topology based on the data to be processed; to construct a classification model to be trained based on the characteristic attributes and preset classification criteria; to acquire multiple training sample data, wherein the training sample data includes sample node data, sample line data and sample equipment operation data; and to train the classification model to be trained based on the training sample data to obtain a trained target classification model.

[0118] Optionally, the device further includes: a topology map testing module, which is used to evaluate the target power grid topology map based on multiple evaluation dimensions to obtain evaluation attributes under each evaluation dimension, wherein the evaluation dimensions include at least: topology map loading dimension, topology map rendering frame rate dimension, and topology map memory usage dimension; determine the target evaluation dimension based on the evaluation attributes under each evaluation dimension, and determine at least one test case to be used corresponding to the target evaluation dimension; perform test processing on the target power grid topology map according to at least one test case to be used to obtain test results, update the target power grid topology map based on the test results, and display the updated target power grid topology map on the target terminal.

[0119] Optionally, the device also includes a topology map and information display module, used to display the preset explanatory text information corresponding to each power device in the target area, the chart information corresponding to the data to be processed, and the target power grid topology map in a preset format on the target terminal.

[0120] Optionally, the device further includes a load detection module, used to perform load detection processing on the power equipment in the target area according to the target power grid topology map, obtain the detection results, and adjust the power equipment based on the detection results.

[0121] Optionally, the device further includes: an equipment fault simulation module, used to perform fault simulation detection on power equipment in the target area based on the target power grid topology map, and obtain simulation results, so as to perform maintenance on the power equipment based on the simulation results when a power fault is detected.

[0122] The power grid topology determination device provided in this embodiment of the invention can execute the power grid topology determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0123] Example 4

[0124] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0125] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0126] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0127] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the power grid topology determination method.

[0128] In some embodiments, the power grid topology determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power grid topology determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the power grid topology determination method by any other suitable means (e.g., by means of firmware).

[0129] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] Computer programs used to implement the power grid topology determination method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0131] Example 5

[0132] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a power grid topology determination method, the method comprising:

[0133] The system receives a topology map construction request and parses the requirement information carried in the request to determine multiple nodes to be used and at least one line to be used within the target area. The nodes to be used correspond to power equipment within the target area, and the lines to be used correspond to transmission lines connecting the power equipment within the target area. It also acquires at least one set of data to be processed from at least one source system. This data includes at least: node data of the nodes to be used, line data of the lines to be used, and equipment operation data of the power equipment corresponding to the nodes to be used. Based on the multiple nodes to be used, at least one line to be used, and the data to be processed, the system uses the G6 visualization engine to determine the target power grid topology map corresponding to the target area and displays the target power grid topology map on the target terminal.

[0134] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0135] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0136] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0137] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0138] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.< / node>

Claims

1. A method for determining a power grid topology, characterized in that, include: Receive a topology map construction request, and parse the requirement information carried in the topology map construction request to determine multiple nodes to be used and at least one line to be used in the target area. The nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area. Acquire at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used; Based on the multiple nodes to be used, at least one line to be used, and the data to be processed, the target power grid topology map corresponding to the target area is determined using the G6 visualization engine, and the target power grid topology map is displayed on the target terminal. After determining the target power grid topology map corresponding to the target area, the method further includes: The target power grid topology is evaluated based on multiple evaluation dimensions to obtain evaluation attributes under each evaluation dimension. The evaluation dimensions include at least: topology loading dimension, topology rendering frame rate dimension, and topology memory usage dimension. Based on the evaluation attributes under each evaluation dimension, determine the target evaluation dimension and at least one test case to be used corresponding to the target evaluation dimension; The target power grid topology is tested according to at least one test case to be used, the test results are obtained, the target power grid topology is updated based on the test results, and the updated target power grid topology is displayed on the target terminal.

2. The method according to claim 1, characterized in that, The acquisition of data to be processed from at least one source system includes: Based on at least one required data type, raw data is obtained from at least one source system, wherein the raw data includes at least power grid topology data, load data of the power equipment, and fault record data of the power equipment; Data preprocessing is performed on the power grid topology data to obtain node data of the node to be used and line data of the line to be used; and data preprocessing is performed on the load data and fault record data to obtain equipment operation data of the power equipment corresponding to the node to be used. The node data, the line data, and the equipment operation data are used as data to be processed. The data preprocessing includes at least one of data verification processing, data cleaning processing, and data format conversion processing.

3. The method according to claim 1, characterized in that, The step of determining the target power grid topology map corresponding to the target area using the G6 visualization engine, based on multiple nodes to be used, at least one line to be used, and the data to be processed, includes: The data to be processed is input into a pre-trained target classification model to obtain a classification result, wherein the classification result contains data to be processed in at least one category; The classification results are imported into the G6 visualization engine, and a power grid topology map to be rendered is generated based on the pre-configured ring layout method and the corresponding preset layout algorithm in the G6 visualization engine. Based on at least one power grid planning stage and corresponding scene information corresponding to the target area, the power grid topology map to be rendered is rendered to obtain the target power grid topology map.

4. The method according to claim 3, characterized in that, The method further includes: Based on the data to be processed, determine the characteristic attributes associated with the power grid topology; Based on the aforementioned feature attributes and preset classification criteria, a classification model to be trained is constructed; Acquire multiple training sample data, wherein the training sample data includes sample node data, sample line data, and sample device operation data; The training sample data is used to train the classification model to obtain a trained target classification model.

5. The method according to claim 1, characterized in that, Also includes: The preset explanatory text information corresponding to each power device in the target area, the chart information corresponding to the data to be processed, and the target power grid topology map are displayed on the target terminal in a preset format.

6. The method according to claim 1, characterized in that, Also includes: Load detection processing is performed on the power equipment in the target area based on the target power grid topology map to obtain the detection results, and the power equipment is adjusted based on the detection results.

7. The method according to claim 1, characterized in that, Also includes: Based on the target power grid topology map, fault simulation detection is performed on the power equipment in the target area to obtain simulation results. When a power fault is detected in the power equipment, the power equipment is repaired based on the simulation results.

8. A device for determining a power grid topology, characterized in that, include: The information parsing module is used to receive a topology map construction request and parse the requirement information carried in the topology map construction request to determine multiple nodes to be used and at least one line to be used in the target area. The nodes to be used correspond to the power equipment in the target area, and the lines to be used correspond to the transmission lines connecting the power equipment in the target area. The data acquisition module is used to acquire at least one source system of data to be processed, wherein the data to be processed includes at least: node data of the node to be used, line data of the line to be used, and equipment operation data of the power equipment corresponding to the node to be used; The topology display module is used to determine the target power grid topology corresponding to the target area based on multiple nodes to be used, at least one line to be used, and the data to be processed, using the G6 visualization engine, and to display the target power grid topology on the target terminal. The topology graph testing module is used to evaluate the target power grid topology graph based on multiple evaluation dimensions to obtain evaluation attributes under each evaluation dimension. The evaluation dimensions include at least: topology graph loading dimension, topology graph rendering frame rate dimension, and topology graph memory usage dimension. Based on the evaluation attributes under each evaluation dimension, a target evaluation dimension is determined, and at least one test case corresponding to the target evaluation dimension is identified. The target power grid topology graph is then tested using the at least one test case to obtain test results. The target power grid topology graph is updated based on the test results, and the updated target power grid topology graph is displayed on the target terminal.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the power grid topology determination method according to any one of claims 1-7.

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