A carbon emission topology construction method and system based on type characteristics

Abstract

This application relates to the field of carbon emission topology analysis technology, and discloses a carbon emission topology construction method and system based on type characteristics. The method includes: identifying node types and constructing a node topology graph based on pre-acquired power grid topology data; analyzing the power values ​​of each line during operation, identifying nodes with missing power and interpolating to complete them, analyzing the power flow direction of nodes, and constructing a power flow direction matrix; calculating the carbon emission factor of each node based on real-time carbon emission data to obtain a first carbon emission factor set; identifying missing factors in the first carbon emission factor set, setting corresponding missing factor reconstruction methods according to node types to obtain a second carbon emission factor set; and matching the corresponding carbon emission factor values ​​in the second carbon emission factor set to the corresponding nodes in the node topology graph to obtain the carbon emission topology. This application improves the completeness and accuracy of the carbon emission analysis process through data interpolation completion and missing factor reconstruction.

Description

Technical Field

[0001] This application relates to the field of carbon emission topology analysis technology, and more specifically to a carbon emission topology construction method and system based on type characteristics. Background Technology

[0002] Currently, with the intensification of global climate change and the proposal of dual-carbon targets, the power industry, as one of the major sources of carbon emissions, requires accurate accounting and tracking of carbon emissions. The power grid, as the bridge connecting the generation and consumption sides, directly impacts the allocation of regional carbon reduction responsibilities, the implementation of green electricity trading, and the tracing of carbon footprints. Traditional carbon emission analysis methods estimate emissions based on fossil fuel consumption within a region, failing to pinpoint specific grid nodes and lines, and thus failing to reflect real-time carbon flow changes during electricity transmission.

[0003] Existing technologies suffer from the following problems: carbon flow calculations rely on complete power grid measurement data, resulting in poor handling of missing data and a lack of effective identification and interpolation mechanisms for missing data nodes; uniformly processing power grid nodes ignores the physical meaning and computational logic differences of different types of nodes in carbon flow calculations, leading to low accuracy of carbon flow analysis results; and using a single completion method for missing data nodes results in completed data that fails to reflect the accurate carbon emission status of the nodes, affecting the accuracy of the carbon emission topology map. To address at least one of these problems, this application proposes a carbon emission topology construction method and system based on type characteristics. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this application is to provide a carbon emission topology construction method and system based on type characteristics, which can effectively solve the problems in the background technology. The specific technical solution of this application is as follows: A carbon emission topology construction method based on type characteristics, comprising: Based on the pre-acquired power grid topology data, the node types are identified and a node topology graph is constructed. The node types include generating nodes, load nodes, ring nodes, and radial nodes. Analyze the power value of each line in the node topology diagram during operation, identify nodes with missing power and interpolate to fill in the missing power, analyze the power flow direction of the nodes, and construct a power flow direction matrix; The first carbon emission factor of the power generation node is set according to the real-time carbon emission data. The second carbon emission factor of the radial node is calculated according to the topology direction, the ring subgraph is constructed for the ring node and the third carbon emission factor of the ring node is calculated. The fourth carbon emission factor of the load node is calculated to obtain the first carbon emission factor set. Identify the missing factors in the first carbon emission factor set, set the corresponding missing factor reconstruction method according to the node type, and obtain the second carbon emission factor set; The carbon emission factor values ​​in the second set of carbon emission factors are matched to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

[0005] Specifically, the step of identifying node types and constructing a node topology graph based on pre-acquired power grid topology data includes: Based on the pre-acquired power grid topology data, identify power nodes, analyze the electrical connection relationships between power nodes, and construct a node set and a connection edge set; Analyze the equipment attributes corresponding to the nodes, and divide the node set into a set of power generation nodes, a set of load nodes, and a set of intermediate nodes; Calculate the degree of each node in the set of intermediate nodes, and designate nodes with a degree of 1 as radial nodes and nodes with a degree greater than 1 as ring nodes. Based on the set of connecting edges, corresponding connecting edges are established between power generation nodes, load nodes, ring nodes, and radial nodes to construct a node topology graph.

[0006] Specifically, the analysis of the power values ​​of each line in the node topology graph during operation identifies nodes with missing power and interpolates to fill in the missing power, analyzes the power flow direction of the nodes, and constructs a power flow direction matrix, including: Based on the bidirectional active power value of each line in the node topology diagram during the operation of the preset time window, nodes with missing power values ​​and abnormal power accumulation are identified, and a set of nodes with missing power is obtained. According to the node type, the power values ​​of nodes in the set of missing power nodes are interpolated to complete the data, the power flow direction of the nodes is analyzed, and a power flow direction matrix is ​​constructed.

[0007] Specifically, the step of interpolating and completing the power values ​​of nodes in the power missing node set according to node type, analyzing the power flow direction of nodes, and constructing a power flow direction matrix includes: For radial node connections in a set of missing power nodes, interpolation is performed to complete the connection by analyzing the power change trend of the upstream nodes. For ring node connections in the set of missing power nodes, interpolation is performed to complete the connection by analyzing the power of normal lines in the ring network. For power generation nodes, interpolation is performed by analyzing the real-time power generation status. For load nodes, interpolation is performed by analyzing historical load data; Based on the completed node power data, the power values ​​in the two directions of each line are compared to determine the power flow direction of the node and construct the power flow direction matrix.

[0008] Specifically, the process involves setting a first carbon emission factor for power generation nodes based on real-time carbon emission data, calculating a second carbon emission factor for radial nodes according to the topological direction by combining the node topology graph and power flow direction matrix, constructing a ring network subgraph for ring nodes and calculating a third carbon emission factor for ring nodes, and calculating a fourth carbon emission factor for load nodes, thus obtaining a first carbon emission factor set, including: The first carbon emission factor of the power generation node is set based on real-time carbon emission data; Combining the node topology diagram and the power flow direction matrix, the second carbon emission factor of radial nodes is calculated according to the topological direction based on the power generation nodes in the upstream nodes. Analyze the connectivity of the ring nodes, construct a ring network subgraph for the ring nodes, and calculate the third carbon emission factor of the ring nodes; Obtain the upstream lines supplying power to the load nodes and calculate the fourth carbon emission factor of the load nodes; By integrating the first carbon emission factor, the second carbon emission factor, the third carbon emission factor, and the fourth carbon emission factor, we obtain the first carbon emission factor set.

[0009] Specifically, the calculation of the second carbon emission factor for radial nodes based on the power generation nodes in the upstream nodes, according to the topological direction, by combining the node topology graph and the power flow direction matrix, includes: By combining the node topology diagram and power flow direction matrix, the topological hierarchy of each radial node is analyzed, the power generation nodes in the upstream nodes corresponding to the radial nodes are extracted, and the power value transmitted by the line is integrated based on the first carbon emission factor of the corresponding power generation node to obtain the cumulative power. According to the topology hierarchy, for each radial node, the cumulative sum of the cumulative electricity of the upstream line and the corresponding first carbon emission factor is calculated, and then divided by the sum of the cumulative electricity of the upstream lines to obtain the second emission factor.

[0010] Specifically, the analysis of the connectivity of the ring nodes, the construction of a ring network subgraph for the ring nodes, and the calculation of the third carbon emission factor for the ring nodes include: Analyze the connectivity of the ring nodes, filter out the interconnected ring nodes, and construct a set of ring network subgraphs; For each ring network subgraph, the injection nodes where power flows into the ring network subgraph from external nodes and the output nodes where power flows out of the ring network subgraph to external nodes are determined according to the power flow direction matrix. Calculate the total injected power of the injection nodes and the total output power of the output nodes, and dynamically allocate the third carbon emission factor of each ring node according to the degree of the ring nodes in the ring network subgraph.

[0011] Specifically, obtaining the upstream lines supplying power to the load nodes and calculating the fourth carbon emission factor of the load nodes includes: Obtain the upstream lines supplying power to the load nodes, their corresponding power, and the carbon emission factors of the upstream nodes; Calculate the sum of the products of the power values ​​of all upstream lines and their corresponding carbon emission factors, and divide by the sum of the power values ​​of the upstream lines to obtain the fourth carbon emission factor of the load node.

[0012] Specifically, identifying missing factors in the first set of carbon emission factors and setting corresponding missing factor reconstruction methods according to node type includes: Identify the missing factors in the first set of carbon emission factors to obtain the set of missing factor nodes; For radial nodes in the missing factor node set, the missing factors are reconstructed by analyzing the historical carbon emission factors of upstream nodes and fitting the data; for ring nodes in the missing factor node set, the missing factors are reconstructed by analyzing the carbon flow balance in the ring subnetwork; for power generation nodes and load nodes, the missing factors are reconstructed by analyzing historical carbon emission factor data and interpolating the data.

[0013] A type-feature-based carbon emission topology construction system, used to implement the aforementioned type-feature-based carbon emission topology construction method, includes: The node topology construction module identifies node types and constructs a node topology graph based on the pre-acquired power grid topology data. The node types include generation nodes, load nodes, ring nodes, and radial nodes. The power flow direction analysis module analyzes the power value of each line in the node topology diagram during operation, identifies nodes with missing power and interpolates to fill in the missing power, analyzes the power flow direction of the nodes, and constructs a power flow direction matrix. The carbon emission analysis module sets the first carbon emission factor of the power generation node based on real-time carbon emission data, calculates the second carbon emission factor of the radial node according to the topology direction by combining the node topology graph and the power flow direction matrix, constructs a ring network subgraph for the ring node and calculates the third carbon emission factor of the ring node, calculates the fourth carbon emission factor of the load node, and obtains the first carbon emission factor set. The carbon emission optimization module identifies missing factors in the first carbon emission factor set, sets the corresponding missing factor reconstruction method according to the node type, and obtains the second carbon emission factor set. The carbon emission topology construction module matches the corresponding carbon emission factor values ​​in the second carbon emission factor set to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

[0014] The beneficial effects of this application are as follows: Power grid nodes are divided into four categories: power generation, load, ring network, and radial network. For each category, corresponding interpolation and carbon emission factor calculations are performed. Power data is supplemented according to node type using upstream trend analysis, normal ring network line analysis, power generation status analysis, and historical load analysis, improving the accuracy of the supplemented data. For ring network structures, a ring network subgraph is constructed, and dynamic carbon flow allocation is performed based on node degree, improving the accuracy of the carbon emission factor calculation results. Missing factors in the first carbon emission factor set are reconstructed and fitted, improving the completeness and accuracy of the carbon emission factor data. The output carbon emission topology graph ensures that all nodes possess carbon emission factors, improving the comprehensiveness and accuracy of the carbon emission analysis process. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a carbon emission topology construction method based on type characteristics, as described in Embodiment 1 of this application. Figure 2 This is a schematic diagram of the node topology in Embodiment 1 of this application; Figure 3 This is a schematic diagram of the ring network sub-diagram in Embodiment 1 of this application; Figure 4 This is a schematic diagram of a carbon emission topology construction system based on type characteristics in Embodiment 1 of this application. Detailed Implementation

[0016] The present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0017] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0018] Hereinafter, the terms "first," "second," and other generic terms are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more. Example 1:

[0019] refer to Figure 1 The image shows a specific implementation of a carbon emission topology construction method based on type characteristics according to this application, including: S101. Based on the pre-acquired power grid topology data, identify the node types and construct a node topology diagram, wherein the node types include generation nodes, load nodes, ring nodes, and radial nodes; S102. Analyze the power value of each line in the node topology diagram during operation, identify nodes with missing power and interpolate to fill in the missing power, analyze the power flow direction of the nodes, and construct the power flow direction matrix. S103. Set the first carbon emission factor of the power generation node according to the real-time carbon emission data. Combine the node topology diagram and the power flow direction matrix to calculate the second carbon emission factor of the radial node according to the topology direction. Construct a ring network subgraph for the ring node and calculate the third carbon emission factor of the ring node. Calculate the fourth carbon emission factor of the load node to obtain the first carbon emission factor set. S104. Identify the missing factors in the first carbon emission factor set, set the corresponding missing factor reconstruction method according to the node type, and obtain the second carbon emission factor set. S105. Match the corresponding carbon emission factor values ​​in the second carbon emission factor set to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

[0020] In this embodiment, power grid topology data is pre-acquired from the power grid dispatch automation system or power grid GIS platform. This data is typically stored in CIM standard format and describes the connection relationships of equipment such as busbars, circuit breakers, disconnectors, and transmission lines within the substation. From the power grid topology data, each independent electrical node is identified, and a node set is constructed. The presence of directly connected electrical devices between nodes is analyzed, and a connection edge set is constructed. The equipment attributes corresponding to each electrical node are analyzed. For example, nodes connected to generator sets are identified and assigned to the generator node set; nodes connected to user-side load equipment or distribution transformers are identified and assigned to the load node set; the remaining nodes are assigned to the intermediate node set.

[0021] For each set of intermediate nodes, the number of connecting edges connected to each node is calculated to obtain the node's degree. When the degree of an intermediate node is 1, it indicates that there is only one connecting edge, appearing at the end of a radial distribution network or the beginning of a branch line, and this node is designated as a radial node. When the degree of an intermediate node is greater than 1, it indicates that there are multiple connecting edges, appearing in a ring power supply network or a hub substation, and this node is designated as a ring node. Based on the defined node types and the set of connecting edges, a node topology graph is constructed, containing four types of nodes—generator nodes, load nodes, ring nodes, and radial nodes—and their connection relationships.

[0022] It should be noted that by constructing a node topology diagram, the power grid structure can be clearly displayed, and by calculating the degree of intermediate nodes, radial nodes and ring nodes can be distinguished, providing corresponding structural information for subsequent carbon emission analysis. Different analyses can be performed based on the structural characteristics of nodes, improving the accuracy of carbon emission factor calculations for different types of nodes.

[0023] Specifically, the bidirectional active power value of each line in the node topology map within a preset time window is obtained. The data comes from the data acquisition and monitoring control system, which records the magnitude and direction of active power at both ends of the line. From the data, missing nodes with empty power values ​​and abnormal power accumulation nodes at both ends of the line that are seriously inconsistent with line loss are identified, thus obtaining a set of missing power nodes.

[0024] Furthermore, interpolation is performed to complete the set of nodes with missing power according to node type. For lines connected to radial nodes, since radial structures are usually tree-like and their power flow has strong upstream and downstream correlations, interpolation is performed by analyzing the power change trend of upstream nodes before and after the current moment and using linear trend extrapolation. For lines connected to ring nodes, since the power flow distribution inside the ring network follows Kirchhoff's laws and the power of each line is coupled, the power value of the missing line is calculated by analyzing the power data of other non-missing lines in the same ring network subgraph and allocating power according to the inverse ratio of line impedance. For generating nodes, the power is completed by calling the real-time output curve of the generating unit. For load nodes, the load data of the node in the same historical period is analyzed and interpolated by combining factors such as temperature and holidays. After the power data is completed, the complete line power data is obtained. The power flow direction is determined by comparing the active power values ​​at both ends of each line and constructing the corresponding power flow direction matrix. The elements in the matrix define the power flow relationship between nodes. For example, if node i sends power to node j, the corresponding position (i, j) in the matrix is ​​marked as 1, otherwise it is 0.

[0025] It should be noted that by setting different interpolation completion methods for different node types, the integrity and reliability of the input data are improved, avoiding interruption of the entire carbon flow calculation process due to missing local data. Radial nodes are completed using upstream trends, and ring nodes are completed using ring network coupling relationships. The completed data retains the original physical current flow characteristics, improving the accuracy of subsequent carbon emission factor calculation results. The constructed current flow direction matrix reflects the carbon flow propagation path, which can improve the efficiency of carbon emission factor calculation.

[0026] For power generation nodes, real-time carbon emission monitoring data is obtained, such as continuous emission monitoring system data for thermal power units or coal consumption conversion data. Based on the current output level and emission rate of the unit, the first carbon emission factor of the power generation node at the current moment is calculated.

[0027] For radial nodes, combining the node topology graph and power flow direction matrix, starting from the power generation node, the process traverses layer by layer according to the direction of power outflow. For each radial node, all upstream lines supplying power to that node and their corresponding upstream nodes are extracted. All power generation nodes are then identified among the upstream nodes. For each upstream line, the transmitted power value is integrated over time to obtain the cumulative electricity transmitted from that upstream power generation node to the current node. The sum of the products of the cumulative electricity transmitted by all upstream lines and the carbon emission factors of their corresponding upstream nodes is then divided by the total cumulative electricity transmitted by all upstream lines to obtain the second carbon emission factor of the radial node.

[0028] For ring nodes, based on the node topology graph, all interconnected ring nodes forming a ring network are selected, and a corresponding ring network subgraph is constructed for each ring network. For each ring network subgraph, based on the power flow direction matrix, the injection nodes from outside the subgraph into the subgraph and the output nodes from outside the subgraph into the subgraph are identified. The total injected power of all injection nodes and the total output power of all output nodes are calculated. As a whole, the carbon emission factors of each node in the ring network subgraph are coupled. The carbon emission factors are dynamically allocated based on the node degree. The higher the degree of the ring node, the stronger its energy transfer function in the ring network. Based on the degree of each ring node, the total carbon emissions of the injection nodes are dynamically allocated, and the third carbon emission factor of each ring node is calculated.

[0029] For each load node, all upstream power lines are located using the power flow direction matrix. The power value of each upstream line and the carbon emission factor of the upstream node from which that line originates are obtained. The sum of the products of the power values ​​of all upstream lines and their corresponding upstream node carbon emission factors is calculated and divided by the sum of the power values ​​of all upstream lines to obtain the fourth carbon emission factor of the load node. The carbon emission factors of all nodes are then integrated to obtain the first set of carbon emission factors.

[0030] It should be noted that for radial nodes, the calculation method of hierarchical integration and upstream weighted averaging reflects the characteristics of carbon flow transmission and mixing in long-distance radial lines. For ring nodes, by constructing a ring network subgraph and dynamically allocating it in combination with node degree, the error caused by forcibly de-ringing the ring network can be avoided, making the carbon emission factor allocation of nodes within the ring network more consistent with its topological connectivity and energy convergence degree. By taking the power generation node as the source of carbon flow and calculating it step by step, the fourth carbon emission factor of the load node can be obtained, which can truly reflect the average carbon emission level of the electricity used by users, providing a direct basis for carbon footprint verification.

[0031] Specifically, the first set of carbon emission factors is traversed, and nodes with empty values ​​are selected to obtain the set of missing factor nodes. For the missing factors of radial nodes, since radial nodes are in a chain structure, carbon emission factors are strongly correlated with upstream nodes and have relative stability over a certain period of time. The carbon emission factor data of the radial node in the same historical period (e.g., the same day and time of the previous week) is retrieved, and the current carbon emission factor of the node is fitted and predicted by linear regression.

[0032] For the missing factor of the ring node, the carbon flow in and out of the ring subgraph is balanced. Based on the data of the known nodes in the ring network and the topology of the ring subgraph, the carbon emission factor of the missing ring node is solved by the known injection node data and some known ring node data, so that it satisfies the carbon conservation of the entire ring subgraph.

[0033] For missing factors at generation and load nodes, historical data interpolation is used. The carbon emission factor of a generation node depends on the operating conditions and fuel consumption of the generator, exhibiting a relatively obvious daily and weekly periodicity. Interpolation is performed to complete the factor by analyzing the historical carbon emission factor of the generation node at the same output level. The carbon emission factor of a load node is determined by the upstream power source. If the upstream node data is complete, it can be recalculated. If the upstream data is also missing, interpolation is performed by combining the historical load curve of the load node itself with the average carbon emission factor of the entire grid in the same period. After reconstruction, the carbon emission factors of all nodes are obtained. The reconstructed carbon emission factors are then merged with the carbon emission factors in the first carbon emission factor set to obtain the complete second carbon emission factor set.

[0034] It should be noted that by reconstructing and optimizing carbon emission factors, the integrity and robustness of carbon emission topology data are improved. For radial nodes, historical data fitting is performed, and the time stability and chain propagation characteristics of carbon factors can be used to quickly repair single-point calculation failures. For the carbon flow balance reconstruction of ring nodes, the physical constraints of the ring network can be used to ensure the consistency of carbon factor calculations within the ring network, avoiding the loss of ring network carbon flow data due to local data problems. Reconstruction according to node type improves the integrity and accuracy of the carbon emission factor set, providing accurate data support for the subsequent generation of a complete carbon emission topology map.

[0035] Specifically, based on the node topology graph, each node in the graph is traversed, and a match is made with the second set of carbon emission factors. The corresponding carbon emission factor value is then added to the data structure of the corresponding node in the node topology graph. After matching, a carbon emission topology is obtained, with real-time carbon emission factor data labeled on each node. This allows for rapid analysis of high-carbon and low-carbon areas and can also be used for data analysis, including but not limited to calculating the total carbon emissions of all load nodes within a specific region.

[0036] It should be noted that by associating carbon emission factor data with the node topology graph, the constructed carbon emission topology reflects both the topological structure and the carbon emission factors, providing direct data support for carbon flow analysis, carbon emission hotspot identification, and refined carbon accounting for different regions and users, thereby improving the efficiency and accuracy of the carbon emission analysis process.

[0037] This application classifies power grid nodes into four categories: power generation, load, ring network, and radial network. For each type of node, corresponding interpolation and carbon emission factor calculation are performed. Power data is supplemented according to node type using upstream trend analysis, normal ring network line analysis, power generation status analysis, and historical load analysis to improve the accuracy of the supplemented data. For ring network structures, a ring network subgraph is constructed and dynamic carbon flow allocation is performed based on node degree to improve the accuracy of carbon emission factor calculation results. Missing factors in the first carbon emission factor set are reconstructed and fitted to improve the completeness and accuracy of carbon emission factor data. This ensures that all nodes in the output carbon emission topology graph have carbon emission factors, improving the comprehensiveness and accuracy of the carbon emission analysis process.

[0038] Furthermore, based on the pre-acquired power grid topology data, node types are identified, and a node topology graph is constructed, including: S201. Identify power nodes based on the pre-acquired power grid topology data, analyze the electrical connection relationships between power nodes, and construct a node set and a connection edge set; S202. Analyze the equipment attributes corresponding to the nodes and divide the node set into a power generation node set, a load node set, and an intermediate node set; S203. Calculate the degree of each node in the intermediate node set, and take the nodes with a degree of 1 as radial nodes and the nodes with a degree greater than 1 as ring nodes. S204. Based on the set of connecting edges, establish corresponding connecting edges between power generation nodes, load nodes, ring nodes, and radial nodes to construct a node topology graph.

[0039] In this embodiment, power grid topology data is pre-acquired from a power grid dispatch automation system or a power grid geographic information system. This data describes detailed information about equipment such as buses, circuit breakers, disconnectors, lines, and transformers within a substation, along with their corresponding connections. Independent power nodes are identified from the power grid topology data, and a corresponding identifier ID is assigned to each power node, constructing a node set. The connections between nodes are analyzed; for example, an AC line may connect to two different power nodes at each end; the high-voltage and low-voltage sides of a transformer may also connect to different power nodes. All transmission lines, transformer windings, series reactors, and other energy transmission equipment are traversed, identifying the power nodes at both ends of each device. For each identified connection, a connection edge is generated, and the node IDs at both ends of the edge and the electrical parameters of the edge, including but not limited to line length, impedance value, and rated capacity, are recorded, constructing a connection edge set.

[0040] It should be noted that by identifying electrical nodes and connection edges, data support is provided for topology analysis, improving analysis efficiency. By constructing node sets and connection edge sets, short-term topology fluctuations caused by changes in the state of auxiliary equipment such as switches and disconnectors in the power grid are removed, and only stable electrical connection points used for energy transmission are retained, thereby improving the robustness and applicability of the constructed topology map and enhancing the accuracy of the carbon emission analysis process.

[0041] Specifically, the process iterates through each power node in the node set and filters out all electrical devices connected to that node. For each node, the device type attribute of the connected devices is analyzed. If a node is found to be connected to a device of type generator or power generation unit, the node is identified as a power generation node. If a node is found to be connected to a device of type load or distribution line, the node is identified as a load node. Nodes that are neither connected to any power generation equipment nor any load equipment, or that are connected to intermediate transmission equipment such as transmission lines, busbars, circuit breakers, and reactors, are identified as intermediate nodes. The node set is then divided into a power generation node set, a load node set, and an intermediate node set.

[0042] It should be noted that classifying nodes by associating device attributes allows for the analysis of the position and role of each node in the energy flow process. Power generation nodes serve as the source of carbon flow calculations, reflecting the initial boundary conditions of the entire carbon flow calculation; load nodes serve as the sink of carbon flow calculations, reflecting the ultimate goal of carbon footprint tracing; the intermediate node set provides the node range to distinguish between radial and annular nodes, effectively limiting the calculation range to the intermediate node set and reducing the subsequent computational complexity.

[0043] Specifically, based on the set of connecting edges, for each node in the set of intermediate nodes, the set of connecting edges is traversed, and the number of connecting edges with the current node as one of their endpoints is counted. Each connecting edge is counted once as long as it contains the current node. After the traversal, the statistical value obtained is the degree of the intermediate node. For intermediate nodes with a degree of 1, they are identified as radial nodes, appearing at the end of radial distribution networks, at the connection points of branch lines, or in the middle of long-distance transmission lines. Their topological characteristic is that one line enters and another line exits, with a clear upstream and downstream relationship. For intermediate nodes with a degree greater than 1, they are identified as ring nodes, appearing in ring power supply networks, multi-source power supply areas, or hub substations. Their topological characteristic is that multiple lines converge here, and the energy source and flow are multidirectional.

[0044] It should be noted that by calculating the degree of intermediate nodes and classifying nodes, radial nodes are identified. Radial nodes have a unique source of carbon flow, and an efficient layer-by-layer recursive algorithm can be used in the subsequent calculation of carbon emission factors. Ring nodes are effectively screened out. Ring nodes have multiple energy input and output paths, and the carbon emission factor calculation process considers the carbon flow balance of the entire ring network. Node classification based on degree allows for setting different calculation strategies for different types of nodes, improving the accuracy of the analysis process.

[0045] Furthermore, iterate through each edge in the set of connecting edges, obtain the node IDs at both ends of the edge, and treat the power nodes as nodes in the graph. Each node contains a corresponding type label, including power generation node, load node, radial node, or ring node. For example... Figure 2 As shown, connect the nodes according to the connection status between the corresponding nodes in the connection edge set, and construct the node topology graph.

[0046] It should be noted that by constructing a node topology diagram, accurate data support is provided for the subsequent analysis process. Each node in the node topology diagram has a corresponding type, providing a basis for subsequent differential analysis calculations. The node topology diagram preserves the original connection relationship of the power grid, ensuring the physical accuracy of subsequent power flow direction analysis and carbon flow analysis, and providing accurate data support for the carbon emission analysis process.

[0047] Furthermore, the power values ​​of each line in the node topology graph are analyzed during operation, nodes with missing power are identified and interpolated to fill in the missing power, the power flow direction of the nodes is analyzed, and a power flow direction matrix is ​​constructed, including: S301. Based on the bidirectional active power value of each line in the node topology diagram during the operation of the line within a preset time window, identify the nodes with missing power values ​​and abnormal power accumulation, and obtain the set of nodes with missing power. S302. According to the node type, interpolate and complete the node power values ​​in the power missing node set, analyze the node power flow direction, and construct the power flow direction matrix.

[0048] In this embodiment, a preset time window is set according to the real-time requirements and data volume. For example, 15 or 30 minutes before the current time is taken as an analysis cycle. For each line in the node topology diagram, the bidirectional active power time series data of the line within the time window is obtained from the real-time database. Bidirectional active power refers to the measurement devices installed at both ends of the line in the power grid measurement system to record the values ​​of the power outflow end and the power inflow end. Ideally, these two values ​​are equal in magnitude and opposite in direction, but in reality, there will be slight differences due to line loss.

[0049] Specifically, the acquired bidirectional active power data undergoes anomaly identification. Each sampling point is scanned to check if the power value is null, non-numerical, or outside a reasonable range. If any of these conditions are found, the corresponding line and node at that sampling point are marked as having missing power values. Anomaly identification is also performed for accumulated power. The power values ​​within a time window are integrated over time to obtain the accumulated power values ​​at both ends of the line. Under normal circumstances, the accumulated power flowing out of one end of the line, minus line losses, should approximately equal the accumulated power flowing in from the other end. The difference between the accumulated power at both ends is calculated and divided by the accumulated power at the outflow end to obtain a relative error rate. If the relative error rate exceeds a preset anomaly threshold, such as 5% or 10%, it indicates that the power data of that line has a cumulative anomaly during this period and cannot be directly used for carbon flow calculation. All nodes associated with lines exhibiting missing power values ​​or accumulated power anomalies are aggregated to obtain a set of nodes with missing power values.

[0050] It should be noted that by identifying power missing nodes, incomplete or quality-prone data can be quickly identified, avoiding the decrease in accuracy caused by direct calculation. The set of power missing nodes provides accurate data support for the subsequent interpolation and completion process, improving data processing efficiency.

[0051] Specifically, power values ​​of nodes in the missing power node set are interpolated and completed according to node type, and power flow direction is analyzed to construct a power flow direction matrix. By using differentiated interpolation strategies based on node type, the physical characteristics of the power grid can be fully utilized for data completion. The upstream trend extrapolation of radial nodes utilizes the strong correlation of chain power supply, and the ring network coupling calculation of ring nodes utilizes the constraints of circuit laws. The completed data is not only numerically reasonable but also conforms to the physical operation law of the power grid. For generation nodes and load nodes, completion is performed using two different data sources: scheduling plans and load periodicity, respectively, to achieve accurate estimation of missing data on the power generation and load sides. The power flow direction matrix is ​​constructed through power flow direction analysis, providing clear path guidance for the subsequent calculation of carbon emission factors for radial and ring nodes, improving the efficiency and accuracy of the carbon emission analysis process.

[0052] Furthermore, the power values ​​of nodes in the missing power node set are interpolated and completed according to node type, the power flow direction of the nodes is analyzed, and a power flow direction matrix is ​​constructed, including: S401. For radial node connections in a set of missing power nodes, interpolation is performed to complete the connection by analyzing the power change trend of the upstream nodes. S402. For ring node connections in the set of missing power nodes, interpolation is performed to complete the connection by analyzing the power of normal lines in the ring network. S403. For power generation nodes, interpolation is performed by analyzing the real-time power generation status. S404. For load nodes, interpolation is performed by analyzing historical load data; S405. Based on the completed node power data, compare the power values ​​in the two directions of each line to determine the node power flow direction and construct a power flow direction matrix.

[0053] In this embodiment, the upstream node of the radial node is located. The upstream node refers to the adjacent node that is closer to the power source in the power flow direction, and the energy flows through this node before flowing to the current node. By traversing the node topology graph and combining the radial structure characteristics of the power grid, starting from the current radial node, the search proceeds along the direction away from the load to find the first node that is directly connected to it and is in the power inflow direction, which is then taken as the upstream node.

[0054] Specifically, power time-series data of the upstream node is obtained within 5 minutes before and after the missing time period. The power change trend of the upstream node is analyzed. Based on several consecutive sampling points of the upstream node before the missing time period, the average rate of change of the sampling points is calculated, and the power value of the upstream node during the missing time period is estimated according to the average rate of change. Since there is a proportional relationship between the upstream and downstream power in the radial structure, the power ratio coefficient between the radial node and the upstream node under normal operating conditions is further retrieved from the historical database. The ratio coefficient is a stable value obtained by statistically comparing the power values ​​of the two nodes at the same moment over the past week or month. The estimated upstream node power value is multiplied by the ratio coefficient to obtain the interpolated power data of the current radial node.

[0055] It should be noted that by using the physical coupling relationship between upstream and downstream nodes in the radial structure for data interpolation, the inherent physical laws of power grid operation are fully utilized. The completed data is not only numerically continuous, but also conforms to the actual energy transfer logic of the power system. By introducing historical power ratio coefficients, the relatively accurate values ​​of downstream nodes can be calculated based on the trend information of upstream nodes, thereby improving the accuracy and robustness of the interpolation results.

[0056] Specifically, the ring subgraph to which the current ring node belongs is identified. The ring subgraph refers to a closed loop structure composed of interconnected ring nodes and their corresponding connecting edges. For example... Figure 3 As shown, traverse the node topology graph, filter out all loop nodes that are directly or indirectly connected by connecting edges, obtain a connected subgraph, and take the closed loops in the subgraph as the ring network subgraph.

[0057] Furthermore, the power data of all lines within the ring network sub-graph is scanned to identify lines with complete and missing data. For lines with missing data, calculations are performed based on the fundamental principles of ring network power flow distribution. In an AC power grid, the active power distribution within a ring network primarily depends on the impedance parameters of each line, and power is distributed across parallel paths according to the inverse relationship of impedance. The impedance parameters of each line in the ring network sub-graph are obtained and stored in the line ledger database. For lines with missing data, the power distribution on other parallel paths connected to that line is analyzed.

[0058] Specifically, a set of normal lines that form a parallel relationship with the missing line is selected. The sum of the power of the normal lines is the total power passing through this parallel branch group. Based on the impedance ratio between the missing line and the normal lines, the power share borne by the missing line is calculated. For example, if the impedance of the missing line is twice that of the normal lines, the power flowing through the missing line in the parallel branching is half that of the normal lines. The interpolated power of the missing line is then calculated.

[0059] Preferably, if multiple line data are missing in the ring network subgraph, the impedance shunting method cannot be directly applied. Instead, the node power balance method is used. For each node in the ring network subgraph, according to Kirchhoff's first law, the sum of the power flowing into the node should be equal to the sum of the power flowing out of the node. Using the known line power data, combined with the generation or load data connected to the node, the missing line power can be solved through power balance.

[0060] It should be noted that data interpolation based on the electrical coupling patterns within the ring network can effectively repair missing power data in complex ring networks. The completed data can meet the power balance constraints of the entire ring network. Impedance shunting improves the interpretability and accuracy of the interpolation results, providing accurate data support for the subsequent accurate calculation of carbon emission factors at ring nodes.

[0061] Specifically, real-time power generation status information of power generation nodes is obtained from the monitoring system of the power plant or the automatic power generation control system of the power grid dispatch center. This includes the output command value of each unit. The power generation node power data is obtained by data interpolation using the output command value. Interpolation using the output command value can restore the true operating status of the power generation node to the greatest extent and improve the robustness and accuracy of the power generation node power data.

[0062] Specifically, historical data samples with the same or similar time characteristics are selected from the historical load database of load nodes. Temperature data within the missing time period is obtained, along with temperature data corresponding to each sample from the same historical period. The similarity between the current temperature and the historical sample temperature is calculated, prioritizing historical samples with similar temperatures. For each selected historical sample, the system calculates its similarity weight with the current scene; the higher the similarity, the greater the weight. The similarity calculation can combine time feature matching degree and temperature similarity, and a weighted average of the load values ​​of multiple historical samples at the missing time is performed to obtain the interpolation result.

[0063] It should be noted that by analyzing the periodicity and regularity of load changes to perform data interpolation, the interpolation results can reflect the real variation patterns of load under different scenarios. The contribution of historical samples can be differentiated and weighted according to their similarity to the current scenario, thereby improving the accuracy of the interpolation results.

[0064] Furthermore, by integrating the original complete data and the interpolated and completed data, a complete bidirectional active power dataset for each line within the current time window is obtained. For each line, the dataset contains two key values: the active power flowing from node A to node B, denoted as... The active power flowing from node B to node A is denoted as... .

[0065] For each line, the power flow direction is determined. In most cases, the power flow direction is clear, meaning the power value in one direction is significantly greater than in the other. A direction determination threshold is set, for example, the power value in the main direction must exceed twice the power value in the opposite direction. If... Greater than If the value is twice that of the node, the power flow direction is determined to be from node A to node B; if Greater than If the value is twice that of the node, it is determined to be a flow from node B to node A.

[0066] After determining the power flow direction for all routes, a power flow direction matrix is ​​constructed. This matrix is ​​an N×N two-dimensional matrix, where N is the total number of nodes in the network. The row index represents the node from which power flows out, and the column index represents the node from which power flows in. Each route is traversed, and based on the determined power flow direction, a mark is made at the corresponding position in the matrix. For example, for a route flowing from node i to node j, the system sets the element in the i-th row and j-th column of the matrix to 1, indicating the existence of a directed connection from i to j; otherwise, it is set to 0.

[0067] It should be noted that analyzing the tidal current direction provides a clear path for the subsequent calculation of carbon emission factors, and the tidal current direction matrix and node topology diagram provide input data for the subsequent radial node hierarchical calculation and annular node subgraph analysis.

[0068] Furthermore, based on real-time carbon emission data, a first carbon emission factor is set for the power generation nodes. Combining the node topology diagram and power flow direction matrix, a second carbon emission factor is calculated for radial nodes according to the topological direction. A ring network subgraph is constructed for the ring nodes, and a third carbon emission factor for the ring nodes is calculated. A fourth carbon emission factor is calculated for the load nodes, resulting in the first carbon emission factor set, which includes: S501. Set the first carbon emission factor of the power generation node based on real-time carbon emission data; S502. Combining the node topology diagram and the power flow direction matrix, the second carbon emission factor of the radial nodes is calculated according to the topology direction based on the power generation nodes in the upstream nodes. S503. Analyze the connection of the ring nodes, construct a ring network subgraph for the ring nodes and calculate the third carbon emission factor of the ring nodes; S504. Obtain the upstream lines supplying power to the load nodes and calculate the fourth carbon emission factor of the load nodes; S505. Integrate the first carbon emission factor, the second carbon emission factor, the third carbon emission factor and the fourth carbon emission factor to obtain the first carbon emission factor set.

[0069] In this embodiment, based on real-time measured carbon emission data, including but not limited to parameters such as carbon dioxide concentration, flow rate, and temperature and pressure, as well as the active power output of the generator set at the current moment, the real-time carbon emission rate is divided by the real-time active power to obtain the first carbon emission factor of the power generation node at the current moment. By using real-time data from a continuous emission monitoring system, the true emission intensity of the generator set during dynamic operation can be reflected, avoiding the errors caused by using a fixed emission factor, and capturing the impact of factors such as unit start-up and shutdown, load changes, and coal quality fluctuations on carbon emissions.

[0070] Specifically, by combining the node topology diagram and the power flow direction matrix, the second carbon emission factor of the radial nodes is calculated based on the power generation nodes in the upstream nodes according to the topological direction. Through a layer-by-layer weighted average algorithm, the propagation path of carbon flow is ensured to be consistent with the actual power flow path, avoiding calculation deviations caused by incorrect direction judgment. By analyzing the topological hierarchy and calculating according to the hierarchical order, the logic of the calculation process is ensured, avoiding circular dependencies or data loss caused by improper calculation order.

[0071] Specifically, the connection of ring nodes is analyzed, a ring network subgraph is constructed for the ring nodes, and the third carbon emission factor of the ring nodes is calculated. By constructing the ring network subgraph and using dynamic allocation based on node degree, the carbon emission factor in the ring network can be calculated quickly. Dynamic allocation based on node degree utilizes topological information to reflect the importance of nodes in the ring network. Nodes with higher degrees connect more lines, undertake more power exchange, and are allocated more carbon flow, thus improving the accuracy of the calculation results.

[0072] Specifically, the upstream lines supplying power to the load nodes are obtained, and the fourth carbon emission factor of the load nodes is calculated. By using a weighted average calculation based on the upstream power supply lines, the source path of the load node's electrical energy is fully traced. The weighted average algorithm reflects the carbon mixing effect in the multi-source power supply scenario, thus improving the accuracy of the calculation results.

[0073] Furthermore, the first, second, third, and fourth carbon emission factors are integrated according to node ID to obtain the first carbon emission factor set. This data integration consolidates the calculated carbon emission factors, providing accurate data support for subsequent reconstruction of missing factors.

[0074] Furthermore, combining the node topology diagram and power flow direction matrix, based on the power generation nodes in the upstream nodes, the second carbon emission factor of the radial nodes is calculated according to the topological direction, including: S601. Combining the node topology diagram and power flow direction matrix, analyze the topology level of each radial node, extract the power generation nodes in the upstream nodes corresponding to the radial nodes, and integrate the power value transmitted by the line based on the first carbon emission factor of the corresponding power generation node to obtain the cumulative power. S602. According to the topology hierarchy, for each radial node, calculate the cumulative sum of the cumulative electricity of the upstream line and the corresponding first carbon emission factor, and divide it by the sum of the cumulative electricity of the upstream lines to obtain the second emission factor.

[0075] In this embodiment, based on the node topology graph and combined with the power flow direction matrix, a breadth-first traversal is performed starting from each power generation node along the direction indicated by the power flow direction matrix, and a corresponding topology level number is assigned to each radial node traversed. The topology levels include: radial nodes directly connected to power generation nodes are defined as the first level; nodes that can only be reached through an intermediate radial node are defined as the second level, and so on.

[0076] For each radial node to be calculated, based on the power flow direction matrix, all upstream lines flowing to that node are selected, with each upstream line corresponding to an upstream node. For radial structures, typically each node has only one upstream node; however, in multi-source power supply or at the junction of a ring network and a radial structure, multiple upstream nodes may appear, which are then used as the direct power supply path for that node. The origin of the generating nodes is traced back from the upstream nodes, recursively upwards, to select the generating nodes that ultimately contribute power. Each complete path from a generating node to the current node, along with every line segment along that path, is recorded.

[0077] After determining all power supply paths, for each segment of each path, the active power time series data of that segment within the current time window is obtained. The power value at each sampling point within the time window is multiplied by the sampling interval to obtain the electricity within that sampling interval. Then, the electricity values ​​of all sampling intervals are summed to obtain the cumulative electricity of that segment within the time window. It should be noted that for a complete power supply path, the distance from the generating node to the current node may pass through multiple segments, but the carbon flow is not lost during propagation. Therefore, there is a certain proportional relationship between the electricity flowing out of the generating node and the electricity reaching the current node.

[0078] It should be noted that by using topology hierarchy analysis and power supply path tracing, the topology hierarchy determines the order basis, which can avoid data dependency errors caused by improper calculation order and ensure the logical rigor of the calculation process. By recursively tracing the power generation nodes, the power transmission process can be reflected. By integrating discrete power sampling points into the total energy over a period of time to obtain the cumulative power, the average situation over a period of time can be reflected, rather than instantaneous values, thus improving the stability and representativeness of the results.

[0079] Specifically, following the topological hierarchy, processing proceeds layer by layer upwards from the lowest-level node. For the currently being calculated radial node, information on all power supply paths to that node is obtained. Each power supply path is associated with a generator node's first carbon emission factor and the cumulative electricity contributed by that path within the current time window. The sum of the cumulative electricity of all power supply paths is calculated; this sum represents the total electricity transmitted from all generator nodes to the current node within the current time window. For each power supply path, its cumulative electricity is multiplied by the corresponding generator node's first carbon emission factor to obtain the carbon flow contributed by that path. The carbon flows contributed by all paths are summed to obtain the total carbon flow. The total carbon flow is divided by the total cumulative electricity to obtain the second carbon emission factor of the current radial node.

[0080] Preferably, in some cases, the power supply path is not a single path directly from the generating node to the current node, but rather passes through multiple intermediate nodes. Since the intermediate nodes themselves also mix the carbon emission factors of their upstream sources, during the recursive calculation, when processing higher-level radial nodes, their upstream nodes are already calculated radial nodes, and their carbon emission factors already reflect the mixing results of multiple upstream power sources. The second carbon emission factor of the upstream node can be directly used to replace the first carbon emission factor of the generating node for calculation, without having to trace back to the source generating node each time, which can reduce the amount of computation while ensuring the accuracy of the results. The second carbon emission factor of each radial node is calculated sequentially according to the topology hierarchy from smallest to largest.

[0081] It should be noted that the weighted average algorithm based on cumulative electricity strictly follows the physical laws of energy mixing in the power system. The calculation results reflect the average carbon intensity of the mixed electrical energy flowing into the node. By using cumulative electricity as the weight, the calculation results can reflect the average situation over a period of time, which can smooth the instantaneous changes caused by power fluctuations. The calculation is performed layer by layer according to the topology hierarchy, making full use of the calculation results of intermediate nodes, avoiding repeated recursive tracing, and improving the calculation efficiency and accuracy of the calculation results.

[0082] Furthermore, the connectivity of the ring nodes is analyzed, a ring network subgraph is constructed for the ring nodes, and the third carbon emission factor of the ring nodes is calculated, including: S701. Analyze the connection of the ring nodes, filter out the interconnected ring nodes, and construct a set of ring network subgraphs; S702. For each ring network subgraph, determine the injection nodes where power flows into the ring network subgraph from the external nodes and the output nodes where power flows out of the ring network subgraph to the external nodes based on the power flow direction matrix. S703. Calculate the total injected power of the injection node and the total output power of the output node, and dynamically allocate the third carbon emission factor of each ring node according to the degree of the ring node in the ring network subgraph.

[0083] In this embodiment, all nodes marked as loop nodes are selected from the node topology graph to obtain the loop nodes and their corresponding connecting edge information. Each loop node is traversed, starting from the loop node and performing a breadth-first search, traversing all reachable loop nodes along the connecting edges. During the traversal, each new loop node is marked as visited and added to the node set of the current subgraph. Simultaneously, the connecting edges traversed during the traversal are also added to the edge set of the current subgraph. When no new unvisited loop node can be found starting from the current point, a complete connected component is obtained. This connected component is treated as a loop subgraph and added to the loop subgraph set. The process continues to traverse the next unvisited loop node, repeating the above steps, until all loop nodes have been visited and assigned to their corresponding loop subgraphs, resulting in a loop subgraph set.

[0084] It should be noted that by using connected component analysis, the power grid is decomposed into several independent ring network subgraphs, which can effectively segment the ring structure, reduce the difficulty of subsequent calculations and the consumption of computing resources. By ensuring that the nodes within each ring network subgraph are interconnected, the boundaries of subsequent carbon flow allocation calculations are guaranteed to be clear, mutual interference between different ring network regions is avoided, and the accuracy of the calculation is improved.

[0085] For each ring network subgraph, determine the set of nodes contained in the ring network subgraph, obtain a list of all nodes in the entire network and a power flow direction matrix, and identify injection nodes. An injection node is a node inside the ring network subgraph and has at least one path from an external node to that node. Traverse each node in the ring network subgraph, find all directed edges ending at that node, and filter out directed edges whose starting point is not in the set of nodes in the ring network subgraph. This indicates that power is flowing into the node from outside the ring network, and the node is identified as an injection node.

[0086] Specifically, output nodes are identified when they are internal nodes of the ring network subgraph and have at least one path leading from that node to an external node. Each node in the ring network subgraph is traversed, and all directed edges originating from that node are searched. Edges whose endpoints are not in the set of nodes in the ring network subgraph are selected, indicating power flowing from that node to the outside of the ring network; these nodes are then identified as output nodes. After identifying the injection and output nodes, a list of injection nodes and their corresponding injection power values ​​are constructed, as are a list of output nodes and their corresponding output power values.

[0087] It should be noted that by identifying the injection and output nodes, the energy exchange relationship between the ring network subgraph and the external power grid is determined. The identification of the injection and output nodes determines the energy input and output situation, providing accurate data support for calculating the total carbon input and total carbon output of the ring network.

[0088] For each sub-circular network, calculate the total injected electricity and total carbon injection. Iterate through all injection nodes, and for each node, obtain its injected power value and its own carbon emission factor. The carbon emission factor of the injection node is brought in from outside the network and represents the carbon intensity of the electricity entering the network. Sum the injected power of all nodes to obtain the total injected electricity of the sub-circular network. Calculate the product of the injected power value and the corresponding carbon emission factor to obtain the carbon flow contributed by each injection path. Sum the carbon flows of all injection paths to obtain the total carbon injection of the sub-circular network.

[0089] Specifically, the degree of each ring node within the ring network subgraph is calculated. The degree of a node refers to the number of edges connected to it, including edges connecting to other nodes within the ring network and edges connecting to external nodes. The system traverses each ring node within the ring network subgraph, counting the total number of edges connected to that node, denoted as . Calculate the sum of the degrees of all ring nodes within the ring network subgraph, denoted as . .

[0090] The total carbon injection within the ring network subgraph is allocated to each ring node according to its degree as a proportion of the sum of the degrees of all nodes. The more lines a node connects to, the greater its energy exchange role in the ring network, and therefore the more carbon flux it should be allocated. The total carbon injection is multiplied by... Divide by Calculate the carbon flux allocated to each annular node. The total inflow power to a node refers to the sum of the power flowing into that node through all connecting edges. Based on the power flow direction matrix, for each loop node, identify all directed edges terminating at that node, and sum the power values ​​on these edges to obtain the total inflow power to that node. The calculated carbon flow allocated to this node. Dividing by the calculated total inflow power of the node yields the node's third carbon emission factor. The carbon flow carried by each unit of electricity flowing into the node is the node's carbon emission intensity at the current moment. The third carbon emission factor is calculated for each ring node.

[0091] It should be noted that by employing a dynamic allocation strategy based on node degree, the ring network carbon flow calculation problem is transformed into a topology-based allocation problem, improving computational efficiency. Node degree is used as the allocation weight; node degree reflects the connectivity and importance of a node in the network. Nodes with higher degrees typically undertake more power collection and allocation functions, thus receiving more carbon flow. The total carbon flow from all injected nodes is fully allocated to each ring node, and the carbon emission factor of the output nodes reflects the carbon intensity of the ring network's external power supply, achieving input-output matching. Dynamic allocation can adaptively handle various power flow distributions, exhibiting strong robustness and applicability, suitable for ring network structures of varying complexity.

[0092] Furthermore, the upstream lines supplying power to the load nodes are obtained, and the fourth carbon emission factor of the load nodes is calculated, including: S801, Obtain the upstream line supplying power to the load node, the corresponding power, and the carbon emission factor of the upstream node; S802. Calculate the sum of the products of the power values ​​of all upstream lines and their corresponding carbon emission factors, and divide it by the sum of the power values ​​of the upstream lines to obtain the fourth carbon emission factor of the load node.

[0093] In this embodiment, all nodes marked as load nodes are traversed. For each load node, the upstream line supplying power to that load node is identified, and the power value of each upstream line is obtained to obtain the active power value flowing from the upstream node to the load node, i.e., the transmission power of the line, reflecting the contribution of the upstream node to the load node's power supply, which is used as the power supply power corresponding to that upstream line. The carbon emission factor of the upstream node corresponding to each upstream line is obtained, and the carbon emission factor is used as the power supply carbon intensity corresponding to that upstream line. After traversing all load nodes, the power supply information of each load node is obtained.

[0094] It should be noted that by obtaining the power supply path information of the load nodes, the actual power supply lines to the load nodes at the current moment can be accurately identified, reflecting the dynamic changes in the power grid operation mode and ensuring the timeliness and accuracy of the data.

[0095] For each load node, the total power supply is calculated based on the power supply information list. The power supply information for that load node is iterated through, and the power supply values ​​recorded for each upstream line are summed to obtain the total power supply for that load node. The total power supply represents the sum of electrical energy delivered to that load node by all upstream lines within the current time window. The power supply information is iterated through again, and for each upstream line, its power supply value is multiplied by the corresponding upstream node's carbon emission factor to obtain the carbon flow contributed by that line. This reflects the total carbon emissions carried by the electrical energy flowing into the load node from this upstream line. The carbon flows contributed by all upstream lines are summed to obtain the total carbon flow, which is the total carbon emissions carried by the electrical energy flowing into the load node.

[0096] Dividing the calculated total carbon flow by the total power supply yields the fourth carbon emission factor for that load node, reflecting the average carbon emissions per unit of electricity consumed by that load node, i.e., the carbon intensity of electricity consumption at that load node at the current moment. The fourth carbon emission factors for all load nodes are calculated, and the results are combined with the carbon emission factors for power generation nodes, radial nodes, and ring nodes to form a complete set of first carbon emission factors.

[0097] It should be noted that the weighted average algorithm based on power supply strictly follows the physical laws of energy mixing in the power system, reflects the average carbon intensity of the mixed electrical energy supplied to the load, and uses power supply as the weight. The calculation results can reflect the instantaneous or average situation within the current time window, match the real-time power consumption behavior of the load node, and improve the calculation efficiency and accuracy.

[0098] Furthermore, missing factors in the first carbon emission factor set are identified, and corresponding missing factor reconstruction methods are set according to node type, including: S901. Identify the missing factors in the first carbon emission factor set and obtain the missing factor node set; S902. For radial nodes in the missing factor node set, the missing factors are reconstructed by analyzing the historical carbon emission factors of upstream nodes and fitting the data. For ring nodes in the missing factor node set, the missing factors are reconstructed by analyzing the carbon flow balance in the ring subnetwork. For power generation nodes and load nodes, the missing factors are reconstructed by analyzing historical carbon emission factor data and interpolating the data.

[0099] In this embodiment, each record in the first carbon emission factor set is traversed, and the carbon emission factor value field is checked for validity. Validity checks include null value checks, numerical validity checks, and numerical range checks. Null value checks determine if the field contains null values ​​from the database or specially marked null values; numerical validity checks determine if the field value is a valid numerical type; and numerical range checks determine if the field value is within a reasonable physical range. For example, carbon emission factors should typically be between zero and a certain upper limit; negative or excessively large values ​​are treated as outliers. If any check fails, the node is marked as a missing factor node. All nodes marked as missing factors are compiled into a missing factor node set.

[0100] It should be noted that by performing a validity check on the first set of carbon emission factors, the completeness of the calculation results can be assessed and problems identified; multi-dimensional validity checks ensure the comprehensiveness and accuracy of missing factor identification, avoiding the misclassification of invalid data as valid data; and by constructing a set of missing factor nodes, an accurate data range is provided for data repair, saving computational resources.

[0101] Specifically, the set of missing factor nodes is traversed, and different reconstruction methods are used according to the type label of each node. For missing factor nodes marked as radial nodes, a fitting reconstruction method based on historical data of upstream nodes is used. According to the power flow direction matrix, the direct upstream node of the radial node is found, and the carbon emission factor of the upstream node at the current moment is obtained. If the upstream node is also missing, it is recursively traced upwards until a valid upstream node is found. After obtaining a valid upstream node, a historical proportional relationship between the radial node and the upstream node is established. The carbon emission factor data of the radial node over a period of time is retrieved from the historical database, and the carbon emission factor data of the upstream node at the corresponding moment is also retrieved. The historical data pairs are analyzed, and the proportional coefficient sequence between the two is calculated. The median of the proportional coefficient is calculated to obtain the representative proportional coefficient. The carbon emission factor of the upstream node at the current moment is multiplied by the representative proportional coefficient to obtain the reconstructed carbon emission factor of the radial node.

[0102] For missing factor nodes marked as ring nodes, a reconstruction method based on ring network carbon flow balance is used to determine the ring network subgraph to which the ring node belongs, and to obtain all boundary information of the ring network subgraph, including injection nodes and output nodes. The total carbon flow to the injection nodes should be equal to the sum of the carbon flows from all nodes within the ring to the output nodes. Within the ring network, the distribution of carbon flow is related to the power flow distribution. Using known data on nodes within the ring and line power flow data, a set of linear equations is established, and the carbon emission factor of the missing nodes is calculated by treating the unknown carbon emission factor of the nodes within the ring as a variable.

[0103] For missing factor nodes marked as power generation nodes or load nodes, an interpolation reconstruction method based on historical data is used. For power generation nodes, the historical carbon emission factor database for that node is obtained, and historical data with similar operating conditions to the current moment are selected. The criteria for judging similar operating conditions include similar power generation output levels, similar seasons, and similar times of day. From the selected historical data, the most similar samples are selected, and the carbon emission factors of the samples are weighted and averaged to obtain the reconstructed value for the current moment. The weights can be allocated according to the similarity of operating conditions, with higher similarity resulting in higher weights.

[0104] For load nodes, historical data with similar temporal characteristics to the current moment are selected from the historical database. Taking into account the similarity of meteorological factors, a weighted average interpolation is performed on the selected historical carbon emission factors. After reconstructing all missing factor nodes, the reconstructed carbon emission factors are merged with the valid data in the original first carbon emission factor set to obtain a complete second carbon emission factor set.

[0105] It should be noted that by reconstructing missing factors in a differentiated manner for different node types, carbon emission factor data can be quickly repaired. For the upstream historical proportion fitting method for radial nodes, the strong correlation and temporal stability between upstream and downstream in the radial structure are utilized, and the reconstruction results are both in line with physical laws and supported by historical data. For the carbon flow balance reconstruction of ring nodes, the physical constraint equations of the ring network itself are used to ensure the carbon balance within the ring network after reconstruction. For the historical data interpolation method for power generation nodes and load nodes, the periodicity and regularity of node carbon emission factors are combined to ensure that the reconstruction results can reflect normal operating characteristics. Through differentiated reconstruction strategies, the integrity and accuracy of carbon emission factors can be restored to the greatest extent in the case of missing data, ensuring that the final output carbon emission topology map can cover all nodes and provide complete and accurate data support for carbon emission topology analysis.

[0106] like Figure 4 As shown, a carbon emission topology construction system based on type features is used to implement a carbon emission topology construction method based on type features, including: The node topology construction module identifies node types and constructs a node topology graph based on the pre-acquired power grid topology data. The node types include generation nodes, load nodes, ring nodes, and radial nodes. The power flow direction analysis module analyzes the power value of each line in the node topology diagram during operation, identifies nodes with missing power and interpolates to fill in the missing power, analyzes the power flow direction of the nodes, and constructs a power flow direction matrix. The carbon emission analysis module sets the first carbon emission factor of the power generation node based on real-time carbon emission data, calculates the second carbon emission factor of the radial node according to the topology direction by combining the node topology graph and the power flow direction matrix, constructs a ring network subgraph for the ring node and calculates the third carbon emission factor of the ring node, calculates the fourth carbon emission factor of the load node, and obtains the first carbon emission factor set. The carbon emission optimization module identifies missing factors in the first carbon emission factor set, sets the corresponding missing factor reconstruction method according to the node type, and obtains the second carbon emission factor set. The carbon emission topology construction module matches the corresponding carbon emission factor values ​​in the second carbon emission factor set to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

[0107] Example 2: This embodiment provides a complete description of the technical solution in the context of a real industrial park power grid application scenario. The industrial park power grid includes a gas generator set G1, an external tie-line access point G2, a ring network structure consisting of three substation nodes, and multiple radial distribution networks and industrial loads.

[0108] The system obtains topology data in CIM format from the power grid dispatching system, parses it, and identifies 10 power nodes. By analyzing the equipment attributes, node 1 is connected to gas turbine unit G1 and is marked as a power generation node; node 2 is connected to the external tie line and is marked as a power generation node; nodes 8, 9, and 10 are connected to different industrial users and are marked as load nodes. The remaining nodes 3 to 7 are intermediate nodes. Calculate the degree of the intermediate nodes. Node 3 connects nodes 1, 4, and 5, and has a degree of 3, which is greater than 1, so it is marked as a loop node. Node 4 connects nodes 3 and 6, and has a degree of 2, which is greater than 1, so it is marked as a loop node. Node 5 connects nodes 3 and 7, and has a degree of 2, so it is marked as a loop node. Node 6 connects nodes 4 and 8, and has a degree of 2. However, node 8 is a load node, so node 6, as an intermediate node, has a degree of 2 and is connected to the loop node, so it is included in the loop node set. Node 7 connects nodes 5, 9, and 10, where nodes 9 and 10 are load nodes. Node 7 has a degree of 3, so it is marked as a loop node.

[0109] The system collects bidirectional active power data of each line within the previous 15 minutes. During the collection process, it was found that the power data on line L4-6 (connecting node 4 and node 6) was missing. Although the power data on line L5-7 (connecting node 5 and node 7) existed, the cumulative power error at both ends reached 8%, exceeding the set abnormal threshold of 5%. Nodes 4, 6, 5, and 7 associated with these two lines were identified as the set of nodes with missing power.

[0110] Different interpolation completion strategies are employed for different types of missing nodes. For ring nodes, the ring network containing node 4 consists of nodes 3, 4, and 5. Data for lines L3-4 and L3-5 is complete, with powers of 50 MW and 40 MW respectively. Based on the principle of inverse impedance distribution of power flow in ring networks, the impedance of line L4-6 is twice that of line L3-4, and the calculated power on line L4-6 is approximately 25 MW. For generating nodes, the real-time output data of the gas turbine unit at node 1 is complete and requires no completion; the tie-line power data at node 2 is also complete and requires no completion. For load nodes, the data for nodes 8, 9, and 10 are all complete. Complete line power datasets are obtained through completion.

[0111] Based on the complete line power dataset, power flow direction analysis was performed. On line L1-3, the power flowing from node 1 to node 3 is 100 MW, and the reverse flow is almost zero, so the direction is determined to be from node 1 to node 3. On line L2-3, the power flowing from node 2 to node 3 is 80 MW, so the direction is determined to be from node 2 to node 3. On line L3-4, the power flowing from node 3 to node 4 is 90 MW, so the direction is determined to be from node 3 to node 4. On line L3-5, the power flowing from node 3 to node 5 is 90 MW, so the direction is determined to be from node 3 to node 5. On line L4-6, based on the completed data, the power is determined to be from node 4 to node 6, with a power of 25 MW. On line L5-7, after correction, the power is determined to be from node 5 to node 7, with a power of 85 MW. On line L6-8, the power flowing from node 6 to node 8 is 25 MW. On line L7-9, the power flowing from node 7 to node 9 is 40 MW. On line L7-10, power flows from node 7 to node 10 at a rate of 45 MW. A 10x10 power flow direction matrix is ​​constructed based on the power flow direction information. A 1 is entered at each position in the matrix to indicate the presence of a power flow in that direction.

[0112] After completing the power flow direction analysis, the carbon emission factor is calculated hierarchically for each power generation node. The gas turbine unit at node 1, with a current carbon emission rate of 50 tons per hour and a power output of 100 MW, is measured by the continuous emission monitoring system, and the first carbon emission factor is calculated to be 0.5 tons per MWh. Node 2 is an external interconnection line, and its real-time average carbon emission factor obtained from the upstream grid is 0.8 tons per MWh, which is used as the first carbon emission factor.

[0113] In another radial branch, node 11 exists as a radial node, and its upstream node is node 4. After the carbon emission factor of node 4 is obtained through ring network calculation, the carbon emission factor of node 11 is equal to the factor of node 4 multiplied by the line loss correction factor.

[0114] Specifically, nodes 3, 4, and 5 are interconnected, forming a ring network subgraph. Boundary nodes are identified. According to the power flow direction matrix, lines L1-3 and L2-3 flow to node 3, therefore node 3 is an injection node. Line L4-6 flows out of the ring from node 4, making node 4 an output node. Line L5-7 flows out of the ring from node 5, making node 5 an output node. Node 3, as an injection node, has an injection power equal to the sum of the injections from nodes 1 and 2, i.e., 100 plus 80 equals 180 MW. The carbon emission factor of node 3 needs to be calculated based on its upstream power source. Node 3 receives 100 MW from node 1 and 80 MW from node 2, and its carbon emission factor is a weighted average, i.e., 100 multiplied by 0.5 plus 80 multiplied by 0.8 divided by 180, which yields approximately 0.63 tons per megawatt-hour. The degree of node 3 is 3, the degree of node 4 is 2, and the degree of node 5 is 2. The total carbon injection flow into the ring network is 180 MW multiplied by 0.63 tons per MWh, which equals 113.4 tons per hour. The sum of the degrees of the ring network nodes is 3 plus 2 plus 2, which equals 7. Based on the degree allocation, node 3 is allocated a carbon flow of 113.4 multiplied by 3 divided by 7, approximately 48.6 tons per hour; node 4 is allocated a carbon flow of 113.4 multiplied by 2 divided by 7, approximately 32.4 tons per hour; and node 5 is allocated a carbon flow of 113.4 multiplied by 2 divided by 7, approximately 32.4 tons per hour. The total inflow power (injected power) at Node 3 is 180 MW, therefore the third carbon emission factor for Node 3 is 48.6 divided by 180, approximately 0.27 tons per megawatt-hour. The total inflow power at Node 4 is 90 MW from line L3-4, therefore the third carbon emission factor for Node 4 is 32.4 divided by 90, equal to 0.36 tons per megawatt-hour. The total inflow power at Node 5 is 90 MW from line L3-5, therefore the third carbon emission factor for Node 5 is 32.4 divided by 90, equal to 0.36 tons per megawatt-hour.

[0115] Furthermore, the fourth carbon emission factor of the load nodes is calculated. The upstream line of node 8 is only L6-8 with a power of 25 MW. The carbon emission factor of upstream node 6 has been calculated through the ring network to be 0.36 tons per megawatt-hour. Therefore, the carbon emission factor of node 8 is equal to 0.36 tons per megawatt-hour. The upstream line of node 9 is only L7-9 with a power of 40 MW. The carbon emission factor of upstream node 7 is 0.36 tons per megawatt-hour. Therefore, the carbon emission factor of node 9 is equal to 0.36 tons per megawatt-hour. The upstream line of node 10 is only L7-10 with a power of 45 MW. The carbon emission factor of upstream node 7 is 0.36 tons per megawatt-hour. Therefore, the carbon emission factor of node 10 is equal to 0.36 tons per megawatt-hour.

[0116] The values ​​in the second set of carbon emission factors were matched back to the node topology graph. In the final carbon emission topology graph, node 1 is marked as 0.5, node 2 as 0.8, node 3 as 0.27, node 4 as 0.36, node 5 as 0.36, node 6 as 0.36, node 7 as 0.36, node 8 as 0.36, node 9 as 0.36, and node 10 as 0.36. This complete carbon emission topology graph clearly shows the real-time carbon emission intensity of each node in the park's power grid. Specifically, the ring network structure plays a role in mixing and homogenizing the carbon flow, ensuring that the load nodes downstream of the ring network all have the same carbon emission factor of 0.36, while the gas turbine units and tie lines on the power supply side maintain their respective characteristic values. This result can be directly used for carbon emission accounting, green electricity certification, and low-carbon dispatch decisions. For example, park managers can use this carbon emission topology map to identify carbon emission hotspots and optimize energy consumption structure; they can also provide legally valid electricity carbon intensity certificates to users at load nodes 8, 9, and 10 to support their product carbon footprint certification.

[0117] The above description is merely a preferred embodiment of this application. The scope of protection of this application is not limited to the above embodiments. All technical solutions falling within the scope of this application's concept are within the scope of protection of this application. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of this application should also be considered within the scope of protection of this application.

Claims

1. A carbon emission topology construction method based on type characteristics, characterized in that, include: Based on the pre-acquired power grid topology data, the node types are identified and a node topology graph is constructed. The node types include generating nodes, load nodes, ring nodes, and radial nodes. Analyze the power value of each line in the node topology diagram during operation, identify nodes with missing power and interpolate to fill in the missing power, analyze the power flow direction of the nodes, and construct a power flow direction matrix; The first carbon emission factor of the power generation node is set according to the real-time carbon emission data. The second carbon emission factor of the radial node is calculated according to the topology direction, the ring subgraph is constructed for the ring node and the third carbon emission factor of the ring node is calculated. The fourth carbon emission factor of the load node is calculated to obtain the first carbon emission factor set. Identify the missing factors in the first carbon emission factor set, set the corresponding missing factor reconstruction method according to the node type, and obtain the second carbon emission factor set; The carbon emission factor values ​​in the second set of carbon emission factors are matched to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

2. The carbon emission topology construction method based on type characteristics according to claim 1, characterized in that, The step of identifying node types and constructing a node topology graph based on pre-acquired power grid topology data includes: Based on the pre-acquired power grid topology data, identify power nodes, analyze the electrical connection relationships between power nodes, and construct a node set and a connection edge set; Analyze the equipment attributes corresponding to the nodes, and divide the node set into a set of power generation nodes, a set of load nodes, and a set of intermediate nodes; Calculate the degree of each node in the set of intermediate nodes, and designate nodes with a degree of 1 as radial nodes and nodes with a degree greater than 1 as ring nodes. Based on the set of connecting edges, corresponding connecting edges are established between power generation nodes, load nodes, ring nodes, and radial nodes to construct a node topology graph.

3. The carbon emission topology construction method based on type characteristics according to claim 1, characterized in that, The analysis examines the power values ​​of each line in the node topology diagram during operation, identifies nodes with missing power and interpolates to fill in the missing power, analyzes the power flow direction of the nodes, and constructs a power flow direction matrix, including: Based on the bidirectional active power value of each line in the node topology diagram during the operation of the preset time window, nodes with missing power values ​​and abnormal power accumulation are identified, and a set of nodes with missing power is obtained. According to the node type, the power values ​​of nodes in the set of missing power nodes are interpolated to complete the data, the power flow direction of the nodes is analyzed, and a power flow direction matrix is ​​constructed.

4. The carbon emission topology construction method based on type characteristics according to claim 3, characterized in that, The process of interpolating and completing the power values ​​of nodes in the power missing node set according to node type, analyzing the power flow direction of nodes, and constructing a power flow direction matrix includes: For radial node connections in a set of missing power nodes, interpolation is performed to complete the connection by analyzing the power change trend of the upstream nodes. For ring node connections in the set of missing power nodes, interpolation is performed to complete the connection by analyzing the power of normal lines in the ring network. For power generation nodes, interpolation is performed by analyzing the real-time power generation status. For load nodes, interpolation is performed by analyzing historical load data; Based on the completed node power data, the power values ​​in the two directions of each line are compared to determine the power flow direction of the node and construct the power flow direction matrix.

5. The carbon emission topology construction method based on type characteristics according to claim 1, characterized in that, The process involves setting a first carbon emission factor for power generation nodes based on real-time carbon emission data, calculating a second carbon emission factor for radial nodes according to the topological direction using a node topology graph and power flow direction matrix, constructing a ring network subgraph for ring nodes and calculating a third carbon emission factor for ring nodes, and calculating a fourth carbon emission factor for load nodes, thus obtaining a set of first carbon emission factors, including: The first carbon emission factor of the power generation node is set based on real-time carbon emission data; Combining the node topology diagram and the power flow direction matrix, the second carbon emission factor of radial nodes is calculated according to the topological direction based on the power generation nodes in the upstream nodes. Analyze the connectivity of the ring nodes, construct a ring network subgraph for the ring nodes, and calculate the third carbon emission factor of the ring nodes; Obtain the upstream lines supplying power to the load nodes and calculate the fourth carbon emission factor of the load nodes; By integrating the first carbon emission factor, the second carbon emission factor, the third carbon emission factor, and the fourth carbon emission factor, we obtain the first carbon emission factor set.

6. The carbon emission topology construction method based on type characteristics according to claim 5, characterized in that, The method of combining the node topology graph and the power flow direction matrix, and calculating the second carbon emission factor of the radial nodes based on the power generation nodes in the upstream nodes according to the topological direction, includes: By combining the node topology diagram and power flow direction matrix, the topological hierarchy of each radial node is analyzed, the power generation nodes in the upstream nodes corresponding to the radial nodes are extracted, and the power value transmitted by the line is integrated based on the first carbon emission factor of the corresponding power generation node to obtain the cumulative power. According to the topology hierarchy, for each radial node, the cumulative sum of the cumulative electricity of the upstream line and the corresponding first carbon emission factor is calculated, and then divided by the sum of the cumulative electricity of the upstream lines to obtain the second emission factor.

7. The carbon emission topology construction method based on type characteristics according to claim 6, characterized in that, The analysis of the connectivity of the ring nodes, the construction of a ring network subgraph for the ring nodes, and the calculation of the third carbon emission factor for the ring nodes include: Analyze the connectivity of the ring nodes, filter out the interconnected ring nodes, and construct a set of ring network subgraphs; For each ring network subgraph, the injection nodes where power flows into the ring network subgraph from external nodes and the output nodes where power flows out of the ring network subgraph to external nodes are determined according to the power flow direction matrix. Calculate the total injected power of the injection nodes and the total output power of the output nodes, and dynamically allocate the third carbon emission factor of each ring node according to the degree of the ring nodes in the ring network subgraph.

8. The carbon emission topology construction method based on type characteristics according to claim 7, characterized in that, The process of obtaining the upstream lines supplying power to the load nodes and calculating the fourth carbon emission factor of the load nodes includes: Obtain the upstream lines supplying power to the load nodes, their corresponding power, and the carbon emission factors of the upstream nodes; Calculate the sum of the products of the power values ​​of all upstream lines and their corresponding carbon emission factors, and divide by the sum of the power values ​​of the upstream lines to obtain the fourth carbon emission factor of the load node.

9. The carbon emission topology construction method based on type characteristics according to claim 1, characterized in that, The process of identifying missing factors in the first set of carbon emission factors and setting corresponding missing factor reconstruction methods according to node type includes: Identify the missing factors in the first set of carbon emission factors to obtain the set of missing factor nodes; For radial nodes in the missing factor node set, the missing factors are reconstructed by analyzing the historical carbon emission factors of upstream nodes and fitting the data; for ring nodes in the missing factor node set, the missing factors are reconstructed by analyzing the carbon flow balance in the ring subnetwork; for power generation nodes and load nodes, the missing factors are reconstructed by analyzing historical carbon emission factor data and interpolating the data.

10. A carbon emission topology construction system based on type characteristics, characterized in that, A method for implementing a type-feature-based carbon emission topology construction method as described in any one of claims 1 to 9, comprising: The node topology construction module identifies node types and constructs a node topology graph based on the pre-acquired power grid topology data. The node types include generation nodes, load nodes, ring nodes, and radial nodes. The power flow direction analysis module analyzes the power value of each line in the node topology diagram during operation, identifies nodes with missing power and interpolates to fill in the missing power, analyzes the power flow direction of the nodes, and constructs a power flow direction matrix. The carbon emission analysis module sets the first carbon emission factor of the power generation node based on real-time carbon emission data, calculates the second carbon emission factor of the radial node according to the topology direction by combining the node topology graph and the power flow direction matrix, constructs a ring network subgraph for the ring node and calculates the third carbon emission factor of the ring node, calculates the fourth carbon emission factor of the load node, and obtains the first carbon emission factor set. The carbon emission optimization module identifies missing factors in the first carbon emission factor set, sets the corresponding missing factor reconstruction method according to the node type, and obtains the second carbon emission factor set. The carbon emission topology construction module matches the corresponding carbon emission factor values ​​in the second carbon emission factor set to the corresponding nodes in the node topology graph to obtain the carbon emission topology.

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