Carbon metering method based on transaction path credible tracking carbon emission flow positioning identification

Abstract

The invention provides a carbon metering method based on transaction path credible tracking carbon emission flow positioning identification, and belongs to the field of power supply chain carbon emission. The block chain intelligent contract is in butt joint with a power market, a dispatching center and a power generation enterprise information system, and real-time and multi-source heterogeneous data collection and safe chaining are achieved. According to the method, multi-dimensional feature clustering and path topology analysis are fused, a power transaction path is dynamically decomposed, and a high-frequency carbon flow joint point is identified. And finely calculating the carbon emission of each path section by combining real-time equipment parameters and multivariate correction factors through a segmented weighted correction carbon emission algorithm. And introducing subject-object collaborative weight allocation and credibility adjustment to realize carbon flow attribution and whole-process tracing. The system supports carbon footprint traceability authentication, enterprise green purchase, quota distribution and multi-level visual analysis, and ensures that the whole process of carbon measurement data is safe, transparent and traceable.

Description

Technical Field

[0001] This invention belongs to the field of carbon emissions in the power supply chain, and more specifically relates to a carbon metering method based on the reliable tracking of carbon emission flows in the transaction path. Background Technology

[0002] With increasing global pressure from climate change, carbon peaking and carbon neutrality have become a focus of widespread attention in the energy industry and society at large. Achieving accurate carbon emission measurement, end-to-end tracking, and effective verification has become fundamental to promoting green and low-carbon transformation and the construction of market-based carbon trading mechanisms. Currently, the power industry, as a major link in carbon emissions, faces increasingly complex challenges in managing and allocating carbon emission data across the entire chain of power generation, transmission, distribution, trading, and consumption. However, existing carbon measurement methods generally suffer from problems such as fragmented carbon emission data, fragmented information along trading paths, ambiguous attribution of responsibility, and large errors in manual calculations. These issues make it difficult to support dynamic carbon emission measurement and real-time traceability in cross-regional, multi-entity, and high-frequency power trading environments. Against this backdrop, the development of blockchain, smart contracts, big data, and encryption technologies provides a new technological path for achieving precise location and ownership identification of carbon emission flows through the entire power trading chain and with reliable traceability.

[0003] However, efficiently integrating multi-source, heterogeneous power contracts, dispatch paths, unit operating conditions, and energy consumption data, and relying on a trusted digital foundation to achieve fine-grained, segmented carbon emission measurement, allocation, and intelligent traceability, still faces numerous technical challenges, including ensuring data credibility, algorithm efficiency, distributed evidence storage, and verifiable closed-loop responsibility chains. Therefore, there is an urgent need for a novel carbon measurement and identification method that integrates power trading path characteristics, smart contracts, and on-chain trusted evidence storage, while also considering data security, real-time performance, and multi-party ownership allocation, to support diverse application needs such as green power trading, carbon market management, and carbon footprint compliance certification. Summary of the Invention

[0004] This invention aims to address the problems of carbon data silos, fragmented path information, unclear responsibility attribution, and large errors in manual calculation in existing carbon emission measurement processes. Addressing the technical challenges of dynamic tracking and accurate responsibility allocation of carbon emissions throughout the entire process in a multi-party electricity trading environment, this invention proposes a carbon measurement method that integrates multi-source heterogeneous data acquisition, electricity trading path clustering and analysis, fine-grained segmented quantification, on-chain allocation via smart contracts, and a traceability mechanism. This enables reliable tracking, precise location, and ownership identification of carbon emission flows throughout the entire electricity trading chain, effectively supporting the real-time and intelligent management needs of green electricity trading decisions and carbon market quota allocation.

[0005] To achieve the above objectives, the present invention employs the following technical solution: the method comprises:

[0006] Data collection and storage automatically connects to the information systems of the power market trading platform, dispatch center and power generation companies through smart contracts, and collects multi-source heterogeneous data in real time, including power trading contracts, power grid transmission routes, unit operating conditions and energy consumption.

[0007] The power trading path clustering decomposition method designs a similarity matrix that integrates multi-dimensional features of trading volume, time period, contract type and participating entities for historical and real-time power trading contracts. It also introduces a weighting factor to give priority to high carbon emission associated systems and adopts density-based scalable clustering to dynamically identify high-frequency and highly associated trading sub-paths.

[0008] A fine-grained quantitative mapping of path-level carbon emissions is proposed, and a segmented weighted correction carbon emission algorithm is put forward: based on the real-time operating parameters of the unit, the unit power transmission carbon emission intensity of each segment of the trading path is dynamically mapped, and the carbon content of coal, carbon oxidation rate, and carbon capture correction value are incorporated into the carbon emission calculation model in a hierarchical manner to form a segmented weighted correction factor certificate.

[0009] Carbon flow attribution and source identification: Establish a carbon emission responsibility sharing model for market participants in multi-path transactions. Use linear attribution weight correction + trust adjustment factor to organically allocate the carbon flow of each transaction to each market participant. Track the entire process in a chain and generate source traceability certificates.

[0010] Carbon measurement results are applied and feedback corrections are made. Based on a transparent and reliable on-chain data foundation, a multi-level real-time visualized carbon measurement and tracking panel is developed to support enterprises' green electricity procurement decisions, carbon footprint traceability and certification, carbon market quota allocation, and cross-regional carbon accounting.

[0011] In one approach, the data collection and storage includes: deploying efficient data collection interfaces within the power market trading platform, dispatch center, and power generation enterprise main information system; automatically integrating power trading contracts, actual power flow, real-time dispatch paths, generator unit operating status parameters, and multi-source energy consumption data through a standardized adaptation layer; and utilizing blockchain smart contracts for automatic dispatch to achieve event-triggered data collection and verification.

[0012] In one scheme, the power trading path clustering decomposition includes: extracting multi-dimensional features including the trading volume, contract signing period, contract type, set of participating entities, and actual power transmission path based on historical and real-time collected power trading contract data; constructing a weighted feature similarity matrix; introducing carbon emission intensity weight and normalization weight; and using a density-based spatial clustering algorithm to cluster into high-frequency, high-similarity trading sub-paths.

[0013] Subsequently, by utilizing path topology analysis, based on node weight vectors and link weight matrices, the main carbon flow nodes in the path are automatically identified and decomposed, enabling dynamic location and source tracing of carbon emissions at the path level.

[0014] In one scheme, the path-level carbon emission fine-quantification mapping includes: based on the clustered and decoupled high-frequency trading path segments and their corresponding carbon flow nodes, relying on real-time collected generator set operating parameters, using a segmented weighted correction carbon emission algorithm, dynamically calculating the basic carbon emission coefficient for each segment of the power transmission path based on information such as real-time load, unit coal consumption, fuel carbon content, carbon oxidation rate, and online carbon capture efficiency, and performing multi-dimensional correction on each sub-segment using a carbon emission segmented weighted correction factor.

[0015] In one scheme, the carbon flow attribution and source identification includes: adopting a subject-object collaborative carbon flow attribution discrimination algorithm, firstly establishing subject behavior modeling and subject-object weight relationship based on path-level carbon emissions, combining contract proportion and energy flow allocation to allocate preliminary attribution weights, then setting basic attribution parameters for the subject and object based on the subject-object role binary relationship, and introducing a subject-object collaborative adjustment mechanism, using historical compliance records and carbon performance to generate a trust adjustment factor, and dynamically adjusting the subject-object weight allocation;

[0016] Ultimately, the entire carbon traceability allocation process, including transaction paths, segmented attribution, subject and object identities and weights, adjustment factors, and carbon allocation results, is recorded on the blockchain through smart contracts, forming a unique traceability certificate. This achieves accurate carbon emission attribution from the path level to the subject level and reliable traceability throughout the entire process. In one solution, the application and feedback correction of the carbon measurement results include: constructing a multi-level, real-time carbon measurement and tracking panel based on carbon emission data recorded on the distributed ledger. This panel provides queryable, visual, and verifiable carbon information services to various users, including enterprises, market regulators, and policy decision-makers, displaying the distribution of carbon emission flows across the entire chain, by path, and by subject. It also supports the generation of carbon footprint traceability certificates, carbon asset certification and quota allocation, and compliance verification.

[0017] In one approach, the collected data is locally encrypted using elliptic curve cryptography, preprocessed using a hash algorithm to generate a unique and irreversible hash fingerprint, and then the encrypted data is segmented and packaged into data packets with timestamps and digital signatures. Distributed nodes use an improved PBFT consensus mechanism for verification. Data that passes the consensus is uploaded to the blockchain and stored in a decentralized and redundant manner in a multi-party distributed ledger. Each piece of data on the chain is accompanied by a traceability path, the generating entity, a timestamp, an encrypted signature, and a hash fingerprint, achieving full-process traceability and tamper-proof data security.

[0018] In one scheme, the weighted correction factors include: coal carbon content correction, network batch energy loss factor and scheduling change correction. After the carbon emissions of the whole path are superimposed in segments, they are mapped to the blockchain ledger through hash processing and smart contracts. Combined with traceability and encrypted links, the trajectory of carbon emission data of the whole process can be found, the data can be traced, and the contract can be verified.

[0019] Beneficial effects of this invention:

[0020] This invention significantly improves the accuracy and real-time performance of carbon emission measurement by introducing trusted carbon emission tracking, segmented quantification, and smart contract on-chain technology based on electricity trading paths. It enables automatic collection, weighted allocation, and traceable evidence storage of carbon emission data in cross-regional, multi-entity electricity trading scenarios.

[0021] Compared to traditional data processing methods that rely on manual calculations and offline statistics, this invention effectively breaks down carbon data silos, eliminates fragmentation and information distortion in the carbon flow tracking process, and ensures that the carbon emission responsibilities for each electricity transaction are clear and verifiable. By automatically executing carbon allocation and on-chain registration through smart contracts, this invention also reduces collaboration costs, improves system security and transparency, and provides strong technical support for applications such as carbon trading, green finance, and carbon footprint compliance certification, thus promoting the green and low-carbon transformation of the energy industry and the healthy development of the carbon market. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method of the present invention;

[0023] Figure 2 This is a flowchart of the carbon flow attribution and source identification process of the present invention. Detailed Implementation

[0024] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0025] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0026] like Figure 1 As shown, a carbon metering method based on reliable tracking of carbon emission streams in trading paths is described, with the following specific steps:

[0027] Step 1: Data Acquisition and Storage

[0028] By automatically connecting to the information systems of the power market trading platform, dispatch center, and power generation enterprises through smart contracts, multi-source heterogeneous data is collected in real time, including power trading contracts, grid transmission routes, unit operating conditions, and energy consumption. Elliptic curve cryptography (ECC) is used to ensure the privacy and security of the collected data, and the data is preprocessed using a hash function and uploaded to the blockchain. Combined with a periodic consensus mechanism among independent nodes (such as a multi-view fault-tolerant algorithm based on PBFT), data traceability, immutability, and highly reliable sharing are achieved.

[0029] Firstly, efficient data acquisition interfaces need to be deployed in the information systems of various entities, including power market trading platforms, dispatch centers, and power generation companies. These interfaces, through a standardized adaptation layer, integrate the signing and performance of power trading contracts, actual power flow and real-time dispatch paths, and generator unit operating status parameters (including real-time load, start-stop records, fuel consumption, online carbon emission capture, etc.) with multi-source energy consumption data, achieving automatic data aggregation for multiple business scenarios. This data acquisition process does not rely on manual intervention but is automated through blockchain smart contracts—for example, whenever a new trading contract is generated, a path is adjusted, or key unit operating parameters change, the smart contract can detect these events in real time and automatically trigger the data acquisition and verification process. To prevent the leakage of sensitive commercial data and corporate ownership information in an interconnected environment, all raw data undergoes elliptic curve encryption (ECC) locally before leaving the network, achieving lightweight, efficient, and difficult-to-crack data transmission security.

[0030] Subsequently, the collected encrypted business data is preprocessed using a hash algorithm to obtain a unique and irreversible hash fingerprint, minimizing redundancy and locking content snapshots. This operation not only significantly reduces the data volume burden of subsequent transmission and storage but also ensures the efficiency of subsequent traceability and identity verification. Before entering the blockchain network, data is segmented and encapsulated into data packets with timestamps and digital signatures. Distributed nodes verify the legality and timeliness of the received data through a consensus mechanism. To address the security and efficiency issues of synchronizing large-scale heterogeneous data, an improved method is adopted for the consensus mechanism among nodes. This method involves multi-view adjudication and weighted grouping optimization of the traditional PBFT (Practical Byzantine Fault Tolerance) algorithm, enabling rapid consensus among a majority of nodes in the network and providing strong fault tolerance against risks such as malicious node operations and network jitter. This consensus process not only improves transmission efficiency but also ensures the synchronization and tamper resistance of data across the entire blockchain.

[0031] Data that has completed consensus and verification will be officially recorded on the blockchain and stored in a decentralized, redundant manner within a multi-party distributed ledger. Each piece of data on the chain includes its traceability path, the entity that generated it, a full-process timestamp, a cryptographic signature, and a hash fingerprint, ensuring that any read or access can be traced back to its source and verified. This means that once data at any stage of the power business is recorded, it acquires the characteristics of immutability and permanent traceability, providing a solid data foundation and security guarantee for subsequent path-based carbon emission tracking, transaction responsibility attribution, and even cross-entity investigations. Furthermore, even if the original and processed data stored on the blockchain network fails due to the downtime of individual nodes or malicious attacks, it can still be efficiently recovered from other copies across the network, effectively eliminating the risks of single points of failure and data loss inherent in traditional centralized systems.

[0032] Step 2: Clustering and Decomposition of Electricity Trading Paths and Trustworthy Decoupling

[0033] A weighted similarity-based dynamic clustering algorithm is introduced: For historical and real-time electricity trading contracts, a similarity matrix integrating multi-dimensional features such as trading volume, time period, contract type, and participating entities is designed, and a weighting factor is introduced to prioritize high-carbon emission associated systems. Density-based scalable clustering (such as the improved DBSCAN) is employed to dynamically identify high-frequency, highly correlated trading sub-paths. For a complex transmission path, the main carbon flow nodes and their link weights are automatically decoupled from each stage, forming a traceable trading path grouping system.

[0034] First, based on historical and real-time collected electricity trading contract data, core multi-dimensional features need to be extracted from each transaction, including the traded electricity volume Q, contract signing period T, contract type C, participating entity set S, and actual power transmission path P. To systematically measure the similarity between different transactions, a weighted feature similarity matrix M needs to be constructed. Specifically, for any two transactions i and j, the basic feature Euclidean distance is calculated based on the standardized attributes of each dimension. If power consumption and time period are standardized using Z-score, then the definition is... For discrete features such as contract type and participating entity category, one-hot encoding and Hamming distance are used. For the actual physical path of the transaction, the system uses topological mapping projection to transform each path into a set of nodes and links, and uses Jaccard similarity to measure the overlap between paths, with the formula being: .

[0035] To highlight the importance of high-carbon-emission pathways in path segmentation, a weighting factor is introduced when constructing the comprehensive distance matrix. Let the carbon emission intensity weight be... The final weighted similarity It can be represented as:

[0036] in , For the unit carbon emission factor at both ends of the transaction, ( ... ) for normalized weights, Sensitive clustering response for enhancing carbon emissions.

[0037] Subsequently, an improved density-based spatial clustering algorithm (optimized version DBSCAN) was employed, based on the aforementioned... This serves as a clustering criterion. Unlike traditional DBSCAN's density connectivity determination in Euclidean space, it uses weighted similarity to define dynamic neighborhoods. A threshold is used to adaptively adjust the cluster radius, prioritizing the merging of transaction samples with high carbon emissions and high correlation. All transaction contracts grouped into one category correspond to a set of high-frequency, high-similarity transaction sub-paths in the physical path mapping.

[0038] For each complex multi-node transmission path, the main carbon flow nodes in the path are automatically identified and decomposed through path topology analysis of the grouping and clustering results—that is, those hub nodes and lines that bear large amounts of electrical energy transmission or account for a significant proportion of carbon emissions. This is achieved by constructing node weight vectors. The link weight matrix L characterizes the spatial distribution of carbon emissions in each transaction sub-path, as shown in the formula: ,in Indicates the flow relationship between nodes u and v along the path. This represents the actual amount of electricity carried by the link. The formation of the grouping system provides a solid foundation for the subsequent adaptive tracking and allocation of carbon flows, enabling dynamic location and source tracing of carbon emissions at the path level.

[0039] Step 3: Fine-scale quantification and mapping of path-level carbon emissions

[0040] A segmented weighted correction carbon emission algorithm is proposed: based on real-time operating parameters of generating units (such as load, coal consumption, unit fuel carbon content, and online carbon capture efficiency), the unit power transmission carbon emission intensity is dynamically mapped for each segment of the transaction path. Simultaneously, the carbon content of coal, carbon oxidation rate, and carbon capture correction value are hierarchically incorporated into the carbon emission calculation model to form a segmented weighted correction factor, achieving high-precision calculation of carbon emissions throughout the entire path and on-chain storage of block-level data.

[0041] Based on the clustered and decoupled high-frequency trading path segments and their corresponding carbon flow key points, and relying on real-time collected generator unit operating parameters, a segmented weighted correction carbon emission algorithm is proposed to achieve high-resolution mapping of carbon emission intensity during the cross-regional power flow process. For each segmented power transmission path, the system first retrieves the real-time load of the associated units in that segment. Unit coal consumption Fuel carbon content and carbon oxidation rate And simultaneously obtain online carbon capture efficiency The dynamic calculation formula for the basic carbon emission factor is as follows:

[0042]

[0043] in The carbon emission intensity (kgCO2 / kWh) of the unit's power generation at time t. Coal consumption per unit of electricity generation This refers to the carbon content of coal (including corrections based on high and low calorific values). This is the carbon oxidation rate correction factor (reflecting the actual oxidation ratio). The online carbon capture rate.

[0044] For a complete electricity trading path, suppose it is decomposed into n sub-segments, each corresponding to a different dominant generating unit (or set) and transmission node, and let the carbon emission intensity per unit of electricity transmission for each segment be denoted as . The corresponding power transmission volume for the interval is The cumulative carbon emissions along the route. It can be calculated by segmented weighted superposition:

[0045]

[0046] in The carbon emission segmentation weighted correction factor further incorporates compensation terms such as coal type gradient correction (high / low calorific value coal), carbon capture / loss fluctuation, network scheduling, and batch energy loss. Specifically, this can be expressed as follows:

[0047]

[0048] in This refers to the correction of carbon content in segmented coal combustion. Reflects the network batch energy loss factor. Used to correct temporary adjustments or sudden changes during actual scheduling.

[0049] Once the total carbon emissions along the entire path are calculated, the data for each segment and the total are hashed and mapped to the blockchain ledger via smart contracts. Combined with the aforementioned traceability and encryption links, this ensures that every carbon emissions calculation result is traceable, verifiable, and contract-verifiable. This precise quantification process ensures highly accurate carbon emissions allocation from the source of the power source, through the energy flow to various nodes and across regions, to the load terminal. Whether used for policy verification, responsibility sharing, or carbon accounting and trading, it provides a scientific and operational data foundation.

[0050] Step 4: Carbon Stream Attribution and Source Identification

[0051] Design a subject-object collaborative carbon flow attribution algorithm: Establish a carbon emission responsibility sharing model for market entities participating in multi-path transactions, and adopt a linear attribution weight correction + trust adjustment factor (based on the entity's historical compliance and carbon compliance behavior) to organically allocate the carbon flow of each transaction to each market entity, realize path-level and subject-level two-layer traceability, and generate traceability certificates through full-process chain tracking.

[0052] like Figure 2 As shown, in the carbon flow attribution and source identification steps, the subject-object collaborative carbon flow attribution algorithm focuses on the carbon flow allocation problem under the full-process participation and multi-path interaction of all market parties (subject and object). By combining linear attribution weights and trust factors, it achieves quantitative, dynamic, and traceable carbon responsibility allocation. Its detailed process includes four key steps: subject behavior modeling, subject-object weight relationship characterization, collaborative carbon flow allocation, and multi-dimensional source traceability.

[0053] S401, the algorithm establishes an initial allocation structure based on path-level carbon emissions. For a power trading path P, let the total carbon emissions of the entire path be... The participants along the path (such as power generators, grid transmitters, intermediaries, and end users) constitute a set. During the transaction, each party obtains an initial attribution weight based on factors such as agreement allocation, actual energy transfer, or contractual proportion. ,satisfy The specific allocation can be automatically generated based on energy flow allocation, contract percentage, or proportion negotiated.

[0054] S402, to embody the subject-object synergy concept, needs to consider the actual behavior, status, and responsibilities of the subjects. Define a binary relationship between subject and object roles: the subject (e.g., power generation companies) is responsible for the actual carbon output, while the object (e.g., electricity purchasers and consumers) corresponds to the final carbon consumption. Assign basic attribution parameters to both roles, such as the initial attribution ratio for the subject. (Typically covering the vast majority of emission sources), customer (The distribution between the purchaser and the user is further subdivided according to the actual electricity distribution), and the allocation coefficients of the two satisfy the carbon flow closed loop.

[0055] S403 introduces a subject-object collaborative regulation mechanism. For each participating subject... The algorithm generates a trust adjustment factor based on the entity's historical compliance record and carbon compliance performance (such as joint compliance rate, historical violations, and carbon reduction investment). If the entity has high compliance and excellent performance, it can obtain a better weighting allocation. ), and vice versa. This can be formally modeled as:

[0056]

[0057] in To adjust the weights, Norm() represents normalization. Trust adjustment applies to the subject and object weights, ensuring that collaborative allocation dynamically responds to market participants' behavior while strengthening the incentives and constraints of carbon responsibility traceability.

[0058] The core mathematical expression of attribution allocation is: Subject Final carbon assignment along path P

[0059]

[0060] in and They are respectively As the sharing ratio of the host or the guest (allocated within the host or guest side of this path), , The allocation results will be dynamically adjusted based on actual performance.

[0061] S404: The entire carbon traceability allocation process is implemented by smart contracts hosted on the blockchain. The contracts automatically record each transaction path, segment ownership, subject and object identities and weights, adjustment factors, and carbon allocation results, generating a unique traceability certificate. This certificate links upstream and downstream transactions and historical allocations, providing a verifiable and traceable data foundation for subsequent cross-entity audits, carbon liability accounting, and regulatory verification.

[0062] Step 5: Application and Feedback Correction of Carbon Measurement Results

[0063] Based on a transparent and trustworthy on-chain data foundation, a multi-level, real-time visualized carbon metering and tracking panel is developed to support enterprises' green electricity procurement decisions, carbon footprint traceability and certification, carbon market quota allocation, and cross-regional carbon accounting. The system automatically analyzes and provides feedback on the deviation between actual carbon flows and contractual expectations, and proposes a dynamic calibration incentive mechanism—providing carbon incentive feedback for actions that optimize carbon flow paths and energy consumption structures, driving all entities to continuously optimize low-carbon trading and production methods.

[0064] Based on carbon emission data uploaded to the blockchain using a distributed ledger, a multi-level, real-time carbon measurement and tracking dashboard has been built, providing queryable, visual, and verifiable accurate carbon information services for various users, including enterprises, market regulators, and policymakers. This visualization dashboard not only displays the distribution of carbon emission flows across the entire chain, by path, and by entity, but also dynamically presents the current, historical, and predicted changes in an enterprise's carbon footprint in the form of charts and GIS maps. For enterprises, the system supports green electricity procurement decisions based on carbon intensity and traceability results; for third-party certification bodies and regulatory departments, the system automatically generates carbon footprint traceability certificates and customized reports to support carbon asset certification and compliance audits; for the carbon market, the system automatically drives quota allocation, compliance verification, and multi-regional carbon accounting docking through smart contracts based on the carbon emission data of the entities involved, promoting accurate fulfillment of responsibilities by market participants and cross-regional collaborative trading.

[0065] In terms of carbon measurement result feedback and correction, the system integrates contract management and real-time carbon flow tracking modules to automatically compare actual carbon flows with contractually agreed carbon emissions, pathway structures, and energy consumption composition, identifying and quantifying the sources of deviation. When a significant difference is identified between contractual expectations and reality, the system triggers feedback correction logic—running a dynamic calibration incentive mechanism based on trading behavior, carbon flow optimization performance, and the effectiveness of energy consumption structure adjustments. This mechanism sets thresholds for carbon optimization behavior and, in conjunction with dimensions such as multi-period carbon emission reductions, the proportion of green electricity introduced, or the rate of reduction in pathway losses, automatically assigns enterprises carbon incentive points, credit rewards, or a certain carbon credit boost. For example, if an enterprise introduces more low-carbon electricity by optimizing its procurement pathways and effectively reduces carbon losses in power transmission, the system will assess the optimization effect in real time and record historical performance. After verification, it will automatically release carbon incentive rights, such as increasing the weight of future quota allocations, reducing carbon trading compliance costs, and improving credit ratings, ultimately driving all entities to continuously and efficiently adjust their production and trading methods, forming a positive cycle of low-carbon operations.

[0066] Example:

[0067] Taking a typical inter-regional power transaction of a provincial power grid on June 1, 2024 as an example, the technical solution is systematically explained.

[0068] Assume the region contains:

[0069] Power generators: Thermal power plant A, hydropower plant B, and new energy power plant C;

[0070] Power transmission links: Regional high-voltage transmission company X, inter-regional trunk transmission company Y;

[0071] Electricity users: Large manufacturing company D and data center company E.

[0072] The power transfer paths between the various entities cover multi-level transmission and multi-energy complementarity, which is highly consistent with the typical power grid supply chain.

[0073] During implementation, all nodes are connected to the data acquisition terminal designed in this invention to acquire and record core indicators in real time, including power generation, node input and output power, power loss, and real-time carbon emission coefficient (dynamically updated based on unit type and fuel structure). The data is automatically uploaded to the blockchain preprocessing platform and automatically and strictly verified by smart contract rules to ensure that the data is accurate before entering the allocation and authentication process.

[0074] Core Data Collection Sample Form

[0075] time Power source Power transmission company User terminal Power supply (MWh) Loss (MWh) <![CDATA[Carbon emission factor (tCO2 / MWh)]]> 2024-06-01 A X D 1200 24 0.950 2024-06-01 B Y E 900 28 0.200 2024-06-01 C - E 500 5 0.000

[0076] The system automatically identifies the electricity flow paths for each day, clusters them according to the physical and contractual paths between power generation users, transmission nodes, and electricity consumers, and performs differentiated carbon weight allocation calculations for each path segment. The carbon emission allocation follows these principles:

[0077] By statistically analyzing power loss and electricity flow along the path segment, the carbon emissions corresponding to each kilowatt-hour can be accurately calculated.

[0078] The zero-emission pathway for new energy sources is automatically categorized into the green electricity block, facilitating the generation of green electricity rights certificates in the future.

[0079] By merging and allocating electricity through the same path, the total carbon input and output of the system is kept constant, and duplicate statistics and misallocation of responsibility are prevented.

[0080] Carbon Emission Allocation Results Table

[0081] Path number Electricity trading path Electricity generation (MWh) Transmission loss (MWh) Actual user electricity consumption (MWh) <![CDATA[Carbon emissions (tCO2)]]> On-chain certificate number 01 A → X → D 1200 24 1176 1111.2 20240601-0001 02 B → Y → X → D 600 15 585 117 20240601-0002 03 B → Y → E 300 13 287 57.4 20240601-0003 04 C → E 500 5 495 0 20240601-0004

[0082] After calculation, the system automatically generates digital carbon emission rights certificates (corresponding to path numbers) and invokes a smart contract to execute the allocation, registration, and transfer of carbon emissions. The smart contract logic includes:

[0083] For the same node (such as user D), electricity from different sources and its carbon allocation are recorded on the blockchain separately, clarifying the responsibilities of each part.

[0084] Once the contract is executed, the certificate cannot be altered, ensuring the openness and transparency of subsequent carbon verification and supervision, and preventing tampering or evasion of carbon responsibilities.

[0085] The contract supports concurrent queries and traceability by multiple parties, enabling transparency and convenient verification of carbon flow to end-users.

[0086] On-chain contract execution summary table

[0087] Contract execution steps time node <![CDATA[Carbon emission sharing (tCO2)]]> Responsible Entity Successful / failed blockchain upload Contract generation 2024-06-01 08:00 A → X → D 1111.2 Company D success Contract execution 2024-06-01 08:10 B → Y → X → D 117 Company D success Contract execution 2024-06-01 09:05 B → Y → E 57.4 Enterprise E success Contract execution 2024-06-01 09:15 C → E 0 Enterprise E success

[0088] The system supports full-process tracking of historical data on the blockchain. Taking manufacturing company D as an example, when it applies for product carbon footprint, participates in carbon quota allocation, or obtains green finance credit in the future, it can query all historical electricity sources and carbon allocation details with one click, obtain an immutable carbon footprint report on the blockchain, and achieve full traceability from "source to path to use".

[0089] For example, Company D is required to declare its product carbon emissions to the regulatory agency in June 2024. By retrieving on-chain data from the system, the following details are obtained:

[0090] Power source Power supply (MWh) <![CDATA[Responsible carbon emissions (tCO2)]]> Carbon certificate number A 1176 1111.2 20240601-0001 B 585 117 20240601-0002

[0091] Manufacturing companies can use on-chain data to apply for carbon quotas. At the same time, if they reach a cross-regional green electricity transaction with new energy user E, the system will automatically generate a green electricity certificate (zero carbon emissions), which will facilitate their application for green certification and green financial services.

[0092] By implementing this invention, the scientific rigor and transparency of carbon tracking management in the power grid supply chain are ensured. It also enhances data credibility and decision-making intelligence across multiple scenarios, including regulation, carbon trading, and corporate financial management, reducing errors from manual statistics and the risk of secondary human intervention. Combined with the immutability of blockchain technology, it can be extended to new power systems such as distributed power sources, energy storage, and electric vehicles in the future, achieving comprehensive low-carbon management across the entire ecosystem.

[0093] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0094] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of carbon accounting based on transaction path trustable tracking carbon emission flow positioning identification, characterized in that: The method comprises: Data acquisition and storage, automatically connecting the information systems of the power market trading platform, dispatch center and power generation enterprises through smart contracts, and collecting real-time multi-source heterogeneous data including power trading contracts, power transmission paths, unit operating conditions and energy consumption; Power trading path clustering decomposition, for historical and real-time power trading contracts, a similarity matrix is designed by integrating multi-dimensional features such as transaction volume, time period, contract type and participating subjects, and a density-based scalable clustering algorithm is used to dynamically identify high-frequency and highly associated sub-paths of transactions; Path-level fine quantification of carbon emissions, a segmented weighted correction carbon emission algorithm is proposed: based on real-time operating parameters of the unit, the unit carbon emission intensity of each segmented transaction path is dynamically mapped, the carbon oxidation rate, carbon capture correction value and other factors are layered into the carbon emission calculation model, and a segmented weighted correction factor is formed; Carbon flow attribution and traceability identification, a carbon emission responsibility allocation model is established under the condition of market subject participation in multi-path trading, linear attribution weight correction and trust degree adjustment factor are used to organically allocate the carbon flow of each transaction to each market subject, and the whole process is chain tracked and traceability certificate is generated; Carbon measurement result application and feedback correction, based on the transparent and reliable data base on the chain, a multi-level real-time visual carbon measurement and tracking panel is developed to support enterprise green power procurement decision, carbon footprint traceability certification, carbon market quota allocation and cross-region carbon accounting.

2. The carbon metering method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The data acquisition and storage comprises: deploying efficient data acquisition interfaces in the information systems of the power market trading platform, dispatch center and power generation enterprises, automatically integrating power trading contracts, actual power flow, real-time dispatch path, generator operating state parameters and multi-source energy consumption data through a standardized adaptation layer, and automatically scheduling using a blockchain smart contract to realize event-triggered data acquisition and verification.

3. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The power trading path clustering decomposition comprises: extracting multi-dimensional features including transaction electricity, contract signing period, contract type, participating subject set and power transmission actual path from historical and real-time collected power trading contract data, constructing a weighted feature similarity matrix, introducing carbon emission intensity weight and normalization weight, and using a density-based spatial clustering algorithm to cluster and form high-frequency and high-similarity transaction sub-paths; Then, the main carbon flow nodes in the path are automatically identified and decomposed based on the node weight vector and link weight matrix through path topology analysis, realizing dynamic positioning and tracing of carbon emissions at the path level.

4. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The path-level fine quantification of carbon emissions comprises: based on the clustered and decoupled high-frequency transaction path segments and their corresponding carbon flow nodes, relying on real-time collected generator operating parameters, using a segmented weighted correction carbon emission algorithm, dynamically calculating the basic carbon emission coefficient of each segmented power transmission path based on real-time load, unit coal consumption, fuel carbon content, carbon oxidation rate and online carbon capture efficiency information, and correcting each sub-section using a carbon emission segmented weighted correction factor in multiple dimensions.

5. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The carbon flow attribution and traceability identification includes: adopting a principal-agent collaborative carbon flow attribution algorithm, first establishing a principal behavior modeling and principal-agent weight relationship based on path-level carbon emissions, combining contract proportion, energy flow allocation and distribution to preliminarily attribute weights, then setting the basic attribution parameters of the principal and the agent according to the principal-agent role binary relationship, and introducing a principal-agent collaborative adjustment mechanism, using historical compliance records and carbon performance to generate a trust adjustment factor to dynamically adjust the principal-agent weight allocation; Finally, through the smart contract, the whole process of carbon traceability allocation, including transaction path, segmented attribution, principal-agent identity and weight, adjustment factor and carbon allocation results are on-chain, forming a unique traceability certificate, realizing the accurate attribution of carbon emissions from the path level to the principal level and the whole process of traceable and reliable traceability.

6. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The carbon measurement result application and feedback correction includes: based on the carbon emission data on the distributed ledger, a multi-level and real-time carbon measurement and tracking panel is constructed, which provides queryable, visual and verifiable carbon information services to enterprise, market supervision and policy decision users, displays the carbon emission flow distribution of the whole link, path and principal, and supports carbon footprint traceability certificate generation, carbon asset authentication and quota allocation, and compliance verification.

7. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 1, characterized in that: The collected data is encrypted by elliptic curve after local collection, preprocessed by hash algorithm to generate a unique and irreversible hash fingerprint, then the encrypted data is segmented and packaged into a data packet with timestamp and digital signature, the distributed nodes use an improved PBFT consensus mechanism for verification, the consensus data is on-chain and decentralized redundant storage in the multi-party distributed ledger, each on-chain data is attached with traceability path, principal, timestamp, encryption signature and hash fingerprint, realizing the whole process traceable, tamper-proof data security protection.

8. The carbon accounting method based on transaction path trustable tracking carbon emission flow positioning and identification according to claim 4, characterized in that: The weighted correction factor includes: coal carbon content correction, network batch energy loss factor and dispatching variation correction, after the segmented superposition of the whole path carbon emissions, through hash processing and smart contract mapping to the blockchain ledger, combined with the traceability and encryption link, realizing the traceable, data traceable and contract verifiable of the whole process carbon emission data.

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