Electricity-certificate-carbon multi-source data traceability checking method oriented to green certificate full coverage

Abstract

The invention relates to the technical field of green electric power certificates, carbon emission reduction verification and electric power data collaborative traceability, and discloses an electric-certificate-carbon multi-source data traceability verification method oriented to green certificate full coverage. The system is internally provided with a deployment design module, a real-time receiving module, a data processing module, a consistency analysis module, an edge preprocessing module, an on-chain evidence storage module, an intelligent contract module and a security interaction gateway module, and a data monitoring network is constructed by deploying a collection station and used for executing edge side real-time monitoring of multi-source heterogeneous data. The monitoring data is collected and preprocessed, and the multi-source monitoring effective coverage rate, the electricity-certificate consistency deviation rate and the certificate-carbon anti-duplication electric quantity are calculated based on the preprocessed data; the method is used for dynamically evaluating and improving the coverage blind area of a monitoring network, preliminarily identifying the abnormal deviation between the positioning electric quantity and the green certificate issuing, and preliminarily screening out suspicious data repeatedly applied across the market.

Description

Technical Field

[0001] This invention relates to the fields of green electricity certificates, carbon emission reduction verification, and collaborative traceability of electricity data, specifically a method for traceability and verification of multi-source data on electricity, certificates, and carbon, covering full coverage of green certificates. Background Technology

[0002] With the full implementation of China's renewable energy power consumption responsibility weighting mechanism and the accelerated construction of a unified national electricity market and carbon emission trading market, the green electricity certificate (hereinafter referred to as "green certificate") serves as the sole credential for calculating green electricity consumption on the consumption side, and the reliable traceability of its entire process of issuance, trading and consumption is of paramount importance.

[0003] Currently, the tracking and management of the environmental attributes (green certificates) of green electricity and physical electricity generation and carbon emission reductions (such as CCERs) still suffers from the phenomenon of "data silos." Specifically, this manifests in the following ways: 1) Real-time electricity generation data, electricity consumption data, green certificate issuance records, and carbon market project data are managed by different entities, resulting in heterogeneous systems and inconsistent standards, making automated cross-verification difficult; 2) Verification of the consistency of "certificate-electricity-carbon" data relies on manual comparison after the fact, which is not timely and makes it difficult to detect and block false reporting, duplicate applications, and other behaviors in real time; 3) The monitoring network coverage is incomplete, and it is difficult to collect power generation data in remote areas or distributed energy sources in real time and accurately, resulting in blind spots in traceability; 4) Cross-system data interaction poses privacy and security risks, which restricts the depth of data integration.

[0004] Therefore, existing technologies lack a systematic solution that can integrate multi-source data from power generation, electricity consumption, green certificates, and the carbon market, and achieve closed-loop management of the entire process from data collection, fusion analysis, automatic consistency verification to trusted storage and secure interaction. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a multi-source data traceability and verification method for green certificates covering all aspects of electricity, certificates, and carbon. It has the advantages of achieving accurate traceability across the entire chain, dynamic quantitative assessment of the monitoring network, automatic closed-loop verification and risk warning, and ensuring cross-domain data privacy and security. It solves the problems of fragmented and difficult-to-interoperable electricity, certificate, and carbon data, delayed and inefficient verification, incomplete monitoring coverage with blind spots, and low security and reliability of cross-system data interaction in existing technologies.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a multi-source data traceability and verification method for green certificates covering all aspects of electricity, certificates, and carbon, comprising the following steps:

[0009] Step 1: Establish a multi-source data traceability and verification system for electricity, certificates, and carbon. The system includes a deployment design module, a real-time receiving module, a data processing module, a consistency analysis module, an edge preprocessing module, an on-chain evidence storage module, a smart contract module, and a secure interaction gateway module.

[0010] Step 2: Deploy the design module to build a data monitoring network by deploying acquisition stations, which is used to perform real-time edge monitoring of multi-source heterogeneous data;

[0011] Step 3: The real-time receiving module is used to receive multi-source heterogeneous data in real time, including power generation data, electricity consumption data, green certificate transaction records and carbon asset registration data;

[0012] Step 4: The edge preprocessing module performs format standardization and cleaning on each acquisition station before the data is connected to the data processing module.

[0013] Step 5: The data processing module adds a multi-source data fusion and coverage improvement calculation formula to the preprocessed data from the edge preprocessing module, and calculates the effective coverage of multi-source monitoring. It is used to dynamically assess and improve the coverage blind spots of the monitoring network;

[0014] Step Six: The consistency analysis module adds the formulas for the consistency deviation rate between the electronic certificate and the certificate-carbon anti-suspicion quantity based on the preprocessed data from the edge preprocessing module, and calculates the consistency deviation rate between the electronic certificate and the certificate. With evidence - carbon defense against suspicious electricity It is used to initially identify abnormal discrepancies between the measured electricity consumption and the issuance of green certificates, and to initially screen out suspicious data of repeated applications across markets;

[0015] Step 7: The on-chain evidence storage module uses consortium blockchain technology to put the standard data packets output from edge preprocessing, data fusion calculation and consistency analysis, as well as the hash value and timestamp of their processing logs, onto the blockchain, forming a full-link data fingerprint chain from power generation to transaction;

[0016] Step 8: The smart contract module deploys an automatic verification and alarm contract through the blockchain platform, and analyzes the data fingerprints stored on the chain and the consistency deviation rate of the electronic certificate output by the consistency analysis module. With evidence - carbon defense against suspicious electricity Perform real-time logical judgment and status monitoring to achieve second-level detection, automatic early warning and intervention of data anomalies and conflicts of interest;

[0017] Step 9: The secure interaction gateway module manages cross-domain privacy computing and high-performance communication by integrating a secure multi-party computation engine and a TLS two-way authentication mechanism based on national cryptographic certificates, enabling trusted, real-time, and high-concurrency data exchange between the power generation side, the power consumption side, the green certificate platform, and the carbon market under encryption and privacy protection.

[0018] Preferably, the deployment design module divides the data sources required for traceability and verification into power generation monitoring stations, power consumption monitoring stations, green certificate issuance stations, and carbon market data stations, and designs the deployment of sensors and communication networks for the four data acquisition stations to connect to the real-time receiving module.

[0019] Preferably, the power generation side monitoring station is responsible for monitoring the real-time production data of renewable energy wind turbines, photovoltaic arrays, and hydropower units.

[0020] Preferably, the electricity consumption monitoring station is responsible for monitoring the real-time load data of green electricity consumers.

[0021] Preferably, the green certificate issuance station is responsible for monitoring and synchronizing the issuance and transaction data of the National Renewable Energy Power Generation Project Information Management Platform.

[0022] Preferably, the carbon market data station is responsible for monitoring and synchronizing project approval, emission reduction issuance and registration data from the National Greenhouse Gas Voluntary Emission Reduction Trading System and other carbon markets.

[0023] Preferably, the data processing module calculates the effective coverage rate of multi-source monitoring based on the preprocessed data. The calculation formula is as follows: In the formula, Indicates the effective coverage rate of multi-source monitoring. Indicates the number of monitoring points for smart meters. Indicates the number of IoT sensor monitoring points. Indicates the number of carbon satellite remote sensing grids. This indicates the total number that needs to be monitored according to the regional theory. This indicates the weighting of the accuracy and reliability of smart meter monitoring. This indicates the density and diversity weights of IoT sensor monitoring. Indicates the spatial coverage integrity weight of carbon satellite remote sensing. This represents the correction coefficient for multi-source data fusion.

[0024] Preferably, the consistency analysis module calculates the statistical deviation between the actually reported settlement electricity volume and the electricity volume represented by the officially issued green certificate based on the preprocessed data. This is used to quickly locate abnormal deviations between electricity consumption and green certificate issuance; the calculation formula is as follows: In the formula, This represents the electricity-certificate consistency deviation rate, which is the statistical deviation between the actual reported settlement electricity volume and the electricity volume represented by the green certificate issued by the official authority. This indicates the actual amount of electricity reported to the grid by the power generation side for settlement. This refers to the green electricity volume represented by the issued green certificates that are monitored at the green certificate issuance station and correspond to the power generation project and time period.

[0025] Preferably, the consistency analysis module calculates, based on preprocessed data, the suspected electricity consumption that may be counted twice in the green certificate system and the carbon market system for the same project, indicating potential duplicate calculations of environmental rights. This is used for preliminary screening of suspicious data related to duplicate claims across markets. The calculation formula is as follows: In the formula, This refers to suspected electricity volume for green certificate-carbon market protection, which means that the environmental rights of the same project may be calculated repeatedly in both the green certificate system and the carbon market system. This indicates the declared electricity volume of project i as monitored at the green certificate issuance station. This represents the amount of emission reductions issued for project i as monitored by the carbon market data station.

[0026] Preferably, the on-chain evidence storage module calls Fabric or Chang'an Chain evidence storage contract to generate a unique hash fingerprint for the processed data packet and attach a Beidou time stamp. After consensus, it is written into an immutable block to form a full-link data fingerprint chain from power generation to transaction.

[0027] The smart contract module deploys on-chain smart contracts containing logic for judging the consistency deviation threshold between electricity and certificates and logic for screening duplicate calculations of certificates and carbon. When the consistency deviation rate between electricity and certificates is calculated in real time... >Preset threshold or certificate - Carbon anti-suspicious electricity volume When the value is >0, the smart contract will automatically trigger an early warning event and lock the trading permissions of the relevant green certificates or CCER assets. At the same time, it will push an immutable alarm containing abnormal details, timestamps and data fingerprints to the regulatory node.

[0028] The secure interactive gateway module uses the national cryptographic SM9 algorithm to achieve encrypted data transmission, and combines zero-knowledge proof technology to verify the authenticity of data without leaking the original information. Through dynamic load balancing and message queue middleware, it ensures the real-time and reliable transmission of tens of thousands of data packets per second between each collection station and the central node, realizing secure data interoperability between the power generation side, the power consumption side, the green certificate platform and the carbon market.

[0029] Compared with existing technologies, this invention provides a multi-source data traceability and verification method for green certificates covering all aspects of electricity, certificates, and carbon, which has the following beneficial effects:

[0030] 1. This invention deploys a categorized monitoring system across four stations—power generation, power consumption, green certificate issuance, and carbon market data—through a modular design. This system constructs a full-chain monitoring network encompassing power generation, green electricity consumption, environmental rights issuance, and carbon emission reduction issuance. This enables precise traceability and cross-verification of the entire lifecycle of electricity data, from generation and transmission to verification and trading. Specifically, BeiDou / GPS spatiotemporal tags on the power generation side ensure data spatiotemporal accuracy of ≤5 meters and 20 nanoseconds. On the power consumption side, unified social credit codes and industry codes accurately depict energy consumption profiles. The on-chain synchronization mechanism between the green certificate issuance station and the carbon market data station ensures the real-time nature and authority of the official benchmark for rights data. Ultimately, cross-verification of data from the four stations eliminates the risk of single-point data falsification, achieving the beneficial effects of improving the credibility of green electricity traceability, preventing double-counting of environmental rights, and supporting the precise consumption of electricity through the integration of certificates and electricity.

[0031] 2. This invention calculates the effective coverage rate of multi-source monitoring. The deployment integrity of the above four monitoring stations was quantitatively evaluated. When the effective coverage rate Fa of multi-source monitoring was in the range of [0%, 90%), the system automatically identified coverage blind spots and triggered a sensor supplementation deployment work order, prioritizing the addition of IoT sensors and smart meters in the blind spots. When the effective coverage rate Fa of multi-source monitoring was in the range of [90%, 95%), a dynamic weight optimization program was initiated to increase the weight of satellite data. With adjustment and fusion correction coefficient Achieve low-cost coverage enhancement; when the effective coverage rate Fa of multi-source monitoring is in the range of [95%, 100%], maintain the current monitoring network configuration and periodically verify the effectiveness of weights, and dynamically allocate surplus monitoring resources to newly emerging project areas. Finally, through continuous calculation of Fa and weight feedback adjustment, improve the system network coverage and reduce monitoring costs, thereby increasing resource utilization compared to traditional methods.

[0032] 3. This invention calculates and represents the consistency deviation rate between electronic and electronic certificates. Real-time consistency verification is performed between the actual reported settlement electricity volume and the electricity volume represented by the green certificate issuance on the power generation side. When the deviation is within the range of (0%, 1%), it is considered a normal statistical deviation, the system automatically verifies and generates a compliance report; when... When the value is within the range of (1%, 5%), a yellow alert is triggered, and a manual review process is initiated to verify the power generation curve and the timestamp on the green certificate platform. When the value is within the range of (5%, +∞), a red alert is triggered. The smart contract automatically locks the trading permissions of the green certificate assets corresponding to the power generation project and pushes an anomaly alert to the regulatory node. At the same time, it retrieves the data fingerprint stored on the chain for traceability and accountability. Ultimately, through the hierarchical early warning and automated handling mechanism, the beneficial effects of quickly locating abnormal deviations between electricity volume and green certificates, preventing false reporting of electricity volume to obtain subsidies, and improving the accuracy of green certificate issuance are achieved.

[0033] 4. This invention calculates the suspected electricity consumption for environmental benefits. This is used to identify the risk of duplicate reporting of the same renewable energy project between the green certificate system and the carbon market system, and will be calculated. Substituting this into the automatic screening logic of the smart contract, a dual matching judgment is performed, combining spatial geofencing (overlap of power generation equipment coordinates with PPID boundary > 80%) and temporal overlap rate (> 80%). When the value is 0, it is determined that there is no risk of duplication, and the environmental rights of the project are transferred normally; when... When the value is greater than 0, it is determined that there is suspicious overlap, triggering a deep verification process and freezing the green certificate trading and CCER issuance authority of the project. At the same time, a list of suspicious projects is pushed to the cross-market regulatory alliance nodes. Ultimately, by quantifying suspicious electricity and linking it with automated risk control, the project achieves the beneficial effects of accurately screening cross-market duplicate applications, avoiding multiple calculations of environmental rights, and maintaining the credibility of the carbon-electricity market. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0035] 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.

[0036] Please see Figure 1 A multi-source data traceability and verification method for green certificates covering all electricity, certificates, and carbon sources includes the following steps:

[0037] Step 1: Establish a multi-source data traceability and verification system for electricity, certificates, and carbon. The system includes a deployment design module, a real-time receiving module, a data processing module, a consistency analysis module, an edge preprocessing module, an on-chain evidence storage module, a smart contract module, and a secure interaction gateway module.

[0038] Step 2: Deploy the design module to build a data monitoring network by deploying acquisition stations, which is used to perform real-time edge monitoring of multi-source heterogeneous data;

[0039] Step 3: The real-time receiving module is used to receive multi-source heterogeneous data in real time, including power generation data, electricity consumption data, green certificate transaction records and carbon asset registration data;

[0040] Step 4: The edge preprocessing module performs format standardization and cleaning on each acquisition station before the data is connected to the data processing module.

[0041] Step 5: The data processing module adds a multi-source data fusion and coverage improvement calculation formula to the preprocessed data from the edge preprocessing module, and calculates the effective coverage of multi-source monitoring. It is used to help solve the deficiencies in real-time monitoring and coverage. By adjusting the weights and introducing satellite data, it can dynamically assess and improve the coverage blind spots of the monitoring network.

[0042] Step Six: The consistency analysis module adds the formulas for the consistency deviation rate between the electronic certificate and the certificate-carbon anti-suspicion quantity based on the preprocessed data from the edge preprocessing module, and calculates the consistency deviation rate between the electronic certificate and the certificate. With evidence - carbon defense against suspicious electricity It is used to initially identify abnormal discrepancies between the measured electricity consumption and the issuance of green certificates, and to initially screen out suspicious data of repeated applications across markets;

[0043] Step 7: The on-chain evidence storage module uses consortium blockchain technology to put the standard data packets output from edge preprocessing, data fusion calculation and consistency analysis, as well as the hash value and timestamp of their processing logs, onto the blockchain, forming a full-link data fingerprint chain from power generation to transaction;

[0044] Step 8: The smart contract module deploys an automatic verification and alarm contract through the blockchain platform, and analyzes the data fingerprints stored on the chain and the consistency deviation rate of the electronic certificate output by the consistency analysis module. With evidence - carbon defense against suspicious electricity Perform real-time logical judgment and status monitoring to achieve second-level detection, automatic early warning and intervention of data anomalies and conflicts of interest;

[0045] Step Nine: The secure interaction gateway module manages cross-domain privacy computing and high-performance communication by integrating a secure multi-party computation (MPC) engine and a TLS two-way authentication mechanism based on national cryptographic certificates. This enables trusted, real-time, and high-concurrency data exchange between the power generation side, the power consumption side, the green certificate platform, and the carbon market under encryption and privacy protection. This module can support tens of thousands of data packets per second for secure cross-domain exchange and collaborative computing while ensuring that the data of each participant is available but not visible.

[0046] The deployment design module divides the data sources required for traceability and verification into power generation monitoring stations, power consumption monitoring stations, green certificate issuance stations, and carbon market data stations, and designs the deployment of sensors and communication networks for the four data acquisition stations to connect with the real-time receiving module.

[0047] The power generation monitoring station is responsible for monitoring the real-time production data of renewable energy wind turbines, photovoltaic arrays and hydropower units, including but not limited to total power generation (active / reactive), cumulative on-grid electricity, equipment operating conditions (environmental and status parameters such as temperature, speed, and irradiance), and precise spatiotemporal tags (latitude and longitude, timestamp) based on BeiDou / GPS, to ensure that the electricity data can be traced back to the specific power generation equipment and the precise time period.

[0048] The electricity consumption monitoring station is responsible for monitoring the real-time load data of green electricity consumers (enterprises, industrial parks, aggregators, etc.), including but not limited to total active power, time-of-use metered electricity, power factor, load characteristic curve, and user identification (unified social credit code, industry classification code), providing an accurate energy consumption profile for the unified electricity consumption certification.

[0049] The Green Certificate Issuance Station is responsible for monitoring and synchronizing the issuance and transaction data of the National Renewable Energy Power Generation Project Information Management Platform (Green Certificate Platform), including but not limited to the green certificate issuance batch number, corresponding project filing code, approved electricity volume, generation time, initial owner, ownership change records (trading counterparty, time, electricity volume), and green certificate status (issued / traded / cancelled), serving as the official traceability benchmark for green power environmental rights.

[0050] The carbon market data station is responsible for monitoring and synchronizing project approval, emission reduction issuance, and registration data from the National Greenhouse Gas Voluntary Emission Reduction Trading System (such as CCER) and other carbon markets. This includes, but is not limited to, the unique emission reduction project code (PPID), methodology type, cumulative emission reduction during the monitoring period, issuance date, project owner information, project geographical boundary coordinates, and key parameters of the filed monitoring plan (MP). This provides a verification benchmark to prevent the double-counting and claiming of multiple environmental rights related to electricity, certificates, and carbon.

[0051] The advantages are as follows: By deploying the above-mentioned classified monitoring of four stations—power generation, power consumption, green certificate issuance, and carbon market data—a full-chain monitoring network is constructed, encompassing power generation, green electricity consumption, environmental rights issuance, and carbon emission reduction issuance. This enables precise traceability and cross-verification of the entire lifecycle of electricity data generation, transmission, verification, and trading. Specifically, the BeiDou / GPS spatiotemporal tags on the power generation side ensure data spatiotemporal accuracy of ≤5 meters and 20 nanoseconds, while the unified social credit code and industry code on the power consumption side enable precise depiction of energy consumption profiles. The on-chain synchronization mechanism between the green certificate issuance station and the carbon market data station ensures the real-time nature and authority of the official benchmark for rights data. Ultimately, cross-verification of data from the four stations eliminates the risk of single-point data falsification, achieving the beneficial effects of improving the credibility of green electricity traceability, preventing double-counting of environmental rights, and supporting the precise consumption of electricity through the integration of certificates and electricity.

[0052] The data processing module, based on preprocessed data, adds a multi-source data fusion and coverage improvement calculation formula to calculate the effective coverage of multi-source monitoring. It is used to help solve the deficiencies in real-time monitoring and coverage, and its calculation formula is: In the formula, Indicates the effective coverage rate of multi-source monitoring. Indicates the number of monitoring points for smart meters. Indicates the number of IoT sensor monitoring points. Indicates the number of carbon satellite remote sensing grids. This indicates the total number that needs to be monitored according to the regional theory. This indicates the weighting of the accuracy and reliability of smart meter monitoring. This indicates the density and diversity weights of IoT sensor monitoring. Indicates the spatial coverage integrity weight of carbon satellite remote sensing. This represents the multi-source data fusion correction coefficient, used to correct overestimation of coverage caused by data overlap or redundancy. By adjusting weights and introducing satellite data, it dynamically assesses and improves the coverage blind spots of the monitoring network.

[0053] The advantage is that it calculates the effective coverage rate of multi-source monitoring. The deployment integrity of the above four monitoring stations was quantitatively evaluated. When the effective coverage rate Fa of multi-source monitoring was in the range of [0%, 90%), the system automatically identified coverage blind spots and triggered a sensor supplementation deployment work order, prioritizing the addition of IoT sensors and smart meters in the blind spots. When the effective coverage rate Fa of multi-source monitoring was in the range of [90%, 95%), a dynamic weight optimization program was initiated to increase the weight of satellite data. With adjustment and fusion correction coefficient Achieve low-cost coverage enhancement; when the effective coverage rate Fa of multi-source monitoring is in the range of [95%, 100%], maintain the current monitoring network configuration and periodically verify the effectiveness of weights, and dynamically allocate surplus monitoring resources to newly emerging project areas. Finally, through continuous calculation of Fa and weight feedback adjustment, improve the system network coverage and reduce monitoring costs, thereby increasing resource utilization compared to traditional methods.

[0054] The consistency analysis module, based on preprocessed data, adds a formula for calculating the statistical deviation between the actual reported settlement electricity volume and the electricity volume represented by the officially issued green certificate. This is used to quickly locate abnormal deviations between electricity consumption and green certificate issuance; the calculation formula is as follows: In the formula, This represents the electricity-certificate consistency deviation rate, which is the statistical deviation between the actual reported settlement electricity volume and the electricity volume represented by the green certificate issued by the official authority. This indicates the actual amount of electricity reported to the grid by the power generation side for settlement. This refers to the green electricity volume represented by the issued green certificates that are monitored at the green certificate issuance station and correspond to the power generation project and time period.

[0055] The advantage is that it can calculate and represent the consistency deviation rate between the electronic and electronic certificates. Real-time consistency verification is performed between the actual reported settlement electricity volume and the electricity volume represented by the green certificate issuance on the power generation side. When the deviation is within the range of (0%, 1%), it is considered a normal statistical deviation, the system automatically verifies and generates a compliance report; when... When the value is within the range of (1%, 5%), a yellow alert is triggered, and a manual review process is initiated to verify the power generation curve and the timestamp on the green certificate platform. When the value is within the range of (5%, +∞), a red alert is triggered. The smart contract automatically locks the trading permissions of the green certificate assets corresponding to the power generation project and pushes an anomaly alert to the regulatory node. At the same time, it retrieves the data fingerprint stored on the chain for traceability and accountability. Ultimately, through the hierarchical early warning and automated handling mechanism, the beneficial effects of quickly locating abnormal deviations between electricity volume and green certificates, preventing false reporting of electricity volume to obtain subsidies, and improving the accuracy of green certificate issuance are achieved.

[0056] Based on preprocessed data, the consistency analysis module adds a formula for calculating suspected duplicate electricity amounts for the same project in the green certificate system and the carbon market system, which may result in duplicate calculations of environmental rights. This is used for preliminary screening of suspicious data related to duplicate claims across markets. The calculation formula is as follows: In the formula, This refers to suspected electricity volume for green certificate-carbon market protection, which means that the environmental rights of the same project may be calculated repeatedly in both the green certificate system and the carbon market system. This indicates the declared electricity volume of project i as monitored at the green certificate issuance station. This represents the issued emission reductions monitored by project i at the carbon market data station, converted into equivalent electricity generation according to the corresponding methodology. This formula estimates the overlapping electricity generation at risk of duplicate reporting by taking the smaller of the two values, thus providing a target range for subsequent accurate verification.

[0057] The advantage is: by calculating the suspected electricity consumption for environmental rights. This is used to identify the risk of duplicate reporting of the same renewable energy project between the green certificate system and the carbon market system, and will be calculated. Substituting this into the automatic screening logic of the smart contract, a dual matching judgment is performed, combining spatial geofencing (overlap of power generation equipment coordinates with PPID boundary > 80%) and temporal overlap rate (> 80%). When the value is 0, it is determined that there is no risk of duplication, and the environmental rights of the project are transferred normally; when... When the value is greater than 0, it is determined that there is suspicious overlap, triggering a deep verification process and freezing the green certificate trading and CCER issuance authority of the project. At the same time, a list of suspicious projects is pushed to the cross-market regulatory alliance nodes. Ultimately, by quantifying suspicious electricity and linking it with automated risk control, the project achieves the beneficial effects of accurately screening cross-market duplicate applications, avoiding multiple calculations of environmental rights, and maintaining the credibility of the carbon-electricity market.

[0058] The on-chain evidence storage module calls Fabric or Changan Chain evidence storage contract to generate a unique hash fingerprint for the processed data packets (edge ​​preprocessing stage, data fusion calculation stage and consistency analysis stage) and attach a Beidou time stamp. After consensus, it is written into an immutable block to form a full-link data fingerprint chain from power generation to transaction.

[0059] (1) Edge preprocessing stage: Generate hash values ​​for each site's cleaned and standardized data packets, and attach a BeiDou time stamp;

[0060] (2) Data fusion calculation stage: Evaluation of coverage results after multi-source data fusion ( The data (value) and intermediate process data are used to generate a proof hash;

[0061] (3) Consistency analysis stage: The consistency deviation rate between the electronic and physical certificates ( ) and the certificate - carbon defense against suspicious electricity ( The calculation results and triggering logic are recorded and stored on the blockchain for evidence preservation.

[0062] The smart contract module deploys on-chain smart contracts containing logic for judging the consistency deviation threshold between electricity and certificates and logic for screening duplicate calculations of certificates and carbon. When the consistency deviation rate between electricity and certificates is calculated in real time... >Preset threshold (e.g., 1%) or certificate - carbon anti-suspicious electricity volume When the value is >0, the smart contract will automatically trigger an early warning event and lock the trading permissions of the relevant green certificates or CCER assets. At the same time, it will push an immutable alarm containing abnormal details, timestamps and data fingerprints to the regulatory node.

[0063] The secure interactive gateway module uses the national cryptographic SM9 algorithm to achieve encrypted data transmission, and combines zero-knowledge proof technology to verify the authenticity of data without leaking the original information. Through dynamic load balancing and message queue middleware, it ensures the real-time and reliable transmission of tens of thousands of data packets per second between each collection station and the central node, realizing secure data interoperability between the power generation side, the power consumption side, the green certificate platform and the carbon market.

[0064] The advantages are: through the collaborative work of the on-chain evidence storage module, smart contract module, and secure interaction gateway module, a trustworthy, efficient, and secure cross-domain data verification and rights management system is constructed. Specifically, the on-chain evidence storage module uses consortium blockchain technology to solidify the hash fingerprints and BeiDou timestamps of key processing steps, ensuring that the entire process of data is auditable, traceable, and tamper-proof; the smart contract module... and The threshold judgment logic is coded to achieve automatic detection, early warning and asset locking of data anomalies and rights conflicts within seconds; the secure interaction gateway module integrates MPC and national cryptographic TLS technology to ensure that the data of each participant can be used but not seen to complete tens of thousands of concurrent secure exchanges and collaborative calculations. Finally, through the organic linkage of the three modules, the system achieves the beneficial effect of improving verification efficiency and reducing the cost of manual intervention.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for tracing and verifying multi-source data on electricity, certificates, and carbon for full coverage of green certificates, characterized in that: Includes the following steps: Step 1: Establish a multi-source data traceability and verification system for electricity, certificates, and carbon. The system includes a deployment design module, a real-time receiving module, a data processing module, a consistency analysis module, an edge preprocessing module, an on-chain evidence storage module, a smart contract module, and a secure interaction gateway module. Step 2: Deploy the design module to build a data monitoring network by deploying acquisition stations, which is used to perform real-time edge monitoring of multi-source heterogeneous data; Step 3: The real-time receiving module is used to receive multi-source heterogeneous data in real time, including power generation data, electricity consumption data, green certificate transaction records and carbon asset registration data; Step 4: The edge preprocessing module performs format standardization and cleaning on each acquisition station before the data is connected to the data processing module. Step 5: The data processing module adds a multi-source data fusion and coverage improvement calculation formula to the preprocessed data from the edge preprocessing module, and calculates the effective coverage of multi-source monitoring. It is used to dynamically assess and improve the coverage blind spots of the monitoring network; Step Six: The consistency analysis module adds the formulas for the consistency deviation rate between the electronic certificate and the certificate-carbon anti-suspicion quantity based on the preprocessed data from the edge preprocessing module, and calculates the consistency deviation rate between the electronic certificate and the certificate. With evidence - carbon defense against suspicious electricity It is used to initially identify abnormal discrepancies between the measured electricity consumption and the issuance of green certificates, and to initially screen out suspicious data of repeated applications across markets; Step 7: The on-chain evidence storage module uses consortium blockchain technology to put the standard data packets output from edge preprocessing, data fusion calculation and consistency analysis, as well as the hash value and timestamp of their processing logs, onto the blockchain, forming a full-link data fingerprint chain from power generation to transaction; Step 8: The smart contract module deploys an automatic verification and alarm contract through the blockchain platform, and analyzes the data fingerprints stored on the chain and the consistency deviation rate of the electronic certificate output by the consistency analysis module. With evidence - carbon defense against suspicious electricity Perform real-time logical judgment and status monitoring to achieve second-level detection, automatic early warning and intervention of data anomalies and conflicts of interest; Step 9: The secure interaction gateway module manages cross-domain privacy computing and high-performance communication by integrating a secure multi-party computation engine and a TLS two-way authentication mechanism based on national cryptographic certificates, enabling trusted, real-time, and high-concurrency data exchange between the power generation side, the power consumption side, the green certificate platform, and the carbon market under encryption and privacy protection.

2. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 1, characterized in that: The deployment design module divides the data sources required for traceability and verification into power generation monitoring stations, power consumption monitoring stations, green certificate issuance stations, and carbon market data stations, and designs the deployment of sensors and communication networks for the four data acquisition stations to connect with the real-time receiving module.

3. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 2, characterized in that: The power generation side monitoring station is responsible for monitoring the real-time production data of renewable energy wind turbines, photovoltaic arrays, and hydropower units.

4. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 2, characterized in that: The electricity consumption monitoring station is responsible for monitoring the real-time load data of green electricity consumers.

5. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 2, characterized in that: The green certificate issuance station is responsible for monitoring and synchronizing the issuance and transaction data of the National Renewable Energy Power Generation Project Information Management Platform.

6. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 2, characterized in that: The carbon market data station is responsible for monitoring and synchronizing project approval, emission reduction issuance, and registration data from the National Greenhouse Gas Voluntary Emission Reduction Trading System and other carbon markets.

7. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 1, characterized in that: The data processing module calculates the effective coverage rate of multi-source monitoring based on the preprocessed data. The calculation formula is as follows: In the formula, Indicates the effective coverage rate of multi-source monitoring. Indicates the number of monitoring points for smart meters. Indicates the number of IoT sensor monitoring points. Indicates the number of carbon satellite remote sensing grids. This indicates the total number that needs to be monitored according to the regional theory. This indicates the weighting of the accuracy and reliability of smart meter monitoring. This indicates the density and diversity weights of IoT sensor monitoring. Indicates the spatial coverage integrity weight of carbon satellite remote sensing. This represents the correction coefficient for multi-source data fusion.

8. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 1, characterized in that: The consistency analysis module calculates the statistical deviation between the actually reported settlement electricity volume and the electricity volume represented by the officially issued green certificates based on preprocessed data. This is used to quickly locate abnormal deviations between electricity consumption and green certificate issuance; the calculation formula is as follows: In the formula, This represents the electricity-certificate consistency deviation rate, which is the statistical deviation between the actual reported settlement electricity volume and the electricity volume represented by the green certificate issued by the official authority. This indicates the actual amount of electricity reported to the grid by the power generation side for settlement. This refers to the green electricity volume represented by the issued green certificates that are monitored at the green certificate issuance station and correspond to the power generation project and time period.

9. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates as described in claim 1, characterized in that: The consistency analysis module calculates, based on preprocessed data, the suspected electricity consumption that might have been counted twice in the green certificate system and carbon market system for the same project, indicating potential duplicate calculations of environmental rights. This is used for preliminary screening of suspicious data related to duplicate claims across markets. The calculation formula is as follows: In the formula, This refers to suspected electricity volume for green certificate-carbon market protection, which means that the environmental rights of the same project may be calculated repeatedly in both the green certificate system and the carbon market system. This indicates the declared electricity volume of project i as monitored at the green certificate issuance station. This represents the amount of emission reductions issued for project i as monitored by the carbon market data station.

10. The method for multi-source data traceability and verification of electricity, certificates, and carbon for full coverage of green certificates according to claim 1, characterized in that: The on-chain evidence storage module calls Fabric or Chang'an Chain evidence storage contract to generate a unique hash fingerprint for the processed data packet and attach a Beidou time stamp. After consensus, it is written into an immutable block to form a full-link data fingerprint chain from power generation to transaction. The smart contract module deploys on-chain smart contracts containing logic for judging the consistency deviation threshold between electricity and certificates and logic for screening duplicate calculations of certificates and carbon. When the consistency deviation rate between electricity and certificates is calculated in real time... >Preset threshold or certificate - Carbon anti-suspicious electricity volume When the value is >0, the smart contract will automatically trigger an early warning event and lock the trading permissions of the relevant green certificates or CCER assets. At the same time, it will push an immutable alarm containing abnormal details, timestamps and data fingerprints to the regulatory node. The secure interactive gateway module uses the national cryptographic SM9 algorithm to achieve encrypted data transmission, and combines zero-knowledge proof technology to verify the authenticity of data without leaking the original information. Through dynamic load balancing and message queue middleware, it ensures the real-time and reliable transmission of tens of thousands of data packets per second between each collection station and the central node, realizing secure data interoperability between the power generation side, the power consumption side, the green certificate platform and the carbon market.

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