A method and apparatus for determining the carbon property factors of electricity

Abstract

This application discloses a method and apparatus for determining power carbon attribute factors, comprising: acquiring power carbon attribute factors of one or more power supply nodes associated with the power grid; for each power supply node, determining its corresponding geographic power supply range in a geographic information system, and storing each geographic power supply range in a database in the form of corresponding spatial surface data, while using the power carbon attribute factor as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the association between geographic power supply range and power carbon attribute factors; acquiring the geographic spatial location of a specified terminal consumption point; performing a spatial query in the spatial database based on the geographic spatial location of the specified terminal consumption point to determine that the specified terminal consumption point belongs to one or more geographic power supply ranges; and determining the power carbon attribute factors associated with the geographic power supply range to which the specified terminal consumption point belongs as the power carbon attribute factors applicable to the specified terminal consumption point.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and apparatus for determining the carbon property factor of electricity. Background Technology

[0002] With the advancement of the "dual carbon" goals, applications such as corporate carbon accounting, product carbon footprint accounting, industrial park / building carbon management, and green electricity consumption certification all require the support of refined decision-making based on electricity carbon attribute factors at the "point of use" level.

[0003] In existing technologies, one common approach is to uniformly assign values ​​to the provincial, regional, or annual average electricity carbon attribute factors. While this is simple to implement, it has low spatial resolution and makes it difficult to reflect the differences between different power supply nodes or different grid areas. Another approach is to infer the power supply source of the electricity consumption point and calculate the electricity carbon attribute factor based on the grid topology, power generation structure, and power flow tracing. Theoretically, this is more precise, but it is highly dependent on grid operation data, calculation models, and computing power, resulting in high engineering implementation costs. It is also difficult to balance real-time performance and maintainability when facing a large number of end users. Some other approaches rely on manual rules or coarse-grained geographical division for matching, which is easily affected by factors such as inaccurate boundaries, changes in power supply range, and maintenance difficulties, leading to long-term operational instability. Summary of the Invention

[0004] This specification provides one or more embodiments of a method and apparatus for determining the carbon attribute factor of an electric power source, which is used to solve the technical problems mentioned in the background art.

[0005] One or more embodiments of this specification employ the following technical solutions: This specification provides one or more embodiments of a method for determining the carbon attribute factor of an electrical system, the method comprising: Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, the power supply nodes being used to characterize key nodes on the power supply side, the power carbon attribute factors including power carbon footprint factors and / or power carbon emission factors; For each power supply node, the corresponding geographic power supply range is determined in the geographic information system, and each geographic power supply range is stored in the database in the form of corresponding spatial surface data. At the same time, the power carbon attribute factor is used as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographic power supply range and power carbon attribute factor. The geographic power supply range is used to describe the geographic area covered by each power supply node in space for external power supply. Obtain the geospatial location of the specified terminal consumption point; Based on the geospatial location of the designated terminal consumption point, a spatial query is performed in the spatial database to determine whether the designated terminal consumption point belongs to one or more of the geographical power supply ranges. Based on the spatial query results, the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the electricity carbon attribute factor applicable to the specified terminal consumption point.

[0006] It should be noted that this method no longer attempts to perform complex real-time power grid tracing calculations for each power consumption point in every query. Instead, it innovatively binds the core correspondence of "power supply node - carbon factor" with the spatial entity of "geographic power supply range" in a pre-emptive and structured manner, and constructs a spatial database that can be queried efficiently.

[0007] In this way, when it is necessary to determine the electricity carbon attribute factors of a massive number of end-consumer points, the computational challenge that originally relied on complex power grid models and real-time data is transformed into a mature and efficient geospatial inclusion relationship query problem. The system only needs to obtain the geographical location of the consumption points and, through a quick spatial query, can directly obtain or weighted calculate the refined factors that match their actual power supply relationship from the database, without having to call a huge power grid model for real-time simulation.

[0008] Therefore, this method successfully strikes a balance between the "low-precision but easy-to-use" average value scheme and the "high-precision but difficult-to-use" real-time calculation scheme, achieving a spatial resolution far superior to the provincial average at an acceptable engineering cost (building and maintaining the spatial database). Simultaneously, it completely avoids the heavy reliance on data and computing power inherent in real-time power flow calculations. This allows it to stably, efficiently, and sustainably support the urgent need for refined "point-of-use" electricity carbon accounting in massive terminal scenarios such as enterprises, products, and industrial parks.

[0009] Furthermore, the geographical power supply range is determined based on one or more of the following: power grid topology, historical operating data, and power flow analysis results, to ensure that the power supply range delineation results match the actual power supply relationship.

[0010] It should be noted that this method abandons subjective and static geographical division, and instead uses objective data reflecting the physical connection, past operating patterns, and electrical characteristics of the power grid as the basis for defining the power supply range. In this way, the final generated "geographical power supply range" can approximate the real-world power supply service area as closely as possible, thus ensuring the basic reliability of all subsequent spatial queries and factor matching work from the source.

[0011] Therefore, this technical solution uses objective data sources closely related to the actual operation of the power grid to delineate the geographical power supply range, fundamentally overcoming the inaccuracy and rigidity of manual or coarse-grained division methods. This ensures that the constructed "power supply range-carbon factor" spatial mapping relationship has a solid engineering basis and reliability, laying a reliable data foundation for the subsequent accurate and stable assignment of carbon attribute factors to the electricity at the end-consumer point.

[0012] Furthermore, obtaining the geospatial location of the specified terminal consumption point includes: When the designated terminal consumption point is a point-like object, its geographical location is represented by latitude and longitude coordinates; When the designated terminal consumption point is a planar area object or a collection of multiple consumption points, its geographic spatial location is represented by the area boundary information, the coordinates of the center point, or the coordinates of multiple points.

[0013] It should be noted that the core value of this method lies in its systematic definition and support for the diversity of spatial forms of end-consumer points. It clearly distinguishes between "point objects" (such as a single electricity meter or charging pile) and "area objects or sets" (such as an entire factory park, commercial buildings, or a collection of multiple branches), and specifies the most appropriate and complete way of expressing geospatial information for each form (for example, using "area boundary information" to accurately describe an area, rather than just using a central point).

[0014] Therefore, by establishing a standardized geospatial location input specification corresponding to the physical form of consumption points, this technical solution ensures that the method can flexibly, accurately, and unambiguously receive and process spatial information of various terminal consumption points, from single devices to complex regional sets. This fundamentally guarantees the input accuracy and wide applicability of subsequent spatial query and factor matching operations, thereby meeting the common needs of entities of different scales and forms in the real world for refined electricity carbon accounting.

[0015] Furthermore, the step of determining the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs, based on the spatial query result, as the electricity carbon attribute factor applicable to the specified terminal consumption point, includes: When the spatial query determines that the specified terminal consumption point belongs to only one geographical power supply range, the power carbon attribute factor associated with that geographical power supply range is directly read as the final power carbon attribute factor applicable to the specified terminal consumption point.

[0016] It should be noted that this method addresses the clear and common scenario where "the consumption point belongs to only one geographical power supply range." It stipulates the most direct and deterministic processing rules, directly reading the factors associated with that range as the final result. This design eliminates any unnecessary intermediate calculations or human intervention, transforming the previously established precise mapping relationship between "power supply range and factors" into the final output with minimal overhead (directly reading attributes) after a single deterministic spatial query.

[0017] Therefore, this technical solution, by setting a deterministic rule of "direct reading" for the most common single-attribution scenario, makes the final stage of the entire factor determination process extremely efficient and reliable. This simplifies the operation of assigning factors to massive numbers of terminal consumer points to the greatest extent, ensuring that in most cases, the system can output accurate and consistent results at a near-instantaneous speed, thus providing crucial performance and deterministic guarantees for the large-scale, high-concurrency engineering applications of this method.

[0018] Furthermore, the method also includes: When the geographic location of the designated terminal consumption point belongs to the overlapping area of ​​multiple geographic power supply ranges, the power carbon attribute factors associated with the multiple geographic power supply ranges are weighted according to preset rules or spatial weighting algorithms to determine the final power carbon attribute factor applicable to the designated terminal consumption point.

[0019] It is important to note that the core value of this method lies in providing a structured, computable, and deterministic solution to the pervasive engineering challenge of "overlapping regions." It presupposes the existence of overlap and designs a standardized response mechanism: based on pre-defined objective rules (such as distance attenuation and primary / backup power priority) or quantifiable spatial weighting algorithms, it weights and fuses multiple candidate power carbon attribute factors. This design transforms what might otherwise be a vague boundary problem reliant on manual judgment into an automated, uniquely achievable, and traceable mathematical computation process.

[0020] Therefore, this technical solution, by introducing preset rules and weighted calculation mechanisms for overlapping regions, ensures that the method can systematically and unambiguously process all spatial query results, including complex multiple attribution scenarios. This not only greatly enhances the robustness and applicability of the method in complex real-world power grid scenarios, avoiding computational failures or result jumps caused by boundary ambiguity, but also guarantees that the final output power carbon attribute factors have consistent computational logic and credibility under any circumstances by providing deterministic and interpretable weighted results, thereby supporting the stable and reliable operation of refined carbon accounting in all scenarios.

[0021] Furthermore, when the geographic location of the designated terminal consumption point belongs to an overlapping area of ​​multiple geographic power supply ranges, the preset rule or spatial weight algorithm includes: Distance attenuation weight calculated based on the distance between the designated terminal consumption point and the boundary of each geographical power supply range; The weight of power supply contribution ratio for each geographical power supply area determined based on historical power supply data. Alternatively, the priority weight of the primary and backup supply relationship determined based on the power grid operation mode.

[0022] It is important to note that the core value of this method lies in providing a variety of quantifiable and executable rules based on objective data and power grid operation patterns for the crucial operation of "weighted calculation." It explicitly outlines three weight determination paths: "distance attenuation weight" based on spatial geometric characteristics, "power supply contribution ratio weight" based on historical factual data, and "primary and backup power priority weight" based on power grid operation logic. These rules are all derived from measurable and traceable objective parameters (such as distance, historical power generation, and dispatch relationships), rather than subjective experience.

[0023] Therefore, this technical solution transforms the crucial step of weighted calculation of overlapping region factors from a potentially ambiguous and subjective operation into a rigorously defined, automated, and transparently verifiable scientific calculation process by specifying a series of explicit weighting algorithms based on objective measurements and power grid operation laws. This ensures that even in complex scenarios with multiple power supply affiliations, the final determined power carbon attribute factors have solid objective basis, high consistency of results, and good engineering interpretability, thus fundamentally guaranteeing the reliability and credibility of the output results of this method in various practical boundary scenarios.

[0024] Furthermore, the method also includes: The geographic power supply range and its associated power carbon attribute factors are rendered or visualized in a hierarchical manner on a geographic information system map or other visualization platform.

[0025] It should be noted that this method, by introducing a "hierarchical rendering or visualization" step, transforms the structured data and spatial relationships generated by all the aforementioned technical steps into a directly perceptible geographic information layer. It intuitively maps different geographic power supply ranges and their corresponding electricity carbon attribute factors onto the geographic base map using visual elements such as color, color gradation, and area filling.

[0026] Therefore, this technical solution transforms abstract data associations and spatial query results into intuitive, spatially identifiable visual information, making the complex spatial distribution pattern of electricity carbon attributes readily apparent. This not only greatly improves the efficiency and depth of understanding of massive data analysis results, but more importantly, it provides users (such as grid planners, regional carbon managers, and corporate energy efficiency departments) with a visual tool for direct spatial location-based insight, comparison, and decision-making. Ultimately, it translates refined carbon accounting data into intuitive capabilities that support low-carbon planning, electricity strategy optimization, and management decisions.

[0027] Furthermore, the method also includes an update and maintenance step: The spatial database stores version identifiers and valid time periods for each geographical power supply range and its associated power carbon attribute factor records; In response to adjustments in the power grid structure, changes in power supply relationships, or updates to power carbon attribute factor data, the spatial surface data or its associated attribute values ​​in the spatial database are incrementally updated or fully updated.

[0028] It should be noted that the technical solution described in this method is a key safeguard mechanism designed to address the aforementioned core challenges. By "recording version identifiers and effective time periods," it endows each piece of data with a time dimension and traceability, transforming it from a static snapshot into a manageable dynamic data asset. By defining clear triggering conditions (power grid structure adjustments, changes in power supply relationships, factor data updates) and response actions (incremental or full version updates), it establishes a sustainable data maintenance process that is linked to external changes.

[0029] Therefore, this technical solution, by introducing versioning management and a responsive update mechanism, upgrades the method from a static model that relies on precise mapping at a "certain moment" to a lifecycle system capable of co-evolving with dynamically changing power grid physical structures and carbon data. This not only solves the fundamental problem of result distortion caused by outdated data, but also ensures that the constructed "power supply range-carbon factor" spatial mapping relationship can continuously and reliably reflect the latest state of the real world. This supports the accuracy, stability, and maintainability of the entire refined power carbon accounting system in long-term operation, achieving a key leap from "one-time calculation" to "sustainable service."

[0030] Furthermore, the update and maintenance steps support maintaining and associating spatial range data and their electricity carbon attribute factors for the same geographical power supply range under different time versions, so as to support historical electricity carbon attribute factor tracing or future scenario analysis for the specified terminal consumption point.

[0031] It's important to note that the core value of this method lies in endowing the update and maintenance steps with time-dimensional data management capabilities. Instead of simply replacing old data with new data, it maintains and correlates complete data for the same geographic power supply range across different time versions, thus constructing a coherent and queryable historical data sequence within the system. This ensures that each geographic power supply range and its associated carbon factor have a timeline-based status record.

[0032] Therefore, this technical solution upgrades the method from a static tool that only provides a snapshot of the "current moment" to a dynamic analysis system with time-based tracking and simulation capabilities by establishing and maintaining data associations with time versions. This allows users to not only obtain real-time carbon factors at consumption points based on the latest data, but also accurately query carbon factors at any historical moment for compliance tracking and auditing. Furthermore, it enables users to perform "carbon factor simulation analysis under future power grid scenarios" by combining different versions of data. This greatly expands the application value of this method in in-depth analysis scenarios such as carbon footprint backtracking, emission reduction effect assessment, and low-carbon power grid planning, solving the key challenges of long-term data consistency and traceability in dynamic environments.

[0033] This specification provides one or more embodiments of an electrical carbon attribute factor determination device, comprising: At least one processor and bus; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, the power supply nodes being used to characterize key nodes on the power supply side, the power carbon attribute factors including power carbon footprint factors and / or power carbon emission factors; For each power supply node, the corresponding geographic power supply range is determined in the geographic information system, and each geographic power supply range is stored in the database in the form of corresponding spatial surface data. At the same time, the power carbon attribute factor is used as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographic power supply range and power carbon attribute factor. The geographic power supply range is used to describe the geographic area covered by each power supply node in space for external power supply. Obtain the geospatial location of the specified terminal consumption point; Based on the geospatial location of the designated terminal consumption point, a spatial query is performed in the spatial database to determine whether the designated terminal consumption point belongs to one or more of the geographical power supply ranges. Based on the spatial query results, the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the electricity carbon attribute factor applicable to the specified terminal consumption point.

[0034] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: This method no longer attempts to perform complex real-time power grid tracing calculations for each power consumption point in every query. Instead, it innovatively binds the core correspondence of "power supply node - carbon factor" with the spatial entity of "geographic power supply range" in a pre-emptive and structured manner, and constructs a spatial database that can be queried efficiently.

[0035] In this way, when it is necessary to determine the electricity carbon attribute factors of a massive number of end-consumer points, the computational challenge that originally relied on complex power grid models and real-time data is transformed into a mature and efficient geospatial inclusion relationship query problem. The system only needs to obtain the geographical location of the consumption points and, through a quick spatial query, can directly obtain or weighted calculate the refined factors that match their actual power supply relationship from the database, without having to call a huge power grid model for real-time simulation.

[0036] Therefore, this method successfully strikes a balance between the "low-precision but easy-to-use" average value scheme and the "high-precision but difficult-to-use" real-time calculation scheme, achieving a spatial resolution far superior to the provincial average at an acceptable engineering cost (building and maintaining the spatial database). Simultaneously, it completely avoids the heavy reliance on data and computing power inherent in real-time power flow calculations. This allows it to stably, efficiently, and sustainably support the urgent need for refined "point-of-use" electricity carbon accounting in massive terminal scenarios such as enterprises, products, and industrial parks. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a method for determining the carbon property factor of an electric power source, provided for one or more embodiments of this specification; Figure 2 This is a schematic diagram of a device for determining the carbon property factor of an electric power source, provided for one or more embodiments of this specification. Detailed Implementation

[0038] This specification provides a method and apparatus for determining the carbon attribute factor of electricity.

[0039] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0040] Existing technologies face a fundamental contradiction: the "provincial / regional average factor assignment" method sacrifices spatial resolution and accuracy, but is easy to implement; while the "fine-grained tracing based on power grid topology and power flow" method is theoretically more accurate, but its high requirements for data, models and computing power result in high engineering costs and make it difficult to scale up to a large number of users and maintain in the long term.

[0041] Figure 1 This diagram illustrates a process for determining the carbon property factor of an electrical system, provided for one or more embodiments of this specification. This process can be executed by a system for determining the carbon property factor of an electrical system. Certain input parameters or intermediate results in the process can be manually adjusted to help improve accuracy.

[0042] The method flow steps of the embodiments in this specification are as follows: S101, Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, wherein the power supply nodes are used to characterize key nodes on the power supply side, and the power carbon attribute factors include power carbon footprint factors and / or power carbon emission factors.

[0043] In the embodiments of this specification, a list of power supply nodes that need to be included in the accounting scope can be obtained from the dispatching department, planning department, or asset management system of the power grid company, such as specific power plants, hub substations, converter stations, etc.

[0044] It connects with carbon accounting systems, energy big data platforms, or authoritative carbon factor databases, and uses node codes (such as power plant codes, substation IDs) or names as key association fields to obtain the power carbon footprint factor (covering carbon emissions throughout the entire life cycle) and / or power carbon emission factor (mainly covering carbon emissions during the operation phase) corresponding to each power supply node.

[0045] The obtained "power supply node identifier - power carbon attribute factor value" pairs are stored and managed in a structured form (such as a database table or JSON file) to prepare for subsequent association with geospatial information.

[0046] S102, for each power supply node, determine its corresponding geographical power supply range in the geographic information system, and store each geographical power supply range in the database in the form of corresponding spatial surface data. At the same time, use the power carbon attribute factor as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographical power supply range and power carbon attribute factor. The geographical power supply range is used to describe the geographical area covered by each power supply node in space for external power supply.

[0047] In the embodiments described in this specification, a geographic information system platform is used, combined with a power grid topology map, historical load data, feeder power supply range information of the distribution automation system, and planning drawings, with the assistance of power grid technicians, to delineate the geographical power supply range of each power supply node on a map. This range is a continuous geographical area.

[0048] Each geographic power supply area outlined above is created as a spatial polygon data feature in GIS. Each polygon data feature is assigned a unique ID and associated with the corresponding power supply node identifier in step S101.

[0049] In the attribute table of each spatial surface data feature created above, add a new field (such as carbon_factor) and fill in the power carbon attribute factor value corresponding to the power supply node. Finally, import or store all spatial surface data with attribute information into a spatial database (such as a database managed by PostGIS or GeoServer) to complete the construction of the "Geographic Power Supply Range Spatial Surface - Power Carbon Attribute Factor" relationship database.

[0050] S103, obtain the geospatial location of the specified terminal consumption point.

[0051] In the embodiments of this specification, the geographical location of the terminal consumption point is obtained in different ways depending on the type of the terminal consumption point.

[0052] For point objects (such as a single factory or building): obtain a coordinate point by address resolution (geocoding) or by directly reading the latitude and longitude coordinates registered in its electricity meter file.

[0053] For area objects (such as a park or an administrative region): obtain the coordinate string of the polygons governing its jurisdiction.

[0054] For a collection of multiple consumption points (such as all branches of a group company): obtain a list of coordinates of all its individual points, or the smallest boundary polygon that can cover these points.

[0055] The coordinates or boundary information obtained above are uniformly converted into the same geographic coordinate system as the spatial database in step S102 to ensure the accuracy of spatial queries.

[0056] S104, based on the geographic location of the designated terminal consumption point, perform a spatial query in the spatial database to determine that the designated terminal consumption point belongs to one or more of the geographic power supply ranges.

[0057] In the embodiments of this specification, the geographic spatial location (point coordinates or polygon) of the terminal consumption point prepared in step S103 is used as the query input in the query interface of the geographic information system or the spatial database.

[0058] The system uses a spatial database engine to automatically perform spatial inclusion relationship determination calculations between "points (or surfaces) and surfaces", that is, to determine which geographical power supply range spatial surfaces the location of the consumption point falls within in step S102.

[0059] The query engine returns a list of spatial surface IDs for all geographic power supply ranges containing that consumption point. The result may be a single (single attribution) or multiple (overlapping area attribution).

[0060] S105, based on the spatial query result, the power carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the power carbon attribute factor applicable to the specified terminal consumption point.

[0061] In the embodiments described in this specification, the system receives the list of geographic power supply range IDs returned in step S104.

[0062] If the list has only one ID (single affiliation): the system directly reads the associated power carbon attribute factor value from the spatial database based on this ID and outputs this value as the final result.

[0063] If the list has multiple IDs (overlapping area attribution): The system reads multiple power carbon attribute factors associated with all these IDs. Then, according to preset rules (e.g., taking the average value, taking the main power supply factor) or a more complex spatial weighting algorithm (e.g., setting weights based on the reciprocal of the distance from the consumption point to the boundary of each power supply range), these factor values ​​are weighted and calculated to obtain a comprehensive factor value as the final output.

[0064] The final determined electricity carbon attribute factor value is returned to the user or upstream application system to complete the factor assignment for the specified terminal consumption point.

[0065] It should be noted that this method no longer attempts to perform complex real-time power grid tracing calculations for each power consumption point in every query. Instead, it innovatively binds the core correspondence of "power supply node - carbon factor" with the spatial entity of "geographic power supply range" in a pre-emptive and structured manner, and constructs a spatial database that can be queried efficiently.

[0066] In this way, when it is necessary to determine the electricity carbon attribute factors of a massive number of end-consumer points, the computational challenge that originally relied on complex power grid models and real-time data is transformed into a mature and efficient geospatial inclusion relationship query problem. The system only needs to obtain the geographical location of the consumption points and, through a quick spatial query, can directly obtain or weighted calculate the refined factors that match their actual power supply relationship from the database, without having to call a huge power grid model for real-time simulation.

[0067] Therefore, this method successfully strikes a balance between the "low-precision but easy-to-use" average value scheme and the "high-precision but difficult-to-use" real-time calculation scheme, achieving a spatial resolution far superior to the provincial average at an acceptable engineering cost (building and maintaining the spatial database). Simultaneously, it completely avoids the heavy reliance on data and computing power inherent in real-time power flow calculations. This allows it to stably, efficiently, and sustainably support the urgent need for refined "point-of-use" electricity carbon accounting in massive terminal scenarios such as enterprises, products, and industrial parks.

[0068] Furthermore, the geographical power supply range is determined based on one or more of the following: power grid topology, historical operating data, and power flow analysis results, to ensure that the power supply range delineation results match the actual power supply relationship.

[0069] It should be noted that this method abandons subjective and static geographical division, and instead uses objective data reflecting the physical connection, past operating patterns, and electrical characteristics of the power grid as the basis for defining the power supply range. In this way, the final generated "geographical power supply range" can approximate the real-world power supply service area as closely as possible, thus ensuring the basic reliability of all subsequent spatial queries and factor matching work from the source.

[0070] Therefore, this technical solution uses objective data sources closely related to the actual operation of the power grid to delineate the geographical power supply range, fundamentally overcoming the inaccuracy and rigidity of manual or coarse-grained division methods. This ensures that the constructed "power supply range-carbon factor" spatial mapping relationship has a solid engineering basis and reliability, laying a reliable data foundation for the subsequent accurate and stable assignment of carbon attribute factors to the electricity at the end-consumer point.

[0071] Furthermore, in the process of obtaining the geospatial location of the specified terminal consumption point, the method flow steps of this embodiment are as follows: S201, when the designated terminal consumption point is a point object, its geographical location is represented by latitude and longitude coordinates.

[0072] In the embodiments described in this specification, the first step is to determine whether the "designated terminal consumption point" to be processed is a point-like object. Typical point-like objects include: an independent electricity meter (such as a factory main meter), an independent building, an independent charging pile, an independent communication base station, etc.

[0073] For consumption points that have been identified as point objects, their latitude and longitude coordinates are obtained through one or more of the following methods: Address resolution (geocoding): If the detailed address of the object is known, call the geocoding service interface to convert the text address into the corresponding latitude and longitude coordinates.

[0074] Direct reading: If the object has already had its coordinate information pre-recorded in an existing asset management system, electricity information collection system, or IoT platform, then the latitude and longitude coordinates can be read directly from it.

[0075] Manual plotting: On the map interface of the geographic information system, the operator manually selects the known location, and the system records the coordinates of the point.

[0076] The obtained coordinates are uniformly converted into a coordinate system consistent with that of the entire system (especially the spatial database built in step S102) (such as WGS84, CGCS2000).

[0077] S202, when the designated terminal consumption point is a planar area object or a collection of multiple consumption points, its geographic spatial location is represented by the area boundary information, the coordinates of the center point, or the coordinates of multiple points.

[0078] In the embodiments of this specification, it is determined whether the current "designated terminal consumption point" is a planar area object or a collection of multiple consumption points. Typical examples include: Area objects: a large industrial park, a university campus, an administrative village, and a commercial complex plot.

[0079] A collection of multiple consumption points: all branches of a group company located in different geographical locations; all user meters under a distribution area.

[0080] Choose the appropriate representation method based on the object type: For areal areas: Obtain their boundary information. This is typically a closed polygon formed by connecting a series of consecutive coordinate points. This boundary information can be extracted and digitized from planning drawings, real estate registration information, administrative division data, or high-resolution satellite imagery.

[0081] For a set of multiple consumption points: obtain a list of coordinates of all members within the set (coordinates of multiple points). Alternatively, to simplify subsequent calculations, the center point (center point coordinates) of the smallest bounding rectangle (also a type of region boundary information) or polygon that covers all member points can be calculated as an approximate representation of its spatial location.

[0082] The obtained boundary polygon coordinate strings, center point coordinates, or lists of multiple point coordinates are also unified into the coordinate system agreed upon by the system and organized in a standard spatial data format (such as GeoJSON or WKT format) so that the spatial database engine can recognize and process them.

[0083] It should be noted that the core value of this method lies in its systematic definition and support for the diversity of spatial forms of end-consumer points. It clearly distinguishes between "point objects" (such as a single electricity meter or charging pile) and "area objects or sets" (such as an entire factory park, commercial buildings, or a collection of multiple branches), and specifies the most appropriate and complete way of expressing geospatial information for each form (for example, using "area boundary information" to accurately describe an area, rather than just using a central point).

[0084] Therefore, by establishing a standardized geospatial location input specification corresponding to the physical form of consumption points, this technical solution ensures that the method can flexibly, accurately, and unambiguously receive and process spatial information of various terminal consumption points, from single devices to complex regional sets. This fundamentally guarantees the input accuracy and wide applicability of subsequent spatial query and factor matching operations, thereby meeting the common needs of entities of different scales and forms in the real world for refined electricity carbon accounting.

[0085] Furthermore, in the process of determining the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs as the electricity carbon attribute factor applicable to the specified terminal consumption point based on the determination result of the spatial query, the method flow steps of this embodiment are as follows: S301, when the spatial query determines that the specified terminal consumption point belongs to only one geographical power supply range, the power carbon attribute factor associated with the geographical power supply range is directly read as the final power carbon attribute factor applicable to the specified terminal consumption point.

[0086] In this embodiment of the specification, the system receives a determination result from the spatial query step (S104). This result is a data list containing unique identifiers for all geographic power supply ranges containing the specified terminal consumption point.

[0087] The system checks the result list obtained in the previous step. If there is one and only one identifier for a geographic power supply range in the list, the system determines that the condition of "belonging to only one geographic power supply range" is met, and then triggers the "direct read" processing flow of S301.

[0088] The system uses the aforementioned unique geographic power supply range identifier (such as ID) as a key query condition to directly initiate a precise query to the spatial database. The purpose of this query is not to perform spatial calculations again, but to quickly retrieve and read the power carbon attribute factor value that has been permanently associated with the spatial surface data in the previous step (S102) based on the ID.

[0089] The system will retrieve the electricity carbon attribute factor values ​​from the spatial database and directly assign them to the current designated end-consumer point as the final factor result suitable for carbon accounting. This result can then be output to the user interface, stored in a results repository, or passed to downstream applications.

[0090] It should be noted that this method addresses the clear and common scenario where "the consumption point belongs to only one geographical power supply range." It stipulates the most direct and deterministic processing rules, directly reading the factors associated with that range as the final result. This design eliminates any unnecessary intermediate calculations or human intervention, transforming the previously established precise mapping relationship between "power supply range and factors" into the final output with minimal overhead (directly reading attributes) after a single deterministic spatial query.

[0091] Therefore, this technical solution, by setting a deterministic rule of "direct reading" for the most common single-attribution scenario, makes the final stage of the entire factor determination process extremely efficient and reliable. This simplifies the operation of assigning factors to massive numbers of terminal consumer points to the greatest extent, ensuring that in most cases, the system can output accurate and consistent results at a near-instantaneous speed, thus providing crucial performance and deterministic guarantees for the large-scale, high-concurrency engineering applications of this method.

[0092] Furthermore, in real-world power grids, there are areas with overlapping power supply ranges, switching between primary and backup power supplies, or complex network structures. This means that some end-consumer points may be located in overlapping or intersecting areas of two or more geographical power supply ranges simultaneously. If the technical solution cannot properly handle such situations, it will force the system to choose between "forcibly assigning a single affiliation (which may be erroneous)" or "being unable to provide a definite result," thereby compromising the completeness and practicality of the method. The method flow steps of the embodiments in this specification are as follows: S401, when the geographic location of the designated terminal consumption point belongs to the overlapping area of ​​multiple geographic power supply ranges, the power carbon attribute factors associated with the multiple geographic power supply ranges are weighted according to preset rules or spatial weighting algorithms to determine the final power carbon attribute factor applicable to the designated terminal consumption point.

[0093] In the embodiments described in this specification, the system receives the determination result from the spatial query step (S104). When the system detects that the result list contains unique identifiers for two or more geographic power supply ranges, it determines that the specified terminal consumption point is located in an overlapping area, and then triggers the weighted calculation process of this step (S401), instead of the direct reading process of S301.

[0094] Based on the list of multiple geographic power supply range identifiers obtained in the previous step, the system reads all corresponding power carbon attribute factors from the spatial database at once, as a candidate factor set. Simultaneously, according to preset rules, the system acquires or calculates the parameters required for weighting. These parameters may include: Distance parameter: Calculates the geometric distance from the location of the consumption point to the boundary of each geographical power supply area.

[0095] Historical data parameters: Query the historical power supply data of each jurisdiction for this consumption point or region from the power business system.

[0096] Operation rule parameters: Obtain the priority of primary and backup supply relationships between each belonging range defined in the power grid dispatching rules.

[0097] The system performs a weighted calculation based on pre-configured rules or spatial weighting algorithms, using the candidate factors and corresponding parameters obtained in the previous step. For example: If the rule is "distance decay weight", then the weight is calculated based on the distance from the consumption point to the boundary of each range (usually the closer the distance, the higher the weight), and a weighted average is performed.

[0098] If the rule is "power supply contribution ratio weight", then the power supply ratio of each range is calculated based on historical power supply data as the weight, and a weighted average is performed.

[0099] If the rule is "Priority weight of primary and backup power supply", then the factor of the primary power supply may be directly specified as the final result, or different fixed weights may be assigned to the primary and backup power supplies.

[0100] The weighted calculation produces a numerical value, which is the final electricity carbon attribute factor applicable to the specified end-consumer point. The system assigns this result value to the consumption point and completes the output. The entire calculation process, as well as the rules, parameters, and weights used, can be recorded to ensure the traceability and interpretability of the results.

[0101] It is important to note that the core value of this method lies in providing a structured, computable, and deterministic solution to the pervasive engineering challenge of "overlapping regions." It presupposes the existence of overlap and designs a standardized response mechanism: based on pre-defined objective rules (such as distance attenuation and primary / backup power priority) or quantifiable spatial weighting algorithms, it weights and fuses multiple candidate power carbon attribute factors. This design transforms what might otherwise be a vague boundary problem reliant on manual judgment into an automated, uniquely achievable, and traceable mathematical computation process.

[0102] Therefore, this technical solution, by introducing preset rules and weighted calculation mechanisms for overlapping regions, ensures that the method can systematically and unambiguously process all spatial query results, including complex multiple attribution scenarios. This not only greatly enhances the robustness and applicability of the method in complex real-world power grid scenarios, avoiding computational failures or result jumps caused by boundary ambiguity, but also guarantees that the final output power carbon attribute factors have consistent computational logic and credibility under any circumstances by providing deterministic and interpretable weighted results, thereby supporting the stable and reliable operation of refined carbon accounting in all scenarios.

[0103] Furthermore, when the geographic location of the designated terminal consumption point belongs to an overlapping area of ​​multiple geographic power supply ranges, the method flow steps in the preset rule or spatial weight algorithm embodiment of this specification are as follows: S501, the distance attenuation weight is calculated based on the distance between the designated terminal consumption point and the boundary of each geographical power supply range.

[0104] In the embodiments described in this specification, the system calculates the shortest geometric distance from a specified terminal consumer point to the boundary of each assigned geographical power supply range based on the precise geographical location (point coordinates) of that point and the spatial surface data boundaries of its respective geographical power supply ranges, using the spatial analysis function of a geographic information system. This distance reflects the spatial proximity between the consumer point and the edge of each power supply range's "service area".

[0105] The system calculates a set of corresponding distance attenuation weight values ​​based on a pre-defined distance attenuation rule (e.g., higher weight for closer distances and lower weight for farther distances), using a set of distance values ​​obtained in the previous step. This rule ensures that consumption points spatially closer to the core area of ​​a power supply range are more significantly affected by that range in terms of their electricity carbon attribute factor.

[0106] The calculated set of distance decay weights will be standardized (e.g., to ensure that the sum of all weights is 1) so that they can be directly used for the weighted calculation in step S401.

[0107] S502, the power supply contribution ratio weight of each geographical power supply area determined based on historical power supply data, or the priority weight of the main power supply and backup power supply relationship determined based on the power grid operation mode.

[0108] Based on historical power supply data, the system determines the power supply contribution ratio weight. According to the identifier of the designated terminal consumption point (or its location), the system queries the actual historical power supply data of each geographical power supply area for the designated point (or area) within the specified historical period from business databases such as the power marketing system and the electricity consumption information collection system.

[0109] The system calculates the proportion of power supplied to the total power supply for each service area based on the historical power supply data. This proportion is the power supply contribution weight, reflecting the actual share of electricity contributed by each service area to the consumption point historically.

[0110] Based on the power grid operation mode, the priority weight of the primary and backup supply relationship is determined. The system obtains a clear definition of the primary and backup supply relationship of each geographical power supply range for the area where the designated terminal consumption point is located from the power grid dispatch operation procedures, distribution network power supply reliability schemes or related protection configuration strategies.

[0111] Based on the aforementioned operating rules (e.g., specifying a certain area as the primary power source and other areas as backup power sources), the system assigns priority weights to each assigned area according to preset mapping rules (e.g., the primary power source has a weight of 1, and the backup power source has a weight of 0; or the primary and backup power sources are assigned weights in a fixed ratio). This reflects the logic of power grid planning and operation.

[0112] It is important to note that the core value of this method lies in providing a variety of quantifiable and executable rules based on objective data and power grid operation patterns for the crucial operation of "weighted calculation." It explicitly outlines three weight determination paths: "distance attenuation weight" based on spatial geometric characteristics, "power supply contribution ratio weight" based on historical factual data, and "primary and backup power priority weight" based on power grid operation logic. These rules are all derived from measurable and traceable objective parameters (such as distance, historical power generation, and dispatch relationships), rather than subjective experience.

[0113] Therefore, this technical solution transforms the crucial step of weighted calculation of overlapping region factors from a potentially ambiguous and subjective operation into a rigorously defined, automated, and transparently verifiable scientific calculation process by specifying a series of explicit weighting algorithms based on objective measurements and power grid operation laws. This ensures that even in complex scenarios with multiple power supply affiliations, the final determined power carbon attribute factors have solid objective basis, high consistency of results, and good engineering interpretability, thus fundamentally guaranteeing the reliability and credibility of the output results of this method in various practical boundary scenarios.

[0114] Furthermore, the method flow steps of the embodiments in this specification are as follows: S601, on a geographic information system map or other visualization platform, the geographic power supply range and its associated power carbon attribute factors are rendered or visualized in a hierarchical manner.

[0115] In the embodiments of this specification, a real-time connection or periodic data synchronization channel is established with the spatial database (constructed in step S102) on a selected geographic information system map platform (such as ArcGIS Online, SuperMap iPortal, etc.) or other visualization platform (such as a BI tool equipped with map components). This ensures that the visualization platform can obtain a complete dataset containing the spatial geometry of the geographic power supply range and its associated power carbon attribute factor values.

[0116] In the visualization platform, the visualization style is configured around the "Geographic Power Supply Range" layer accessed in the previous step, using its power carbon attribute factor field value as the core. This typically includes: Based on the numerical range of the factor values, they are divided into several consecutive intervals (levels), such as "high carbon", "medium-high", "medium", "medium-low" and "low carbon".

[0117] Assign an easily distinguishable visual variable to each numerical range, most commonly color (color band). For example, a gradient from red to green can be used to represent the transition from high carbon to low carbon.

[0118] Configure clear legends for the generated visualization layers, illustrating the correspondence between colors and factor value ranges. Optionally, label factor values ​​for key areas on the graph.

[0119] After configuration, the generated thematic map will be published as an interactive layer or a static map image. Users can use this platform to intuitively see the color differences in different geographic power supply areas due to their associated electricity carbon attribute factors, thus gaining a clear understanding of the overall spatial distribution of electricity carbon emissions, the location of high- and low-value areas, and their boundaries.

[0120] It should be noted that this method, by introducing a "hierarchical rendering or visualization" step, transforms the structured data and spatial relationships generated by all the aforementioned technical steps into a directly perceptible geographic information layer. It intuitively maps different geographic power supply ranges and their corresponding electricity carbon attribute factors onto the geographic base map using visual elements such as color, color gradation, and area filling.

[0121] Therefore, this technical solution transforms abstract data associations and spatial query results into intuitive, spatially identifiable visual information, making the complex spatial distribution pattern of electricity carbon attributes readily apparent. This not only greatly improves the efficiency and depth of understanding of massive data analysis results, but more importantly, it provides users (such as grid planners, regional carbon managers, and corporate energy efficiency departments) with a visual tool for direct spatial location-based insight, comparison, and decision-making. Ultimately, it translates refined carbon accounting data into intuitive capabilities that support low-carbon planning, electricity strategy optimization, and management decisions.

[0122] Furthermore, the embodiments of this specification also include update and maintenance steps, and the method flow steps of the embodiments of this specification are as follows: S701 is the version identifier and valid time period of each geographical power supply range and its associated power carbon attribute factor record stored in the spatial database.

[0123] In this embodiment of the specification, a new field for version management is added to the data table in the spatial database used to store spatial surface data of geographic power supply range and its associated attributes. This field includes at least: a version identifier (such as V1.0, V2.0, or a timestamp sequence number), a valid start time, and a valid end time. A blank valid end time indicates the current valid version.

[0124] When constructing the spatial database in step S102 for the first time, all geographic power supply range records entering the database are uniformly assigned an initial version identifier (e.g., V1.0) and a uniform valid start time (e.g., data release date) is set. At this time, the valid end time is empty.

[0125] Through the above fields, any spatial graph of a geographic power supply range and its associated power carbon attribute factor are clearly marked with the effective time range, forming a strong "data-version-time" correlation.

[0126] S702, in response to adjustments in the power grid structure, changes in power supply relationships, or updates to power carbon attribute factor data, incremental updates or full version updates are performed on the spatial surface data or its associated attribute values ​​in the spatial database.

[0127] In the embodiments described in this specification, the system continuously monitors or receives update event notifications from external sources. The update process is triggered when any of the following conditions are confirmed: Adjustments to the power grid structure: such as the addition of new substations or changes in line connections leading to changes in power supply zones.

[0128] Changes in power supply relationships: such as adjustments to the operation mode of the distribution network, which cause a switch in the power supply to certain areas.

[0129] Updates to power carbon attribute factor data: such as the release of new versions of generator set carbon factor calculation results.

[0130] Depending on the scope and nature of the change, select one of the following update modes to execute: Incremental Update: Applicable to localized, small-scale changes. For example, only the boundary of a specific geographic power supply area may be slightly adjusted due to line modifications, or only the associated electricity carbon attribute factor value may need correction. The process is as follows: a) Mark the original record in the database with its effective end time as one day before the change takes effect; b) Create a new record with its spatial graphic or attribute value being the updated content, assign it a new version identifier (e.g., V1.1), and set a new effective start time.

[0131] Full version update: Applicable to large-scale, global changes. For example, overall adjustment of provincial power grid zones, or complete recalculation of the entire power carbon factor. The operation process is as follows: a) Mark the effective end time for all currently valid records in the database; b) Based on the new power grid and carbon data source, re-execute the complete process of steps S101 to S102 to generate a new spatial database version with all records assigned a new version identifier (e.g., V2.0), and set a new effective start time.

[0132] After the update is complete, the new version of the data will automatically take effect after the set effective start time, serving new spatial queries. The old version of the data is fully retained in history, ensuring that any query request at any point in history can be traced back to the correct data version.

[0133] It should be noted that the technical solution described in this method is a key safeguard mechanism designed to address the aforementioned core challenges. By "recording version identifiers and effective time periods," it endows each piece of data with a time dimension and traceability, transforming it from a static snapshot into a manageable dynamic data asset. By defining clear triggering conditions (power grid structure adjustments, changes in power supply relationships, factor data updates) and response actions (incremental or full version updates), it establishes a sustainable data maintenance process that is linked to external changes.

[0134] Therefore, this technical solution, by introducing versioning management and a responsive update mechanism, upgrades the method from a static model that relies on precise mapping at a "certain moment" to a lifecycle system capable of co-evolving with dynamically changing power grid physical structures and carbon data. This not only solves the fundamental problem of result distortion caused by outdated data, but also ensures that the constructed "power supply range-carbon factor" spatial mapping relationship can continuously and reliably reflect the latest state of the real world. This supports the accuracy, stability, and maintainability of the entire refined power carbon accounting system in long-term operation, achieving a key leap from "one-time calculation" to "sustainable service."

[0135] Furthermore, the update and maintenance steps support maintaining and associating spatial range data and their electricity carbon attribute factors for the same geographical power supply range under different time versions, so as to support historical electricity carbon attribute factor tracing or future scenario analysis for the specified terminal consumption point.

[0136] It's important to note that the core value of this method lies in endowing the update and maintenance steps with time-dimensional data management capabilities. Instead of simply replacing old data with new data, it maintains and correlates complete data for the same geographic power supply range across different time versions, thus constructing a coherent and queryable historical data sequence within the system. This ensures that each geographic power supply range and its associated carbon factor have a timeline-based status record.

[0137] Therefore, this technical solution upgrades the method from a static tool that only provides a snapshot of the "current moment" to a dynamic analysis system with time-based tracking and simulation capabilities by establishing and maintaining data associations with time versions. This allows users to not only obtain real-time carbon factors at consumption points based on the latest data, but also accurately query carbon factors at any historical moment for compliance tracking and auditing. Furthermore, it enables users to perform "carbon factor simulation analysis under future power grid scenarios" by combining different versions of data. This greatly expands the application value of this method in in-depth analysis scenarios such as carbon footprint backtracking, emission reduction effect assessment, and low-carbon power grid planning, solving the key challenges of long-term data consistency and traceability in dynamic environments.

[0138] In the scenarios of low-carbon management of electricity and carbon accounting of electricity consumption, electricity carbon attribute factors (including but not limited to electricity carbon footprint factors and electricity carbon emission factors) need to accurately serve a massive number of end-user electricity consumers with higher spatial resolution and an engineerable approach. However, in reality, electricity carbon attribute factors often exist in macro-level partitions or statistical calibers, making it difficult to directly, automatically, and cost-effectively map them to a large number of end-user consumption points. If grid topology tracing or power flow calculation is used to determine the electricity source and electricity carbon attribute factors of end-user consumption points, it relies on complex data, large computational loads, and is difficult to achieve rapid assignment and continuous updating in massive user scenarios. Therefore, the technical problem to be solved by this invention is to propose a method that establishes a correlation between the electricity carbon attribute factors of power supply nodes and their geographical power supply range, and uses geospatial queries to quickly and accurately determine the applicable electricity carbon attribute factors for end-user consumption points, thereby achieving efficient assignment and continuous updating of massive consumption points and supporting the ability to build high-resolution dynamic electricity carbon maps.

[0139] The existing implementation approaches most similar to the objectives of this invention can be broadly categorized into three types. The first type is the "direct application of macro-level zoning / average electricity carbon attribute factors" scheme: This scheme directly assigns the average electricity carbon attribute factor of a specific administrative region, market zone, or power grid region to all users within that region. While simple to implement and with low data thresholds, it suffers from insufficient spatial resolution due to neglecting differences in power supply nodes and grid power supply boundaries. This makes it difficult to support high-resolution assessment and refined management, and it is prone to significant deviations in areas with users across regions, near boundaries, or in areas with complex power supply relationships. The second type is the "grid topology and power flow tracing" scheme: This scheme traces the source of electricity for end-consumer points by combining grid topology, power generation structure, power flow, or other operational information, and then calculates the corresponding electricity carbon attribute based on the source structure. Theoretically, this type of scheme is more refined, but it requires high-quality grid models and operational data. The calculation process is complex, making it difficult to achieve rapid response and low-cost maintenance in scenarios with a large number of end-users. Furthermore, changes in operating methods can cause frequent fluctuations in results, making engineering deployment difficult. The third category is the "artificial rule / coarse geographical boundary mapping" scheme: relying on experience or coarse-grained geographical division to bind a certain type of region to a certain type of power carbon attribute factor. This type of scheme can work on a small scale, but it is difficult to form a unified, verifiable, and sustainably updated data foundation. When the power supply boundary changes or multiple power supply ranges overlap, inconsistencies and unexplainable problems are easily generated. In contrast, this invention forms an engineering chain from macro-level partitioning factors to micro-level precise user empowerment through "power supply range spatial surface data + spatial query," and replaces complex power flow tracing calculations with geospatial queries. This is precisely the improvement direction proposed to address the shortcomings of the aforementioned existing technologies.

[0140] (a) Obtaining the power carbon attribute factors of power supply nodes In step S1, the system first obtains the power carbon attribute factors of one or more power supply nodes associated with the power grid. The power supply nodes are used to characterize key nodes on the power supply side, and may specifically include, but are not limited to, power plants, substations, converter stations, and electrical interconnection points in the regional power grid.

[0141] The electricity carbon attribute factor is used to reflect the carbon emission or carbon footprint level corresponding to a unit of electricity, and includes at least one of the following or a combination thereof: electricity carbon footprint factor covering the entire life cycle of power generation facilities and the upstream fuel supply chain, and electricity carbon emission factor mainly covering the power generation operation stage.

[0142] In practice, the power carbon attribute factors can be obtained through carbon accounting systems, statistical systems, scheduling systems or external carbon factor databases, and a unique correspondence can be established with the corresponding power supply nodes to provide basic data support for subsequent spatial correlation.

[0143] (II) Determination of Geographic Power Supply Scope and Construction of Spatial Database In step S2, for each power supply node for which the electricity carbon attribute factor has been acquired, the corresponding geographical power supply range is determined in the geographic information system. The geographical power supply range describes the geographical area covered by the power supply node in terms of spatial coverage for external power supply.

[0144] In the specific implementation process, the delineation of the geographical power supply range can be determined based on the power grid topology, historical operating data, power flow analysis results, or a combination of the above data, so as to ensure that the power supply range delineation results match the actual power supply relationship.

[0145] Furthermore, the system stores each geographic power supply range in the form of spatial surface data, and uses the corresponding power carbon attribute factor as the associated attribute of the spatial surface data to construct a database for storing spatial data. The spatial surface data can be in polygonal or multi-polygonal form, and its geometric information is used for subsequent spatial queries. Its attribute information includes at least the power carbon attribute factor corresponding to the geographic power supply range, thus forming a spatialized data structure of "geographic power supply range - power carbon attribute factor".

[0146] (iii) Obtaining the geographical location of the terminal consumption point In step S3, the system obtains the geospatial location of the terminal consumption point for subsequent spatial relationship determination. The terminal consumption point can be an independent power facility, a building, a charging pile, a collection of multiple consumption points, or a custom geographical area.

[0147] In practical implementation, when the terminal consumption point is a point object, its geographic spatial location can be represented by latitude and longitude coordinates; when the terminal consumption point is a region object or a collection of multiple consumption points, it can be spatially represented by region boundary information, center point coordinates or multiple point coordinates to meet the input requirements of spatial query.

[0148] (iv) Determination of power supply range ownership based on spatial relationships In step S4, the system performs a spatial query operation on the geographic location of the terminal consumption point in the spatial database based on spatial relationship judgment to determine the geographic power supply range to which the terminal consumption point belongs.

[0149] Specifically, the spatial query is a judgment operation based on the spatial inclusion relationship between point and spatial surface data, that is, determining whether the spatial location of the terminal consumption point is located within the spatial surface data corresponding to a certain geographical power supply range. Through the determination of this spatial inclusion relationship, the system can determine one or more geographical power supply ranges to which the terminal consumption point belongs.

[0150] (v) Determination of applicable electricity carbon attribute factors at end-consumer points In step S5, the system will determine the electricity carbon attribute factor associated with the geographical power supply range to which it belongs, and identify it as the electricity carbon attribute factor applicable to the terminal consumption point.

[0151] When the terminal consumption point belongs to only a single geographical power supply area, the system directly reads the power carbon attribute factor associated with that geographical power supply area as the final result, thereby completing the factor assignment for the terminal consumption point.

[0152] (vi) Factor weighting determination in the case of overlapping power supply ranges When the geographic location of the terminal consumption point belongs to the overlapping area of ​​multiple geographic power supply ranges, the system further calculates the power carbon attribute factors associated with the multiple geographic power supply ranges according to preset rules or spatial weighting algorithms, and determines the final applicable power carbon attribute factor.

[0153] The preset rules or spatial weighting algorithm can be set based on distance, weight ratio or other spatial factors, so that even when the power supply range overlaps, it can still output a unique and interpretable power carbon attribute factor result.

[0154] (vii) Visualization of geographical power supply range and electricity carbon attribute factors In a preferred embodiment, the method further includes a visualization step: on a geographic information system map or other visualization platform, the geographic power supply range and its associated power carbon attribute factors are rendered or visualized in a hierarchical manner.

[0155] Through this visualization process, different geographical power supply ranges can be distinguished and displayed by color, level, or layer according to their associated power carbon attribute factors, thereby intuitively presenting the spatial distribution of power carbon attributes and providing visualization support for power carbon management, analysis, and decision-making.

[0156] Compared to schemes that use regional average factors for coarse-grained assignment, this invention uses spatial surface data of power supply range as a carrier to structurally bind the power carbon attribute factors of power supply nodes to their geographical power supply range. This allows the power carbon attribute factors to be expressed in a refined spatial manner according to the actual power supply structure, thereby significantly improving the spatial resolution and application accuracy of power carbon attribute assignment. Compared to refined schemes that rely on grid topology tracing and power flow calculation, this invention pre-solidifies the power supply impact in the form of "queryable spatial power supply range," simplifying the process of determining consumption point factors into efficient spatial querying and attribute reading, reducing the burden on complex grid operations. The invention avoids modeling, data coupling, and reliance on computing power, making it easier to deploy at scale. Furthermore, addressing practical engineering challenges such as overlapping power supply boundaries and the coexistence of primary and backup power supplies, this invention introduces a weighted determination mechanism based on preset rules or spatial weight algorithms. This ensures that even in scenarios with overlapping power supply ranges, it can still output unique, stable, and interpretable power carbon attribute factor results. Combined with visualization capabilities such as hierarchical rendering, this invention can generate a continuously updated expression of the "spatial distribution of power carbon attribute factors" and a dynamic power carbon map, providing more engineering-feasible underlying methods and data support for enterprise carbon accounting, park management, and green electricity assessment.

[0157] To expand the scope of patent protection and reduce the possibility of others circumventing it to achieve the same purpose, this invention, without deviating from the core idea of ​​"determining the applicable electricity carbon attribute factor for target consumption points based on spatial information," allows for various equivalent alternative implementation methods. Specifically: at the spatial relationship determination level, in addition to using the spatial inclusion relationship between a point and the power supply range surface, equivalent determination methods such as spatial coverage relationship, intersection relationship, and proximity relationship (e.g., nearest neighbor attribution) can also be used. Furthermore, strategies such as buffer zones, tolerance thresholds, or boundary adsorption can be introduced for boundary points to improve the robustness and consistency of spatial matching. At the power supply range expression level, the power supply range can be stored in the form of vector surface data (polygons or multi-polygons) or discretely expressed using rasterized or gridded spatial units (e.g., regular grids, hexagonal grids, etc.), and factor matching is completed through "point placement in grid / grid unit" method. Essentially, it still belongs to the same technical concept of "spatial range data + attribute association + spatial query." At the power supply range generation level, in addition to being based on the power grid topology, historical operating data, and power flow analysis... In addition to defining the boundaries based on the analysis results, data such as distribution network area information, load density distribution, feeder boundary information, and geographical constraints can be introduced as supplements or constraints. A combined or hierarchical division strategy can be adopted to obtain a power supply range boundary that is more in line with the actual engineering situation. At the level of overlapping area processing, when the target point falls into the overlapping area of ​​multiple power supply ranges, the preset rule weighting can be equivalently replaced by a weight determination method based on distance attenuation, historical power supply contribution ratio, main and backup power priority, or model inference, thereby outputting a unique and interpretable final applicable factor. At the level of update and maintenance, the correspondence between power supply range and power carbon attribute factor at different time periods can be recorded through version number and validity period mechanism, and incremental or periodic updates can be supported to adapt to grid structure adjustments, changes in operation mode, and factor updates, ensuring that the method is long-term operable, scalable, and reusable.

[0158] Figure 2 A schematic diagram of a device for determining the carbon property factor of an electrical system, provided for one or more embodiments of this specification, includes: At least one processor and bus; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, the power supply nodes being used to characterize key nodes on the power supply side, the power carbon attribute factors including power carbon footprint factors and / or power carbon emission factors; For each power supply node, the corresponding geographic power supply range is determined in the geographic information system, and each geographic power supply range is stored in the database in the form of corresponding spatial surface data. At the same time, the power carbon attribute factor is used as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographic power supply range and power carbon attribute factor. The geographic power supply range is used to describe the geographic area covered by each power supply node in space for external power supply. Obtain the geospatial location of the specified terminal consumption point; Based on the geospatial location of the designated terminal consumption point, a spatial query is performed in the spatial database to determine whether the designated terminal consumption point belongs to one or more of the geographical power supply ranges. Based on the spatial query results, the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the electricity carbon attribute factor applicable to the specified terminal consumption point.

[0159] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0160] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0161] 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 implementation should not be considered beyond the scope of this application.

[0162] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The aforementioned units can be implemented in hardware or software.

[0165] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0166] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the carbon attribute factors of electricity, characterized in that, The method includes: Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, the power supply nodes being used to characterize key nodes on the power supply side, the power carbon attribute factors including power carbon footprint factors and / or power carbon emission factors; For each power supply node, the corresponding geographic power supply range is determined in the geographic information system, and each geographic power supply range is stored in the database in the form of corresponding spatial surface data. At the same time, the power carbon attribute factor is used as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographic power supply range and power carbon attribute factor. The geographic power supply range is used to describe the geographic area covered by each power supply node in space for external power supply. Obtain the geospatial location of the specified terminal consumption point; Based on the geospatial location of the designated terminal consumption point, a spatial query is performed in the spatial database to determine whether the designated terminal consumption point belongs to one or more of the geographical power supply ranges. Based on the spatial query results, the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the electricity carbon attribute factor applicable to the specified terminal consumption point.

2. The method of claim 1, wherein, The geographical power supply range is determined based on one or more of the following: power grid topology, historical operating data, and power flow analysis results, to ensure that the power supply range delineation results match the actual power supply relationship.

3. The method of claim 1, wherein, The step of obtaining the geospatial location of the specified terminal consumption point includes: When the designated terminal consumption point is a point-like object, its geographical location is represented by latitude and longitude coordinates; When the designated terminal consumption point is a planar area object or a collection of multiple consumption points, its geographic spatial location is represented by the area boundary information, the coordinates of the center point, or the coordinates of multiple points.

4. The method of claim 1, wherein, The step of determining the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs, based on the spatial query result, as the electricity carbon attribute factor applicable to the specified terminal consumption point includes: When the spatial query determines that the specified terminal consumption point belongs to only one geographical power supply range, the power carbon attribute factor associated with that geographical power supply range is directly read as the final power carbon attribute factor applicable to the specified terminal consumption point.

5. The method of claim 4, wherein, The method further includes: When the geographic location of the designated terminal consumption point belongs to the overlapping area of ​​multiple geographic power supply ranges, the power carbon attribute factors associated with the multiple geographic power supply ranges are weighted according to preset rules or spatial weighting algorithms to determine the final power carbon attribute factor applicable to the designated terminal consumption point.

6. The method according to claim 5, characterized in that, When the geographic location of the specified terminal consumption point belongs to an overlapping area of ​​multiple geographic power supply ranges, the preset rule or spatial weight algorithm includes: Distance attenuation weight calculated based on the distance between the designated terminal consumption point and the boundary of each geographical power supply range; The weight of power supply contribution ratio for each geographical power supply area determined based on historical power supply data. Alternatively, the priority weight of the primary and backup supply relationship determined based on the power grid operation mode.

7. The method according to claim 1, characterized in that, The method further includes: The geographic power supply range and its associated power carbon attribute factors are rendered or visualized in a hierarchical manner on a geographic information system map or other visualization platform.

8. The method according to claim 1, characterized in that, The method also includes update and maintenance steps: The spatial database stores version identifiers and valid time periods for each geographical power supply range and its associated power carbon attribute factor records; In response to adjustments in the power grid structure, changes in power supply relationships, or updates to power carbon attribute factor data, the spatial surface data or its associated attribute values ​​in the spatial database are incrementally updated or fully updated.

9. The method according to claim 8, characterized in that, The update and maintenance steps support maintaining and associating spatial range data and their electricity carbon attribute factors for the same geographical power supply range under different time versions, so as to support the historical electricity carbon attribute factor tracing or future scenario analysis for the specified terminal consumption point.

10. A device for determining the carbon property factors of electricity, characterized in that, include: At least one processor and bus; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Obtain the power carbon attribute factors of one or more power supply nodes associated with the power grid, the power supply nodes being used to characterize key nodes on the power supply side, the power carbon attribute factors including power carbon footprint factors and / or power carbon emission factors; For each power supply node, the corresponding geographic power supply range is determined in the geographic information system, and each geographic power supply range is stored in the database in the form of corresponding spatial surface data. At the same time, the power carbon attribute factor is used as an attribute associated with the corresponding spatial surface data to construct a spatial database containing the relationship between geographic power supply range and power carbon attribute factor. The geographic power supply range is used to describe the geographic area covered by each power supply node in space for external power supply. Obtain the geospatial location of the specified terminal consumption point; Based on the geospatial location of the designated terminal consumption point, a spatial query is performed in the spatial database to determine whether the designated terminal consumption point belongs to one or more of the geographical power supply ranges. Based on the spatial query results, the electricity carbon attribute factor associated with the geographical power supply range to which the specified terminal consumption point belongs is determined as the electricity carbon attribute factor applicable to the specified terminal consumption point.

Images