Multi-source electricity-carbon collaborative atlas construction method based on electric power topological characteristics

Abstract

The invention relates to the technical field of power-carbon collaborative data processing, in particular to a multi-source power-carbon collaborative atlas construction method based on power topology characteristics, which comprises the following steps of: firstly, integrating power topology and carbon emission multi-source heterogeneous data, and constructing an initial atlas after ensuring data quality through characteristic parameter analysis, so that the problem of inconsistency of multi-source data is solved; secondly, through node stability evaluation and optimization, the reliability of the atlas structure is improved; and finally, accurate analysis and application of the electrical-carbon coupling relationship are realized. According to the method, through data quality control and topological optimization, the accuracy and stability of the power-carbon collaborative atlas are enhanced, quantitative support is provided for low-carbon scheduling, carbon flow tracking and cross-domain collaborative decision making of the power system, the power-carbon management efficiency is effectively improved, the carbon emission reduction target of the power system is assisted to be achieved, and the method has high engineering application value.

Description

Technical Field

[0001] This invention relates to the field of electric carbon synergistic data processing technology, and in particular to a method for constructing a multi-source electric carbon synergistic map based on power topology features. Background Technology

[0002] In existing technologies, the construction of multi-source electric carbon collaborative maps based on power topology features involves multiple aspects; graph data-based electric carbon tracking technology: by generating graph data representations from power network topology and other data, the graph search problem is transformed, and the impact of energy storage is considered to achieve refined spatiotemporal electric carbon tracking; electrical segmentation-based carbon footprint tracking technology: by relying on energy big data to achieve full-chain carbon footprint monitoring and tracing of the source, grid, load and storage, and by combining power gene technology to achieve precise control of user carbon emissions under complex network topologies; spatiotemporal heterogeneous electric carbon factor calculation technology: by combining the time-varying nature of the power grid topology, a refined model is used to calculate time- and zone-specific electric carbon factors to improve the accuracy of carbon accounting; these technologies provide multi-dimensional support for the construction of multi-source electric carbon collaborative maps from data tracking and accounting to model construction.

[0003] For example, Chinese invention patent CN112632287B discloses a method and apparatus for constructing an electric power knowledge graph, relating to the field of data processing technology. The method includes: acquiring data to be processed; acquiring domain text from the data to be processed; extracting keywords from the domain text to obtain multiple candidate words; scoring the multiple candidate words; determining multiple domain ontology from the multiple candidate words based on the scoring results; preprocessing the data to be processed to obtain candidate terms; filtering the candidate terms and extracting relationships to obtain extraction results; and using the verified extraction results as multiple core ontology; acquiring character vectors and word vectors for each statement in the data to be processed; concatenating the character vectors and word vectors and inputting them into a long short-term memory network for entity recognition to obtain multiple entities; and constructing an electric power knowledge graph based on the multiple core ontology, multiple entities, and multiple domain ontology.

[0004] For example, Chinese invention patent CN113946684A discloses a method for constructing a knowledge graph for power infrastructure, including the following steps: acquiring raw data by obtaining raw data from documents, tables, and news through a data acquisition module; data preprocessing by preprocessing the acquired raw data to obtain preprocessed data for structured and unstructured data respectively; knowledge extraction by extracting triples from structured data; for unstructured data, firstly using BiLSTM-CRF for named entity recognition, and then using the BERT model to extract entity relations from the unstructured data after named entity recognition; knowledge fusion by fusing the extracted knowledge to obtain entity-relation-entity triples; after obtaining entity-relation-entity triples, knowledge representation is performed to form a knowledge graph, which is then stored in the Neo4j graph database.

[0005] However, in the process of implementing the embodiments of this application, the above-mentioned technology has at least the following technical problems: different data sources lead to inconsistent sampling frequency, accuracy and format, and the uncertainty of data association characteristics also affects data quality; node stability is affected by external disturbances such as defects in the topology of the electro-carbon co-location graph (such as insufficient redundant connection of key nodes, unreasonable power allocation of tie lines), fluctuations in the operating status of the power system, and abnormal fluctuations in carbon data. In addition, data quality defects will be transmitted to node stability assessment, and insufficient node stability will have a reverse effect on data quality, forming a two-way influence. Summary of the Invention

[0006] To address the technical problems of data quality issues caused by differences in data sources and node instability in existing technologies, this invention provides a method and apparatus for constructing a multi-source electric carbon collaborative map based on power topology features. The technical solution is as follows: On the one hand, a method for constructing a multi-source electric-carbon synergistic map based on power topology features is provided. This method includes: Step 1, collecting multi-source heterogeneous data on power topology features and carbon emissions and integrating them into a multi-source heterogeneous dataset, analyzing the characteristic parameters of the multi-source heterogeneous dataset, determining whether the quality of the multi-source heterogeneous dataset meets the standards, and constructing an initial multi-source electric-carbon synergistic map based on the qualified multi-source heterogeneous dataset; Step 2, obtaining the stable operating parameters of each node in the initial multi-source electric-carbon synergistic map, determining whether each node in the initial multi-source electric-carbon synergistic map is qualified, and simultaneously counting the unqualified nodes in the initial multi-source electric-carbon synergistic map, thereby optimizing the initial multi-source electric-carbon synergistic map, and marking the optimized map after optimization as an electric-carbon synergistic optimization map; Step 3, constructing a multi-dimensional analysis framework based on the optimized electric-carbon synergistic map to analyze and apply the electric-carbon coupling relationship.

[0007] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) This invention provides a method for constructing a multi-source electric carbon collaborative map based on power topology features. This method achieves deep integration and accurate modeling of power topology and carbon emission data through progressive processing of data layer, map layer and application layer. Its core value lies in constructing a high-quality and highly reliable electric carbon collaborative map, providing a quantitative tool for the low-carbon operation of the power system and the refined management of carbon emissions, effectively breaking the island status of power and carbon data, supporting the collaborative decision-making of "power-carbon flow", and helping the efficient regulation of the energy system under the dual carbon target.

[0008] (2) By collecting multi-source heterogeneous data and analyzing characteristic parameters (such as time synchronization, spatial overlap rate, etc.), the data quality is guaranteed from the source, avoiding interference from low-quality data in map construction; the process of integrating power topology and carbon emission data realizes the standardized docking of cross-domain information, providing complete and consistent basic data support for the initial map, and reducing map deviation caused by data loss or heterogeneity.

[0009] (3) By evaluating the stable operating parameters of the nodes and optimizing the nodes to be optimized, the topological defects that may exist in the initial map (such as node mismatch and association error) were specifically addressed, which significantly improved the structural accuracy and stability of the map. The optimized electric-carbon synergistic map can more realistically reflect the coupling relationship between electric power and carbon flow, providing a highly reliable model basis for subsequent analysis and reducing the risk of decision misjudgment.

[0010] (4) Based on the multi-dimensional analysis framework constructed by the optimized graph, the deep mining of the coupling relationship between electricity and carbon (such as the correlation between power flow and carbon flow path) was realized. The analysis results can directly serve practical scenarios such as energy planning and emission reduction strategy formulation. This step transforms the abstract graph model into a practical application value, improves the collaborative efficiency of power system and carbon management, and promotes the low-carbon transformation practice in the energy field. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for constructing a multi-source electric carbon synergistic map based on power topology features, provided by an embodiment of the present invention. Figure 2 This is a flowchart of a method for judging the quality of multi-source heterogeneous datasets provided in an embodiment of the present invention; Figure 3 This is a flowchart of the overall method for secondary optimization of multi-source heterogeneous datasets provided in an embodiment of the present invention; Figure 4 This is a detailed flowchart of a method for secondary optimization of multi-source heterogeneous datasets provided in an embodiment of the present invention; Figure 5 This is a flowchart of a method for judging the quality fluctuation of multi-source heterogeneous datasets provided in an embodiment of the present invention; Figure 6 The flowchart of a method for stabilizing each node of an initial multi-source electrocarbon synergistic spectrum provided by an embodiment of the present invention is shown below; Figure 7 This is a flowchart of a method for generating a multi-source electrocarbon synergistic optimization spectrum, provided by an embodiment of the present invention. Detailed Implementation

[0013] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0014] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0015] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0016] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0017] This invention provides a method for constructing a multi-source electrocarbon synergistic map based on power topology features, such as... Figure 1The flowchart shown illustrates a method for constructing a multi-source electric-carbon synergistic map based on power topology features. The process can include the following steps: Step 1: Collect multi-source heterogeneous data on power topology features and carbon emissions, integrate them into a multi-source heterogeneous dataset, analyze the characteristic parameters of the dataset, determine if the dataset meets quality standards, and then construct an initial multi-source electric-carbon synergistic map based on the qualified dataset. This process involves data mapping, topology modeling, correlation verification, and... Storage optimization involves four core steps: First, mapping electricity (e.g., power plants) and carbon emissions (e.g., industrial sources) entities to graph nodes, constructing power edges, carbon flow edges, and coupling edges based on physical connections, carbon flow paths, and electricity-carbon correlations; second, constructing a layered power topology layer (including backbone and edge networks), a carbon flow topology layer (including carbon source and carbon sink networks), and an electricity-carbon coupling layer (quantifying the correlation strength between power and carbon emissions); subsequently, filtering abnormal correlations through physical constraints (e.g., power balance) and data consistency checks (cross-source comparison) to ensure graph logic compliance; finally, adopting... The graph database is used to store and establish a hierarchical index. The quality is evaluated using connectivity, integrity, and accuracy indicators, ultimately forming an initial graph containing multi-dimensional node attributes and edge relationships, laying the foundation for subsequent node analysis and optimization. Step two involves obtaining stable operating parameters for each node in the initial multi-source electricity-carbon synergy graph and determining whether each node is qualified. Unqualified nodes in the initial multi-source electricity-carbon synergy graph are also counted, thus optimizing the initial graph. After optimization, it is marked as an optimized electricity-carbon synergy graph. Step three involves constructing a multi-dimensional analysis framework based on the optimized electricity-carbon synergy graph to analyze and apply the electricity-carbon coupling relationship. The electricity-carbon coupling relationship is analyzed through three core dimensions: spatiotemporal, physical coupling, and risk-regulation. The spatiotemporal dimension analyzes the dynamic synchronization and regional distribution differences between electricity and carbon flow. The physical coupling dimension relies on the connection relationship between the electricity topology and carbon flow network in the graph to construct a quantitative equation for "electricity-carbon" (such as total carbon emissions and node power), clarifying the hard correlation strength between electricity conversion, transmission, and carbon emissions. The risk-regulation dimension identifies coupling vulnerabilities and evaluates intervention effects. In practice, time-series and spatial data are extracted from the graph, and multi-dimensional analysis is carried out by combining correlation analysis, coupling equations, risk models and other tools. The results are then applied to real-world scenarios, such as optimizing intraday scheduling strategies, tracking the entire carbon footprint, setting early warnings for high-risk nodes and simulating policy impacts. Ultimately, this achieves the transformation from graph information to precise regulation and scientific decision-making, supporting the efficient collaboration between the power system and the carbon market.

[0018] Specifically, the characteristic parameters of the multi-source heterogeneous dataset are analyzed. The specific analysis process is as follows: the characteristic parameters of the multi-source heterogeneous dataset include the timestamp deviation factor, the spatial coverage overlap factor, and the power flow measurement error contribution factor.

[0019] The timestamp deviation factor represents the ratio of the timestamp deviation of a multi-source heterogeneous dataset to its corresponding boundary value; the spatial coverage overlap factor represents the ratio of the spatial coverage overlap of a multi-source heterogeneous dataset to its corresponding boundary value; and the power flow measurement error contribution factor represents the ratio of the power flow measurement error contribution of a multi-source heterogeneous dataset to its corresponding boundary value.

[0020] In the simulated database, the importance weights of timestamp deviation, spatial coverage overlap rate, and power flow measurement error factor are set. Based on these weights, the contribution of each factor to the multi-source data quality index is quantified. Finally, the multi-source data quality index is obtained by weighted summation of these contributions. The multi-source data quality index represents the overall quality level of the multi-source heterogeneous dataset. The specific evaluation method is as follows: ; ; ; ; In the formula, REWD is the multi-source data quality index, PLK is the timestamp deviation factor of the multi-source heterogeneous dataset, DCV is the timestamp deviation of the multi-source heterogeneous dataset, YHB is the preset defined timestamp deviation in the simulation database, EDG is the spatial coverage overlap rate factor of the multi-source heterogeneous dataset, TFX is the spatial coverage overlap rate of the multi-source heterogeneous dataset, WSK is the preset defined spatial coverage overlap rate in the simulation database, DCB is the power flow measurement error contribution factor of the multi-source heterogeneous dataset, BTL is the power flow measurement error contribution of the multi-source heterogeneous dataset, SPM is the preset defined power flow measurement error contribution in the simulation database, fp is the importance weight corresponding to the preset timestamp deviation factor in the simulation database, fe is the importance weight corresponding to the preset spatial coverage overlap rate factor in the simulation database, and fd is the importance weight corresponding to the preset power flow measurement error contribution factor in the simulation database.

[0021] The aforementioned timestamp deviation value is used to measure the degree of inconsistency in timestamps of multi-source heterogeneous data (such as power topology data and carbon emission data collected by different sensors), obtained by comparing the difference between the timestamps of multi-source data and the high-precision reference time; the aforementioned spatial coverage overlap rate characterizes the proportion of overlap in the spatial coverage of multi-source heterogeneous data, reflecting the monitoring redundancy or complementarity of different data sources for the same spatial area, obtained by calculating the proportion of overlapping area of ​​the spatial coverage of multi-source data; the aforementioned power flow measurement error contribution rate is used to quantify the contribution ratio of different data sources (such as SCADA systems) to the overall error in power flow measurement data. Power flow measurement data refers to the measurement values ​​of power flow state quantities such as voltage, current, active power, and reactive power in the power system, used to reflect the distribution and flow of power in the network. Through statistical methods (such as variance decomposition), the proportion of the variance of each data source error in the total error variance is calculated to quantify the power flow measurement error contribution rate.

[0022] The defined timestamp deviation value represents the maximum value of the timestamp deviation value within the specified range; the defined spatial coverage overlap rate represents the minimum value of the spatial coverage overlap rate within the specified range; and the defined power flow measurement error contribution rate represents the maximum value of the power flow measurement error contribution rate within the specified range.

[0023] The timestamp deviation determines the temporal synchronization of multi-source data. If the deviation is too large, the measurement values ​​from different data sources at the same spatial location will lose their temporal correspondence, directly affecting the accuracy of spatial coverage overlap calculation. When the spatial coverage overlap is too low, more heterogeneous data sources are needed to supplement information, and the difference in measurement accuracy between different data sources will amplify the volatility of the contribution of power flow measurement error. Conversely, the stability of the contribution of power flow measurement error will have a reverse effect on the efficiency of timestamp deviation correction—data sources with a high error ratio are more difficult to accurately calibrate their timestamp deviation, thus forming a chain of influence between "spatial-temporal consistency and error accuracy," which together determine the quality and reliability of multi-source heterogeneous datasets.

[0024] The importance weights corresponding to the timestamp deviation factor mentioned above represent the changes in the multi-source data quality index when the factor changes by a unit; the importance weights corresponding to the spatial coverage overlap rate factor mentioned above represent the changes in the multi-source data quality index when the factor changes by a unit; the importance weights corresponding to the power flow measurement error contribution factor mentioned above represent the changes in the multi-source data quality index when the factor changes by a unit; the simulation database stores the mapping relationships between the timestamp deviation factor and its corresponding importance weight, the spatial coverage overlap rate factor and its corresponding importance weight, and the power flow measurement error contribution factor and its corresponding importance weight; for example, when the timestamp deviation factor, the spatial coverage overlap rate factor, and the power flow measurement error contribution factor are input into the simulation database, the simulation database will generate the corresponding importance weights for the timestamp deviation factor, the spatial coverage overlap rate factor, and the power flow measurement error contribution factor based on preset mapping rules, and the numerical range of each importance weight is strictly controlled between 0 and 1.

[0025] The larger the timestamp deviation factor, the more serious the deviation of the actual timestamp from the defined value, the worse the time synchronization of the data, which directly reduces the time consistency of multi-source data and leads to a decrease in the multi-source data quality index. The larger the spatial coverage overlap factor, the more the spatial coverage overlap of multi-source data meets expectations, the more reasonable the complementarity and redundancy of data in the spatial dimension, the better the spatial integrity of the data, and the higher the multi-source data quality index.

[0026] In one specific embodiment, by analyzing the spatiotemporal characteristics (time synchronization / spatial overlap rate) of multi-source heterogeneous data, low-confidence data streams are pre-filtered to ensure the quality of input for map construction; relying on the structured alignment of electricity-carbon emission data, a cross-domain data mapping paradigm is established to generate an unambiguous ground-state map, significantly reducing modeling errors caused by the data gap.

[0027] Furthermore, the quality of the multi-source heterogeneous dataset is determined to meet the standards. The specific determination process is as follows: the multi-source data quality index is compared with the preset multi-source data quality threshold in the simulation database. If the multi-source data quality index is greater than or equal to the multi-source data quality threshold, the quality of the multi-source heterogeneous dataset is determined to meet the standards. Then, an initial electrocarbon synergistic spectrum is constructed. At the same time, a data quality fluctuation coefficient is introduced to determine whether to issue an early warning for data quality fluctuations. It should be explained that the aforementioned multi-source data quality threshold represents the minimum value of the multi-source data quality index within a specified range. The aforementioned data quality fluctuation coefficient represents the stability index of the multi-source heterogeneous dataset quality index over multiple sampling periods. Within a set time window, the data quality fluctuation coefficient is obtained by calculating the deviation statistic (such as standard deviation) between the index and the mean at each time point, based on the mean value of the multi-source data quality index.

[0028] The process for determining whether to issue an early warning for data quality fluctuations is as follows: The data quality fluctuation coefficient is compared with a preset data quality fluctuation threshold in the simulated database. If the data quality fluctuation coefficient is greater than the threshold, an early warning is issued. If the data quality fluctuation coefficient is less than or equal to the threshold, no early warning is issued. If the multi-source data quality index is less than the multi-source data quality threshold, the quality of the multi-source heterogeneous dataset is deemed substandard, and adjustments are made to the dataset. The aforementioned data quality fluctuation threshold represents the maximum value of the data quality fluctuation coefficient within a specified range. Issuing an early warning for data quality fluctuations refers to pushing the warning information to the data management terminal and related systems (such as the power dispatch platform).

[0029] like Figure 2 The flowchart of a method for judging the quality of multi-source heterogeneous datasets provided in this embodiment of the invention is shown. The process starts from the beginning, firstly calculating the multi-source data quality index, and then determining whether the index is greater than or equal to the multi-source data quality threshold. If it is, the data quality is determined to be good, and a quality fluctuation coefficient is introduced. If not, the interpolation order is increased based on the multi-source data quality deviation value, the boundary buffer radius is increased, and a second-order multi-source data quality index is obtained. For the obtained second-order multi-source data quality index, it is also determined whether it is greater than or equal to the multi-source data quality threshold. If it is, a quality fluctuation coefficient is introduced. Otherwise, the wavelet decomposition level is increased based on the second-order multi-source data quality deviation value, and the data association entropy is obtained.

[0030] Specifically, an adjustment is performed on the multi-source heterogeneous dataset. The adjustment process is as follows: Based on the multi-source data quality index and multi-source data quality threshold, the multi-source data quality deviation value is obtained. Based on the multi-source data quality deviation value, a high-order interpolation is matched from the simulation database to improve the time series data in the multi-source heterogeneous dataset. This can accurately fill missing values ​​or correct anomalous jump points in the time series data, preserving the trend characteristics of the original data while avoiding smoothing distortion caused by low-order interpolation. This can significantly improve the continuity and integrity of the time series data and reduce the interference of time dimension data breaks on subsequent time series correlation analysis of map nodes. Edges are matched based on the multi-source data quality deviation value. The boundary buffer radius is increased by a factor, thereby expanding the spatial matching range between power topology nodes and carbon data nodes. This allows for flexible relaxation of spatial matching constraints between power topology nodes and carbon data nodes, resolving the "node data island" problem caused by insufficient initial spatial coverage overlap. The expanded matching range can incorporate more neighboring carbon data with high correlation, enhancing the spatial correlation between power nodes and carbon flow nodes, improving the complementarity of multi-source data in the spatial dimension, and providing more comprehensive spatial data support for the integrity of the map topology. The multi-source data quality index after the first adjustment is obtained, marked as the multi-source data secondary quality index, and it is determined whether to perform secondary optimization on the multi-source heterogeneous dataset.

[0031] The aforementioned multi-source data quality deviation value refers to the result of subtracting the multi-source data quality index from the multi-source data quality threshold. The process of matching higher-order interpolation from the simulation database based on the multi-source data quality deviation value is as follows: the simulation database pre-stores mapping rules between different levels of multi-source data quality deviation and corresponding higher-order interpolation algorithms. For example, mild deviation corresponds to cubic spline interpolation, suitable for correcting small fluctuations; moderate deviation corresponds to Kriging interpolation, suitable for time-series data with strong spatial correlation; and severe deviation corresponds to piecewise polynomial interpolation, which can handle large-scale missing values ​​or jumps. Simultaneously, the simulation database stores the parameter thresholds (such as interpolation windows) for each algorithm. (Size); The above-mentioned boundary buffer radius amplification factor is matched based on the multi-source data quality deviation value. The specific matching process is as follows: The boundary buffer radius amplification factor corresponding to each multi-source data quality deviation value interval is stored in the simulation database. The obtained multi-source data quality deviation value is input into the simulation database to determine its interval. Then, the boundary buffer radius amplification factor corresponding to the interval is the required amplification factor. The obtained boundary buffer radius amplification factor is multiplied by the original boundary buffer radius, and the result is the boundary buffer radius that needs to be adjusted. The boundary buffer radius amplification factor is greater than 1, which indicates that the boundary buffer radius needs to be increased by a certain factor.

[0032] Specifically, the process for determining whether to perform secondary optimization on the multi-source heterogeneous dataset is as follows: the secondary quality index of the multi-source data is compared with the multi-source data quality threshold; if the secondary quality index of the multi-source data is greater than or equal to the multi-source data quality threshold, it is determined that secondary optimization will not be performed on the multi-source heterogeneous dataset, and an initial electrocarbon synergistic spectrum is constructed. At the same time, a data quality fluctuation coefficient is introduced to determine whether to issue an early warning for data quality fluctuations. The process of introducing the data quality fluctuation coefficient to determine whether to issue an early warning for data quality fluctuations is consistent with the previous process for determining whether the quality of the multi-source heterogeneous dataset meets the standards.

[0033] If the secondary quality index of the multi-source data is less than the multi-source data quality threshold, it is determined that secondary optimization should be performed on the multi-source heterogeneous dataset. The specific optimization process is as follows: Based on the secondary quality index and the multi-source data quality threshold, the secondary quality deviation value of the multi-source data is obtained. Based on the secondary quality deviation value of the multi-source data, the wavelet decomposition layer is increased, thereby increasing the wavelet decomposition layer of the power signal, decomposing the signal into a finer frequency band, enhancing the ability to extract subtle features (such as short-term power surges), and thus accurately associating subtle fluctuations in carbon emission data. In addition, a higher decomposition layer can refine the time window, improve the time-series matching degree between power signals and carbon emission data, and amplify potential abnormal mutations, thereby strengthening the sensitivity of anomaly detection.

[0034] The aforementioned secondary quality deviation value of multi-source data refers to the result of subtracting the secondary quality index of multi-source data from the quality threshold of multi-source data. The specific matching process for matching the wavelet decomposition enhancement level based on the secondary quality deviation value of multi-source data is as follows: the mapping relationship between the secondary quality deviation interval of each multi-source data and the wavelet decomposition enhancement level is pre-stored in the simulation database. The obtained secondary quality deviation value of multi-source data is input into the simulation database. Based on the preset mapping rules, the simulation database can match the corresponding wavelet decomposition enhancement level. The obtained wavelet decomposition enhancement level is added to the original wavelet decomposition level, and the final result is the wavelet decomposition level to be adjusted.

[0035] Simultaneously, the data association entropy of the multi-source heterogeneous dataset is acquired and compared with the preset data association entropy threshold in the simulation database. If the data association entropy is less than or equal to the threshold, the multi-source heterogeneous dataset is continuously monitored. If the data association entropy is greater than the threshold, the selection weight of highly correlated features is increased based on the secondary quality deviation value of the multi-source data, thereby increasing the selection weight of highly correlated features of the power topology and specifically enhancing the influence of key features of the power topology while reducing noise interference. At the same time, constraint equations are introduced to standardize feature association boundaries based on physical laws and business logic, avoiding false associations and strengthening the association of key features of the power topology. The synergistic effect of the two can enhance the system's adaptive correction capability to data quality fluctuations, bridge the structural differences of multi-source heterogeneous data, provide more reliable and logical feature support for the construction of the power carbon synergy map and subsequent analysis, and improve the stability and effectiveness of the overall analysis.

[0036] The aforementioned data association entropy is a quantitative indicator that measures the degree of correlation and consistency between different data sources in a multi-source heterogeneous dataset. A higher value indicates weaker correlation and more chaotic structure among the data. Key features (such as voltage levels at substation nodes) are extracted from the multi-source heterogeneous data and standardized. A matrix is ​​formed by calculating the pairwise correlations between features from different data sources. The data association entropy is then calculated based on this matrix using the information entropy formula. The data association entropy threshold represents the maximum allowed value for data association entropy. The matching process for the high-relevance feature selection weight amplification coefficient based on the secondary quality deviation value of multi-source data is as follows: The simulation database stores the high-relevance feature selection weight amplification coefficients corresponding to the secondary quality deviation value intervals of each multi-source data source. The obtained secondary quality deviation values ​​of the multi-source data are input into the simulation database to determine their respective intervals. The high-relevance feature selection weight amplification coefficient corresponding to this interval is the required amplification coefficient. The obtained high-relevance feature selection weight amplification coefficient is multiplied by the original high-relevance feature selection weight, and the result is the high-relevance feature selection weight that needs to be adjusted. A high-relevance feature selection weight amplification coefficient greater than 1 indicates that the high-relevance feature selection weight needs to be increased by a certain factor.

[0037] like Figure 3 The flowchart of the overall method for secondary optimization of multi-source heterogeneous datasets provided in this embodiment of the invention is shown. After obtaining the data association entropy, it is determined whether it is greater than the data association entropy threshold. If it is, the selection weight of highly correlated features is increased based on the secondary quality deviation value of multi-source data, and a constraint equation is introduced. If not, continuous monitoring is performed. Whether it is increasing the selection weight of highly correlated features, introducing a constraint equation, or continuous monitoring, the third quality index of multi-source data will be obtained afterwards.

[0038] In a specific example embodiment, highly correlated features refer to core features that significantly influence the strength of the "power-carbon" correlation, temporal matching degree, or spatial correspondence in the collaborative analysis of power topology and carbon emission data. Their changes can directly or indirectly reflect the authenticity of the power-carbon synergy relationship. Examples include: real-time power of transmission lines, regional carbon flow transmission rate, and the conversion ratio of transmission losses to carbon emissions. Introducing constraint equations refers to matching suitable equations from a pre-set constraint equation library (the greater the deviation, the stricter the constraint conditions). These equations are embedded in the feature correlation logic of the power topology through mathematical modeling to enforce the range of values ​​or interrelationships of highly correlated features, thereby offsetting the correlation distortion caused by data deviations. Examples include: power-carbon emission physical constraints, temporal synchronization constraints, and spatial matching constraints.

[0039] Obtain the multi-source data quality index after secondary optimization, label it as the multi-source data tertiary quality index, and determine whether to issue an early warning for the quality of multi-source heterogeneous datasets.

[0040] Furthermore, the process determines whether to issue an early warning for the quality of the multi-source heterogeneous dataset. Specifically, the process involves comparing the three-dimensional quality index of the multi-source data with a multi-source data quality threshold. If the three-dimensional quality index is greater than or equal to the multi-source data quality threshold, no early warning is issued, and an initial electro-carbon synergistic map is constructed. Simultaneously, a data quality fluctuation coefficient is introduced to determine whether to issue an early warning for data quality fluctuations. If the three-dimensional quality index is less than the multi-source data quality threshold, an early warning is issued for the multi-source heterogeneous dataset, but the initial electro-carbon synergistic map is not constructed. The cumulative number of times the multi-source data quality index is continuously less than the multi-source data quality threshold is obtained and compared with a preset threshold in the simulation database. If the cumulative number is greater than or equal to the threshold, the multi-source heterogeneous dataset is reacquired, and its quality is reassessed. If the cumulative number is less than the threshold, the multi-source heterogeneous dataset is continuously monitored. The process involves constructing a dataset; combining a time-series database and a counter to obtain the cumulative number of times the multi-source data quality index is continuously less than a threshold; the counter periodically reads the latest index from the database, incrementing the count if the index is below the threshold, and resetting it to 0 if it is within the threshold, and writing the real-time count value to the corresponding field in the time-series database; the time-series database records the dynamic changes in the cumulative count in real time; the aforementioned acquisition of the cumulative number of times the multi-source data quality index is continuously less than the multi-source data quality threshold can be achieved by directly reading the latest recorded count value from the time-series database; the aforementioned threshold count refers to the maximum value within a specified range of the cumulative number of times the multi-source data quality index is continuously less than the multi-source data quality threshold; the aforementioned reacquisition of the multi-source heterogeneous dataset refers to re-collecting multi-source heterogeneous data on power topology characteristics and carbon emissions and integrating them into a new multi-source heterogeneous dataset; the aforementioned re-judgment of whether the quality of the multi-source heterogeneous dataset meets the standards is consistent with the previous process of judging whether the quality of the multi-source heterogeneous dataset meets the standards.

[0041] like Figure 4 The flowchart illustrates a detailed method for secondary optimization of a multi-source heterogeneous dataset provided in this embodiment of the invention. It then determines whether the three-dimensional quality index of the multi-source data is greater than or equal to the multi-source data quality threshold. If yes, a quality fluctuation coefficient is introduced; if no, the cumulative number of consecutive non-compliance counts is obtained, and it is determined whether the cumulative number is greater than or equal to the threshold count. If the cumulative number is greater than or equal to the threshold count, the multi-source heterogeneous dataset is re-acquired, and the quality of the multi-source heterogeneous dataset is re-determined; if no, the multi-source heterogeneous dataset is continuously monitored, and an initial electrocarbon synergistic spectrum is constructed simultaneously.

[0042] like Figure 5 The flowchart of a method for judging the quality fluctuation of multi-source heterogeneous datasets provided by an embodiment of the present invention is shown. After introducing a quality fluctuation coefficient, it is determined whether the quality fluctuation coefficient is greater than the fluctuation threshold. If it is, a warning is issued for potential data quality fluctuation risks; otherwise, continuous monitoring is carried out. Whether it is a warning for potential data quality fluctuation risks, continuous monitoring, or continuous monitoring of data, an initial electrocarbon synergistic spectrum will eventually be constructed.

[0043] Specifically, the stable operating parameters of each node in the initial multi-source electrocarbon synergistic spectrum are obtained, and the specific analysis process is as follows: The stable operating parameters of each node in the initial multi-source electro-carbon co-location map include the coupling node connectivity factor, the node power injection fluctuation factor, and the node carbon flow direction transformation frequency factor; and the final value of the comprehensive evaluation of multi-source data quality is obtained.

[0044] The aforementioned coupling node connectivity factor characterizes the deviation relationship between the coupling node connectivity of each node in the initial multi-source electro-carbon co-location map and its corresponding reference value; the aforementioned node power injection fluctuation factor characterizes the ratio of the node power injection fluctuation value of each node in the initial multi-source electro-carbon co-location map to its corresponding boundary value; the aforementioned node carbon flow direction change frequency factor characterizes the ratio of the node carbon flow direction change frequency of each node in the initial multi-source electro-carbon co-location map to its corresponding boundary value; the aforementioned multi-source data quality comprehensive evaluation final value represents the final multi-source data quality index during the adjustment process.

[0045] Using the importance weights defined in the simulated database, the contributions of the coupling node connectivity factor, node power injection fluctuation factor, node carbon flow direction change frequency factor, and the final value of the comprehensive evaluation of multi-source data quality to the stability index of the map nodes are calculated. These contributions are then weighted and summed to generate the stability index of each map node. Each map node stability index represents the operational stability of each node in the initial electro-carbon synergistic map. The specific evaluation method is as follows: ; ; ; ; In the formula, AKLO i REWD_e is the stability index of the i-th node in the initial multi-source electrocarbon synergistic map, REWD_e is the final value of the comprehensive evaluation of multi-source data quality, and KRG is the stability index of the map node. i CBD is the coupling node connectivity factor of the i-th node in the initial multi-source electrocarbon synergistic spectrum. i RCB represents the connectivity of the coupled nodes of the i-th node in the initial multi-source electrocarbon synergistic spectrum, CVN is the preset reference connectivity of the coupled nodes in the simulation database, and RCB is the connectivity of the coupled nodes. i Inject a fluctuation factor into the node power of the i-th node in the initial multi-source electrocarbon synergistic spectrum, SXS i Let EFB be the node power injection fluctuation value of the i-th node in the initial multi-source electrocarbon synergistic spectrum, and QCZ be the pre-defined node power injection fluctuation value in the simulation database. i KDZ is the frequency factor for the nodal carbon flow direction transformation of the i-th node in the initial multi-source electrocarbon synergistic spectrum. i Let $\frac{i}{i}$ be the node carbon flow direction transformation frequency of the i-th node in the initial multi-source electro-carbon co-location graph, $WTN$ be the pre-defined node carbon flow direction transformation frequency in the simulation database, $gr$ be the importance weight corresponding to the pre-defined coupling node connectivity factor in the simulation database, $gc$ be the importance weight corresponding to the pre-defined node power injection fluctuation value factor in the simulation database, $gn$ be the importance weight corresponding to the pre-defined node carbon flow direction transformation frequency factor in the simulation database, and $gm$ be the importance weight corresponding to the pre-defined multi-source data quality comprehensive evaluation final value in the simulation database, $i = 1, 2, 3, ..., n$; $i$ is the node number in the initial multi-source electro-carbon co-location graph, and $n$ is the total number of nodes in the initial multi-source electro-carbon co-location graph.

[0046] It should be explained that the above-mentioned connectivity of coupled nodes refers to the comprehensive index of "physical connection strength" and "data association density" between power topology nodes and carbon flow network nodes (such as high-energy-consuming enterprises and carbon sink areas). It is obtained by weighted summation based on the physical connection data of the power and carbon flow networks and the data interaction logs between nodes. The above-mentioned node power injection fluctuation value represents the fluctuation range of the power injection (active / reactive power) of a certain node in the power topology (such as a new energy power plant) within a unit time. It is obtained by the average of the absolute values ​​of the deviations between the power values ​​and the average values ​​within the sliding window, based on the real-time power monitoring data of the power nodes. The above-mentioned node carbon flow direction change frequency is the number of times the carbon flow transmission direction (such as input / output) of a certain node (such as a regional carbon sink center) in the carbon flow network changes within a preset period (the specific time is determined by relevant technical personnel). It is obtained by statistically analyzing the number of changes in the direction state within a unit time based on the carbon flow direction time-series records or changes in node carbon storage.

[0047] The aforementioned reference coupling node connectivity represents a reference value for the connectivity of the coupling nodes; the aforementioned delimited node power injection fluctuation value represents the maximum value of the node power injection fluctuation value within a specified range; the aforementioned delimited node carbon flow direction change frequency represents the maximum value of the delimited node carbon flow direction change frequency within a specified range.

[0048] The connectivity of coupled nodes is fundamental, as its level determines the efficiency of interaction between power and carbon flow nodes. The higher the connectivity, the more directly power fluctuations affect the direction of carbon flow. The fluctuation value of node power injection is a key source of disturbance. The greater the fluctuation, the easier it is to trigger the switching of carbon emission sources / carbon sinks, which in turn increases the frequency of carbon flow direction changes. The change in the frequency of node carbon flow direction changes will in turn affect the stability of power injection. Frequent direction changes may force nodes to adjust their power strategies, exacerbating power fluctuations.

[0049] The importance weights corresponding to the aforementioned coupling node connectivity factors represent the changes in the graph node stability index when the factor changes by a unit; the importance weights corresponding to the aforementioned node power injection fluctuation value factors represent the changes in the graph node stability index when the factor changes by a unit; the importance weights corresponding to the aforementioned node carbon flow direction change frequency factors represent the changes in the graph node stability index when the factor changes by a unit; the importance weights corresponding to the aforementioned multi-source data quality comprehensive evaluation final value represent the changes in the graph node stability index when the final value changes by a unit; the simulation database stores the mapping relationship between coupling node connectivity factors and their corresponding importance weights, and the mapping relationship between node power injection fluctuation value factors and their corresponding importance weights. The mapping relationships between importance weights, the mapping relationships between the node carbon flow direction change frequency factor and its corresponding importance weights, and the mapping relationships between the final value of the comprehensive evaluation of multi-source data quality and its corresponding importance weights are established. For example, the coupling node connectivity factor, the node power injection fluctuation factor, the node carbon flow direction change frequency factor, and the final value of the comprehensive evaluation of multi-source data quality are input into the simulation database. The simulation database will generate the corresponding importance weights for the coupling node connectivity factor, the node power injection fluctuation factor, the node carbon flow direction change frequency factor, and the final value of the comprehensive evaluation of multi-source data quality based on the preset mapping rules. The numerical range of each importance weight is strictly controlled between 0 and 1.

[0050] A larger node connectivity factor indicates that the association between the node and the carbon-electric network deviates from the optimal state, thus reducing node stability. A larger node power injection fluctuation factor indicates poorer power injection stability, higher risk of impact on grid frequency and voltage, and direct threat to node operational stability. A larger node carbon flow direction change frequency factor indicates more frequent and disordered dynamic transfer of carbon flow at the node, which can easily lead to chaos in carbon flow-electricity coordinated regulation (such as source-load mismatch), disrupting the energy-carbon flow balance at the node and thus reducing node stability. A higher final value of the multi-source data quality comprehensive evaluation indicates more accurate monitoring, assessment, and regulation of node status, effectively supporting the formulation and execution of stability control strategies, enhancing the node's ability to resist disturbances, and thus improving node stability.

[0051] In one specific embodiment, based on the evaluation and targeted optimization of node stability parameters, potential topological defects in the initial graph (node ​​mismatch / association error) are eliminated, and the fidelity and robustness of the reconstructed graph structure are improved. The optimized electro-carbon synergistic topology accurately maps the electric-carbon flow coupling dynamic relationship, constructs a high-confidence analysis basis, and suppresses the risk of misjudgment by the decision-making system.

[0052] Furthermore, the following steps determine whether each node in the initial multi-source electro-carbon synergistic spectrum is qualified: The stability index of each node in the initial multi-source electro-carbon synergistic spectrum is compared with the preset stability threshold of the node in the simulation database. If the stability index of a node in the initial multi-source electro-carbon synergistic spectrum is greater than or equal to the stability threshold, the node is considered qualified and marked as a qualified node. If the stability index of a node in the initial multi-source electro-carbon synergistic spectrum is less than the stability threshold, the node is considered unqualified and marked as a node to be optimized. The nodes to be optimized are then identified, and the nodes in the initial multi-source electro-carbon synergistic spectrum are optimized accordingly. The aforementioned stability threshold represents the minimum value of the stability index of each node in the initial multi-source electro-carbon synergistic spectrum within a specified range.

[0053] Specifically, the optimization process involves optimizing each node in the initial multi-source electrocarbon co-processing graph. The optimization process is as follows: based on the stability threshold of the graph nodes, the stability deviation value of each node is obtained. Based on the stability deviation value of each node, the number of redundant connections for each node in the initial electrocarbon co-processing graph is directly matched to increase the number of redundant connections for each node. This enhances the "multi-path association" capability between nodes and other nodes in the electrocarbon co-processing graph, and also improves the redundancy of data interaction between nodes (such as parallel transmission of multi-source verification data). This reduces node coordination failures caused by single-point data distortion and strengthens node stability in the graph from the topology level. Based on the stability deviation value of each node in the graph, a tie-line power reduction coefficient is matched to reduce the tie-line power of the initial electrocarbon coordination graph, thereby reducing the actual transmission power of the tie-line and making it operate in a safer load range. This can directly reduce the risk of physical faults such as line overheating and voltage drop caused by excessive power. Especially when the node stability deviation is large, reducing the power can weaken the conduction intensity of disturbances on the tie-line and prevent local instability from causing a chain reaction.

[0054] It should be explained that the stability deviation value of each node in the graph refers to the result of subtracting the stability index of each node in the initial multi-source electro-carbon synergistic graph from the stability threshold of the graph node. The above-mentioned matching of the stability deviation value of each node directly determines the number of redundant connections increased for each node in the initial electro-carbon synergistic graph. The specific matching process is as follows: the mapping relationship between the stability deviation value range of each node and the number of redundant connections increased for each node is pre-stored in the simulation database. The obtained stability deviation value of each node is input into the simulation database. Based on the preset mapping rules, the simulation database can match the corresponding number of redundant connections increased for each node. The obtained number of redundant connections increased for each node is added to the original number of redundant connections for each node, and the final result is the number of redundant connections that should be adjusted for each node.

[0055] The above-mentioned tie-line power reduction coefficient is matched based on the stability deviation value of each node in the graph. The specific matching process is as follows: the simulation database stores the tie-line power reduction coefficient corresponding to the interval of the stability deviation value of each node in the graph. The obtained stability deviation value of each node in the graph is input into the simulation database to determine its interval. The tie-line power reduction coefficient corresponding to this interval is the required reduction coefficient. The obtained tie-line power reduction coefficient is multiplied by the original tie-line power, and the result is the tie-line power that needs to be adjusted. The tie-line power reduction coefficient is less than 1, which indicates that the tie-line power needs to be reduced by a certain percentage.

[0056] Obtain the stability index of each node in the optimized initial multi-source electro-carbon synergistic spectrum, mark it as the second-order stability index of each node in the initial multi-source electro-carbon synergistic spectrum, and determine whether to issue an early warning for each node in the initial multi-source electro-carbon synergistic spectrum.

[0057] In one specific embodiment, a multidimensional analysis framework based on optimized graphs reveals the electricity-carbon coupling feedback mechanism (such as the dynamic correlation of power flow-carbon flow paths), producing decision knowledge that can directly drive the formulation of energy planning and emission reduction strategies. This framework maps abstract topology into actionable insights, enhances the synergistic effectiveness of power systems and carbon governance, and accelerates the process of low-carbon energy transition.

[0058] like Figure 6The flowchart of a method for stabilizing nodes in an initial multi-source electro-carbon co-location graph provided by an embodiment of the present invention is shown. After constructing the initial electro-carbon co-location graph, the stability index of each node in the graph is obtained. Then, it is determined whether the stability index of each node is greater than or equal to the stability threshold of the graph node. If it is, it is marked as a qualified node; if not, it is marked as a node to be optimized. Based on the stability deviation value of each node, the redundant connection number of each node in the initial electro-carbon co-location graph is increased, the tie-line power is reduced, and the secondary stability index of each node in the initial multi-source electro-carbon co-location graph is obtained. For the secondary stability index of each node in the initial multi-source electro-carbon co-location graph, it is determined whether it is greater than or equal to the stability threshold of the graph node. If it is, it is marked as a qualified node; otherwise, it is marked as an unqualified node and an early warning is issued.

[0059] Furthermore, the determination of whether to issue an early warning for each node in the initial multi-source electro-carbon synergistic spectrum is as follows: The quadratic stability index of each node in the initial multi-source electro-carbon synergistic spectrum is compared with the node stability threshold. If the quadratic stability index of a node in the initial multi-source electro-carbon synergistic spectrum is greater than or equal to the node stability threshold, then no early warning is issued for that node, and the node is marked as a qualified node. If the quadratic stability index of a node in the initial multi-source electro-carbon synergistic spectrum is less than the node stability threshold, then an early warning is issued for that node, and the node is marked as an unqualified node. The aforementioned early warning for that node in the initial multi-source electro-carbon synergistic spectrum refers to a pop-up notification on the background monitoring interface, which is simultaneously recorded in the node risk file for periodic review by maintenance personnel.

[0060] The total number of non-compliant nodes in the initial multi-source electro-carbon synergistic spectrum is counted. If the total number of non-compliant nodes is greater than or equal to the preset number of non-compliant nodes in the simulation database, the initial multi-source electro-carbon synergistic spectrum is optimized and an early warning is issued. At the same time, it is marked as the multi-source electro-carbon synergistic optimization spectrum. If the total number of non-compliant nodes is less than the preset number of non-compliant nodes in the simulation database, the initial multi-source electro-carbon synergistic spectrum is updated and marked as the multi-source electro-carbon synergistic optimization spectrum.

[0061] The total number of non-compliant nodes in the initial multi-source electro-carbon synergistic map, as described above, can be obtained by automatically scanning and identifying all node IDs marked with "non-compliant node" using the node tag retrieval function of the map management module, triggering a built-in counter to accumulate the count of eligible nodes. The optimization and early warning of the initial multi-source electro-carbon synergistic map mentioned above refers to first determining the distribution status of non-compliant nodes. If non-compliant nodes are clustered (e.g., multiple nodes in a certain region are simultaneously unstable), the map topology of that region needs to be locally reconstructed, for example, by increasing the redundant connection density of hub nodes within the region to form a "stable core." The "core-radial support" structure brings unstable nodes within the cluster into the coverage of the stable core. Conversely, in non-cluster distributions, the impact of unqualified nodes on the overall system is limited. Therefore, optimized resources (such as the cost of redundant connection construction) are prioritized for allocation to "high-priority distributed nodes" (such as nodes bearing core loads), while low-priority nodes (such as secondary edge nodes) can be optimized using "gradual optimization" (such as adding redundant connections in stages) to avoid resource waste. Early warning refers to pushing early warning information to the dispatch center and carbon management department. The aforementioned update of the initial multi-source electric carbon coordination map refers to the real-time update of the operational data of each node after adjustment.

[0062] like Figure 7 The flowchart of a method for generating a multi-source electro-carbon synergistic optimization map provided in this embodiment of the invention is shown. The process involves counting the total number of non-compliant nodes, then determining whether the total number of non-compliant nodes is greater than or equal to the defined number of non-compliant nodes. If so, the initial electro-carbon synergistic map is optimized and an alert is issued; otherwise, the initial electro-carbon synergistic map is updated. Whether optimizing the initial electro-carbon synergistic map and issuing an alert, or updating the initial electro-carbon synergistic map, it will ultimately be marked as an electro-carbon synergistic optimization map. After being marked as an electro-carbon synergistic optimization map, a multi-dimensional analysis framework is constructed, followed by analysis and application of the electro-carbon coupling relationship, and finally, the process ends.

[0063] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0064] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0065] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0067] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a multi-source electric carbon synergistic map based on electric topology features, characterized in that, The method includes: Step 1: Collect multi-source heterogeneous data on power topology characteristics and carbon emissions and integrate them into a multi-source heterogeneous dataset. Analyze the characteristic parameters of the multi-source heterogeneous dataset to determine whether the quality of the multi-source heterogeneous dataset meets the standards. Based on the qualified multi-source heterogeneous dataset, construct an initial multi-source power-carbon synergy map. Step 2: Obtain the stable operating parameters of each node in the initial multi-source electro-carbon synergistic spectrum, determine whether each node in the initial multi-source electro-carbon synergistic spectrum is qualified, and count the unqualified nodes in the initial multi-source electro-carbon synergistic spectrum, thereby optimizing the initial multi-source electro-carbon synergistic spectrum. After optimization, it is marked as the electro-carbon synergistic optimization spectrum. Step 3: Construct a multi-dimensional analysis framework based on the electro-carbon synergistic optimization map to analyze and apply the electro-carbon coupling relationship.

2. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 1, characterized in that, The specific analysis process for the feature parameters of the multi-source heterogeneous dataset is as follows: The characteristic parameters of the multi-source heterogeneous dataset include the timestamp deviation factor, the spatial coverage overlap factor, and the power flow measurement error contribution factor. In the simulated database, the importance weights of timestamp deviation, spatial coverage overlap rate, and power flow measurement error factor are set. Based on this, the influence contribution of each factor on the multi-source data quality index is quantified. Finally, the multi-source data quality index is obtained by weighted summation of the influence contributions. The multi-source data quality index represents the overall quality level of the multi-source heterogeneous dataset.

3. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 1, characterized in that, The specific process for determining whether the quality of a multi-source heterogeneous dataset meets the standards is as follows: The multi-source data quality index is compared with the preset multi-source data quality threshold in the simulation database; If the multi-source data quality index is greater than or equal to the multi-source data quality threshold, it is determined that the quality of the multi-source heterogeneous dataset meets the standard, and then an initial electrocarbon synergistic map is constructed. At the same time, a data quality fluctuation coefficient is introduced to determine whether to issue an early warning for data quality fluctuations. The specific process for determining whether to issue an early warning for data quality fluctuations is as follows: the data quality fluctuation coefficient is compared with the preset data quality fluctuation threshold in the simulated database. If the data quality fluctuation coefficient is greater than the data quality fluctuation threshold, an early warning for data quality fluctuations is issued. If the data quality fluctuation coefficient is less than or equal to the data quality fluctuation threshold, no early warning for data quality fluctuations is issued. If the multi-source data quality index is less than the multi-source data quality threshold, the multi-source heterogeneous dataset is deemed to be of substandard quality, and an adjustment is made to the multi-source heterogeneous dataset.

4. The method for constructing a multi-source electric carbon synergistic map based on electric topology features according to claim 3, characterized in that, The specific adjustment process for the multi-source heterogeneous dataset is as follows: Based on the multi-source data quality index and multi-source data quality threshold, the multi-source data quality deviation value is obtained, and a high-order interpolation is matched from the simulation database based on the multi-source data quality deviation value, thereby improving the time series data in the multi-source heterogeneous dataset; The boundary buffer radius increase factor is determined by matching the quality deviation values ​​of multi-source data, thereby increasing the spatial matching range between power topology nodes and carbon data nodes; Obtain the multi-source data quality index after initial adjustment, label it as the multi-source data secondary quality index, and determine whether to perform secondary optimization on the multi-source heterogeneous dataset.

5. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 4, characterized in that, The specific process for determining whether to perform secondary optimization on the multi-source heterogeneous dataset is as follows: Compare the secondary quality index of multi-source data with the quality threshold of multi-source data; If the secondary quality index of multi-source data is greater than or equal to the multi-source data quality threshold, then no secondary optimization is performed on the multi-source heterogeneous dataset, and an initial electrocarbon synergistic map is constructed. At the same time, a data quality fluctuation coefficient is introduced to determine whether to issue an early warning for data quality fluctuations. If the secondary quality index of the multi-source data is less than the multi-source data quality threshold, then the multi-source heterogeneous dataset is optimized in a secondary manner. The specific optimization process is as follows: based on the secondary quality index and the multi-source data quality threshold, the secondary quality deviation value of the multi-source data is obtained. Based on the secondary quality deviation value of the multi-source data, the wavelet decomposition is matched to increase the number of layers, thereby increasing the wavelet decomposition layer of the power signal. Simultaneously, the data association entropy of the multi-source heterogeneous dataset is obtained and compared with the preset data association entropy threshold in the simulation database. If the data association entropy is less than or equal to the data association entropy threshold, the multi-source heterogeneous dataset is continuously monitored. If the data association entropy is greater than the data association entropy threshold, the high correlation feature selection weight increase coefficient is matched based on the secondary quality deviation value of the multi-source data, thereby increasing the high correlation feature selection weight of the power topology. At the same time, constraint equations are introduced to strengthen the association of key features of the power topology. Obtain the multi-source data quality index after secondary optimization, label it as the multi-source data tertiary quality index, and determine whether to issue an early warning for the quality of multi-source heterogeneous datasets.

6. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 5, characterized in that, The specific process for determining whether to issue an early warning regarding the quality of multi-source heterogeneous datasets is as follows: Compare the three quality indices of multi-source data with the quality threshold of multi-source data; If the three quality indices of multi-source data are greater than or equal to the quality threshold of multi-source data, no warning will be issued for the quality of multi-source heterogeneous datasets. Then, an initial electrocarbon synergistic map will be constructed. At the same time, a data quality fluctuation coefficient will be introduced to determine whether to issue a warning for data quality fluctuations. If the three-dimensional quality index of multi-source data is less than the multi-source data quality threshold, then an early warning will be issued for the quality of the multi-source heterogeneous dataset and the initial electrocarbon synergistic map will not be constructed. Obtain the cumulative number of times the multi-source data quality index is continuously less than the multi-source data quality threshold, and compare the cumulative number with the preset threshold number in the simulation database; If the cumulative number of times is greater than or equal to the defined number of times, then the multi-source heterogeneous dataset is re-acquired and the quality of the multi-source heterogeneous dataset is re-evaluated. If the cumulative number of times is less than the defined number of times, the multi-source heterogeneous dataset will be continuously monitored.

7. The method for constructing a multi-source electric carbon synergistic map based on electric topology features according to claim 1, characterized in that, The specific analysis process for obtaining the stable operating parameters of each node in the initial multi-source electrocarbon synergistic spectrum is as follows: The stable operating parameters include the coupling node connectivity factor of each node in the initial multi-source electric carbon co-processing spectrum, the node power injection fluctuation factor of each node in the initial multi-source electric carbon co-processing spectrum, and the node carbon flow direction transformation frequency factor of each node in the initial multi-source electric carbon co-processing spectrum. Obtain the final value of the comprehensive quality assessment of multi-source data; Using the importance weights defined in the simulated database, the contribution values ​​of the coupling node connectivity factor, node power injection fluctuation factor, node carbon flow direction change frequency factor, and the final value of the comprehensive evaluation of multi-source data quality to the stability index of the map node are calculated. These contribution values ​​are weighted and combined to generate the stability index of each map node, where the stability index of each map node represents the operational stability of each node in the initial electrocarbon co-location map.

8. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 1, characterized in that, The specific process for determining whether each node in the initial multi-source electrocarbon synergistic spectrum is qualified is as follows: The stability index of each node in the initial multi-source electrocarbon synergistic spectrum is compared with the preset stability threshold of the spectrum nodes in the simulation database. If the stability index of a node to which the initial multi-source electro-carbon synergistic spectrum belongs is greater than or equal to the stability threshold of the node, then the node to which the initial multi-source electro-carbon synergistic spectrum belongs is deemed qualified and marked as a qualified node. If the stability index of a node in the initial multi-source electro-carbon synergistic map is less than the stability threshold of the node, then the node in the initial multi-source electro-carbon synergistic map is deemed unqualified and marked as a node to be optimized. Each node to be optimized is identified, thereby optimizing each node in the initial multi-source electro-carbon synergistic spectrum.

9. The method for constructing a multi-source electric carbon synergistic map based on electric topology features according to claim 8, characterized in that, The optimization process for each node to be optimized in the initial multi-source electrocarbon synergistic spectrum is as follows: Based on the stability index and stability threshold of each node in the initial multi-source electro-carbon co-location graph, the stability deviation value of each node is obtained. Based on the stability deviation value of each node, the number of redundant connections increased for each node in the initial electro-carbon co-location graph is directly matched, thereby increasing the number of redundant connections for each node in the initial electro-carbon co-location graph. Based on the stability deviation value of each node, the tie-line power reduction coefficient is matched, thereby reducing the tie-line power of the initial electro-carbon co-location graph. Obtain the stability index of each node in the optimized initial multi-source electro-carbon synergistic spectrum, mark it as the second-order stability index of each node in the initial multi-source electro-carbon synergistic spectrum, and determine whether to issue an early warning for each node in the initial multi-source electro-carbon synergistic spectrum.

10. The method for constructing a multi-source electric carbon synergistic map based on power topology features according to claim 9, characterized in that, The specific process for determining whether to issue an early warning for each node in the initial multi-source electrocarbon synergistic spectrum is as follows: The quadratic stability index of each node in the initial multi-source electrocarbon synergistic spectrum is compared with the spectrum. Compare node stability thresholds; If the second-order stability index of a certain node in the initial multi-source electro-carbon synergistic spectrum is greater than or equal to the stability threshold of the node, then no warning will be issued for that node in the initial multi-source electro-carbon synergistic spectrum, and the node will be marked as a qualified node. If the second-order stability index of a node in the initial multi-source electro-carbon synergistic spectrum is less than the stability threshold of the node, an early warning will be issued for that node in the initial multi-source electro-carbon synergistic spectrum, and the node will be marked as an unqualified node. The total number of non-compliant nodes in the initial multi-source electro-carbon synergistic spectrum is counted. If the total number of non-compliant nodes is greater than or equal to the preset number of non-compliant nodes in the simulation database, the initial multi-source electro-carbon synergistic spectrum is optimized and an early warning is issued. At the same time, it is marked as the multi-source electro-carbon synergistic optimization spectrum. If the total number of non-compliant nodes is less than the preset number of non-compliant nodes in the simulation database, the initial multi-source electro-carbon synergistic spectrum is updated and marked as the multi-source electro-carbon synergistic optimization spectrum.

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