Carbon footprint tracking big data processing and analyzing system

Abstract

The invention discloses a carbon footprint tracking big data processing analysis system, and belongs to a carbon footprint analysis technology. Carbon footprint full-chain tracking is realized by incorporating emerging main bodies such as a distributed power supply and a micro-grid, synchronization of data and a power grid state is ensured through a space-time dynamic mechanism, and the real-time performance and accuracy of carbon footprint accounting are improved; by constructing a multi-element main body carbon footprint tracking model, core input can be provided for distributed power supply loss allocation and micro-grid responsibility division; by constructing a distributed power supply scene and a corresponding loss supervision processing scheme, the rationality of loss allocation is improved; by constructing a micro-grid interconnection scene and a corresponding loss supervision processing scheme, responsibilities under two-way exchange are clearly divided; the carbon footprint tracking data is matched and verified by using the carbon footprint tracking chain, and an accurate and compliant carbon footprint list and a loss allocation scheme can be adaptively output, so that the effects of diverse division of loss responsibilities and coverage of multiple scenes are realized.

Description

Technical Field

[0001] This invention relates to carbon footprint analysis technology, specifically to a carbon footprint tracking big data processing and analysis system. Background Technology

[0002] Carbon footprint tracking refers to the process of systematically identifying, measuring, recording, and monitoring the greenhouse gas emissions directly or indirectly generated by a product, service, organization, individual, or activity throughout its entire life cycle.

[0003] Existing technical solutions, such as the Chinese invention with application number 202210069048.2 entitled "A Method and Device for Calculating Electricity Carbon Footprint Based on Colored Petri Nets," disclose the following: assessing the degree and flow direction of carbon emissions based on load-side and grid-side loss responsibility; using Petri net models for modeling and analysis to track the intensity and flow direction of carbon emissions throughout the entire power system process; establishing an electricity carbon footprint model based on colored Petri nets; and calculating the carbon emission content contained in the power system according to different electricity carbon footprint accounting model categories and the established Petri net architecture. This invention improves the refined management of carbon footprint based on two types: load-side and grid-side loss responsibility. Petri nets, as a mesh-like information flow model, can reasonably explain the relationship between resources and processes. Therefore, establishing a Petri net for the entire process of power generation, transmission, distribution, and consumption can effectively describe the dynamic flow process of electricity throughout its entire lifecycle, from generation to transmission and finally to use.

[0004] However, existing technical solutions only provide two loss responsibility modes: load side and grid side. They do not consider scenarios where multiple entities in the actual power system share losses collaboratively, such as loss sharing when distributed power sources participate in power supply, and responsibility division in the scenario of microgrid and main grid interconnection. The existing two single responsibility modes are difficult to achieve fair and reasonable carbon emission sharing, and have the defects of single loss responsibility division and failure to cover multiple scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a carbon footprint tracking big data processing and analysis system to solve the technical problems of existing solutions, such as the single division of loss responsibility and the failure to cover multiple scenarios.

[0006] The objective of this invention can be achieved through the following technical solutions: A carbon footprint tracking big data processing and analysis system includes: Multi-entity carbon footprint tracking processing module: Constructs a multi-entity carbon footprint tracking model, defines a set of multiple entities including load side, grid side, distributed power source side, and microgrid side, collects real-time power, electricity, transmission topology, carbon emission factor and spatiotemporal dynamic update data of each entity, and obtains carbon footprint tracking data; Carbon footprint data analysis and verification output module: Combining the multi-source heterogeneous characteristics of carbon footprint tracking data, and using the carbon footprint tracking chain to match and verify the carbon footprint tracking data, outputting the carbon footprint list and loss sharing scheme of each entity; when the verification fails, data analysis of the negative impact of verification is carried out, and a re-verification scheme is dynamically implemented based on the analysis results.

[0007] Preferably, a unified data collection dimension is established based on the subject's role, including real-time power dimension, power consumption dimension, transmission topology dimension, carbon emission factor dimension, and spatiotemporal dynamic data dimension.

[0008] Preferably, when performing spatiotemporal dynamic updates, the time update includes real-time power data updates and carbon emission factor updates; the spatial update includes transmission topology updates and regional carbon emission factor updates.

[0009] Preferably, when constructing a multi-subject carbon footprint tracking model using the collected data, the model adopts a graph theory model G=(V,E), where V is the set of unique subject IDs and E is the set of route parameters; Furthermore, the spatiotemporal update formula for carbon emission factors is: ;in, The carbon emission factor is dynamically adjusted in time and space; As a regional benchmark carbon emission factor; This represents the adjustment factor for the time period t; s represents the spatial location.

[0010] Preferably, the carbon footprint tracking chain includes a list of distributed power source loss allocations and a report on the division of loss responsibilities between the microgrid and the main grid.

[0011] Preferably, the distributed power source loss allocation list includes the loss-bearing amount of different participating entities, and the loss is allocated according to the weighted contribution ratio, involving the following expression: ;in, The amount of loss borne by the i-th participating loss-sharing entity; The total loss incurred by all participating entities; j is the index variable for traversing all participating entities; For weighted contribution, i is the index of the distributed generation (DG), i = 1, 2, 3, ..., n; n is a positive integer, which is the total number of all distributed generation (DGs).

[0012] Preferably, the loss responsibility allocation report between the microgrid and the main grid includes the results of allocating shared losses according to a weighted ratio, involving the following expression: ;in, This refers to the common energy loss from bidirectional energy exchange that the microgrid (MG) needs to bear. This refers to the total common losses generated by the bidirectional energy exchange between the microgrid and the main grid. These are electricity sold and electricity purchased, respectively. The weighting coefficient for the electricity sales ratio; The weighting factor for the electricity purchase ratio; Energy exchange efficiency.

[0013] Preferably, the loss amortization value is calculated using a formula, involving the following expression: ;in, This is the value for loss allocation; For the fusion correction of the first Time window, first Carbon footprint loss allocation value of spatial nodes; , These are the time window index and the spatial node index, respectively. , These are the total number of time windows and the total number of spatial nodes, respectively. like If the value is 0, the verification passes; otherwise, the verification fails.

[0014] Preferably, if the first verification negative impact coefficient is less than or equal to the first verification negative impact threshold, and the second verification negative impact coefficient is less than or equal to the second verification negative impact threshold, then the first re-verification scheme is implemented; If the negative impact coefficient of the first verification is greater than the negative impact threshold of the first verification, and the negative impact coefficient of the second verification is less than or equal to the negative impact threshold of the second verification, then the second re-verification scheme shall be implemented. If the negative impact coefficient of the second verification is greater than the negative impact threshold of the second verification, the implementation of the re-verification plan will be suspended, and an early warning will be issued to the operation and maintenance personnel to intervene and take over.

[0015] Preferably, the expression for calculating the negative impact coefficient of the first verification is: Where Hf1 is the negative impact coefficient of the first verification; Nf1 is the total number of times the first re-verification scheme is implemented; NH is the total number of times the verification is implemented; The expression for calculating the negative impact coefficient of the second verification is: Where Hf2 is the negative impact coefficient of the second verification; Nf2 is the total number of times the second re-verification scheme is implemented.

[0016] Compared to existing solutions, the beneficial effects achieved by this invention are: This invention incorporates emerging entities such as distributed power sources and microgrids to achieve full-chain carbon footprint tracking, breaking through the limitations of traditional methods that only focus on the load and grid sides. Through a spatiotemporal dynamic mechanism, it ensures that data is synchronized with the grid status, improving the real-time performance and accuracy of carbon footprint accounting. By constructing a multi-entity carbon footprint tracking model, it can provide core inputs for distributed power source loss allocation and microgrid responsibility division, laying the foundation for the overall effectiveness of the system.

[0017] This invention addresses the unfairness of existing technologies that only allocate losses based on output while ignoring differences in transmission paths by constructing a distributed power generation scenario and corresponding loss monitoring and processing scheme, thereby improving the rationality of loss allocation. By constructing a microgrid interconnection scenario and corresponding loss monitoring and processing scheme, the weighting coefficients can be dynamically adjusted, clearly defining responsibilities under bidirectional exchange and avoiding a one-size-fits-all approach to allocation. The hierarchical system of the scenario database covers multiple stakeholders, providing reliable loss data for accurate carbon footprint tracking, effectively filling the gap in loss allocation in multi-stakeholder collaborative scenarios and significantly improving the accuracy and reliability of carbon footprint accounting.

[0018] This invention utilizes a carbon footprint tracking chain to match and verify carbon footprint tracking data, effectively solving the matching problem of multi-source heterogeneous data. It can also adaptively output accurate and compliant carbon footprint lists and loss sharing schemes, achieving diverse loss responsibility allocation and covering multiple scenarios. Attached Figure Description

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating the operation of a carbon footprint tracking big data processing and analysis system according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, this invention is a carbon footprint tracking big data processing and analysis system, comprising: Multi-entity carbon footprint tracking processing module: Constructs a multi-entity carbon footprint tracking model, defining a set of multiple entities including load side, grid side, distributed power generation side, and microgrid side, and collects real-time power, electricity consumption, transmission topology, carbon emission factors, and spatiotemporal dynamic update data of each entity to obtain carbon footprint tracking data; specific steps include: When defining a set of multiple entities including the load side, grid side, distributed generation side, and microgrid side, specifically: Load side: Industrial users, commercial users, and residential users; such as electronic manufacturing plant production lines, chain supermarkets, and residential communities; electricity consumption data is collected through smart meters and energy management systems; Grid side: The power transmission and distribution network operator; for example, a provincial power company of the State Grid Corporation of China; obtains power transmission and distribution operation data through SCADA systems and distribution automation systems; Distributed power generation side: distributed photovoltaic and distributed wind power; such as residential rooftop photovoltaic arrays and small rural wind farms; power output and grid connection data are collected through inverter monitoring systems; Microgrid side: Microgrid operating entity; such as the industrial park microgrid management center; collects bidirectional energy exchange data through the microgrid energy management system; A unified data collection dimension is established based on the main roles, including real-time power dimension, power consumption dimension, transmission topology dimension, carbon emission factor dimension, and spatiotemporal dynamic data dimension; Specifically, real-time power corresponds to instantaneous power in the kW range; for example, the real-time power consumption of a factory production line is 1200kW. Electricity consumption, corresponding to cumulative electricity consumption in kWh; for example, a supermarket's monthly electricity consumption is 50,000 kWh. Transmission topology, corresponding to the grid node connection relationship, line impedance R, and loss coefficient α; grid node connection relationship, for example, substation ID: Sub001, line ID: Line012; Carbon emission factor, which corresponds to the carbon emission intensity per unit of energy type; for example, thermal power corresponds to 0.85 kg CO2 / kWh, and photovoltaic power corresponds to 0.028 kg CO2 / kWh. Spatiotemporal dynamic data corresponds to time and space dimensions; among them, the time dimension corresponds to minute-level power updates and peak-valley period division, which includes morning peak 7-9 am, flat period 10-17 am, and evening peak 18-21 pm; the space dimension corresponds to provincial power grid boundaries and substation geographical coordinates.

[0023] When performing spatiotemporal dynamic updates, the time update includes real-time power data updates and carbon emission factor updates; specifically, real-time power data is updated every minute; the carbon emission factor is adjusted according to peak and valley periods, for example, K=1.1 for the morning peak, K=1.0 for the flat period, and K=1.2 for the evening peak; the peak and valley periods are based on the existing division scheme. Spatial updates include transmission topology updates and regional carbon emission factor updates; specifically, the transmission topology is automatically corrected when power grid lines are put into operation or decommissioned; for example, the topology relationship is updated when a new Line013 line is added. Regional carbon emission factors are updated quarterly according to the provincial energy structure; for example, when the proportion of thermal power in a province decreases by 5%, the factor is adjusted from 0.85 to 0.82. In addition, when there are significant changes in the power grid topology or energy structure, such as the commissioning of cross-regional lines, full data synchronization is triggered.

[0024] When constructing a multi-subject carbon footprint tracking model using the collected data, the model adopts a graph theory model G=(V,E), where V is the set of unique IDs of the subjects, such as user ID: U001, line ID: Line012; E is the set of line parameters, such as line impedance R is 0.3, loss coefficient α is 0.015; Furthermore, the spatiotemporal update formula for carbon emission factors is: ;in, The carbon emission factor is dynamically adjusted in time and space; As a regional benchmark carbon emission factor; This represents the adjustment coefficient for time period t, reflecting changes in the energy supply structure at different times. For example, when peak thermal power output increases, the carbon emission factor rises. Time period t includes morning peak, flat period, and evening peak. s represents spatial location, which can refer to regions, nodes, etc. Regions can be provinces, cities, or counties, and nodes can be microgrid interfaces or distributed power source locations.

[0025] In this embodiment of the invention, by incorporating emerging entities such as distributed power sources and microgrids, the entire carbon footprint tracking chain can be achieved, breaking the limitations of traditional methods that only focus on the load side and grid side. Through a spatiotemporal dynamic mechanism, data synchronization with the grid status is ensured, improving the real-time performance and accuracy of carbon footprint accounting. By constructing a multi-entity carbon footprint tracking model, core inputs can be provided for distributed power source loss allocation and microgrid responsibility division, laying the foundation for the overall effectiveness of the system.

[0026] Carbon footprint data analysis and verification output module: Leveraging the multi-source heterogeneous nature of the collected carbon footprint tracking data, this module matches and verifies the data using the carbon footprint tracking chain, and outputs carbon footprint lists and loss allocation schemes for each entity. If verification fails, it performs data analysis on the negative impacts of the verification and dynamically implements a re-verification scheme based on the analysis results. Specific steps include: Based on the multi-source heterogeneous characteristics of the collected carbon footprint tracking data, data preprocessing is performed, including time granularity alignment, spatial node correlation, and outlier filtering. Among them, time granularity alignment: convert minute-level DG data and hour-level exchange data into hour-level time tags; for example, 2025-11-24 08:00~09:00; Spatial node association: Each data point is spatially labeled; for example, DG1 node, main grid interface node, and microgrid master node; Outlier filtering: Remove invalid data from the collected data, such as abnormal power generation values ​​when the generator is shut down; Temporal granularity alignment, spatial node association, and outlier filtering are all existing conventional technical solutions, and the specific implementation steps will not be elaborated here. Output a standardized dataset with unified spatiotemporal labels, including the original carbon footprint value, calculated loss value, and amortization ratio; When matching carbon footprint tracking data using a carbon footprint tracking chain, the relevant expression is: ;in, For the fusion correction of the first Time window, first Carbon footprint loss allocation value of spatial nodes; , These are the time window index and the spatial node index, respectively. This is the time weighting coefficient, with a value ranging from 0 to 1, and a default value of 0.3. For the first The time consistency score of the time window is calculated based on the data integrity within the hourly window, which is a conventional technical solution. This is the spatial weighting coefficient, with a value ranging from 0 to 1, and a default value of 0.4. For the first The spatial correlation score of spatial nodes is calculated by the electrical distance between spatial nodes, which is a conventional technical solution. For the first Time window, first Original value of loss amortization for spatial nodes; This is the weighting coefficient for the raw carbon footprint data, with a value ranging from 0 to 1, and a default value of 0.3. For the first Time window, first The original carbon footprint value of a spatial node; The steps involved in obtaining a carbon footprint tracking chain include: Obtain all scenarios included in carbon footprint tracking, and sequentially match all included scenarios with preset sample scenarios in the scenario database to obtain the preset loss monitoring and processing scheme corresponding to the matched sample scenarios. It should be noted that the sample scenarios preset in the scenario database in this embodiment of the invention specifically include scenarios involving distributed power sources and scenarios where microgrids are interconnected with the main grid. In addition, those skilled in the art can adaptively edit and supplement sample scenarios and corresponding loss monitoring and processing schemes according to actual application scenarios. For scenarios involving distributed generation (DG) power supply, the corresponding loss monitoring and processing scheme acquires data from several DGs participating in the power supply during implementation; for example, DG1 is a rooftop photovoltaic array, providing real-time power output. Transmission path loss coefficient Path length ; The distributed power source DG2 is a small-scale wind farm with real-time power output. Transmission path loss coefficient Path length Total loss ; Considering both power output and transmission path loss characteristics, the weighted contribution of each distributed generation (DG) is calculated using the following expression: ;in, For weighted contribution, i is the index of the distributed generation (DG), i = 1, 2, 3, ..., n; n is a positive integer, which is the total number of all distributed generation (DGs); These are the real-time output, transmission path loss coefficient, and path length of different distributed generation (DG) generators. Furthermore, the expression for allocating losses through a weighted contribution ratio is as follows: ;in, The amount of loss borne by the i-th participating loss-sharing entity; The total loss incurred by all participating entities; j is the index variable for traversing all participating entities; A distributed generation loss allocation list is generated using the loss-bearing amounts of all participating loss-sharing entities, serving as the basic data for carbon footprint accounting within the microgrid. For microgrid-to-grid interconnection scenarios, the corresponding loss monitoring and processing scheme acquires all bidirectional energy exchanges between the microgrid (MG) and the main grid during implementation; for example, during the electricity sales period from 8:00 AM to 12:00 PM: electricity sales volume. Electricity sales transmission efficiency ; Electricity purchase period: 6 PM - 10 PM: Purchased electricity amount Electricity purchase and transmission efficiency ; Common losses between microgrid and main grid ; The energy exchange efficiency is calculated using the following formula: ;in, Energy exchange efficiency; These are electricity sold and electricity purchased, respectively. These are respectively the electricity sales transmission efficiency and the electricity purchase transmission efficiency; Furthermore, the formula for calculating the weighted proportional weighting coefficients involves the following expression: ; ;in, The weighting coefficient for the electricity sales ratio; The weighting factor for the electricity purchase ratio; The expression for allocating common losses according to a weighted ratio is as follows: ;in, This refers to the common energy loss from bidirectional energy exchange that the microgrid (MG) needs to bear. This refers to the total common losses generated by the bidirectional energy exchange between the microgrid and the main grid. The results of allocating common losses according to a weighted ratio generate a loss responsibility division report between the microgrid and the main grid. Combined with the loss allocation list of distributed power sources, a complete carbon footprint tracking chain is formed.

[0027] It should be noted that the distributed generation loss allocation results obtained through processing can be used as a component of the internal losses of the microgrid; and based on the bidirectional interaction characteristics between the microgrid and the main grid, cross-entity common losses can be further divided, forming a hierarchical loss allocation system of distributed generation, microgrid and main grid, ensuring clear responsibilities and data consistency.

[0028] In this embodiment of the invention, by constructing a distributed power generation scenario and a corresponding loss monitoring and processing scheme, the unfairness of existing technical solutions that only allocate losses based on output while ignoring differences in transmission paths can be addressed, thus improving the rationality of loss allocation. By constructing a microgrid interconnection scenario and a corresponding loss monitoring and processing scheme, weighting coefficients can be dynamically adjusted, clearly delineating responsibilities under bidirectional exchange and avoiding a one-size-fits-all approach to allocation. The hierarchical system of the scenario database covers multiple stakeholders, providing reliable loss data for accurate carbon footprint tracking, effectively filling the gap in loss allocation in existing technologies under multi-stakeholder collaborative scenarios, and significantly improving the accuracy and credibility of carbon footprint accounting.

[0029] Furthermore, the loss amortization value is calculated using a formula, involving the following expression: ;in, This is the value for loss allocation; These are the total number of time windows and the total number of spatial nodes, respectively. like If the value is 0, the verification passes; otherwise, the verification fails. When verification fails, data analysis of the negative impacts is performed, and a re-verification plan is dynamically implemented based on the analysis results; specifically: If the first verification negative impact coefficient is less than or equal to the first verification negative impact threshold, and the second verification negative impact coefficient is less than or equal to the second verification negative impact threshold, then the first re-verification scheme is implemented. Both the first and second verification negative impact thresholds can be determined based on the simulation test training data of the previous sample verification negative impact coefficients. For example, the first verification negative impact threshold can be the median of all first verification negative impact coefficients obtained from all previous simulation test training, and the second verification negative impact threshold can be the median of all second verification negative impact coefficients obtained from all previous simulation test training. Alternatively, it can be customized and adjusted by professionals in the field according to the application requirements and specifications of the actual application scenario. If the negative impact coefficient of the first verification is greater than the negative impact threshold of the first verification, and the negative impact coefficient of the second verification is less than or equal to the negative impact threshold of the second verification, then the second re-verification scheme shall be implemented. If the negative impact coefficient of the second verification is greater than the negative impact threshold of the second verification, the implementation of the re-verification plan will be suspended, and an early warning will be issued to the operation and maintenance personnel to intervene and take over. The expression for calculating the negative impact coefficient of the first verification is as follows: Where Hf1 is the negative impact coefficient of the first verification; Nf1 is the total number of times the first re-verification scheme is implemented; NH is the total number of times the verification is implemented; The expression for calculating the negative impact coefficient of the second verification is: Where Hf2 is the negative impact coefficient of the second verification; Nf2 is the total number of times the second re-verification scheme is implemented; In addition, when implementing the first re-verification scheme, it includes locating the root cause of the problem, correcting the error, and re-performing spatiotemporal fusion. Locating the root cause of the problem and correcting the error are existing conventional technical solutions, and the specific implementation steps will not be elaborated here. When implementing the first re-verification scheme, it includes locating the root cause of the problem, correcting the error, and re-performing spatiotemporal fusion. Locating the root cause of the problem and correcting the error are both existing conventional technical solutions, and the specific implementation steps will not be elaborated here. When implementing the second re-verification scheme, based on the implementation of the first re-verification scheme, after re-performing spatiotemporal fusion, the recalculated loss amortization value is processed. The expression involved in performing a second consistency check is: If the inequality is true, the second-order consistency check is deemed to have passed; if the inequality is false, the second-order consistency check is deemed to have failed, and the data analysis of the negative impact of the verification is repeated. It is worth noting that, unlike existing technical solutions that only use fixed verification schemes to handle abnormal situations and cannot adaptively implement verification schemes dynamically according to different overall verification situations, this embodiment of the invention performs data analysis on the negative impact of verification when verification fails, and adaptively and dynamically implements a re-verification scheme based on the analysis results. This can effectively improve the reliability of carbon footprint tracking data analysis and the robustness of the system, and achieve adaptive verification of defects in multiple scenarios from the verification dimension.

[0030] Based on the fusion correction of the first Time window, first Carbon footprint loss allocation of space nodes The carbon footprint inventory is generated by summarizing the carbon emissions of each time period according to the subject; the time period is, for example, day / month / year; the carbon emissions include carbon emissions from power generation and carbon emissions amortized by losses. The merged loss value is broken down by main body, the bidirectional exchange loss borne by each main body is clarified, and a loss sharing scheme is generated by combining them; for example, the microgrid bears 189.12kWh, and the main grid bears 10.88kWh.

[0031] In this embodiment of the invention, the carbon footprint tracking chain is used to match and verify carbon footprint tracking data, which effectively solves the matching problem of multi-source heterogeneous data. It can also adaptively output accurate and compliant carbon footprint lists and loss sharing schemes, achieving the effect of diverse loss responsibility division and coverage of multiple scenarios.

[0032] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0033] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0034] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A carbon footprint tracking big data processing and analysis system, characterized in that, include: Multi-entity carbon footprint tracking processing module: Constructs a multi-entity carbon footprint tracking model, defines a set of multiple entities including load side, grid side, distributed power source side, and microgrid side, collects real-time power, electricity, transmission topology, carbon emission factor and spatiotemporal dynamic update data of each entity, and obtains carbon footprint tracking data; Carbon footprint data analysis and verification output module: Combining the multi-source heterogeneous characteristics of carbon footprint tracking data, and using the carbon footprint tracking chain to match and verify the carbon footprint tracking data, outputting the carbon footprint list and loss sharing scheme of each entity; when the verification fails, data analysis of the negative impact of verification is carried out, and a re-verification scheme is dynamically implemented based on the analysis results.

2. The carbon footprint tracking big data processing and analysis system according to claim 1, characterized in that, A unified data collection dimension is established based on the main roles, including real-time power dimension, power consumption dimension, transmission topology dimension, carbon emission factor dimension, and spatiotemporal dynamic data dimension.

3. The carbon footprint tracking big data processing and analysis system according to claim 2, characterized in that, When performing spatiotemporal dynamic updates, the time update includes real-time power data updates and carbon emission factor updates; the spatial update includes transmission topology updates and regional carbon emission factor updates.

4. The carbon footprint tracking big data processing and analysis system according to claim 3, characterized in that, When constructing a multi-subject carbon footprint tracking model using the collected data, the model adopts a graph theory model G=(V,E), where V is the set of unique IDs of the subjects; and E is the set of route parameters. Furthermore, the spatiotemporal update formula for carbon emission factors is: ;in, The carbon emission factor is dynamically adjusted in time and space; As a regional benchmark carbon emission factor; This represents the adjustment factor for the time period t; s represents the spatial location.

5. The carbon footprint tracking big data processing and analysis system according to claim 1, characterized in that, The carbon footprint tracking chain includes a list of distributed power generation loss allocations and a report on the division of loss responsibilities between microgrids and the main grid.

6. The carbon footprint tracking big data processing and analysis system according to claim 5, characterized in that, The distributed generation loss allocation list includes the loss-sharing amount borne by different participating entities. Losses are allocated based on a weighted contribution ratio, and the relevant expression is: ;in, The amount of loss borne by the i-th participating loss-sharing entity; The total loss incurred by all participating entities; j is the index variable for traversing all participating entities; For weighted contribution, i is the index of the distributed generation (DG), i = 1, 2, 3, ..., n; n is a positive integer, which is the total number of all distributed generation (DGs).

7. A carbon footprint tracking big data processing and analysis system according to claim 5, characterized in that, The loss responsibility allocation report between the microgrid and the main grid includes the results of allocating shared losses according to a weighted ratio, involving the following expression: ;in, This refers to the common energy loss from bidirectional energy exchange that the microgrid (MG) needs to bear. This refers to the total common losses generated by the bidirectional energy exchange between the microgrid and the main grid. These are electricity sold and electricity purchased, respectively. The weighting coefficient for the electricity sales ratio; The weighting factor for the electricity purchase ratio; Energy exchange efficiency.

8. The carbon footprint tracking big data processing and analysis system according to claim 7, characterized in that, The loss amortization value is calculated using the following formula: ;in, This is the value for loss allocation; For the fusion correction of the first Time window, first Carbon footprint loss allocation value of spatial nodes; , These are the time window index and the spatial node index, respectively. , These are the total number of time windows and the total number of spatial nodes, respectively. like If the value is 0, the verification passes; otherwise, the verification fails.

9. A carbon footprint tracking big data processing and analysis system according to claim 8, characterized in that, If the negative impact coefficient of the first verification is less than or equal to the negative impact threshold of the first verification, and the negative impact coefficient of the second verification is less than or equal to the negative impact threshold of the second verification, then the first re-verification scheme shall be implemented. If the negative impact coefficient of the first verification is greater than the negative impact threshold of the first verification, and the negative impact coefficient of the second verification is less than or equal to the negative impact threshold of the second verification, then the second re-verification scheme shall be implemented. If the negative impact coefficient of the second verification is greater than the negative impact threshold of the second verification, the implementation of the re-verification plan will be suspended, and an early warning will be issued to the operation and maintenance personnel to intervene and take over.

10. A carbon footprint tracking big data processing and analysis system according to claim 9, characterized in that, The expression for calculating the negative impact coefficient of the first verification is: Where Hf1 is the negative impact coefficient of the first verification; Nf1 is the total number of times the first re-verification scheme is implemented; NH is the total number of times the verification is implemented; The expression for calculating the negative impact coefficient of the second verification is: Where Hf2 is the negative impact coefficient of the second verification; Nf2 is the total number of times the second re-verification scheme is implemented.

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