A blockchain-based comprehensive method and system for measuring carbon emission reduction

Abstract

This invention belongs to the field of carbon emission measurement technology. It discloses a comprehensive carbon emission reduction measurement method and system based on blockchain, including establishing a hierarchical data acquisition architecture and introducing a carbon emission description language. It collects and structures heterogeneous internal data through a protocol converter to obtain multi-source heterogeneous data; builds a smart oracle network to dynamically access external data sources; and uses a BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset. It constructs a dual-chain hybrid blockchain architecture including a main chain and side chains, automatically calculating carbon emission intensity based on the basic trusted carbon dataset. Data with carbon emission intensity exceeding a preset carbon emission intensity threshold is marked as high-carbon emission behavior data, triggering account freezing operations. This improves the scientific rigor, effectiveness, and transparency of carbon emission reduction management, providing strong technical support for achieving carbon emission reduction targets.

Description

Technical Field

[0001] This invention relates to the field of carbon emission measurement technology, and more specifically, to a blockchain-based comprehensive carbon emission reduction measurement method and system. Background Technology

[0002] Patent publication number CN114493868A discloses a blockchain-based carbon trading data processing method and system. This invention relates to the field of carbon trading technology. The method includes accepting data from different tenants and customers, then using a unified customer access management center to ensure data security. Different customer data is allocated to different ledgers. The system maintains a configurable correspondence between system organizations and blockchain organizations, querying the blockchain organization and ledger information to determine the final information to be submitted to the blockchain. This blockchain-based carbon trading data processing method and system proposes to build a blockchain-based carbon emission verification internet platform. It uses blockchain technology to directly collect data in real time, automatically generating reports on the platform. The report results can be directly accessed by regulatory and trading systems. Regulators, traders, and financial institutions can all view the original data, thereby ensuring a transparent, standardized, and efficient carbon emission verification process.

[0003] Existing methods for comprehensive carbon emission reduction measurement mainly have the following problems:

[0004] Existing carbon emission reduction measurement technologies face multiple challenges and struggle to adapt to the demands of complex data environments and dynamic industrial structures. These challenges are mainly reflected in the following aspects:

[0005] Existing measurement methods generally use fixed thresholds to determine carbon emissions exceeding limits, which leads to insufficient measurement accuracy and rigid judgment standards. Carbon emission data usually relies on multi-source sensing terminals and remote transmission networks for acquisition. The acquisition process is susceptible to interference from factors such as equipment failure, communication interruption, sensor error and data tampering, resulting in insufficient data integrity and accuracy.

[0006] In blockchain application scenarios, although smart contracts can achieve automated punishment for carbon emission regulation, their "automatic execution and immutability" characteristics make the judgment highly dependent on input data and lack data anomaly identification and fault tolerance mechanisms.

[0007] With industrial upgrading and energy system transformation, carbon emission characteristics are showing a dynamic trend. However, existing methods lack the ability to adapt to multi-source data environments and industrial structure changes. Fixed measurement models and threshold systems are difficult to capture the emission patterns of emerging industries or emission fluctuations caused by energy structure adjustments, resulting in a shortened life cycle of measurement methods and failing to meet long-term and continuous regulatory needs.

[0008] In view of this, the present invention proposes a comprehensive carbon emission reduction measurement method and system based on blockchain to solve the above problems. Summary of the Invention

[0009] To overcome the aforementioned shortcomings of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a comprehensive carbon emission reduction measurement method based on blockchain, comprising:

[0010] S1. Establish a hierarchical data acquisition architecture and introduce a carbon emission description language. Use a protocol converter to collect and structure heterogeneous internal data to obtain multi-source heterogeneous data. Build an intelligent oracle network to dynamically access external data sources and use the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset.

[0011] S2. Construct a dual-chain hybrid blockchain architecture that includes a main chain and side chains, and combine it with a carbon credit staking mechanism to automatically calculate carbon emission intensity based on a basic trusted carbon dataset; mark data with carbon emission intensity greater than a preset carbon emission intensity threshold as high carbon emission behavior data and trigger account freezing operations.

[0012] S3. Based on high carbon emission behavior data, a differential verification mechanism is used to identify changes in carbon emissions, and combined with the carbon flow topology generated by the product structure BOM table, abnormal behavior is tracked; the high carbon emission behavior data is processed for credible verification to obtain credible verification data.

[0013] S4. Map trusted verified data to NFT carbon credit tokens and construct a three-dimensional carbon credit identifier to form tradable carbon assets; introduce mainstream market price information and construct a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets.

[0014] S5. Construct a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions to provide a four-dimensional visual display of NFT carbon credit tokens.

[0015] Preferably, the method for acquiring multi-source heterogeneous data includes:

[0016] Based on the source type of carbon emission data, the internal carbon emission data sources of enterprises are divided into layers to establish a hierarchical data acquisition architecture. The hierarchical data acquisition architecture is used to collect internal heterogeneous data related to carbon emissions of enterprises. The hierarchical data acquisition architecture includes an equipment sensing layer, a business system layer, a monitoring and control layer, and an edge computing layer.

[0017] A carbon emission description language, which includes field definitions, context binding, and emission factor mapping, is introduced to semantically model the collected internal heterogeneous data and represent it as a unified carbon emission data field. The carbon emission data field is mapped to a CEDL format data structure through a protocol converter, thereby outputting unified structured multi-source heterogeneous data.

[0018] Preferably, the method for obtaining the basic reliable carbon dataset includes:

[0019] A smart oracle network is built by deploying n oracle nodes with independent data collection and verification capabilities; external carbon-related data is collected by connecting the oracle nodes to external data sources, and the consistency of the external carbon-related data is verified through the BFT consensus mechanism. The same external carbon-related data that has been verified and submitted by more than two-thirds of the oracle nodes is retained to form a basic carbon data unit with credibility and timestamp; the basic carbon data unit is aggregated with multi-source heterogeneous data to form a basic credible carbon dataset.

[0020] Preferably, the method for obtaining the carbon emission intensity includes:

[0021] A dual-chain hybrid blockchain architecture, consisting of a main chain and side chains, is constructed using a dual-chain structure. The main chain is used for on-chain storage and notarization of basic trusted carbon datasets, while smart contracts are deployed and run on the side chains. The carbon emission intensity calculation logic is automatically executed by running the smart contracts. The carbon emission intensity is calculated by dividing the carbon emissions per unit time by the carbon production per unit time.

[0022] Preferably, the method for triggering the account freeze operation includes:

[0023] By comparing carbon emission intensity with a preset carbon emission intensity threshold, data with carbon emission intensity greater than the preset carbon emission intensity threshold are marked as high carbon emission behavior data; the preset carbon emission intensity threshold is dynamically adjusted using a carbon emission intensity threshold adjustment formula;

[0024] Establish a freezing probability function and adopt a tiered response strategy for accounts under different risk levels; use a phased freezing mechanism to construct a freezing status determination logic function, decompose the account freezing operation into warning status, restriction status and freezing status; and trigger the account freezing operation through the freezing status determination logic function.

[0025] Preferably, the method for tracking abnormal behavior includes:

[0026] By comparing historical carbon emission records before and after marking high carbon emission behavior data, sliding difference analysis is performed according to a preset time window. The first-order difference method is used to calculate the difference in carbon emissions per unit time and compare it with a preset carbon emission difference threshold. If the carbon emission difference is greater than the preset carbon emission difference threshold, it is determined that there is a significant change in carbon emissions.

[0027] Obtain the Bill of Materials (BOM) structure of the product corresponding to the high carbon emission behavior data. Based on the components and processes in the BOM, construct a carbon flow topology graph, which is a directed graph containing m carbon source nodes, carbon emission paths, and emission factors.

[0028] By utilizing carbon flow topology maps, key nodes in carbon emission pathways are located, and carbon source nodes related to high carbon emission behavior data are extracted. A time-series prediction model is constructed based on high carbon emission behavior data, and a long short-term memory network is used to predict the trend of carbon emissions from carbon source nodes to obtain the predicted carbon emissions.

[0029] The actual carbon emissions of a carbon source node are compared with the predicted carbon emissions to calculate the degree of deviation. If the degree of deviation is greater than a preset deviation threshold, the carbon source node is marked as an abnormal behavior point. All abnormal behavior points in carbon source nodes related to high carbon emission behavior data are collected for abnormal behavior tracking.

[0030] Preferably, the method for obtaining the trusted verification data includes:

[0031] For data currently marked as high carbon emission behavior, backtrack for a preset time length; extract the time series corresponding to the high carbon emission behavior data, including carbon emissions, power consumption, process section capacity utilization rate and external temperature, to form a multidimensional carbon emission behavior sequence matrix;

[0032] The time series is discretized to construct an emission probability distribution function, and the information entropy value of the time series is calculated using the Shannon entropy formula. The information entropy value baseline interval is defined as [ , The carbon entropy index is generated through a normalization formula.

[0033] Define a threshold for judging carbon entropy index. If the carbon entropy index is less than or equal to the threshold, it means that the abnormal behavior is within an acceptable fluctuation range and the verification is successful. If the carbon entropy index is greater than the threshold, it means that the abnormal behavior is within an unacceptable fluctuation range and the verification is unsuccessful. Combine the verified high carbon emission behavior data with the corresponding carbon entropy index, carbon flow topology map, and basic credible carbon data corresponding to carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible verification data.

[0034] Preferably, the method for obtaining the three-dimensional carbon credit label includes:

[0035] The trusted verification data is converted into the ERC-1155 standard format, and NFT carbon credit tokens are generated by automatically calling the NFT casting contract. The NFT carbon credit tokens are uploaded to the blockchain and an on-chain ownership certificate is generated. A three-dimensional carbon credit identifier is constructed based on the dimensions of trustworthiness, industry, and time and space. The three-dimensional identifier information is embedded into the NFT carbon credit token as extended metadata and can be read on the chain through a preset query interface.

[0036] Preferably, the method for obtaining the four-dimensional carbon sand table includes:

[0037] The metadata of NFT carbon credit tokens introduces time dimension tags, spatial dimension tags, industry dimension tags, and value dimension tags. The time dimension tags include the time of occurrence of carbon emission reduction behavior, the verification time, and the token on-chain time. The spatial dimension tags include the location coordinates of the verification behavior. The industry dimension tags include the unified industry classification code. The value dimension tags include the carbon entropy index and estimated market value mapped in the NFT token.

[0038] Using the WebGL 3D modeling engine and the on-chain metadata interface of NFT carbon credit tokens, the graphics rendering of the four-dimensional carbon sandbox is performed; using a two-dimensional map as the base map, the spatial dimension labels of NFT tokens are mapped to the corresponding latitude and longitude positions and presented as carbon points in the four-dimensional carbon sandbox.

[0039] Create a timeline slider that allows users to select any time window, automatically filtering NFT carbon credit tokens uploaded to the blockchain within that time range and updating the display area; differentiate tokens from different industries using different layers, icons, or colors; and convert carbon emissions, carbon entropy index, and current estimated market value into visual features.

[0040] A blockchain-based integrated carbon emission reduction measurement system includes:

[0041] The carbon intelligent data acquisition gateway module establishes a hierarchical data acquisition architecture and introduces a carbon emission description language. It collects and unifies internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data. It also builds an intelligent oracle network to dynamically access external data sources and uses the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset.

[0042] The consensus blockchain network module constructs a dual-chain hybrid blockchain architecture that includes a main chain and side chains. Combined with a carbon credit staking mechanism, it automatically calculates carbon emission intensity based on a basic trusted carbon dataset. Data with carbon emission intensity exceeding a preset carbon emission intensity threshold is marked as high-carbon emission behavior data, and an account freeze operation is triggered.

[0043] The Trust Enhancement Verification Module, based on high carbon emission behavior data, uses a differential verification mechanism to identify changes in carbon emissions and combines the carbon flow topology generated from the product structure BOM table to track abnormal behavior; it performs trust verification processing on the high carbon emission behavior data to obtain trust verification data.

[0044] The dynamic carbon asset protocol module maps trusted verified data into NFT carbon credit tokens and constructs a three-dimensional carbon credit identifier to form tradable carbon assets; it introduces mainstream market price information and constructs a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets.

[0045] The Carbon Flow Visualization Hub Module constructs a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions, providing a four-dimensional visual display of NFT carbon credit tokens.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This invention utilizes a carbon emission intensity threshold adjustment formula based on the difference between the current region's and the average carbon emission intensity in a dual-chain hybrid blockchain architecture. This allows for dynamic adjustment of a preset threshold, fully reflecting real-time differences in carbon emission intensity across different regions, effectively improving the accuracy of identifying abnormal carbon emission behavior and reducing false positives. Furthermore, by employing a freeze probability function, the probability of an account being frozen is calculated based on the magnitude of the carbon emission intensity deviation and the system's tolerance range. This makes freeze decisions more flexible and scientific, avoiding excessive freezing caused by a single threshold trigger, and helping to protect the rights and interests of compliant users.

[0048] The system designs a logic function to determine account freeze status, classifying account freezes into three levels: warning, restriction, and freeze. Different levels of restrictions are implemented based on the risk level, improving the precision and humanization of system governance. Through dynamic adjustment and phased response mechanisms, it reduces false triggering of smart contracts due to data anomalies, enhances the reliability and rationality of automatic smart contract execution, maintains trust among system stakeholders, and improves the fairness and transparency of carbon emission regulation. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a blockchain-based comprehensive carbon emission reduction measurement method according to the present invention.

[0050] Figure 2 This is a schematic diagram of a blockchain-based integrated carbon emission reduction measurement system according to the present invention.

[0051] Figure 3 This is a schematic diagram of the basic reliable carbon dataset acquisition method of the present invention. Detailed Implementation

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Example 1

[0054] Please see Figure 1 and Figure 2 As shown in Example 1, a comprehensive carbon emission reduction measurement method based on blockchain proposed in this invention is further illustrated, including:

[0055] Currently, various carbon emission reduction measurement methods are widely used in industrial parks, enterprises, and regional energy systems to assess emission compliance and the effectiveness of emission reduction actions. However, existing comprehensive carbon emission reduction measurement methods generally suffer from insufficient measurement accuracy, rigid judgment criteria, and lack of robustness, making it difficult to meet the management needs of increasingly complex multi-source data environments and dynamic industrial structures.

[0056] On the one hand, existing measurement methods mostly use fixed thresholds to determine carbon emission overruns, failing to fully consider the differences in industrial structure, energy consumption baseline, and carbon emission density across different regions, which can easily lead to distorted results. For example, some high-energy-consuming industrial zones, even with relatively low carbon emission intensity, may be misjudged as non-compliant areas due to unreasonable benchmark threshold settings. On the other hand, carbon emission data typically comes from multi-source sensing terminals and remote transmission networks, and the data collection process may be affected by factors such as equipment failure, communication interruptions, sensor errors, spatiotemporal synchronization failures, and data tampering, resulting in insufficient data integrity and accuracy.

[0057] In some blockchain applications, smart contracts are used to automatically monitor carbon emissions. When monitoring data triggers the violation conditions set in the contract, the smart contract immediately executes actions including account freezing, carbon credit locking, and trading permission revocation. Although such mechanisms can achieve automatic punishment without human intervention, the "automatic execution and immutability" nature of smart contracts makes their judgments highly dependent on input data. They lack data anomaly identification and fault tolerance mechanisms, making it highly susceptible to compliant nodes being misjudged as violators due to erroneous data input. Furthermore, current smart contract systems typically lack independent data appeal and arbitration mechanisms, making it impossible to roll back or manually review erroneous judgments, severely impacting the fairness and credibility of the system.

[0058] To effectively address the above problems, this invention proposes a comprehensive carbon emission reduction measurement method based on blockchain, comprising:

[0059] S1. Establish a hierarchical data acquisition architecture and introduce a carbon emission description language. Use a protocol converter to collect and structure heterogeneous internal data to obtain multi-source heterogeneous data. Build an intelligent oracle network to dynamically access external data sources and use the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset.

[0060] S2. Construct a dual-chain hybrid blockchain architecture that includes a main chain and side chains, and combine it with a carbon credit staking mechanism to automatically calculate carbon emission intensity based on a basic trusted carbon dataset; mark data with carbon emission intensity greater than a preset carbon emission intensity threshold as high carbon emission behavior data and trigger account freezing operations.

[0061] S3. Based on high carbon emission behavior data, a differential verification mechanism is used to identify changes in carbon emissions, and combined with the carbon flow topology generated by the product structure BOM table, abnormal behavior is tracked; the high carbon emission behavior data is processed for credible verification to obtain credible verification data.

[0062] S4. Map trusted verified data to NFT carbon credit tokens and construct a three-dimensional carbon credit identifier to form tradable carbon assets; introduce mainstream market price information and construct a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets.

[0063] S5. Construct a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions to provide a four-dimensional visual display of NFT carbon credit tokens.

[0064] Methods for acquiring multi-source heterogeneous data include:

[0065] Based on the source types of carbon emission data, the internal carbon emission data sources of enterprises are divided into hierarchical levels to establish a layered data acquisition architecture. This architecture is used to collect heterogeneous internal data related to carbon emissions. The layered data acquisition architecture includes an equipment perception layer, a business system layer, a monitoring and control layer, and an edge computing layer. The equipment perception layer is used to collect physical quantity data related to emissions from industrial equipment, such as the operating data of energy-consuming equipment like industrial production equipment, boilers, air compressors, and cooling towers, including electricity consumption, gas consumption, and gas production. The business system layer is used to collect structured production and energy data from enterprise management systems, such as data generated by business systems like Manufacturing Execution System (MES), ERP, and Energy Management System (EMS), including production plans, process parameters, and energy consumption reports. The monitoring and control layer is used to collect emission monitoring data provided by environmental monitoring equipment, such as real-time sensor data collected by environmental monitoring sensors, including carbon dioxide concentration, flue gas velocity, and temperature. The edge computing layer is used to preprocess the data collected by the equipment perception layer, business system layer, and monitoring and control layer, including noise filtering, data completion, and time-series alignment.

[0066] A carbon emission description language, including field definitions, context binding, and emission factor mapping, is introduced to semantically model the collected internal heterogeneous data and represent it as a unified carbon emission data field (achieving a standardized description of carbon emission behavior from different sources). The carbon emission data field is mapped to a CEDL format data structure through a protocol converter, thereby outputting unified structured multi-source heterogeneous data.

[0067] Field definitions are used to uniformly describe the names, data types, units, and semantics of carbon emission-related data items; context binding is used to bind the device identifier, spatial location, system affiliation, and collection cycle of the data collection object; emission factor mapping is used to map fields such as energy consumption data and output data to the carbon emission factor table to support the subsequent calculation of carbon emission intensity.

[0068] Methods for obtaining basic, reliable carbon datasets include:

[0069] A smart oracle network is built by deploying n oracle nodes with independent data collection and verification capabilities. These oracle nodes access external data sources to collect external carbon-related data. The consistency of this external carbon-related data is verified using the BFT consensus mechanism. Data from the same external carbon-related data, verified and submitted by more than two-thirds of the oracle nodes, is retained to form a basic carbon data unit with credibility and a timestamp. This basic carbon data unit is then aggregated with multi-source heterogeneous data to form a basic credible carbon dataset. This dataset serves as the data input for subsequent carbon emission intensity calculations, anomaly emission identification, and carbon credit token generation.

[0070] Methods for obtaining carbon emission intensity include:

[0071] A dual-chain hybrid blockchain architecture, consisting of a main chain and side chains, is constructed using a dual-chain structure. The main chain is used for on-chain storage and notarization of basic trusted carbon datasets, while smart contracts are deployed and run on the side chains. The carbon emission intensity calculation logic is automatically executed by running the smart contracts. The carbon emission intensity is calculated by dividing the carbon emissions per unit time by the carbon production per unit time.

[0072] Methods that trigger account freezing include:

[0073] By comparing carbon emission intensity with a preset carbon emission intensity threshold, data with carbon emission intensity greater than the preset carbon emission intensity threshold are marked as high carbon emission behavior data; the preset carbon emission intensity threshold is dynamically adjusted using a carbon emission intensity threshold adjustment formula;

[0074] The formula for adjusting the carbon emission intensity threshold is as follows: ;in, This represents the dynamically adjusted carbon emission intensity threshold. This indicates a preset carbon emission intensity threshold; This indicates the carbon emission intensity of the region where the current node is located in a dual-chain hybrid blockchain architecture. This represents the average carbon intensity of a dual-chain hybrid blockchain architecture; Indicates the deviation in carbon emission intensity; This represents the adjustment factor, used to adjust the degree of influence of regional carbon intensity differences on the carbon emission intensity threshold;

[0075] Smart contracts are automatically executed and immutable program logic that immediately executes a freeze operation once a specific condition is triggered (such as carbon emission intensity exceeding a threshold). However, their execution is highly dependent on the accuracy and completeness of the input data. If the data source has the following issues: incomplete data (such as monitoring interruption or missing segments), data misuse or forgery (such as human tampering or supplier misreporting), sensor errors, accumulated biases, or spatiotemporal alignment failures (misaligned data timestamps or mismatched regional labels), it may trigger incorrect smart contract logic judgments. For example, it may misjudge compliant nodes as over-emission nodes, resulting in: user accounts being frozen, pledged assets being locked, inability to trade carbon credits, damage to node reputation, and a decline in system credibility, triggering a crisis of trust.

[0076] To address the above issues, a freezing probability function is established to implement a tiered response strategy for accounts under different risk levels. A phased freezing mechanism is used, and a freezing status determination logic function is constructed to break down the account freezing operation into warning status, restriction status, and freezing status. The account freezing operation is triggered through the freezing status determination logic function.

[0077] The freeze probability function is ;in, Indicates the probability of freezing; The freezing point represents the maximum deviation range of carbon emission intensity that the system can tolerate. This represents the slope control factor, which controls the steepness of the curve.

[0078] The logic function for determining the frozen state is: ;in, This indicates that the account is in a warning state; This indicates that the account is under restriction. This indicates that the account is frozen; This represents the lower limit threshold of the probability of freezing. This represents the upper limit threshold of the probability of freezing;

[0079] It should be noted that the freeze probability function uses the Sigmoid function, a widely used probability mapping function in statistics and machine learning, which can map the input risk indicator to... The probability values ​​between these values ​​facilitate quantification of the probability of freezing risk. A critical value for the risk indicator is set as the inflection point for freezing judgment. When the risk indicator is below this value, the freezing probability is close to 0, indicating low risk; when it is above this value, the freezing probability rises rapidly, reflecting the system's sensitivity to risks exceeding the tolerance range. The slope control factor controls the steepness of the curve, ensuring that the freezing probability does not suddenly jump near the threshold, but rather has a smooth transition. This allows the system to respond gradually to changes in risk, avoiding unreasonable freezing caused by hard cutoffs. The freezing status judgment function divides the freezing operation into three levels—warning, restriction, and freezing—by setting two probability thresholds. This reflects a phased and graded intelligent management strategy, reducing the risk of misjudgment and over-freezing, and improving the precision of system governance.

[0080] Overall, this function can scientifically reflect the nonlinear relationship between risk indicators and freezing probability, taking into account both risk tolerance and avoiding the rigid limitation of a single threshold, thus improving the robustness and flexibility of the system.

[0081] This solution addresses the following problems with existing technologies: Traditional carbon emission monitoring systems often use fixed thresholds to determine whether emissions exceed limits, failing to consider regional industrial structures, energy consumption baselines, and differences in carbon density, which can easily lead to misjudgments; smart contracts automatically execute severe actions such as freezing accounts once they are bound to incorrect data (missing, forged, mismatched labels, etc.), without any respite mechanism or fault tolerance. The automatic freezing of non-compliant accounts by smart contracts is highly dependent on the accuracy and completeness of the input data. In reality, data may be defective due to monitoring interruptions, sensor errors, forgery, tampering, spatiotemporal mismatches, etc., directly leading to misjudging compliant accounts as non-compliant accounts and causing unreasonable freezes. Error sources are complex (monitoring anomalies, data forgery, equipment errors, etc.), and existing contract mechanisms lack data anomaly prediction and appeal mechanisms.

[0082] Compared to existing technologies, the advantages are as follows: By using a carbon emission intensity threshold adjustment formula based on the difference between the current region and the average carbon emission intensity in a dual-chain hybrid blockchain architecture, the preset threshold can be dynamically adjusted, fully reflecting the real-time differences in carbon emission intensity in different regions, effectively improving the accuracy of identifying abnormal carbon emission behavior and reducing misjudgments. A freezing probability function is adopted to calculate the probability of an account being frozen based on the magnitude of the carbon emission intensity deviation and the system's tolerance range, making freezing decisions more flexible and scientific, avoiding excessive freezing caused by a single threshold trigger, and helping to protect the rights and interests of compliant users. A freezing status determination logic function is designed to divide account freezing into three levels: warning, restriction, and freeze, taking different levels of restriction measures for different risk levels, improving the refinement and humanization of system governance. Through dynamic adjustment and phased response mechanisms, the false triggering of smart contracts due to data anomalies is reduced, improving the reliability and rationality of automatic smart contract execution, maintaining trust among all parties in the system, and enhancing the fairness and transparency of carbon emission regulation.

[0083] For example, a large steel enterprise group has multiple production bases across the country, located in three different regions: East China, North China, and South China. To improve the transparency and intelligence of carbon emission reduction supervision, the company has introduced a comprehensive carbon emission measurement method based on a dual-chain hybrid blockchain architecture.

[0084] Preset carbon emission intensity threshold 1.0 ton / ton of steel, carbon emission intensity of the region where the current node is located in the dual-chain hybrid blockchain architecture The respective carbon emission intensity is as follows: East China 0.85 tons / ton, North China 1.10 tons / ton, and South China 0.95 tons / ton; The average carbon emission intensity of the dual-chain hybrid blockchain architecture is... It is 1.0 ton / ton; adjustment factor The value is 0.3; the dynamically adjusted carbon emission intensity threshold is:

[0085] East China region tons / ton;

[0086] North China region tons / ton;

[0087] South China region tons / ton;

[0088] The carbon emission intensity in East China is 0.85, which is less than 0.955, and therefore compliant.

[0089] The carbon emission intensity in North China is 1.10, which is greater than 1.03, indicating an anomaly.

[0090] The carbon emission intensity in South China is 0.95, which is less than 0.985, and therefore compliant.

[0091] For the anomalous data from the North China base, calculate the freezing probability function:

[0092] Set the freezing determination inflection point =0.05 (deviation range relative to the threshold), slope control factor The value is 50, and the carbon emission intensity deviation is: The probability of freezing is: ;

[0093] Set a lower threshold for the probability of freezing. The upper limit threshold for the probability of freezing is 0.4. The value is 0.8; since 0.4 < 0.71 < 0.8, the account in the North China region is determined to be in a restricted state, and the transaction restriction and partial pledged asset lock-up operations will be automatically executed.

[0094] Methods for tracking abnormal behavior include:

[0095] By comparing historical carbon emission records before and after marking high carbon emission behavior data, sliding difference analysis is performed according to a preset time window. The first-order difference method is used to calculate the difference in carbon emissions per unit time and compare it with a preset carbon emission difference threshold. If the carbon emission difference is greater than the preset carbon emission difference threshold, it is determined that there is a significant change in carbon emissions.

[0096] Obtain the Bill of Materials (BOM) structure of the product corresponding to the high carbon emission behavior data. Based on the components and processes in the BOM, construct a carbon flow topology graph, which is a directed graph containing m carbon source nodes, carbon emission paths, and emission factors.

[0097] By utilizing carbon flow topology maps, key nodes in carbon emission pathways are located, and carbon source nodes related to high carbon emission behavior data are extracted. A time-series prediction model is constructed based on high carbon emission behavior data, and a long short-term memory network is used to predict the trend of carbon emissions from carbon source nodes to obtain the predicted carbon emissions.

[0098] The actual carbon emissions of a carbon source node are compared with the predicted carbon emissions to calculate the degree of deviation. If the degree of deviation is greater than a preset deviation threshold, the carbon source node is marked as an abnormal behavior point. All abnormal behavior points in carbon source nodes related to high carbon emission behavior data are collected for abnormal behavior tracking.

[0099] Methods for obtaining trusted verification data include:

[0100] For data currently marked as high carbon emission behavior, backtrack for a preset time length; extract the time series corresponding to the high carbon emission behavior data, including carbon emissions, power consumption, process section capacity utilization rate and external temperature, to form a multidimensional carbon emission behavior sequence matrix;

[0101] The time series is discretized to construct an emission probability distribution function, and the information entropy value of the time series is calculated using the Shannon entropy formula. The information entropy value baseline interval is defined as [ , The carbon entropy index is generated using a normalization formula; the normalization formula is: ;in, Represents the carbon entropy index;

[0102] Define a threshold for judging carbon entropy index. If the carbon entropy index is less than or equal to the threshold, it means that the abnormal behavior is within an acceptable fluctuation range and the verification is successful. If the carbon entropy index is greater than the threshold, it means that the abnormal behavior is within an unacceptable fluctuation range and the verification is unsuccessful. Combine the verified high carbon emission behavior data with the corresponding carbon entropy index, carbon flow topology map, and basic credible carbon data corresponding to carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible verification data.

[0103] Methods for obtaining three-dimensional carbon credit labels include:

[0104] The trusted verification data is converted into the ERC-1155 standard format, and NFT carbon credit tokens are generated by automatically calling the NFT casting contract. The NFT carbon credit tokens are uploaded to the blockchain and an on-chain ownership certificate is generated. A three-dimensional carbon credit identifier is constructed based on the dimensions of trustworthiness, industry, and time and space. The three-dimensional identifier information is embedded into the NFT carbon credit token as extended metadata and can be read on the chain through a preset query interface.

[0105] Methods for obtaining a four-dimensional carbon sand table include:

[0106] The metadata of NFT carbon credit tokens introduces time dimension tags, spatial dimension tags, industry dimension tags, and value dimension tags. The time dimension tags include the time of occurrence of carbon emission reduction behavior, the verification time, and the token on-chain time. The spatial dimension tags include the location coordinates of the verification behavior. The industry dimension tags include the unified industry classification code. The value dimension tags include the carbon entropy index and estimated market value mapped in the NFT token.

[0107] Using the WebGL 3D modeling engine and the on-chain metadata interface of NFT carbon credit tokens, the graphics rendering of the four-dimensional carbon sandbox is performed; using a two-dimensional map as the base map, the spatial dimension labels of NFT tokens are mapped to the corresponding latitude and longitude positions and presented as carbon points in the four-dimensional carbon sandbox.

[0108] A timeline slider is built, allowing users to select any time window and automatically filter on-chain NFT carbon credit tokens within that time range, updating the display area accordingly. Different industry-specific tokens are distinguished by different layers, icons, or colors; for example, blue for manufacturing, red for energy, and green for agriculture. Layer switching and aggregated display are supported. Carbon emissions, carbon entropy index, and current estimated market value are converted into visual features. Users can hover over any token marker to automatically pop up an on-chain metadata information box displaying the NFT carbon credit token.

[0109] The preset carbon emission intensity threshold is set by staff. By collecting different carbon emission intensities, the average value of multiple carbon emission intensities is taken as the preset carbon emission intensity threshold. Similarly, preset carbon emission difference threshold and preset deviation threshold are set.

[0110] This embodiment achieves dynamic adjustment of a preset threshold by using a carbon emission intensity threshold adjustment formula based on the difference between the current region and the average carbon emission intensity in a dual-chain hybrid blockchain architecture. This fully reflects the real-time differences in carbon emission intensity across different regions, effectively improving the accuracy of identifying abnormal carbon emission behavior and reducing false positives. A freeze probability function is employed to calculate the probability of an account being frozen based on the magnitude of the carbon emission intensity deviation and the system's tolerance range. This makes freeze decisions more flexible and scientific, avoiding excessive freezing caused by a single threshold trigger, and helping to protect the rights and interests of compliant users.

[0111] The system designs a logic function to determine account freeze status, classifying account freezes into three levels: warning, restriction, and freeze. Different levels of restrictions are implemented based on the risk level, improving the precision and humanization of system governance. Through dynamic adjustment and phased response mechanisms, it reduces false triggering of smart contracts due to data anomalies, enhances the reliability and rationality of automatic smart contract execution, maintains trust among system stakeholders, and improves the fairness and transparency of carbon emission regulation.

[0112] Example 2

[0113] Please see Figure 3 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A blockchain-based comprehensive carbon emission reduction measurement system is provided, including:

[0114] The carbon intelligent acquisition gateway module establishes a hierarchical data acquisition architecture and introduces a carbon emission description language. It acquires and unifies internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data. It also builds an intelligent oracle network to dynamically access external data sources and uses the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset.

[0115] The consensus blockchain network module constructs a dual-chain hybrid blockchain architecture that includes a main chain and side chains. Combined with a carbon credit staking mechanism, it automatically calculates carbon emission intensity based on a basic trusted carbon dataset. Data with carbon emission intensity exceeding a preset carbon emission intensity threshold is marked as high-carbon emission behavior data, and an account freeze operation is triggered.

[0116] The Trust Enhancement Verification Module, based on high carbon emission behavior data, uses a differential verification mechanism to identify changes in carbon emissions and combines the carbon flow topology generated from the product structure BOM table to track abnormal behavior; it performs trust verification processing on the high carbon emission behavior data to obtain trust verification data.

[0117] The dynamic carbon asset protocol module maps trusted verified data into NFT carbon credit tokens and constructs a three-dimensional carbon credit identifier to form tradable carbon assets; it introduces mainstream market price information and constructs a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets.

[0118] The Carbon Flow Visualization Hub Module constructs a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions, providing a four-dimensional visual display of NFT carbon credit tokens.

[0119] Since the electronic device described in this embodiment is the one used to implement the comprehensive carbon emission reduction measurement method based on blockchain in this application, those skilled in the art can understand the specific implementation and various variations of the electronic device in this embodiment based on the comprehensive carbon emission reduction measurement method based on blockchain in this application. Therefore, how the electronic device implements the method in this application will not be described in detail here. Any electronic device used by those skilled in the art to implement the comprehensive carbon emission reduction measurement method based on blockchain in this application falls within the scope of protection of this application.

[0120] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0121] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A comprehensive method for measuring carbon emission reduction based on blockchain, characterized in that, include: S1. Establish a hierarchical data acquisition architecture and introduce a carbon emission description language. Use a protocol converter to collect and structure heterogeneous data from multiple sources to obtain heterogeneous data. Build an intelligent oracle network, dynamically access external data sources, and use the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset; S2. Construct a dual-chain hybrid blockchain architecture that includes a main chain and side chains, and combine it with a carbon credit staking mechanism to automatically calculate carbon emission intensity based on a basic trusted carbon dataset; mark data with carbon emission intensity greater than a preset carbon emission intensity threshold as high carbon emission behavior data and trigger account freezing operations. The methods for triggering account freezing include: By comparing carbon emission intensity with a preset carbon emission intensity threshold, data with carbon emission intensity greater than the preset carbon emission intensity threshold are marked as high carbon emission behavior data; the preset carbon emission intensity threshold is dynamically adjusted using a carbon emission intensity threshold adjustment formula; The formula for adjusting the carbon emission intensity threshold is as follows: ;in, This represents the dynamically adjusted carbon emission intensity threshold. This indicates a preset carbon emission intensity threshold; This indicates the carbon emission intensity of the region where the current node is located in a dual-chain hybrid blockchain architecture. This represents the average carbon intensity of a dual-chain hybrid blockchain architecture; Indicates the deviation in carbon emission intensity; This represents the adjustment factor, used to adjust the degree of influence of regional carbon intensity differences on the carbon emission intensity threshold; Establish a freezing probability function and adopt a tiered response strategy for accounts under different risk levels; use a phased freezing mechanism to construct a freezing status determination logic function, decompose the account freezing operation into warning status, restriction status and freezing status; and trigger the account freezing operation through the freezing status determination logic function. The freeze probability function is ;in, Indicates the probability of freezing; The freezing point represents the maximum deviation range of carbon emission intensity that the system can tolerate. This represents the slope control factor, which controls the steepness of the curve. S3. Based on high carbon emission behavior data, a differential verification mechanism is used to identify changes in carbon emissions, and combined with the carbon flow topology generated by the product structure BOM table, abnormal behavior is tracked; the high carbon emission behavior data is processed for credible verification to obtain credible verification data. S4. Map trusted verified data to NFT carbon credit tokens and construct a three-dimensional carbon credit identifier to form tradable carbon assets; introduce mainstream market price information and construct a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets. S5. Construct a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions to provide a four-dimensional visual display of NFT carbon credit tokens.

2. The method for comprehensive carbon emission reduction measurement based on blockchain according to claim 1, characterized in that, The method for acquiring multi-source heterogeneous data includes: Based on the source type of carbon emission data, the internal carbon emission data sources of enterprises are divided into layers to establish a hierarchical data acquisition architecture. The hierarchical data acquisition architecture is used to collect internal heterogeneous data related to carbon emissions of enterprises. The hierarchical data acquisition architecture includes an equipment sensing layer, a business system layer, a monitoring and control layer, and an edge computing layer. A carbon emission description language, which includes field definitions, context binding, and emission factor mapping, is introduced to semantically model the collected internal heterogeneous data and represent it as a unified carbon emission data field. The carbon emission data field is mapped to a CEDL format data structure through a protocol converter, thereby outputting unified structured multi-source heterogeneous data.

3. The method for comprehensive carbon emission reduction measurement based on blockchain according to claim 2, characterized in that, The methods for obtaining the basic trusted carbon dataset include: A smart oracle network is built by deploying n oracle nodes with independent data collection and verification capabilities; external carbon-related data is collected by connecting the oracle nodes to external data sources, and the consistency of the external carbon-related data is verified through the BFT consensus mechanism. The same external carbon-related data that has been verified and submitted by more than two-thirds of the oracle nodes is retained to form a basic carbon data unit with credibility and timestamp; the basic carbon data unit is aggregated with multi-source heterogeneous data to form a basic credible carbon dataset.

4. The comprehensive carbon emission reduction measurement method based on blockchain according to claim 3, characterized in that, The method for obtaining the carbon emission intensity includes: A dual-chain hybrid blockchain architecture, consisting of a main chain and side chains, is constructed using a dual-chain structure. The main chain is used for on-chain storage and notarization of basic trusted carbon datasets, while smart contracts are deployed and run on the side chains. The carbon emission intensity calculation logic is automatically executed by running the smart contracts. The carbon emission intensity is calculated by dividing the carbon emissions per unit time by the carbon production per unit time.

5. The comprehensive carbon emission reduction measurement method based on blockchain according to claim 4, characterized in that, The method for tracking abnormal behavior includes: By comparing historical carbon emission records before and after marking high carbon emission behavior data, sliding difference analysis is performed according to a preset time window. The first-order difference method is used to calculate the difference in carbon emissions per unit time and compare it with a preset carbon emission difference threshold. If the carbon emission difference is greater than the preset carbon emission difference threshold, it is determined that there is a significant change in carbon emissions. Obtain the Bill of Materials (BOM) structure of the product corresponding to the high carbon emission behavior data. Based on the components and processes in the BOM, construct a carbon flow topology graph, which is a directed graph containing m carbon source nodes, carbon emission paths, and emission factors. By utilizing carbon flow topology maps, key nodes in carbon emission pathways are located, and carbon source nodes related to high carbon emission behavior data are extracted. A time-series prediction model is constructed based on high carbon emission behavior data, and a long short-term memory network is used to predict the trend of carbon emissions from carbon source nodes to obtain the predicted carbon emissions. The actual carbon emissions of a carbon source node are compared with the predicted carbon emissions to calculate the degree of deviation. If the degree of deviation is greater than a preset deviation threshold, the carbon source node is marked as an abnormal behavior point. All abnormal behavior points in carbon source nodes related to high carbon emission behavior data are collected for abnormal behavior tracking.

6. The comprehensive carbon emission reduction measurement method based on blockchain according to claim 5, characterized in that, The method for obtaining the trusted verification data includes: For data currently marked as high carbon emission behavior, backtrack for a preset time length; extract the time series corresponding to the high carbon emission behavior data, including carbon emissions, power consumption, process section capacity utilization rate and external temperature, to form a multidimensional carbon emission behavior sequence matrix; The time series is discretized to construct an emission probability distribution function, and the information entropy value of the time series is calculated using the Shannon entropy formula. The information entropy value baseline interval is defined as [ , The carbon entropy index is generated through a normalization formula. Define a threshold for judging carbon entropy index. If the carbon entropy index is less than or equal to the threshold, it means that the abnormal behavior is within an acceptable fluctuation range and the verification is successful. If the carbon entropy index is greater than the threshold, it means that the abnormal behavior is within an unacceptable fluctuation range and the verification is unsuccessful. Combine the verified high carbon emission behavior data with the corresponding carbon entropy index, carbon flow topology map, and basic credible carbon data corresponding to carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible verification data.

7. The method for comprehensive carbon emission reduction measurement based on blockchain according to claim 6, characterized in that, The method for obtaining the three-dimensional carbon credit label includes: The trusted verification data is converted into the ERC-1155 standard format, and NFT carbon credit tokens are generated by automatically calling the NFT casting contract. The NFT carbon credit tokens are uploaded to the blockchain and an on-chain ownership certificate is generated. A three-dimensional carbon credit identifier is constructed based on the dimensions of trustworthiness, industry, and time and space. The three-dimensional identifier information is embedded into the NFT carbon credit token as extended metadata and can be read on the chain through a preset query interface.

8. The comprehensive carbon emission reduction measurement method based on blockchain according to claim 7, characterized in that, The method for obtaining the four-dimensional carbon sand table includes: The metadata of NFT carbon credit tokens introduces time dimension tags, spatial dimension tags, industry dimension tags, and value dimension tags. The time dimension tags include the time of occurrence of carbon emission reduction behavior, the verification time, and the token on-chain time. The spatial dimension tags include the location coordinates of the verification behavior. The industry dimension tags include the unified industry classification code. The value dimension tags include the carbon entropy index and estimated market value mapped in the NFT token. Using the WebGL 3D modeling engine and the on-chain metadata interface of NFT carbon credit tokens, the graphics rendering of the four-dimensional carbon sandbox is performed; using a two-dimensional map as the base map, the spatial dimension labels of NFT tokens are mapped to the corresponding latitude and longitude positions and presented as carbon points in the four-dimensional carbon sandbox. Create a timeline slider that allows users to select any time window, automatically filter NFT carbon credit tokens uploaded to the blockchain within the time range, and update the display area; differentiate tokens of different industries using different layers, icons, or colors; and convert carbon emissions, carbon entropy index, and current estimated market value into visual features.

9. A blockchain-based comprehensive carbon emission reduction measurement system, used to implement the blockchain-based comprehensive carbon emission reduction measurement method according to any one of claims 1 to 8, characterized in that, include: The carbon intelligent data acquisition gateway module establishes a hierarchical data acquisition architecture and introduces a carbon emission description language. It collects and unifies internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data. It also builds an intelligent oracle network to dynamically access external data sources and uses the BFT consensus mechanism to aggregate multi-source heterogeneous data to form a basic trusted carbon dataset. The consensus blockchain network module constructs a dual-chain hybrid blockchain architecture that includes a main chain and side chains. Combined with a carbon credit staking mechanism, it automatically calculates carbon emission intensity based on a basic trusted carbon dataset. Data with carbon emission intensity exceeding a preset carbon emission intensity threshold is marked as high-carbon emission behavior data, and an account freeze operation is triggered. The methods for triggering account freezing include: By comparing carbon emission intensity with a preset carbon emission intensity threshold, data with carbon emission intensity greater than the preset carbon emission intensity threshold are marked as high carbon emission behavior data; the preset carbon emission intensity threshold is dynamically adjusted using a carbon emission intensity threshold adjustment formula; The formula for adjusting the carbon emission intensity threshold is as follows: ;in, This represents the dynamically adjusted carbon emission intensity threshold. This indicates a preset carbon emission intensity threshold; This indicates the carbon emission intensity of the region where the current node is located in a dual-chain hybrid blockchain architecture. This represents the average carbon intensity of a dual-chain hybrid blockchain architecture; Indicates the deviation in carbon emission intensity; This represents the adjustment factor, used to adjust the degree of influence of regional carbon intensity differences on the carbon emission intensity threshold; Establish a freezing probability function and adopt a tiered response strategy for accounts under different risk levels; use a phased freezing mechanism to construct a freezing status determination logic function, decompose the account freezing operation into warning status, restriction status and freezing status; and trigger the account freezing operation through the freezing status determination logic function. The freeze probability function is ;in, Indicates the probability of freezing; The freezing point represents the maximum deviation range of carbon emission intensity that the system can tolerate. This represents the slope control factor, which controls the steepness of the curve. The Trust Enhancement Verification Module, based on high carbon emission behavior data, uses a differential verification mechanism to identify changes in carbon emissions and combines the carbon flow topology generated from the product structure BOM table to track abnormal behavior; it performs trust verification processing on the high carbon emission behavior data to obtain trust verification data. The dynamic carbon asset protocol module maps trusted verified data into NFT carbon credit tokens and constructs a three-dimensional carbon credit identifier to form tradable carbon assets; it introduces mainstream market price information and constructs a tiered carbon token liquidity pool through an automated market-making mechanism to discover the liquidity and value of carbon assets. The Carbon Flow Visualization Hub Module constructs a four-dimensional carbon sandbox that integrates time, space, industry, and value dimensions, providing a four-dimensional visual display of NFT carbon credit tokens.

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