Power grid full topology automatic mapping method and system based on main and auxiliary micrograph penetration

Abstract

A method and system for automatic full-topology mapping of power grids based on the integration of main grid, distribution grid, and microgrid models collect data from the main grid, distribution grid, and microgrid to construct a dataset of power grid equipment and connection relationships. Based on the dataset, the electrical connection relationships between various devices in the power grid are identified, and an initial topology model is constructed. The initial topology model is then corrected to obtain a corrected power grid topology model. Equipment locations are obtained, and the connection lines between devices are derived based on these locations. Based on the corrected power grid topology model and the connection lines between devices, a unified full-topology map of the power grid is generated. Business scenario instructions are received, and corresponding thematic analysis maps are generated based on the full-topology map. The system monitors the status change signals of power grid equipment in real time, dynamically updates the full-topology map of the power grid when the topology structure changes, and records and traces map model changes. This invention enables partial updates to the topology map and supports topology change recording and tracing.

Description

Technical Field

[0001] This invention belongs to the field of smart grid dispatching technology, and in particular relates to a method and system for automatic mapping of the entire power grid topology based on the connection of main and distribution micro-map modules. Background Technology

[0002] Power grid topology mapping is a core supporting technology for smart grid dispatching, operation monitoring, and fault handling. It requires the integration of graph data from the main grid (high-voltage transmission network), distribution network (medium and low-voltage distribution network), and microgrids (distributed energy network) to form a unified power grid topology view.

[0003] Existing technologies already contain some solutions to the problems of manual dependence on topology construction and contradictions arising from a single algorithm in topology construction: The patent with publication number CN115579864B proposes an automatic power grid topology generation method. By collecting power grid equipment data and connection relationships, it uses an improved graph theory algorithm to automatically construct the topology model, reducing manual intervention. At the same time, it optimizes the topology accuracy through simple electrical rule verification. However, this solution only focuses on data processing at the single network level of the main grid and does not involve the fusion of multi-source heterogeneous data from the distribution network and microgrids, so it cannot form an integrated topology model covering all voltage levels. The patent with publication number CN113363965A discloses a method for verifying power grid topology errors. Based on the node current conservation rule and branch impedance analysis, it identifies and corrects topology connection contradictions, thereby improving the accuracy of the topology model. However, this scheme relies on static electrical rule verification and lacks a machine learning verification mechanism that can dynamically adapt to changes in the power grid's operating state. When faced with complex data error scenarios, the verification accuracy is limited. The patent with publication number CN117913895A designed a dynamic power grid topology update system. It achieves rapid updates of the topology model by monitoring changes in equipment status in real time, which solves the inefficiency problem of traditional full-map redrawing. However, the system does not achieve the fusion of multiple maps such as wiring diagrams, topology diagrams and power flow diagrams, and lacks deep integration with dispatching business scenarios. It cannot quickly generate special analysis diagrams such as power supply path analysis and fault impact range assessment. At the same time, it does not establish a systematic topology change version management and backtracking mechanism, which is not conducive to fault tracing and operation history analysis.

[0004] In summary, while existing technologies have made some progress in reducing manual intervention, verifying basic topology, and enabling simple dynamic updates, the following unresolved technical pain points remain: First, the data of the main and distribution microgrids has not achieved true unified integration, making it difficult to construct an integrated topology model covering all voltage levels; second, the verification mechanism is singular, relying on static rules or a single algorithm, making it difficult to dynamically adapt to data errors and changes in operating status, and there is still room for improvement in topology accuracy; third, after the topology map is generated, it lacks deep integration with scheduling business scenarios, making it impossible to quickly generate thematic analysis maps, resulting in insufficient practicality in supporting scheduling decisions; and fourth, there is a lack of a systematic recording and backtracking mechanism for topology change history, which is not conducive to fault tracing and operational analysis.

[0005] Therefore, there is an urgent need for an automatic topology mapping method for the entire power grid based on the interconnection of master and distribution micro-map modules. Summary of the Invention

[0006] The purpose of this invention is to provide an automatic grid topology mapping method and system based on the integration of main and distribution micro-graph models, in order to solve the problems of existing grid topology mapping methods mentioned in the background art, which rely on manual intervention in topology model construction and are prone to topology contradictions due to data errors when using a single graph theory algorithm to identify electrical connection relationships.

[0007] The present invention adopts the following technical solution.

[0008] This invention proposes an automatic full-topology mapping method for power grids based on the interconnection of master and distribution micro-map modules, including: S1: Collect multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and preprocess it to construct a dataset of power grid equipment and connection relationships; S2, Based on the power grid equipment and connection relationship dataset, identify the electrical connection relationships between various devices in the power grid and construct an initial topology model; then, use a hybrid verification mechanism to correct the initial topology model to obtain a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; S3, obtain the device location, and obtain the connection lines between the devices based on the device location; generate a unified power grid topology map based on the corrected power grid topology model and the connection lines between the devices; S4, Receive business scenario instructions, and based on the power grid topology map, generate corresponding thematic analysis maps through the business rule engine; S5 monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and traces the changes in the map model.

[0009] More preferably, in S2, based on the power grid equipment and connection relationship dataset, the electrical connection relationships between various devices in the power grid are identified, and an initial topology model is constructed; the initial topology model is then corrected using a hybrid verification mechanism to obtain a corrected power grid topology model. Specific steps include: S2.1, Based on the power grid equipment and connection relationship dataset, construct a power grid graph represented by a set of nodes and edges; based on the power grid graph, use a breadth-first search algorithm to traverse the power grid graph, identify electrical islands in the power grid graph, and mark the equipment connecting transformers of different voltage levels as topology boundary interconnection points of the main and distribution microgrids; integrate the attribute data of the electrical islands, topology boundary interconnection points, and the set of nodes and the set of edges to generate an initial topology model of the power grid; S2.2, perform topology anomaly screening on each node in the power grid diagram to obtain topology verification anomaly nodes; for each topology verification anomaly node, extract the number of connected branches, the sum of impedances of adjacent branches, the voltage difference between adjacent nodes, and the power difference between adjacent branches to construct a feature vector; S2.3, Based on the feature vector, use a multi-class support vector machine classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vector; S2.4 Based on the root cause type and confidence level of the topology verification abnormal node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification abnormal node are corrected in a targeted manner to obtain the corrected node. S2.5 Update the corrected node and connection relationships to the initial topology model to obtain the corrected power grid topology model.

[0010] More preferably, in S2.2, the specific implementation steps of topological anomaly screening include: For each node in the power grid diagram, calculate the difference between the sum of the currents flowing into all branches and the sum of the currents flowing out of all branches. If the absolute value of this difference is greater than a preset error threshold, then mark the node as a topology verification abnormal node.

[0011] More preferably, in S2.3, the specific implementation steps for obtaining the root cause type and confidence level of the topology verification anomaly node corresponding to the feature vector include: Input the feature vectors into a pre-trained support vector machine multi-classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vectors; The root cause types include abnormal measurement data, incorrect switch status, missing connection relationships, and incorrect connection relationships.

[0012] More preferably, in S2.4, based on the root cause type and confidence level of the topology verification anomaly node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification anomaly node are directionally corrected to obtain the corrected node. The specific implementation steps include: The specific implementation method of the device connection reliability rules is as follows: A static equipment type base score is predefined for each type of equipment in the power grid, and data source weights are set for different connection relationships that need to be corrected; The overall reliability score of the corresponding connection relationship to be corrected is obtained by weighting and summing the basic score of equipment type and the weight of data source. The overall reliability scores of different connection relationships are sorted from low to high, and connection relationships with low scores are corrected first. The principle of physical proximity means that if the overall reliability scores of multiple connections are tied for the lowest, the connection closest to the node with the current topology error will be selected as the correction target.

[0013] More preferably, in step S3, the device locations are obtained, and the connection lines between the devices are derived based on these locations; based on the corrected power grid topology model and the connection lines between the devices, a unified power grid topology map is generated. Specific implementation steps include: S3.1, Based on the modified power grid topology model, the force-oriented layout algorithm is used to calculate the initial position of the equipment nodes, and the position of the equipment is obtained by iteratively calculating the force on each equipment; S3.2, Based on the device location, a path planning algorithm is used to connect adjacent device nodes and generate connection lines between devices; S3.3 Based on the revised power grid topology model and the connection lines between devices, establish the association mapping of wiring diagram, topology diagram and power flow diagram, and merge them to form a unified power grid topology diagram; S3.4 When a change in the topology of a power grid is detected, an update radius is defined centered on the changed device, and S3.1 to S3.3 are re-executed for devices within the update radius area; S3.5 converts the power grid topology map generated after the update in S3.4 into a standardized graphical data format and outputs it.

[0014] More preferably, in S3.1, the force-guided layout algorithm calculates the forces on each device and obtains the device position based on the comprehensive reliability score obtained in step 2. The specific steps include: Based on the set connection weights and the normalized node spacing, the two are multiplied to obtain the normalized gravity value between them; The ratio of the calculated repulsion coefficient to the square of the normalized node spacing is used to calculate the normalized repulsion value between the devices. The repulsion coefficient and its algorithm are as follows: Set a baseline repulsion coefficient, multiply the difference between 1 and the comprehensive reliability score by the set adjustment coefficient to obtain the multiplication result, and use the multiplication result to weight the baseline repulsion coefficient to obtain the repulsion coefficient.

[0015] More preferably, in step S4, based on the complete power grid topology map, a corresponding thematic analysis map is generated, specifically including the following steps: Business scenario commands include power supply path analysis commands, inter-station communication analysis commands, and fault impact range analysis commands; Based on the business scenario instructions, the corresponding analysis rules are retrieved from the business rule base; The rule for analyzing power supply paths is to select the path with the smallest total path impedance among all connected paths from the power source point to the target load point as the optimal power supply path. Based on the correlation mapping in the power grid topology map, the line impedance parameters required for power supply path analysis and the tie line capacity and load rate data required for inter-station interconnection analysis are extracted. Perform the analysis and calculations defined by the corresponding analysis rules to obtain the thematic analysis results data; among which, the thematic analysis results data includes the total impedance of each candidate path, the optimal path, the tie-line load rate, and the alarm flags; In the power supply path analysis, the total impedance of each candidate path is calculated, and the path with the minimum total impedance is marked as the optimal path. In the inter-station interconnection analysis, the interconnection line load rate is calculated as current load / rated capacity × 100%, and an alarm is set for interconnection lines whose load rate exceeds the set threshold. The thematic analysis results are overlaid with the full power grid topology map to generate and output the thematic analysis map.

[0016] More preferably, in step S5, the status change signals of power grid equipment are monitored in real time. When the topology changes, the entire power grid topology map is dynamically updated, and the map changes are recorded and traced back. Specific steps include: Real-time monitoring of power grid equipment status change signals, including circuit breaker opening and closing status, equipment commissioning and decommissioning status, and line connection status changes; When a change in equipment status is detected, the equipment identifier of the changed equipment is extracted, and the affected topology region is determined based on the power grid topology model formed by S2. Centered on the changing device, update the radius according to S3.4 Identify the affected areas; Re-execute step S3 for the equipment in the affected area to update the equipment connection relationships and operating data in the power grid topology diagram; Create a unique version identifier for each change to the drawing model for backtracking. The specific steps include: Version numbers use a coding rule that combines timestamps and change types; Record the equipment parameters, connection relationships, and graphical layout data before and after the change to form a change log; Establish a version index table to store the creation time, changes, and operator information for each version; When tracing back to historical versions, the corresponding graph data is extracted from the version repository based on the version identifier; It supports version recovery based on a specific point in time, restoring the entire power grid topology to a specified historical state.

[0017] This invention also provides an automatic power grid topology mapping system based on the integration of master and distribution micro-map modules, including a data acquisition module, a correction module, a power grid topology full map generation module, a thematic analysis map generation module, and an update and backtracking module: The data acquisition module collects multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and performs preprocessing to construct a dataset of power grid equipment and connection relationships; The correction module, based on the power grid equipment and connection relationship dataset, identifies the electrical connection relationships between various devices in the power grid and constructs an initial topology model; then, it uses a hybrid verification mechanism to correct the initial topology model, obtaining a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; The power grid topology full map generation module obtains the device locations and the connection lines between the devices based on the device locations; based on the corrected power grid topology model and the connection lines between the devices, it generates a unified power grid topology full map. The thematic analysis diagram generation module receives business scenario instructions and, based on the full power grid topology map, generates corresponding thematic analysis diagrams driven by the business rule engine. The update and backtracking module monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and backtracks the changes in the map model.

[0018] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0019] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention relates to an automatic grid topology mapping method and system based on the integration of main grid, distribution network, and microgrid graph models. By collecting multi-source heterogeneous graph model data from the main grid, distribution network, and microgrids and performing cleaning and normalization processing, it constructs an accurate grid topology model by combining graph theory traversal algorithms, electrical rules, and a hybrid verification mechanism of machine learning. Compared with the problems of data silos in the main grid, distribution network, and microgrids, topology construction relying on manual intervention, and lack of dynamic verification in traditional methods, this invention achieves unified integration of multi-network data and automatic and accurate construction of topology models, solving the defects of insufficient accuracy and poor adaptability of traditional topology models caused by data heterogeneity and manual intervention.

[0021] 2. The present invention is based on a method and system for automatic topology mapping of the power grid with integrated main and distribution micro-maps. It generates a unified full topology map by integrating wiring diagrams, topology diagrams and power flow diagrams through automatic graphic layout and incremental mapping algorithms. Combined with incremental update algorithms and version management technology, it realizes dynamic topology updates and historical backtracking. Compared with the inefficient mode of traditional topology maps that are single and require full map redrawing when equipment status changes, as well as the lack of historical version tracing capabilities, it can complete partial updates without repeatedly building the full map. At the same time, it supports topology change records and backtracking, meeting the needs of the dispatching system for multi-dimensional topology views, real-time updates and historical backtracking. Attached Figure Description

[0022] Figure 1 This is a flowchart of the automatic topology mapping method for the entire power grid based on the interconnection of master and distribution micro-map modules of the present invention. Figure 2 This is a flowchart of the overall method of Embodiment 1 of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0024] like Figure 1 As shown, this invention proposes an automatic full-topology mapping method for power grids based on the interconnection of master and distribution micro-map modules, including: S1: Collect multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and preprocess it to construct a dataset of power grid equipment and connection relationships; S2, Based on the power grid equipment and connection relationship dataset, identify the electrical connection relationships between various devices in the power grid and construct an initial topology model; then, use a hybrid verification mechanism to correct the initial topology model to obtain a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; In step S2, based on the power grid equipment and connection relationship dataset, the electrical connection relationships between various devices in the power grid are identified, and an initial topology model is constructed. A hybrid verification mechanism is then used to correct the initial topology model, resulting in a corrected power grid topology model. Specific steps include: S2.1, Based on the power grid equipment and connection relationship dataset, construct a power grid graph represented by a set of nodes and edges; based on the power grid graph, use a breadth-first search algorithm to traverse the power grid graph, identify electrical islands in the power grid graph, and mark the equipment connecting transformers of different voltage levels as topology boundary interconnection points of the main and distribution microgrids; integrate the attribute data of the electrical islands, topology boundary interconnection points, and the set of nodes and the set of edges to generate an initial topology model of the power grid; S2.2, perform topology anomaly screening on each node in the power grid diagram to obtain topology verification anomaly nodes; for each topology verification anomaly node, extract the number of connected branches, the sum of impedances of adjacent branches, the voltage difference between adjacent nodes, and the power difference between adjacent branches to construct a feature vector; In S2.2, the specific implementation steps for topology anomaly screening include: For each node in the power grid diagram, calculate the difference between the sum of the currents flowing into all branches and the sum of the currents flowing out of all branches. If the absolute value of this difference is greater than a preset error threshold, then mark the node as a topology verification abnormal node.

[0025] S2.3, Based on the feature vector, use a multi-class support vector machine classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vector; In S2.3, the specific implementation steps for obtaining the root cause type and confidence level of the topology verification anomaly node corresponding to the feature vector include: Input the feature vectors into a pre-trained support vector machine multi-classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vectors; The root cause types include abnormal measurement data, incorrect switch status, missing connection relationships, and incorrect connection relationships.

[0026] S2.4 Based on the root cause type and confidence level of the topology verification abnormal node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification abnormal node are corrected in a targeted manner to obtain the corrected node. In S2.4, based on the root cause type and confidence level of the topology verification anomaly node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification anomaly node are directionally corrected to obtain the corrected node. The specific implementation steps include: The specific implementation method of the device connection reliability rules is as follows: A static equipment type base score is predefined for each type of equipment in the power grid, and data source weights are set for different connection relationships that need to be corrected; The overall reliability score of the corresponding connection relationship to be corrected is obtained by weighting and summing the basic score of equipment type and the weight of data source. The overall reliability scores of different connection relationships are sorted from low to high, and connection relationships with low scores are corrected first. The principle of physical proximity means that if the overall reliability scores of multiple connections are tied for the lowest, the connection closest to the node with the current topology error will be selected as the correction target.

[0027] S2.5 Update the corrected node and connection relationships to the initial topology model to obtain the corrected power grid topology model.

[0028] S3, obtain the device location, and obtain the connection lines between the devices based on the device location; generate a unified power grid topology map based on the corrected power grid topology model and the connection lines between the devices; In S3, the device locations are obtained, and the connection lines between the devices are derived based on these locations. Based on the corrected power grid topology model and the connection lines between the devices, a unified power grid topology map is generated. Specific implementation steps include: S3.1, Based on the modified power grid topology model, the force-oriented layout algorithm is used to calculate the initial position of the equipment nodes, and the position of the equipment is obtained by iteratively calculating the force on each equipment; In S3.1, the force-oriented layout algorithm calculates the forces on each device and obtains the device position based on the comprehensive reliability score obtained in step 2. The specific steps include: Based on the set connection weights and the normalized node spacing, the two are multiplied to obtain the normalized gravity value between them; The ratio of the calculated repulsion coefficient to the square of the normalized node spacing is used to calculate the normalized repulsion value between the devices. The repulsion coefficient and its algorithm are as follows: Set a baseline repulsion coefficient, multiply the difference between 1 and the comprehensive reliability score by the set adjustment coefficient to obtain the multiplication result, and use the multiplication result to weight the baseline repulsion coefficient to obtain the repulsion coefficient.

[0029] S3.2, Based on the device location, a path planning algorithm is used to connect adjacent device nodes and generate connection lines between devices; S3.3 Based on the revised power grid topology model and the connection lines between devices, establish the association mapping of wiring diagram, topology diagram and power flow diagram, and merge them to form a unified power grid topology diagram; S3.4 When a change in the topology of a power grid is detected, an update radius is defined centered on the changed device, and S3.1 to S3.3 are re-executed for devices within the update radius area; S3.5 converts the power grid topology map generated after the update in S3.4 into a standardized graphical data format and outputs it.

[0030] S4, Receive business scenario instructions, and based on the power grid topology map, generate corresponding thematic analysis maps through the business rule engine; In S4, based on the complete power grid topology map, a corresponding thematic analysis map is generated. Specific steps include: Business scenario commands include power supply path analysis commands, inter-station communication analysis commands, and fault impact range analysis commands; Based on the business scenario instructions, the corresponding analysis rules are retrieved from the business rule base; The rule for analyzing power supply paths is to select the path with the smallest total path impedance among all connected paths from the power source point to the target load point as the optimal power supply path. Based on the correlation mapping in the power grid topology map, the line impedance parameters required for power supply path analysis and the tie line capacity and load rate data required for inter-station interconnection analysis are extracted. Perform the analysis and calculations defined by the corresponding analysis rules to obtain the thematic analysis results data; among which, the thematic analysis results data includes the total impedance of each candidate path, the optimal path, the tie-line load rate, and the alarm flags; In the power supply path analysis, the total impedance of each candidate path is calculated, and the path with the minimum total impedance is marked as the optimal path. In the inter-station interconnection analysis, the interconnection line load rate is calculated as current load / rated capacity × 100%, and an alarm is set for interconnection lines whose load rate exceeds the set threshold. The thematic analysis results are overlaid with the full power grid topology map to generate and output the thematic analysis map.

[0031] S5 monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and traces the changes in the map model.

[0032] S5, Real-time monitoring of power grid equipment status change signals; When the topology changes, dynamically updating the entire power grid topology map; Recording and retrospectively tracking map changes; Specific steps include: Real-time monitoring of power grid equipment status change signals, including circuit breaker opening and closing status, equipment commissioning and decommissioning status, and line connection status changes; When a change in equipment status is detected, the equipment identifier of the changed equipment is extracted, and the affected topology region is determined based on the power grid topology model formed by S2. Centered on the changing device, update the radius according to S3.4 Identify the affected areas; Re-execute step S3 for the equipment in the affected area to update the equipment connection relationships and operating data in the power grid topology diagram; Create a unique version identifier for each change to the drawing model for backtracking. The specific steps include: Version numbers use a coding rule that combines timestamps and change types; Record the equipment parameters, connection relationships, and graphical layout data before and after the change to form a change log; Establish a version index table to store the creation time, changes, and operator information for each version; When tracing back to historical versions, the corresponding graph data is extracted from the version repository based on the version identifier; It supports version recovery based on a specific point in time, restoring the entire power grid topology to a specified historical state.

[0033] This invention also provides an automatic power grid topology mapping system based on the integration of master and distribution micro-map modules, including a data acquisition module, a correction module, a power grid topology full map generation module, a thematic analysis map generation module, and an update and backtracking module: The data acquisition module collects multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and performs preprocessing to construct a dataset of power grid equipment and connection relationships; The correction module, based on the power grid equipment and connection relationship dataset, identifies the electrical connection relationships between various devices in the power grid and constructs an initial topology model; then, it uses a hybrid verification mechanism to correct the initial topology model, obtaining a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; The power grid topology full map generation module obtains the device locations and the connection lines between the devices based on the device locations; based on the corrected power grid topology model and the connection lines between the devices, it generates a unified power grid topology full map. The thematic analysis diagram generation module receives business scenario instructions and, based on the full power grid topology map, generates corresponding thematic analysis diagrams driven by the business rule engine. The update and backtracking module monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and backtracks the changes in the map model.

[0034] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0035] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0036] Example 1: Please see Figure 2 As shown, this embodiment provides an automatic full-topology mapping method for power grids based on the interconnection of main and distribution micro-map modules, including the following steps: S1. Collect multi-source heterogeneous graph model data from the main grid, distribution network and microgrid, and perform cleaning and normalization processing to construct a dataset of power grid equipment and connection relationships; In this embodiment, the multi-source heterogeneous graph data includes at least device parameters, connection relationships, and real-time operating status data; The equipment parameters include the transformer capacity and the line resistance and reactance. Connection relationships include the connection methods between nodes and lines, and the hierarchical relationships between devices; Real-time operating status data includes voltage, current, and power; In this embodiment, the specific steps involved in constructing the power grid equipment and connection relationship dataset are as follows: Data interfaces are used to collect equipment parameters, connection relationships, and real-time operating status data from dispatch automation systems (such as D5000), distribution management systems (DMS), and microgrid controllers. The collected data is filtered using thresholds to remove outliers that clearly exceed the physical reasonable range; The threshold is set based on the equipment nameplate parameters and historical operating statistics. For example, for a 220kV line, the upper limit of the current threshold is set to the line thermal stability limit current. current value of the collection satisfy Define the data as abnormal and remove it; For missing equipment parameters, fill them in using the statistical average of the parameters of the same model of equipment; For real-time data (such as missing voltage measurements at a certain moment), the moving average of measurements from adjacent moments is used to fill in the missing data. The cleaned data is mapped to a unified CIM / G standard format, and the numerical parameters are subjected to min-max normalization to generate a dataset of power grid equipment and connection relationships.

[0037] S2. Based on the power grid equipment and connection relationship dataset, a graph theory traversal algorithm is used to identify the electrical connection relationships between various devices in the power grid and construct an initial topology model; and a hybrid verification mechanism based on electrical rules and machine learning models is used to correct the initial topology model to obtain a corrected power grid topology model. S2.1 In this embodiment, power grid equipment (such as circuit breakers, disconnectors, transformers, and loads) are set as graph nodes, and electrical connection lines (such as transmission lines and busbars) are set as graph edges, constructing a graph based on a set of nodes. Sum of edges Power grid diagram ; Starting with the substation busbar as the initial node, a breadth-first search algorithm is used to traverse the power grid diagram and identify all electrically connected equipment sets. The breadth-first search algorithm traverses the graph by starting from the starting node, visiting all its adjacent nodes to form the first-level node set; then visiting all unvisited adjacent nodes of each node in the first-level node set to form the second-level node set; repeating this process until all nodes reachable from the starting node have been visited. Based on the traversal results of the breadth-first search algorithm, electrical islands in the power grid are identified, and devices connecting transformers of different voltage levels are marked as topological boundary interconnection points of the main and distribution microgrids. An electrical island refers to an isolated operating area in a power grid that is internally electrically connected but not electrically connected to other parts of the power grid. By integrating the attribute data of the electrical islands, topology boundary interconnection points, and node and edge sets, an initial topology model of the power grid is generated. S2.2, Rapid topology anomaly screening for nodes.

[0038] For each node in the power grid diagram, calculate the difference between the sum of the currents flowing into all its branches and the sum of the currents flowing out of all its branches. If the absolute value of this difference is greater than a preset error threshold... If so, then mark the node as a topology verification abnormal node;

[0039] in, Indicates the inflow node All The sum of the currents in each branch, Indicates outflow node All The sum of the currents in each branch; It is the difference between the sum of the currents flowing into all branches and the sum of the currents flowing out of all branches; For the set error threshold (e.g.) , (Reference value for total node current); For each topology check anomaly node, extract the number of connected branches, the sum of impedances of adjacent branches, the voltage difference between adjacent nodes, and the power difference between adjacent branches to construct a feature vector; S2.3 Root cause diagnosis and confidence assessment.

[0040] The feature vector is input into a pre-trained support vector machine multi-classification model to obtain the root cause type and corresponding confidence level of the topological anomaly of the node; the root cause type includes at least measurement data anomaly, switch state error, missing connection relationship, and connection relationship error; As a preferred example of the present invention, the output of the multi-class support vector machine classification model includes the root cause type and confidence level of the node, for example: [Measurement data anomaly: 0.15, switch state error: 0.70, connection relationship omission: 0.10, connection relationship error: 0.05]; this vector provides a direct and quantifiable decision basis for subsequent differential correction.

[0041] S2.4 Differentiated Correction Decisions and Execution Based on Diagnostic Results. Based on the root cause type and confidence level of the node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the node are corrected in a targeted manner; The device connection reliability rule aims to assign a quantified reliability score to each candidate electrical connection to guide corrective decisions, specifically: Predefine a static device type base score for each type of physical device in the power grid. The score is set based on prior knowledge such as the criticality of the equipment in the power grid, its historical failure rate, and its state stability.

[0042] For example: transformers and busbars that are core hubs of the power grid and operate stably. Set to 1; for circuit breakers and disconnectors that require frequent operation and whose status is easily changed, set to 0.8; for overhead lines, set to 0.9; for specific devices that frequently generate status abnormalities or communication interruption alarms in the system's historical operation records, a discount factor of less than 1, such as 0.8, will be multiplied by their device type base score to dynamically reflect their lower reliability.

[0043] During the data acquisition and processing phase, the data source for each device connection relationship is recorded, such as the main network, distribution network, and microgrid. A data source weight is predefined for the connection relationship data from different data sources. .

[0044] For example, the connection relationships from the main network, distribution network, and microgrid are respectively set with data source weights of 1, 0.9, and 0.7.

[0045] Based on device type and data source weight Calculate a comprehensive reliability score for each candidate connection:

[0046] in, The basic score is based on the equipment type. The weights are assigned to the data sources (e.g., 1.0 for data from the dispatch automation system, 0.9 for data from the power distribution management system, and 0.7 for data from the microgrid controller); among which, weights of 0.4 and 0.6 are a preferred set of weights in this invention.

[0047] When multiple candidate connections suspected of being faulty need adjustment, their overall reliability score should be used as the basis for the adjustment. Sort by reliability from low to high, prioritizing connections with lower reliability scores for adjustment.

[0048] The principle of physical proximity means that among the candidate connections sorted according to the device connection reliability rules, if there are multiple connections with the lowest overall reliability scores, the connection with the closest physical distance to the current node will be selected as the object of the correction operation.

[0049] S2.5 Update the corrected node and connection relationships to the initial topology model to obtain the corrected power grid topology model.

[0050] S3. Based on the modified power grid topology model, the wiring diagram, topology diagram and power flow diagram of the power grid are merged through the automatic graphical layout algorithm and the incremental graph generation algorithm to generate a unified full power grid topology diagram. In this embodiment, S3.1, based on the modified power grid topology model, the force-oriented layout algorithm is used to calculate the initial position of the equipment node, introduces the attraction and repulsion parameters between the equipment, and obtains a stable equipment position by iteratively calculating the force on each equipment. The force-directed layout algorithm treats power grid equipment as charged particles and electrical connections between equipment as springs, establishing a mechanical model:

[0051]

[0052] in, This represents the normalized gravitational force between the devices. For connection weights; Normalized node spacing; This is the normalized repulsive force value between devices, which is related to the device type. This is the repulsion coefficient, used to adjust the repulsion strength between nodes. Its value can be determined based on the overall reliability score. Dynamic adjustments are made, and the calculation formula is as follows: ,in, The set reference repulsion coefficient, The overall reliability score for the corresponding edge between two devices. Adjustment coefficient

[0053] The force-directed layout algorithm iteratively determines the final layout position of nodes by calculating abstract attractive and repulsive forces (which are dimensionless values); The connection weights are set based on a combination of the electrical coupling strength and data reliability of the connections between devices. For connections between transformer windings, the connection weight is set to 1.0; for connections between buses of the same voltage level, it is set to 0.8; and for line connections across voltage levels, it is set to 0.6. As a further embodiment of the invention, the connection weight values ​​can be positively correlated with the comprehensive reliability score calculated in step S2.4, for example, w = 0.5 + 0.5 * R_link, so that the layout results can simultaneously reflect both electrical structure and data quality.

[0054] S3.2 Based on stable device locations, the A* path planning algorithm is used to connect adjacent device nodes and generate connection lines between devices. A* Path Planning Algorithm:

[0055] in, This represents the distance from the starting point to the current node. The actual path cost includes route length, number of turns, etc. Indicates the current node The estimated cost to the target node is calculated using Manhattan distance; in the 220kV transmission line wiring, the turning cost coefficient is set to 2.0 and the crossing cost coefficient is set to 5.0. For the path cost (dimensionless) from the starting point to the target node, prioritize... The path with the smallest value; S3.3 Based on the revised power grid topology model and the connection lines between devices, establish the correlation mapping between wiring diagram device connection relationships, topology diagram hierarchical structure, and power flow diagram power data, and integrate the three types of graphical data to form a unified power grid topology map; S3.4 When a change in the topology of a power grid is detected, the update radius shall be determined centered on the device undergoing the change. (like =3-layer connected devices), re-execute S3.1 to S3.3 for devices within the update radius area; The update radius The basis for the value includes: Electrical impact range constraints: The electrical impact of most topology changes in the power grid (such as switch opening and closing, equipment commissioning and decommissioning) is usually limited to the adjacent 2-3 layers of equipment. The node voltage and power flow changes beyond this range can be ignored. Layout coupling constraint: In the force-directed layout algorithm, the position of a node is affected by its directly connected and indirectly connected neighbor nodes. Usually, after passing through 3 layers, the influence has decayed to an acceptable range. System performance balancing: While ensuring the accuracy of graphics updates, the update range is limited ( Typically, 2-4 is used to avoid repetitive layout calculations across the entire image, thereby achieving rapid local updates and overall performance optimization.

[0056] In practice, update radius It can be configured according to the grid voltage level, the sensitivity of the layout algorithm, and the real-time requirements of the system.

[0057] S3.5. Convert the full power grid topology map generated in S3.4 into a standardized graphical data format and output it.

[0058] S4. Receive business scenario instructions from the scheduling system, and generate corresponding thematic analysis diagrams based on the power grid topology map and driven by the business rule engine; In this embodiment, a service scenario instruction is received from the scheduling system; Among them, the business scenario instructions include power supply path analysis instructions, inter-station communication analysis instructions, and fault impact range analysis instructions; Based on the business scenario instructions, the corresponding analysis rules are retrieved from the business rule base; The rule for analyzing power supply paths is to select the path with the smallest total path impedance among all connected paths from the power source point to the target load point as the optimal power supply path. Based on the correlation mapping established by S3.3, the line impedance parameters required for power supply path analysis and the tie line capacity and load rate data required for inter-station tie analysis are extracted. Perform the analysis and calculations defined by the corresponding analysis rules to obtain the thematic analysis results data; the thematic analysis results data includes the total impedance of each optional path, the optimal path, the tie-line load rate, and alarm indicators; In the power supply path analysis, the total impedance of each optional path is calculated, and the path with the minimum total impedance is marked as the optimal path; in the inter-station interconnection analysis, the interconnection line load rate is calculated as current load / rated capacity × 100%, and an alarm is set for interconnection lines with a load rate exceeding 80%. The thematic analysis results are overlaid with the full power grid topology map to generate and output the thematic analysis map; In the power supply path analysis, the optimal power supply path is displayed using highlighted lines; in the inter-station interconnection analysis, different colored lines represent the load rate status of the interconnection lines, with green indicating normal, yellow indicating warning, and red indicating overload.

[0059] S5. Monitor the status change signals of power grid equipment in real time. When the topology changes, trigger the incremental update algorithm to dynamically update the entire power grid topology map, and use version management technology to record and trace map changes. Specifically, in this embodiment, the status change signals of power grid equipment are monitored in real time, including the circuit breaker opening and closing status, equipment commissioning and decommissioning status, and line connection status changes. When a change in equipment status is detected, the equipment identifier of the changed equipment is extracted, and the affected topology region is determined based on the power grid topology model formed by S2. Centered on the changing device, update the radius according to S3.4 Identify the affected areas; Re-execute S3.1 on the equipment within the affected area, keeping the location of equipment outside the area unchanged, to obtain the updated equipment location; Based on the updated device location, S3.2 is executed again to generate new connection lines; Based on the association mapping established in S3.3, update the device connection relationships and operation data in the power grid topology map; Furthermore, create a unique version identifier for each change to the drawing template; The version number uses a timestamp + change type encoding rule; Record the equipment parameters, connection relationships, and graphical layout data before and after the change to form a complete change log; Establish a version index table to store the creation time, changes, and operator information for each version; When it is necessary to revert to a previous version, the corresponding graph data is extracted from the version repository based on the version identifier; It supports version recovery based on a specific point in time, restoring the entire power grid topology to a specified historical state.

[0060] Example 2: This invention also provides an automatic power grid topology mapping system based on the integration of master and distribution micro-map modules, including a data acquisition module, a correction module, a power grid topology full map generation module, a thematic analysis map generation module, and an update and backtracking module: The data acquisition module collects multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and performs preprocessing to construct a dataset of power grid equipment and connection relationships; The correction module, based on the power grid equipment and connection relationship dataset, identifies the electrical connection relationships between various devices in the power grid and constructs an initial topology model; then, it uses a hybrid verification mechanism to correct the initial topology model, obtaining a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; The power grid topology full map generation module obtains the device locations and the connection lines between the devices based on the device locations; based on the corrected power grid topology model and the connection lines between the devices, it generates a unified power grid topology full map. The thematic analysis diagram generation module receives business scenario instructions and, based on the full power grid topology map, generates corresponding thematic analysis diagrams driven by the business rule engine. The update and backtracking module monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and backtracks the changes in the map model.

[0061] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0062] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0063] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0064] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0065] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0066] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for automatic full-topology mapping of a power grid based on the integration of master and distribution micro-maps, characterized in that, include: S1: Collect multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and preprocess it to construct a dataset of power grid equipment and connection relationships; S2, Based on the power grid equipment and connection relationship dataset, identify the electrical connection relationships between various devices in the power grid and construct an initial topology model; then, use a hybrid verification mechanism to correct the initial topology model to obtain a corrected power grid topology model; the hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines; S3, obtain the device location, and obtain the connection lines between the devices based on the device location; generate a unified power grid topology map based on the corrected power grid topology model and the connection lines between the devices; S4, Receive business scenario instructions, and based on the power grid topology map, generate corresponding thematic analysis maps through the business rule engine; S5 monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and traces the changes in the map model.

2. The automatic power grid topology mapping method based on the interconnection of main and distribution micro-map modules according to claim 1, characterized in that: In S2, based on the power grid equipment and connection relationship dataset, the electrical connection relationships between various devices in the power grid are identified, and an initial topology model is constructed; The initial topology model is corrected using a hybrid verification mechanism to obtain the corrected power grid topology model. The specific steps include: S2.1, Based on the power grid equipment and connection relationship dataset, construct a power grid graph represented by a set of nodes and edges; based on the power grid graph, use a breadth-first search algorithm to traverse the power grid graph, identify electrical islands in the power grid graph, and mark the equipment connecting transformers of different voltage levels as topology boundary interconnection points of the main and distribution microgrids; integrate the attribute data of the electrical islands, topology boundary interconnection points, and the set of nodes and the set of edges to generate an initial topology model of the power grid; S2.2, perform topology anomaly screening on each node in the power grid diagram to obtain topology verification anomaly nodes; for each topology verification anomaly node, extract the number of connected branches, the sum of impedances of adjacent branches, the voltage difference between adjacent nodes, and the power difference between adjacent branches to construct a feature vector; S2.3, Based on the feature vector, use a multi-class support vector machine classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vector; S2.4 Based on the root cause type and confidence level of the topology verification abnormal node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification abnormal node are corrected in a targeted manner to obtain the corrected node. S2.5 Update the corrected node and connection relationships to the initial topology model to obtain the corrected power grid topology model.

3. The automatic topology mapping method for the entire power grid based on the interconnection of main and distribution micro-map modules according to claim 2, characterized in that: In S2.2, the specific implementation steps for topology anomaly screening include: For each node in the power grid diagram, calculate the difference between the sum of the currents flowing into all branches and the sum of the currents flowing out of all branches. If the absolute value of this difference is greater than a preset error threshold, then mark the node as a topology verification abnormal node.

4. The automatic power grid topology mapping method based on the interconnection of main and distribution micro-map modules according to claim 2, characterized in that: In S2.3, the specific implementation steps for obtaining the root cause type and confidence level of the topology verification anomaly node corresponding to the feature vector include: Input the feature vectors into a pre-trained support vector machine multi-classification model to obtain the root cause type and confidence level of the topological verification anomaly node corresponding to the feature vectors; The root cause types include abnormal measurement data, incorrect switch status, missing connection relationships, and incorrect connection relationships.

5. The automatic topology mapping method for a power grid based on the interconnection of main and distribution micro-map modules as described in claim 2, characterized in that: In S2.4, based on the root cause type and confidence level of the topology verification anomaly node, and combined with the device connection reliability rules and the principle of closest physical distance, the connection relationship and device status of the topology verification anomaly node are directionally corrected to obtain the corrected node. The specific implementation steps include: The specific implementation method of the device connection reliability rules is as follows: A static equipment type base score is predefined for each type of equipment in the power grid, and data source weights are set for different connection relationships that need to be corrected; The overall reliability score of the corresponding connection relationship to be corrected is obtained by weighting and summing the basic score of equipment type and the weight of data source. The overall reliability scores of different connection relationships are sorted from low to high, and connection relationships with low scores are corrected first. The principle of physical proximity means that if the overall reliability scores of multiple connections are tied for the lowest, the connection closest to the node with the current topology error will be selected as the correction target.

6. The automatic topology mapping method for a power grid based on the interconnection of main and distribution micro-map modules as described in claim 1, characterized in that: In S3, the device locations are obtained, and the connection lines between the devices are derived based on these locations. Based on the corrected power grid topology model and the connection lines between the devices, a unified power grid topology map is generated. Specific implementation steps include: S3.1, Based on the modified power grid topology model, the force-oriented layout algorithm is used to calculate the initial position of the equipment nodes, and the position of the equipment is obtained by iteratively calculating the force on each equipment; S3.2, Based on the device location, a path planning algorithm is used to connect adjacent device nodes and generate connection lines between devices; S3.3 Based on the revised power grid topology model and the connection lines between devices, establish the association mapping of wiring diagram, topology diagram and power flow diagram, and merge them to form a unified power grid topology diagram; S3.4 When a change in the topology of a power grid is detected, an update radius is defined centered on the changed device, and S3.1 to S3.3 are re-executed for devices within the update radius area; S3.5 converts the power grid topology map generated after the update in S3.4 into a standardized graphical data format and outputs it.

7. The automatic power grid topology mapping method based on the interconnection of main and distribution micro-map modules as described in claim 5 or 6, characterized in that: In S3.1, the force-oriented layout algorithm calculates the forces on each device and obtains the device position based on the comprehensive reliability score obtained in step 2. The specific steps include: Based on the set connection weights and the normalized node spacing, the two are multiplied to obtain the normalized gravity value between them; The ratio of the calculated repulsion coefficient to the square of the normalized node spacing is used to calculate the normalized repulsion value between the devices. The repulsion coefficient and its algorithm are as follows: Set a baseline repulsion coefficient, multiply the difference between 1 and the comprehensive reliability score by the set adjustment coefficient to obtain the multiplication result, and use the multiplication result to weight the baseline repulsion coefficient to obtain the repulsion coefficient.

8. The automatic power grid topology mapping method based on the interconnection of main and distribution micro-map modules according to claim 1, characterized in that: In S4, based on the complete power grid topology map, a corresponding thematic analysis map is generated. Specific steps include: Business scenario commands include power supply path analysis commands, inter-station communication analysis commands, and fault impact range analysis commands; Based on the business scenario instructions, the corresponding analysis rules are retrieved from the business rule library; The rule for analyzing power supply paths is to select the path with the smallest total path impedance among all connected paths from the power source point to the target load point as the optimal power supply path. Based on the correlation mapping in the power grid topology map, the line impedance parameters required for power supply path analysis and the tie line capacity and load rate data required for inter-station tie analysis are extracted. Perform the analysis and calculations defined by the corresponding analysis rules to obtain the thematic analysis results data; among which, the thematic analysis results data includes the total impedance of each candidate path, the optimal path, the tie-line load rate, and the alarm flags; In the power supply path analysis, the total impedance of each candidate path is calculated, and the path with the minimum total impedance is marked as the optimal path. In the inter-station interconnection analysis, the interconnection line load rate is calculated as current load / rated capacity × 100%, and an alarm is set for interconnection lines whose load rate exceeds the set threshold. The thematic analysis results are overlaid with the full power grid topology map to generate and output the thematic analysis map.

9. The automatic topology mapping method for a power grid based on the interconnection of main and distribution micro-map modules according to claim 1, characterized in that: S5, Real-time monitoring of power grid equipment status change signals; When the topology changes, dynamically updating the entire power grid topology map; Recording and retrospectively tracking map changes; Specific steps include: Real-time monitoring of power grid equipment status change signals, including circuit breaker opening and closing status, equipment commissioning and decommissioning status, and line connection status changes; When a change in equipment status is detected, the equipment identifier of the changed equipment is extracted, and the affected topology region is determined based on the power grid topology model formed by S2. Centered on the changing device, update the radius according to S3.4 Identify the affected areas; Re-execute step S3 for the equipment in the affected area to update the equipment connection relationships and operating data in the power grid topology diagram; Create a unique version identifier for each change to the drawing model for backtracking. The specific steps include: Version numbers use a coding rule that combines timestamps and change types; Record the equipment parameters, connection relationships, and graphical layout data before and after the change to form a change log; Establish a version index table to store the creation time, changes, and operator information for each version; When tracing back to historical versions, the corresponding graph data is extracted from the version repository based on the version identifier; It supports version recovery based on a specific point in time, restoring the entire power grid topology to a specified historical state.

10. A fully automatic power grid topology mapping system based on the integration of master and distribution micro-map modules, utilizing the method described in any one of claims 1-9, comprising a data acquisition module, a correction module, a power grid topology full map generation module, a thematic analysis map generation module, and an update and backtracking module, characterized in that: The data acquisition module collects multi-source heterogeneous graph model data from the main grid, distribution network and microgrid and performs preprocessing to construct a dataset of power grid equipment and connection relationships; The correction module, based on the power grid equipment and connection relationship dataset, identifies the electrical connection relationships between various devices in the power grid and constructs an initial topology model; The initial topology model is corrected using a hybrid verification mechanism to obtain a corrected power grid topology model. The hybrid verification mechanism includes topology anomaly screening of nodes and classification of node anomalies using support vector machines. The power grid topology full map generation module obtains the device locations and the connection lines between the devices based on the device locations; based on the corrected power grid topology model and the connection lines between the devices, it generates a unified power grid topology full map. The thematic analysis diagram generation module receives business scenario instructions and, based on the full power grid topology map, generates corresponding thematic analysis diagrams driven by the business rule engine. The update and backtracking module monitors the status change signals of power grid equipment in real time. When the topology changes, it dynamically updates the entire power grid topology map and records and backtracks the changes in the map model.

11. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-9.

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

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