Power grid dynamic topology generation system based on machine account and real-time operation data fusion

Abstract

The invention discloses a power grid dynamic topology generation system based on machine account and real-time operation data fusion, and belongs to the technical field of electric power information. Comprising a historical data acquisition module used for acquiring historical topological data and real-time operation data; the rule base generation module is used for establishing a graph construction rule base; the data acquisition module is used for acquiring machine account data and real-time operation data of a target power grid; the power grid layering and grading module is used for carrying out layering and grading processing on the target power grid; the partition topology construction module is used for generating local power grid topology; and the topology merging module is used for merging the local power grid topologies. According to the method, by establishing the rule base and combining the technical means of hierarchical processing, feature matching and the like, the problems that in the prior art, the power grid topology construction speed is low, and the adaptability to a large-scale power grid is insufficient are solved, and efficient and accurate power grid dynamic topology generation is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power information technology, and in particular to a power grid dynamic topology generation system based on the fusion of account and real-time operation data. BACKGROUND

[0002] With the rapid development of smart grids and the continuous complexity of power systems, the topology structure of the power grid plays a crucial role in power grid planning, operation and management. Accurate and efficient power grid topology generation is of great significance to ensure the safe and stable operation of the power grid and optimize resource allocation.

[0003] Existing power grid topology generation systems usually rely on historical topology data or real-time operation data alone to construct, which has the following limitations: first, when facing large-scale power grids, the topology construction process is complex and time-consuming, and it is difficult to meet the requirements of rapid changes and real-time of the power grid; second, the existing system lacks self-adaptive ability to different types of power grid characteristics when constructing topology, and it is difficult to effectively handle the diversification and complex operation state of the power grid structure. These problems limit the efficiency and accuracy of power grid topology generation, affecting the intelligent level of power grid operation and management. SUMMARY

[0004] The embodiments of the present application provide a power grid dynamic topology generation system based on the fusion of account and real-time operation data, which solves the problem of slow power grid topology construction and insufficient adaptability to large-scale power grids in the prior art, and realizes efficient and adaptive power grid dynamic topology generation.

[0005] The embodiments of the present application provide a power grid dynamic topology generation system based on the fusion of account and real-time operation data, which includes: a historical data acquisition module for acquiring historical topology data and real-time operation data of multiple types of power grid systems; a rule base generation module for extracting feature patterns of multiple types of power grids from the historical topology data and real-time operation data, and establishing a graph construction rule base; a data acquisition module for acquiring account data and real-time operation data of a target power grid; a power grid hierarchical classification module for hierarchical classification processing of the target power grid, and outputting power grid data of each level; a partition topology construction module for generating local power grid topology of each level based on the established graph construction rule base according to the power grid data of each level; a topology merging module for merging the local power grid topology of each level, and outputting the complete dynamic topology structure of the target power grid.

[0006] Further, the step of extracting feature patterns of multiple types of power grids from the historical topology data and real-time operation data includes: identifying power equipment information in the historical topology data, and extracting power equipment attributes; constructing a connection relationship set of physical connections between power equipment based on the extracted power equipment attributes; obtaining topology structure features of multiple types of power grids based on the power equipment attributes and the connection relationship set; extracting multiple types of power grid operation state features associated with the topology structure features in the time and space dimensions based on the real-time operation data; performing feature fusion on the topology structure features and the multiple types of power grid operation state features to form feature patterns of multiple types of power grids.

[0007] Further, the step of establishing the atlas construction rule library comprises: defining node types and edge types used to constitute a topology for each type of power grid feature pattern to form a topology primitive set; setting connection constraints between the topology primitives based on the topology primitive set to form connection rules; establishing an attribute assignment method for the topology primitives to form attribute rules; forming topology construction rules corresponding to each type of power grid feature pattern based on the connection rules and the attribute rules; associating the topology construction rules with the corresponding type of power grid feature pattern and storing them in a structured database to form the atlas construction rule library.

[0008] Further, the step of performing hierarchical and graded processing on the target power grid comprises: identifying voltage level information and region information of the target power grid based on the account data and real-time operation data of the target power grid; dividing the target power grid into multiple voltage levels based on the identified voltage level information, and further dividing each voltage level into multiple regions based on the identified region information; obtaining the type and quantity of power equipment in each region based on each divided region; obtaining the equipment complexity of each region according to the obtained type and quantity of power equipment, combined with the preset power equipment type weight and quantity weight; dividing each region into multiple levels according to the equipment complexity of each region.

[0009] Further, the step of obtaining the equipment complexity of each region comprises: extracting the operation parameters of power equipment in each region from the obtained real-time operation data of the target power grid; According to the preset device type weight and quantity weight, in combination with the power equipment operation parameter, the device complexity of each region is obtained through a device complexity formula; The device complexity formula is: ; In the formula, is the device complexity of the region, is the type quantity of the power equipment, is the type weight of the first class power equipment, is the quantity of the first class power equipment, is the operation parameter weight of the first class power equipment, is the average voltage of the first class power equipment, is the average current of the first class power equipment, is the average power of the first class power equipment.

[0010] Further, the step of generating the local power grid topology of each level includes: Based on the collected account data of the target power grid, the type and quantity of the power equipment in each level and region are extracted; Based on the collected real-time operation data of the target power grid, the operation state information of the power equipment in each level and region is extracted; According to the obtained device complexity of each region, the priority of the equipment is sorted from high to low according to the device complexity; Based on the established rule library of the atlas, the topology construction rule corresponding to the grid type of the level and region is obtained by matching the grid type characteristics of the level and region.

[0011] Further, the step of matching the grid type characteristics of each level and region includes: Through the collected account data of the target power grid, the power equipment type, quantity and connection relationship of each level and region are extracted to obtain the feature vector of the target power grid; Based on the established rule library of the atlas, the feature mode vector corresponding to each topology construction rule is obtained; The similarity between the feature vector of the target power grid and the feature mode vector of each rule is obtained through a feature similarity calculation formula; The values of all calculated feature similarities are compared, the feature mode with the highest feature similarity value is taken as the final mode matched with the level and region, and the corresponding topology construction rule is determined.

[0012] Further, the feature similarity calculation formula is: ; In the formula, To construct the rule base for the target region and the map, the first... Feature similarity of each feature pattern This is the fusion weighting coefficient between equipment composition similarity and equipment complexity similarity. This is the vector representing the device composition of the target area. For the first The historical device composition vector corresponds to each feature pattern. For the equipment complexity of the target area, For the first The historical device complexity corresponding to each feature pattern.

[0013] Furthermore, the step of merging the local power grid topologies at each level includes: Based on the generated local power grid topology at each level, common connection point information is extracted, which includes node identifiers and connection relationships; By extracting the common connection point information, the relationships between local power grid topologies are established; By connecting multiple local power grid topologies through the relationships between them, an initial merged topology is obtained; Verify the nodes and edges in the initial merged topology; Correct nodes and edges that do not meet the attribute consistency requirements, and output the complete dynamic topology of the target power grid.

[0014] Furthermore, the steps for validating and correcting nodes and edges in the initial merged topology, and correcting nodes and edges that do not meet the attribute consistency requirements, include: Extract the attribute information of each node and edge from the initial merged topology; Calculate the attribute consistency index of a node or edge using the attribute consistency index formula: The formula for the attribute consistency index is: ; In the formula, For the first Each attribute value This is the standard value for this attribute. This represents the variance of the attribute across all nodes or edges. The number of this attribute; like If the value exceeds the preset one-time threshold, the attributes of the node or edge are determined to be inconsistent and need to be corrected. The correction process introduces correction weights. To adjust each attribute value: ; In the formula, is the corrected attribute value of the mth attribute, is the correction weight of the mth attribute, and the value range is , which is set according to the importance of the attribute; Record the attribute information of the corrected nodes and edges to obtain the corrected topology structure.

[0015] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through the collaborative work of the historical data acquisition module and the rule library generation module, feature patterns are extracted from the historical topology data and real-time operation data of multiple types of power grid systems, and a rule library is established, thereby providing accurate rule guidance for topology construction of different types of power grids. The problem of slow power grid topology construction speed and insufficient adaptability to large-scale power grids in the prior art is effectively solved, ensuring that the power grid topology can quickly respond to the dynamic changes of the power grid and meet the real-time requirements of the power grid.

[0016] 2. The target power grid is processed by layering and grading through the power grid layering and grading module, and the target power grid is divided into multiple levels and regions according to voltage levels, regional information, and equipment complexity and other multi-dimensional factors. The power grid topology construction is more targeted and hierarchical, so that the local power grid topology can be generated more in line with the actual structure and operating state of the power grid, further improving the accuracy and adaptability of the topology construction.

[0017] 3. Through attribute verification and correction of the nodes and edges in the initial merged topology, parts that do not meet the attribute consistency requirements are identified and corrected, thereby ensuring the accuracy and consistency of the final output of the complete dynamic topology structure of the power grid. The topology error caused by inconsistent attributes is effectively avoided, ensuring the reliability and usability of the power grid topology. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The power grid dynamic topology generation system structure diagram based on the fusion of the account and real-time operation data provided by the embodiments of the present application. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide a power grid dynamic topology generation system based on the fusion of the account and real-time operation data, which solves the problem of slow power grid topology construction speed and insufficient adaptability to large-scale power grids in the prior art. Through the collaborative extraction of feature patterns and the establishment of a rule library by the historical data acquisition module and the rule library generation module, combined with power grid layering and grading processing and feature similarity matching steps, efficient and accurate power grid dynamic topology generation is achieved.

[0020] ​​For better understanding of the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0021] As shown in Figure 1 The system structure diagram of the power grid dynamic topology generation system based on the integration of the ledger and real-time operation data provided by the embodiment of the application includes: a historical data acquisition module: used for acquiring historical topology data and real-time operation data of a plurality of types of power grid systems; A rule library generation module: used for extracting feature modes of the plurality of types of power grids from the historical topology data and real-time operation data, establishing a graph construction rule library according to the feature modes, and storing topology construction rules and rule parameters corresponding to the feature modes in the graph construction rule library; A data acquisition module: used for acquiring ledger data and real-time operation data of a target power grid; A power grid hierarchical classification module: used for receiving the ledger data and real-time operation data, and performing hierarchical classification processing on the target power grid, and outputting power grid data of each level; A partition topology construction module: used for acquiring topology construction rules and rule parameters matched with the result of each level based on the established graph construction rule library according to the power grid data of each level, and generating a local power grid topology of each level; A topology merging module: used for merging the local power grid topologies of each level, and outputting a complete dynamic topology structure of the target power grid.

[0022] In the embodiment, by decomposing the complex process that originally requires a large amount of online calculation, the core recognition and construction steps are changed into fast matching and calling based on the rule library, thereby fundamentally solving the speed problem of the traditional method of constructing topology from scratch. Not only does it greatly shorten the time of generating the whole network topology, but also meets the real-time requirement of dynamic scheduling.

[0023] At the same time, the modular design makes the system easy to extend and maintain, for example, when a new type of power grid structure appears, only the historical database needs to be updated and the rule library needs to be regenerated, without the need to reconstruct the entire system.

[0024] Further, the step of extracting feature modes of the plurality of types of power grids from the historical topology data and real-time operation data includes: Identifying power equipment information in the historical topology data, and extracting basic power equipment attributes including voltage level, equipment type, and belonging station; Based on the extracted power equipment attributes, a connection relationship set of physical connections between power equipment is constructed; Based on the power equipment attributes and the connection relationship set, topology structure features of the plurality of types of power grids are obtained; Based on the real-time operation data, extract a plurality of types of power grid operation state features associated with the topological structure features in the time and space dimensions; Fuse the topological structure features with the plurality of types of power grid operation state features to form feature patterns of the plurality of types of power grids.

[0025] In this embodiment, a "holographic snapshot" containing device types, quantities, connection methods, and various information such as voltage, current, and power is created. Compared with methods that only rely on static account books or a single operation parameter, the essence of different power grid types (such as urban center high-density distribution networks and suburban radial power supply networks) in a specific operation state can be more accurately described, providing a high-quality data basis for the establishment and accurate matching of subsequent rule libraries, and improving the accuracy of topology generation.

[0026] Further, the step of establishing the atlas construction rule library includes: For the feature pattern of each type of power grid, define the node type and edge type used to constitute the topology to form a set of topological primitives; Based on the set of topological primitives, set connection constraints between topological primitives, including but not limited to connection order of nodes and edges, connection direction between nodes, and physical limitations of connection, the connection constraints are used to standardize the combination mode between nodes and edges, nodes and nodes, to form connection rules; Establish an attribute assignment method for the topological primitives, the attribute assignment method is used to configure parameters for nodes and edges in the topology to form attribute rules; Based on the connection rules and attribute rules, form topology construction rules corresponding to the feature pattern of each type of power grid; Associate the topology construction rules with the feature pattern of the corresponding type of power grid, and store them in a structured database to form the atlas construction rule library.

[0027] In this embodiment, through the formed atlas construction rule library, when the system is constructing a topology, it does not need to perform complex real-time analysis and calculation, but only needs to consult the rule library according to the characteristics of the target power grid to quickly and accurately complete the construction by finding the corresponding rules. This greatly reduces the complexity of online calculation and realizes efficient topology generation.

[0028] Further, the step of hierarchically processing the target power grid includes: Based on the account data and real-time operation data of the target power grid, identify the voltage level information and regional information of the target power grid; Based on the identified voltage level information, divide the target power grid into a plurality of voltage levels, and based on the identified regional information, further divide each voltage level into a plurality of regions; Based on each divided area, the type and quantity of power equipment in each area are obtained, including but not limited to the number of transformers, the number of lines and the number of switches; According to the obtained type and quantity of power equipment, the device complexity of each area is obtained by combining the preset type weight and quantity weight of power equipment; According to the device complexity of each area, each area is divided into multiple levels, wherein the area with lower device complexity is divided into a lower level, and the area with higher device complexity is divided into a higher level, and finally the power grid data of each level is output.

[0029] In this embodiment, for the area with dense equipment and complex connection relationship (such as large hub substation), the system divides it into a higher level, and more computing resources or more detailed construction rules can be used; for the area with sparse equipment and simple connection (such as a single power transmission line corridor), it is divided into a lower level, and a simplified rule is used for fast processing. This differentiated processing method avoids the waste of computing resources caused by using the same construction rule for all areas, thereby optimizing the speed and efficiency of topology construction as a whole.

[0030] Further, the step of obtaining the device complexity of each area comprises: By obtaining the real-time operation data of the target power grid, the operation parameters of the power equipment in each area are extracted, including but not limited to voltage, current and power; According to the preset type weight and quantity weight of equipment, the device complexity of each area is obtained by combining the operation parameters of power equipment through the device complexity formula; The device complexity formula is: ; In the formula, is the device complexity of the area, is the type and quantity of power equipment, is the type weight of the first class of power equipment, is the quantity of the first class of power equipment, is the operation parameter weight of the first class of power equipment, is the average voltage of the first class of power equipment, is the average current of the first class of power equipment, is the average power of the first class of power equipment.

[0031] In this embodiment, the system can identify those areas that are truly complex in the current operating state through the device complexity formula. For example, a station with numerous switches but low load may not be as complex as a station with fewer devices but under heavy load. This evaluation method is closer to reality than simply counting the number of devices, providing a more accurate basis for subsequent priority ranking and resource allocation.

[0032] Further, the step of generating the local power grid topology of each level includes: Based on the collected account data of the target power grid, the type and number of power devices in each level and area are extracted; Based on the collected real-time operating data of the target power grid, the operating state information of power devices in each level and area is extracted; According to the obtained device complexity of each area, the priority of the devices is ranked from high to low according to the device complexity, that is, the higher the device complexity, the higher the device priority; Based on the established rule library, the topology construction rule corresponding to the power grid type of each level and area is obtained by matching the power grid type characteristics of each level and area.

[0033] In this embodiment, by prioritizing according to device complexity, the system will prioritize devices with high complexity (such as main transformers and key busbars) and then connect devices with lower complexity (such as branch lines and disconnectors). This construction sequence conforms to the logical structure of the power grid and can effectively avoid topology connection errors or conflicts caused by improper processing order, ensuring the stability and accuracy of local topology construction. Finally, based on feature matching to retrieve the rule library for construction, the local topology is quickly generated.

[0034] Further, the step of matching the power grid type characteristics of each level and area includes: Through the collected account data of the target power grid, the type, number, and connection relationship of power devices in each level and area are extracted to obtain the feature vector of the target power grid; Based on the established rule library, the feature mode vector corresponding to each topology construction rule is obtained; The similarity between the feature vector of the target power grid and the feature mode vector of each rule is obtained through the feature similarity calculation formula; Compare the values of all calculated feature similarities, and take the feature mode with the highest feature similarity value as the final mode matched with the level and area to determine the corresponding topology construction rule.

[0035] Further, the feature similarity calculation formula is: ; In the formula, a feature similarity between the target region and a feature mode of the rule base of the atlas construction, a fusion weight coefficient of the equipment composition similarity and the equipment complexity similarity, a target region equipment composition vector, the vector elements being the number of each type of power equipment (such as the number of transformer, line, switch and other equipment) in the region, representing the equipment composition structure of the region, a historical equipment composition vector corresponding to the i-th feature mode, stored in the rule base of the atlas construction, representing the equipment composition feature of the known power grid type, a target region equipment complexity, a historical equipment complexity corresponding to the i-th feature mode. Further, the step of merging the local power grid topologies of each level comprises: extracting common connection point information based on the generated local power grid topology of each level, the common connection point information including node identification and connection relationship; establishing an association relationship between the local power grid topologies through the extracted common connection point information;

[0036] connecting the multiple local power grid topologies through the association relationship between the local power grid topologies to obtain an initial merged topology; verifying the nodes and edges in the initial merged topology to ensure the attribute consistency of the nodes and edges; correcting the nodes and edges that do not meet the attribute consistency requirement, and outputting the complete dynamic topology structure of the target power grid. In this embodiment, the step of verification and correction is set. When the data sources of different partitions are different or there are measurement errors, direct merging may cause "breakage zone" of inconsistent parameters at the connection point (for example, the voltage or power data of the two ends of the same line do not match). By comprehensively verifying the initial merged topology, these inconsistencies can be actively found, providing targets for subsequent correction, so as to ensure that the final output complete dynamic topology is not only seamless in structure.

[0037] Further, the step of verifying the nodes and edges in the initial merged topology and correcting the nodes and edges that do not meet the attribute consistency requirement comprises:

[0038] extracting attribute information of each node and edge from the initial merged topology, the attribute information including device type, voltage level, connection relationship and operating state; calculating the attribute consistency index of the node or edge through the attribute consistency index formula:

[0039] ​​​The formula for the attribute consistency index is: ; In the formula, For the first Each attribute value This is the standard value for this attribute. This represents the variance of the attribute across all nodes or edges. The number of this attribute; like If the value exceeds the preset one-time threshold, the attributes of the node or edge are determined to be inconsistent and need to be corrected. The correction process introduces correction weights. To adjust each attribute value: ; In the formula, For the revised first Each attribute value For the first The adjusted weights for each attribute, with values ​​ranging from [value range missing]. The settings are based on the importance of the attributes; Record the attribute information of the corrected nodes and edges to obtain the corrected topology.

[0040] In this embodiment, by introducing correction weights, the correction process becomes more refined and controllable. For attributes with high measurement accuracy and high confidence, smaller correction weights can be set to retain more of the original data; For attributes with low confidence levels, a larger correction weight can be set to make them more closely approximate the standard value or system average. This greatly improves the reliability of the final generated topology and avoids failures in subsequent power grid analyses (such as power flow calculations and fault simulations) due to data errors.

[0041] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0042] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart

[0043] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart

[0044] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart

[0045] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims are intended to cover all such variations and modifications as falling within the scope of the application.

[0046] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A power grid dynamic topology generation system based on the fusion of ledger and real-time operational data, characterized in that, include: Historical data acquisition module: used to acquire historical topology data and real-time operation data of various types of power grid systems; Rule base generation module: used to extract feature patterns of various types of power grids from the historical topology data and real-time operation data, and to build a rule base by establishing a graph; Data acquisition module: used to collect ledger data and real-time operation data of the target power grid; Power grid hierarchical module: used to perform hierarchical processing on the target power grid and output power grid data for each level; Partition topology construction module: used to generate local power grid topology for each level based on the established graph construction rule base, according to the power grid data of each level; Topology merging module: used to merge the local power grid topologies of each level and output the complete dynamic topology structure of the target power grid.

2. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps for extracting characteristic patterns of various types of power grids from the historical topology data and real-time operational data include: Identify power equipment information in the historical topology data and extract power equipment attributes; Based on the extracted attributes of the power equipment, a set of physical connection relationships between the power equipment is constructed; Based on the set of power equipment attributes and connection relationships, the topological characteristics of various types of power grids are obtained; Based on the real-time operating data, extract various types of power grid operating status features that are associated with the topological features in the time and space dimensions; By fusing topological features with the operational status features of various types of power grids, feature patterns for various types of power grids can be formed.

3. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps to establish a rule base for graph construction include: For each type of power grid, the node type and edge type used to construct the topology are defined to form a set of topology primitives; Based on the set of topological primitives, connection constraints between the topological primitives are set to form connection rules; Establish the attribute assignment method for the aforementioned topological primitives to form attribute rules; Based on the connection rules and attribute rules, topology construction rules corresponding to the characteristic patterns of each type of power grid are formed; The topology construction rules are associated with the characteristic patterns of the corresponding type of power grid and stored in a structured database to form the graph construction rule library.

4. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps for performing hierarchical processing on the target power grid include: Based on the ledger data and real-time operation data of the target power grid, the voltage level information and regional information of the target power grid are identified; Based on the identified voltage level information, the target power grid is divided into multiple voltage levels, and based on the identified regional information, each voltage level is further divided into multiple regions. Based on each divided region, obtain the type and quantity of power equipment in each region; Based on the types and quantities of power equipment obtained, and combined with the preset weights for power equipment types and quantities, the equipment complexity of each region is obtained; Based on the equipment complexity of each region, each region is divided into multiple levels.

5. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 4, characterized in that, The steps to obtain the device complexity for each region include: By acquiring real-time operating data of the target power grid, the operating parameters of power equipment in each region are extracted; Based on the preset equipment type weights and quantity weights, and combined with the power equipment operating parameters, the equipment complexity of each region is obtained through the equipment complexity formula. The formula for equipment complexity is: ; In the formula, Due to the complexity of the equipment in the region, For the types and quantities of electrical equipment, For the first Type weights for power equipment For the first The number of electrical equipment For the first Weights of operating parameters for power equipment For the first Average voltage of electrical equipment For the first Average current of electrical equipment For the first Average power of electrical equipment.

6. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps for generating the local power grid topology at each level include: Based on the collected data from the target power grid, the types and quantities of power equipment in each level and region are extracted. Based on the real-time operation data of the target power grid, the operating status information of power equipment at each level and within each region is extracted; Based on the obtained equipment complexity of each region, the equipment is prioritized from high to low complexity. Based on the established graph construction rule base, the topology construction rules corresponding to the power grid type of each level and region are obtained by matching the power grid type characteristics of each level and region.

7. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 6, characterized in that, The steps for matching the power grid type characteristics for each level and region include: By collecting the target power grid's ledger data, the types, quantities, and connection relationships of power equipment at each level and region are extracted to obtain the feature vector of the target power grid; Based on the established graph construction rule base, the feature pattern vector corresponding to each topology construction rule is obtained; The similarity between the feature vector of the target power grid and the feature pattern vector of each rule is obtained by calculating the feature similarity using the formula. Compare all the calculated feature similarity values, and take the feature pattern with the highest feature similarity value as the final pattern to match the level and region, and determine the corresponding topology construction rule.

8. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 7, characterized in that, The formula for calculating feature similarity is: ； In the formula, To construct the rule base for the target region and the map, the first... Feature similarity of each feature pattern This is the fusion weighting coefficient between equipment composition similarity and equipment complexity similarity. This is the vector representing the device composition of the target area. For the first The historical device composition vector corresponds to each feature pattern. For the equipment complexity of the target area, For the first The historical device complexity corresponding to each feature pattern.

9. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps for merging the local power grid topologies at each level include: Based on the generated local power grid topology at each level, common connection point information is extracted, which includes node identifiers and connection relationships; By extracting the common connection point information, the relationships between local power grid topologies are established; By connecting multiple local power grid topologies through the relationships between them, an initial merged topology is obtained; Verify the nodes and edges in the initial merged topology; Correct nodes and edges that do not meet the attribute consistency requirements, and output the complete dynamic topology of the target power grid.

10. The power grid dynamic topology generation system based on the fusion of ledger and real-time operation data as described in claim 1, characterized in that, The steps for validating and correcting nodes and edges in the initial merged topology, and correcting nodes and edges that do not meet attribute consistency requirements, include: Extract the attribute information of each node and edge from the initial merged topology; Calculate the attribute consistency index of a node or edge using the attribute consistency index formula: The formula for the attribute consistency index is: ; In the formula, For the first Each attribute value This is the standard value for this attribute. This represents the variance of the attribute across all nodes or edges. The number of this attribute; like If the value exceeds the preset one-time threshold, the attributes of the node or edge are determined to be inconsistent and need to be corrected. The correction process introduces correction weights. To adjust each attribute value: ; In the formula, For the revised first Each attribute value For the first The adjusted weights for each attribute, with values ​​ranging from [value range missing]. The settings are based on the importance of the attributes; Record the attribute information of the corrected nodes and edges to obtain the corrected topology.

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