Block chain-based carbon emission reduction comprehensive determination method and system

Abstract

The invention belongs to the technical field of carbon emission determination, and discloses a block chain-based carbon emission reduction comprehensive determination method and system, and the method comprises the steps: building a hierarchical data collection architecture, introducing a carbon emission description language, carrying out the collection and structural unification of internal heterogeneous data through a protocol converter, and obtaining multi-source heterogeneous data; the method comprises the following steps: establishing an intelligent oracle machine network, dynamically accessing an external data source, and aggregating multi-source heterogeneous data by adopting a BFT consensus mechanism to form a basic credible carbon data set; constructing a double-chain hybrid block chain architecture comprising a main chain and a side chain, and automatically calculating the carbon emission intensity based on a basic credible carbon data set in combination with a carbon right pledge mechanism; marking the data of which the carbon emission intensity is greater than a preset carbon emission intensity threshold value as high carbon emission behavior data, and triggering an account freezing operation; the scientificity, effectiveness and transparency of carbon emission reduction management are improved, and powerful technical support is provided for achieving the purpose of carbon emission reduction.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission measurement. More specifically, the present invention relates to a comprehensive carbon emission reduction measurement method and system based on blockchain. Background Art

[0002] The patent with the publication number CN114493868A discloses a carbon trading data processing method and system based on blockchain. The present invention relates to the technical field of carbon trading, including receiving data from different tenants and different customers, and then a unified customer permission management center ensures the security of the data. Different customer data are allocated to different ledgers. The system maintains a corresponding relationship between the system organization and the blockchain organization in a configured manner, queries the blockchain organization and ledger information to which it belongs, and thus determines the final information to be submitted to the blockchain. This carbon trading data processing method and system based on blockchain, by planning to build an Internet platform for carbon emission verification based on blockchain, directly and real-time collects data using blockchain technology, automatically generates reports on the platform, and the report results can be directly connected to the supervision and trading systems. Supervisory, trading, and financial institutions can all penetrate and view the original data, thereby ensuring the transparency, standardization, and efficiency of the carbon emission verification process.

[0003] The existing comprehensive carbon emission reduction measurement methods mainly have the following problems: The existing comprehensive carbon emission reduction measurement technologies face multiple challenges and are difficult to meet the requirements of complex data environments and dynamic industrial structures, which are mainly reflected in the following aspects: Existing measurement methods generally use fixed thresholds to determine excessive emissions of carbon emissions, which in turn leads to problems such as insufficient measurement accuracy and rigid judgment criteria; carbon emission data usually relies on multi-source sensing terminals and remote transmission networks for acquisition, and the acquisition process is easily interfered by factors such as equipment failures, communication interruptions, sensor errors, and data tampering, resulting in insufficient data integrity and accuracy; In the blockchain application scenario, although smart contracts can achieve automatic punishment for carbon emission supervision, their characteristics of "automatic execution and non-modifiability" lead to a high dependence on input data for judgment, lacking a data anomaly identification and fault tolerance mechanism; With the upgrading of the industrial structure and the transformation of the energy system, the carbon emission characteristics show a dynamic change trend, while the existing methods lack the adaptability to multi-source data environments and industrial structure changes; fixed measurement models and threshold systems are difficult to capture the emission laws of emerging industries or the emission fluctuations brought about by energy structure adjustments, resulting in a shortened life cycle of the measurement method and being unable to meet the long-term and continuous supervision requirements.

[0004] 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

[0005] To overcome the above-mentioned defects of the prior art and achieve the above object, the present invention provides the following technical solutions: A comprehensive carbon emission reduction measurement method based on blockchain, including: S1. Establish a hierarchical data collection architecture, introduce a carbon emission description language, collect and structurally unify internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data; build an intelligent oracle network, dynamically access external data sources, and aggregate multi-source heterogeneous data using the BFT consensus mechanism to form a basic trusted carbon data set; S2. Construct a double-chain hybrid blockchain architecture including a main chain and a side chain, combine a carbon right pledge mechanism, and automatically calculate the carbon emission intensity based on the basic trusted carbon data set; mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high-carbon emission behavior data and trigger an account freeze operation; S3. Based on the high-carbon emission behavior data, use a differential verification mechanism to identify changes in carbon emissions, and combine the carbon flow topology structure generated by the product structure BOM table to track abnormal behaviors; perform trusted certification processing on the high-carbon emission behavior data to obtain trusted certification data; S4. Map the trusted certification data to NFT carbon credit tokens, construct a three-dimensional carbon credit identifier to form tradable carbon assets; introduce mainstream market price information, construct a hierarchical 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 sand table integrating the dimensions of time, space, industry, and value, and perform four-dimensional visualization display on the NFT carbon credit tokens.

[0006] Preferably, the method for obtaining the multi-source heterogeneous data includes: Based on the source type of carbon emission data, divide the carbon emission data sources within the enterprise into levels, establish a hierarchical data collection architecture, and collect internal heterogeneous data related to enterprise carbon emissions through the hierarchical data collection architecture; the hierarchical data collection architecture includes a device perception layer, a business system layer, a monitoring and control layer, and an edge computing layer; Introduce a carbon emission description language including field definitions, context bindings, and emission factor mappings, perform semantic modeling on the collected internal heterogeneous data, and represent it as a unified carbon emission data field; map the carbon emission data field to a CEDL format data structure through a protocol converter, and then output unified structured multi-source heterogeneous data.

[0007] Preferably, the method for obtaining the basic trusted carbon data set includes: An intelligent oracle network is built by deploying n oracle nodes with independent data collection and verification capabilities; the oracle nodes are used to access external data sources to collect external carbon-related data, and the BFT consensus mechanism is used to perform consistency verification on the external carbon-related data. 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 timestamp marking; the basic carbon data unit is aggregated with multi-source heterogeneous data to form a basic credible carbon data set.

[0008] Preferably, the method for obtaining the carbon emission intensity includes: A double-chain hybrid blockchain architecture including a main chain and a side chain is constructed using a double-chain structure. The main chain is used for the on-chain storage and certification of the basic credible carbon data set, and the side chain is used to execute the carbon emission intensity calculation and the carbon right pledge mechanism; a carbon right pledge unit is deployed on the main chain to manage the enterprise's pledged tokens and account status; an exception handling unit is deployed on the side chain to identify high carbon emission intensity and trigger the account freezing mechanism; Based on the carbon right pledge rule, an enterprise needs to pledge tokens anchored to the carbon quota The number of pledged tokens increases dynamically with the enterprise's historical violation times; the number of pledged tokens is calculated based on the benchmark quantity and the historical violation times, and the corresponding amount of pledged tokens is stored in the carbon right pledge unit; An intelligent contract is deployed and run in the side chain, and the carbon emission intensity calculation logic is automatically executed by running the intelligent contract; the carbon emission amount per unit time is divided by the carbon output amount at the same time to calculate the carbon emission intensity.

[0009] Preferably, the method for triggering the account freezing operation includes: By comparing the carbon emission intensity with a preset carbon emission intensity threshold, the data with a carbon emission intensity greater than the preset carbon emission intensity threshold is marked as high carbon emission behavior data; the preset carbon emission intensity threshold is dynamically adjusted through the carbon emission intensity threshold adjustment formula; A freezing probability function is established, and a step-by-step response strategy is adopted for the account at different risk levels; a phased freezing mechanism is used to construct a freezing state determination logic function, and the account freezing operation is disassembled into a warning state, a restricted state, and a frozen state; and the account freezing operation is triggered through the freezing state determination logic function.

[0010] Preferably, the method for tracking abnormal behaviors includes: The historical carbon emission records before and after marking the high carbon emission behavior data are compared, sliding difference analysis is performed according to a preset time window, the first-order difference method is used to calculate the difference in carbon emission amount per unit time, and it is compared with a preset carbon emission amount difference threshold. If the carbon emission amount difference is greater than the preset carbon emission amount difference threshold, it is determined that there is a significant change in the carbon emission amount; Obtain the Bill of Materials (BOM) structure of the product corresponding to the high-carbon emission behavior data. Based on the components and process flows in the Bill of Materials, construct a carbon flow topology map, where the carbon flow topology map is a directed graph containing m carbon source nodes, carbon emission paths, and emission factors; Using the carbon flow topology map, locate the key nodes in the carbon emission path and extract the carbon source nodes related to the high-carbon emission behavior data; construct a time series prediction model based on the high-carbon emission behavior data, and use a long short-term memory network to predict the trend of the carbon emissions of the carbon source nodes to obtain the predicted carbon emissions; Compare the actual carbon emissions of the carbon source nodes with the predicted carbon emissions, and calculate the deviation degree; if the deviation degree is greater than the preset deviation degree threshold, mark the carbon source node as an abnormal behavior point; collect all the abnormal behavior points among the carbon source nodes related to the high-carbon emission behavior data for abnormal behavior tracking.

[0011] Preferably, the method for obtaining the credible certification data includes: For the currently marked high-carbon emission behavior data, trace back a preset time length forward; extract the time series corresponding to the high-carbon emission behavior data, where the time series includes carbon emissions, power consumption, process section production capacity utilization rate, and external temperature, to form a multi-dimensional carbon emission behavior sequence matrix; Discretize the time series, construct an emission value probability distribution function, and calculate the information entropy value of the time series using the Shannon entropy formula ; Define the information entropy value reference interval as , and generate a carbon entropy index through a normalization formula; Define a carbon entropy index judgment threshold. If the carbon entropy index is less than or equal to the carbon entropy index judgment threshold, it means that the abnormal behavior is within an acceptable fluctuation range and the certification passes; if the carbon entropy index is greater than the carbon entropy index judgment threshold, it means that the abnormal behavior is within an unacceptable fluctuation range and the certification fails; combine the high-carbon emission behavior data that passes the certification, the corresponding carbon entropy index, the carbon flow topology map, and the basic credible carbon data corresponding to the carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible certification data.

[0012] Preferably, the method for obtaining the three-dimensional carbon credit identifier includes: Convert the credible certification data according to the ERC-1155 standard, and generate an NFT carbon credit token by automatically calling the NFT minting contract; upload the NFT carbon credit token to the blockchain and generate a chain-based rights confirmation certificate; construct a three-dimensional carbon credit identifier based on the credibility dimension, industry dimension, and space-time dimension, embed the three-dimensional identifier information as extended metadata into the NFT carbon credit token, and read it through a preset query interface on the chain.

[0013] Preferably, the method for obtaining the four-dimensional carbon sand table includes: Introduce time dimension tags, space dimension tags, industry dimension tags, and value dimension tags into the metadata of NFT carbon credit tokens; the time dimension tags include the occurrence time, verification time, and token on-chain time of carbon emission reduction behaviors; the space dimension tags include the location coordinates of the verification behavior; the industry dimension tags include unified industry classification codes; the value dimension tags include the carbon entropy index and estimated market value mapped in the NFT token; Use the WebGL three-dimensional modeling engine, combined with the on-chain metadata interface of NFT carbon credit tokens, to perform graphic rendering of the four-dimensional carbon sand table; use a two-dimensional map as the base map, map the space dimension tags of the NFT token to the corresponding longitude and latitude positions, and present them as carbon points in the four-dimensional carbon sand table; Build a time axis slider, which can slide to select any time window, automatically filter the NFT carbon credit tokens that are on-chain within this time range, and update the display area; distinguish different industry tokens with different layers, icons, or colors; convert carbon emissions, carbon entropy index, and current estimated market value into visual features.

[0014] A blockchain-based comprehensive carbon emission reduction measurement system includes: The carbon intelligent collection gateway module establishes a hierarchical data collection architecture, introduces a carbon emission description language, collects and structurally unifies internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data; builds an intelligent oracle network, dynamically accesses external data sources, and aggregates multi-source heterogeneous data using the BFT consensus mechanism to form a basic trusted carbon data set; The consensus blockchain network module constructs a double-chain hybrid blockchain architecture including a main chain and a side chain, combines a carbon right pledge mechanism, and automatically calculates the carbon emission intensity based on the basic trusted carbon data set; mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high carbon emission behavior data and trigger an account freezing operation; The trusted enhanced verification module, based on the high carbon emission behavior data, uses a differential verification mechanism to identify changes in carbon emissions, and combines the carbon flow topology structure generated by the product structure BOM table to track abnormal behaviors; perform trusted verification processing on the high carbon emission behavior data to obtain trusted verification data; The dynamic carbon asset protocol module maps the trusted verification data to NFT carbon credit tokens, constructs a three-dimensional carbon credit identifier, and forms tradable carbon assets; introduce mainstream market price information, build a hierarchical carbon token liquidity pool through an automated market-making mechanism, and conduct liquidity and value discovery of carbon assets; The carbon flow visualization center module constructs a four-dimensional carbon sand table integrating time, space, industry, and value dimensions, and performs four-dimensional visualization display of NFT carbon credit tokens.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Through the carbon emission intensity threshold adjustment formula based on the difference between the current area and the average carbon emission intensity in the double-chain hybrid blockchain architecture, the present invention realizes the dynamic adjustment of the preset threshold, fully reflects the real-time differences in carbon emission intensity in different regions, effectively improves the recognition accuracy of abnormal carbon emission behaviors, and reduces misjudgments. By adopting the freezing probability function, according to the magnitude of the carbon emission intensity deviation and the system tolerance range, the probability of an account being frozen is calculated, making the freezing decision more flexible and scientific, avoiding excessive freezing caused by the triggering of a single threshold, and helping to protect the rights and interests of compliant users.

[0016] Design the freezing state determination logic function, divide the account freezing into three levels: warning, restriction, and freezing, and take different degrees of restriction measures for different risk levels to improve the refinement and humanization level of system governance. Through the dynamic adjustment and phased response mechanism, reduce the mis-triggering of smart contracts caused by data anomalies, improve the reliability and rationality of the automatic execution of smart contracts, maintain the trust of all parties in the system, and enhance the fairness and transparency of carbon emission supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a blockchain-based comprehensive carbon emission reduction measurement method of the present invention; Figure 2 It is a schematic structural diagram of a blockchain-based comprehensive carbon emission reduction measurement system of the present invention; Figure 3 It is a schematic flow chart of the method for obtaining the basic credible carbon data set of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1 Please refer to Figure 1 and Figure 2 As shown, Embodiment 1 further illustrates a blockchain-based comprehensive carbon emission reduction measurement method proposed by the present invention, including: Currently, a variety of carbon emission reduction measurement methods are widely used in industrial parks, enterprise organizations, and regional energy systems to evaluate emission compliance and the effectiveness of emission reduction behaviors. However, the existing comprehensive carbon emission reduction measurement methods generally have the defects of insufficient measurement accuracy, rigid judgment criteria, and lack of robustness, making it difficult to meet the management needs of the increasingly complex multi-source data environment and dynamic industrial structure.

[0020] On the one hand, most of the existing measurement methods use fixed thresholds to determine whether carbon emissions exceed the limit, without fully considering differentiated factors such as the industrial structure, energy consumption baseline, and carbon emission density in different regions, which is likely to lead to distorted judgment results. For example, even if the carbon emission intensity in some high-energy-consuming industrial areas is relatively low, it may be misjudged as a non-compliant area due to unreasonable setting of the benchmark threshold. On the other hand, carbon emission data usually comes from multi-source sensing terminals and remote transmission networks, and its collection process may be interfered by factors such as equipment failures, communication interruptions, sensor errors, spatio-temporal synchronization failures, and data tampering, resulting in insufficient data integrity and accuracy.

[0021] In some blockchain application scenarios, smart contracts are used to automatically monitor carbon emission behaviors. When the monitoring data triggers the violation conditions set in the contract, the smart contract will immediately execute operations including account freezing, carbon credit locking, and trading permission deprivation. Although such a mechanism can achieve automatic punishment without human intervention, due to the "automatic execution and non-changeable" characteristics of smart contracts, their judgment behaviors highly rely on input data, lack data anomaly recognition and fault tolerance mechanisms, and are extremely likely to misjudge compliant nodes as violating entities due to incorrect data input. Furthermore, currently, smart contract systems usually do not establish an independent data appeal and arbitration mechanism, and cannot roll back or manually review misjudgment results, seriously affecting the fairness and credibility of the system.

[0022] To effectively solve the above problems, the present invention proposes a comprehensive carbon emission reduction measurement method based on blockchain, including: S1. Establish a hierarchical data collection architecture, introduce a carbon emission description language, collect and structurally unify internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data; build a smart oracle network, dynamically access external data sources, and aggregate multi-source heterogeneous data using the BFT consensus mechanism to form a basic trusted carbon data set; S2. Construct a double-chain hybrid blockchain architecture including a main chain and a side chain, combine a carbon right pledge mechanism, and automatically calculate the carbon emission intensity based on the basic trusted carbon data set; mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high-carbon emission behavior data and trigger an account freezing operation; S3. Based on the high-carbon emission behavior data, adopt a differential verification mechanism to identify the changes in carbon emissions, and combine with the carbon flow topological structure generated by the product structure BOM table to conduct abnormal behavior tracking; perform credible certification processing on the high-carbon emission behavior data to obtain credible certification data; S4. Map the credible certification 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 hierarchical 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 sand table integrating time, space, industry, and value dimensions to perform four-dimensional visualization of NFT carbon credit tokens.

[0023] The methods for obtaining multi-source heterogeneous data include: Based on the source types of carbon emission data, divide the carbon emission data sources within the enterprise into levels, establish a hierarchical data collection architecture, and collect the internal heterogeneous data related to enterprise carbon emissions through the hierarchical data collection architecture; the hierarchical data collection architecture includes a device perception layer, a business system layer, a monitoring and control layer, and an edge computing layer; the device perception layer is used to collect physical quantity data related to industrial equipment emissions, such as the operation data of energy-consuming equipment such as industrial production equipment, boilers, air compressors, and cooling towers, including electricity consumption, gas consumption, gas production, etc.; the business system layer is used to collect structured production and energy data in the enterprise management system, such as data generated by business systems such as the Manufacturing Execution System (MES), ERP, and Energy Management System (EMS), including production plans, process parameters, energy consumption reports, etc.; the monitoring and control layer is used to collect emission monitoring data provided by environmental monitoring equipment, such as real-time sensing data collected by environmental monitoring sensors, including carbon dioxide concentration, flue gas velocity, temperature, etc.; the edge computing layer is used to preprocess the data collected by the device perception layer, the business system layer, and the monitoring and control layer, including noise filtering, data completion, and time series alignment; Introduce a carbon emission description language including field definition, context binding, and emission factor mapping to perform semantic modeling on the collected internal heterogeneous data, and represent it as a unified carbon emission data field (realize the standardized description of carbon emission behaviors from different sources); map the carbon emission data field to a CEDL format data structure through a protocol converter, and then output unified structured multi-source heterogeneous data.

[0024] Field definition is used to uniformly describe the data item names, data types, units, and semantics related to carbon emissions; context binding is used to bind the device identifier, spatial location, system attribution, and collection period 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 subsequent calculation of carbon emission intensity.

[0025] The method for obtaining the basic trusted carbon dataset includes: Build an intelligent oracle network by deploying n oracle nodes with independent data collection and verification capabilities; use the oracle nodes to access external data sources to collect external carbon-related data, and perform consistency verification on the external carbon-related data through the BFT consensus mechanism. Retain the same external carbon-related data that has been verified and submitted by more than two-thirds of the oracle nodes to form basic carbon data units with credibility and timestamp markings; aggregate the basic carbon data units with multi-source heterogeneous data to form a basic trusted carbon dataset, which is used as data input support for subsequent carbon emission intensity calculation, abnormal emission identification, and carbon credit token generation.

[0026] The method for obtaining the carbon emission intensity includes: Adopt a double-chain structure to construct a double-chain hybrid blockchain architecture including a main chain and a side chain. The main chain is used for the on-chain storage and certification of the basic trusted carbon dataset, and the side chain is used to execute the carbon emission intensity calculation and carbon right pledge mechanism; deploy a carbon right pledge unit on the main chain to manage the enterprise's pledged tokens and account status; deploy an exception handling unit on the side chain to identify high carbon emission intensity and trigger the account freezing mechanism; Based on the carbon right pledge rule, an enterprise needs to pledge tokens anchored to the carbon quota. The number of pledged tokens increases dynamically with the enterprise's historical violation times; calculate and obtain the number of pledged tokens according to the benchmark quantity and historical violation times, and store the corresponding amount of pledged tokens in the carbon right pledge unit; ; where the benchmark quantity refers to the number of tokens that an enterprise initially needs to pledge, which is defined as 20% of the enterprise's annual carbon emission quota. For example, if an enterprise's annual carbon emission quota is 1000 tons, the benchmark quantity is 200 pledged tokens; the historical violation times refer to the number of times that an enterprise has been detected with excessive carbon emissions cumulatively in history; the purpose of this formula is to dynamically adjust through economic means, so that the enterprise's violation cost rises rapidly with the number of times, and it is forced to reduce emissions and comply with regulations; Deploy and run a smart contract in the side chain, and automatically execute the carbon emission intensity calculation logic by running the smart contract; divide the carbon emissions within a unit time by the carbon output within the same time to calculate the carbon emission intensity.

[0027] The method for triggering the account freezing operation includes: By comparing the carbon emission intensity with the preset carbon emission intensity threshold, mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high carbon emission behavior data; dynamically adjust the preset carbon emission intensity threshold through the carbon emission intensity threshold adjustment formula; The carbon emission intensity threshold adjustment formula is ; where represents the carbon emission intensity threshold after dynamic adjustment; represents the preset carbon emission intensity threshold; represents the carbon emission intensity of the area where the current node is located in the double-chain hybrid blockchain architecture; represents the average carbon emission intensity of the double-chain hybrid blockchain architecture; represents the deviation of carbon emission intensity; represents the adjustment factor, which is used to adjust the influence degree of regional carbon intensity difference on the carbon emission intensity threshold; The smart contract is an automatically executed and tamper-proof program logic. Once a specific condition is triggered (such as the carbon emission intensity exceeding the threshold), the freezing operation will be immediately executed. However, its execution highly depends on the accuracy and integrity of the input data. If the data source has the following situations: incomplete data (such as monitoring interruption, missing sections), data abuse or forgery (such as human tampering, false reporting by suppliers), sensor errors, deviation accumulation, spatio-temporal alignment failure (data timestamp misalignment, regional label mismatch), it may trigger incorrect smart contract logic judgments, such as misjudging a compliant node as an over-emission node, resulting in: user accounts being frozen, pledged assets being locked, unable to trade carbon credits, node reputation being damaged, and system credibility decreasing, leading to a trust crisis.

[0028] To address the above problems, a freezing probability function is established to adopt a step-by-step response strategy for accounts at different risk levels; a phased freezing mechanism is used to construct a freezing state determination logic function, and the account freezing operation is disassembled into a warning state, a restricted state, and a frozen state; and the account freezing operation is triggered through the freezing state determination logic function.

[0029] The freezing probability function is ; where represents the freezing probability; represents the freezing determination inflection point, which is the maximum deviation range of carbon emission intensity tolerated by the system; represents the slope control factor, which controls the steepness of the curve; The freezing state determination logic function is: ; where represents that the account is in the warning state; represents that the account is in the restricted state; represents that the account is in the frozen state; represents the lower threshold of the freezing probability; represents the upper threshold of the freezing probability; For example: Define the lower threshold of the freezing probability as , and the upper threshold of the freezing probability as ; when the freezing probability of the account is less than When the probability of freezing an account is When the account is frozen, the restriction state is triggered, limiting the carbon trading quota of the enterprise by 50%, and the base amount of pledged tokens is increased by 15%; when the freezing probability of the account is greater than or equal to When a company violates the regulations, the freeze status is triggered, the pledged tokens are fully frozen, and all carbon trading of the company is prohibited; through the gradient penalty mechanism, the company is given room for rectification and a quick response is ensured in the event of serious violations; It should be noted that the freezing probability function uses the Sigmoid function, which is a probability mapping function widely used in statistics and machine learning. The probability value between and is convenient for quantifying the probability of freezing risk. As the inflection point for freezing determination, a critical value of the risk indicator is set. When the risk indicator is lower than this value, the freezing probability is close to 0, indicating that the risk is low; when it is higher than this value, the freezing probability rises rapidly, reflecting the system's sensitivity to risks beyond the tolerance range. The slope control factor controls the steepness of the curve to ensure that the freezing probability does not suddenly jump near the threshold, but has a smooth transition, which is conducive to the system's gradual response when the risk changes and avoids unreasonable freezing caused by hard cut-off. The freezing state determination function divides the freezing operation into three states: warning, restriction, and freezing by setting two probability thresholds, reflecting a phased and graded intelligent management strategy, reducing the risk of misjudgment and excessive freezing, and improving the level of refinement of system governance.

[0030] On the whole, this function can scientifically reflect the nonlinear relationship between risk indicators and freezing probability, taking into account risk tolerance while avoiding the rigid limitations of a single threshold, thereby improving the robustness and flexibility of the system.

[0031] The following problems existing in the existing technology are solved: Traditional carbon emission monitoring systems mostly use fixed thresholds to determine whether there is an over-emission, without considering regional industrial structure, energy consumption baseline, and carbon density differences, which can easily lead to misjudgment; once the smart contract is bound to wrong data (missing, forged, label mismatch, etc.), it will automatically execute serious actions such as freezing, without a maneuver mechanism or fault tolerance space. The operation of automatically freezing illegal accounts by smart contracts is highly dependent on the accuracy and completeness of the input data. In reality, the data may be defective due to monitoring interruptions, sensor errors, forgery and tampering, time and space mismatch, etc., which directly leads to the misjudgment of compliant accounts as illegal accounts, resulting in unreasonable freezing. The sources of errors are complex (monitoring anomalies, data forgery, equipment errors, etc.), and the existing contract mechanism lacks a data anomaly prediction and complaint mechanism.

[0032] The beneficial effects compared with the prior art are as follows: By means of the carbon emission intensity threshold adjustment formula based on the difference between the current region and the average carbon emission intensity in the double-chain hybrid blockchain architecture, the dynamic adjustment of the preset threshold is realized, which fully reflects the real-time differences in carbon emission intensity in different regions, effectively improves the recognition accuracy of abnormal carbon emission behaviors, and reduces misjudgments. The freezing probability function is adopted to calculate the probability of an account being frozen according to the magnitude of the carbon emission intensity deviation and the system tolerance range, making the freezing decision more flexible and scientific, avoiding over-freezing caused by the triggering of a single threshold, and helping to protect the rights and interests of compliant users. The freezing status determination logic function is designed to divide the account freezing into three levels: early warning, restriction, and freezing, and different degrees of restriction measures are taken for different risk levels, improving the refinement and humanization levels of system governance. Through the dynamic adjustment and phased response mechanism, the mis-triggering of smart contracts caused by data anomalies is reduced, the reliability and rationality of the automatic execution of smart contracts are improved, the trust among all parties in the system is maintained, and the fairness and transparency of carbon emission supervision are enhanced.

[0033] 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 intelligent level of carbon emission reduction supervision, the enterprise introduced a comprehensive carbon emission measurement method based on the double-chain hybrid blockchain architecture; Preset carbon emission intensity threshold is 1.0 ton per ton of steel, and the carbon emission intensity of the region where the current node is located in the double-chain hybrid blockchain architecture is respectively: 0.85 tons / ton in East China, 1.10 tons / ton in North China, and 0.95 tons / ton in South China; the average carbon emission intensity of the double-chain hybrid blockchain architecture is 1.0 ton / ton; the adjustment factor is 0.3; the dynamically adjusted carbon emission intensity threshold is: East China region ; North China region ; South China region ; The carbon emission intensity in East China, 0.85, is less than 0.955, compliant; The carbon emission intensity in North China, 1.10, is greater than 1.03, abnormal; The carbon emission intensity in South China, 0.95, is less than 0.985, compliant; For the abnormal data of the North China base, calculate the freezing probability function: Let the freezing determination inflection point (deviation range relative to the threshold), and the slope control factor is 50, and the carbon emission intensity deviation is: ; then the freezing probability is ; Set the lower threshold of the freezing probability to 0.4, and the upper threshold of the freezing probability to 0.8; Since 0.4 < 0.71 < 0.8, it is determined that the account in the North China region is in a restricted state, and restricted trading and partial pledge of assets locking operations are automatically executed.

[0034] The methods for abnormal behavior tracking include: Compare the historical carbon emission records before and after marking the high carbon emission behavior data, perform sliding difference analysis according to the preset time window, calculate the difference in carbon emissions per unit time using the first-order difference method, and compare it with the 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, and construct a carbon flow topological structure map based on the components and process flows in the bill of materials. The carbon flow topological structure map is a directed graph including m carbon source nodes, carbon emission paths, and emission factors; Using the carbon flow topological structure map, locate the key nodes in the carbon emission path and extract the carbon source nodes related to the high carbon emission behavior data; construct a time series prediction model based on the high carbon emission behavior data, and use a long short-term memory network to predict the trend of carbon emissions of the carbon source nodes to obtain the predicted carbon emissions; Compare the actual carbon emissions of the carbon source nodes with the predicted carbon emissions, and calculate the deviation degree; if the deviation degree is greater than the preset deviation degree threshold, mark the carbon source node as an abnormal behavior point; collect all the abnormal behavior points among the carbon source nodes related to the high carbon emission behavior data for abnormal behavior tracking.

[0035] The methods for obtaining trustworthy certification data include: For the currently marked high carbon emission behavior data, trace back a preset time length forward; extract the time series corresponding to the high carbon emission behavior data, and the time series includes carbon emissions, power consumption, process section production capacity utilization rate, and external temperature, to form a multi-dimensional carbon emission behavior sequence matrix; Discretize the time series, construct an emission value probability distribution function, and calculate the information entropy value of the time series using the Shannon entropy formula ; Define the information entropy value reference interval as , and generate a carbon entropy index through the normalization formula; the normalization formula is ; where represents the carbon entropy index; Define the carbon entropy index judgment threshold. If the carbon entropy index is less than or equal to the carbon entropy index judgment threshold, it means that the abnormal behavior is within the acceptable fluctuation range and the certification passes; if the carbon entropy index is greater than the carbon entropy index judgment threshold, it means that the abnormal behavior is within the unacceptable fluctuation range and the certification fails; Combine the high-carbon emission behavior data with the corresponding carbon entropy index, carbon flow topology map, and basic credible carbon data corresponding to the carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible certification data.

[0036] The method for obtaining a three-dimensional carbon credit identifier includes: Convert the credible certification data according to the ERC-1155 standard, and generate an NFT carbon credit token by automatically calling the NFT minting contract; Upload the NFT carbon credit token to the blockchain and generate a on-chain rights confirmation certificate; Construct a three-dimensional carbon credit identifier based on the credibility dimension, industry dimension, and time-space dimension, embed the three-dimensional identifier information into the NFT carbon credit token as extended metadata, and read it through a preset query interface on the chain.

[0037] The method for obtaining a four-dimensional carbon sand table includes: Introduce time dimension tags, space dimension tags, industry dimension tags, and value dimension tags into the metadata of the NFT carbon credit token; The time dimension tags include the occurrence time, certification time, and token on-chain time of the carbon emission reduction behavior; The space dimension tags include the location coordinates of the certification 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. Use the WebGL three-dimensional modeling engine, combined with the on-chain metadata interface of the NFT carbon credit token, to perform graphic rendering of the four-dimensional carbon sand table; Use a two-dimensional map as the base map, map the space dimension tags of the NFT token to the corresponding longitude and latitude positions, and present them as carbon points in the four-dimensional carbon sand table. Construct a time axis slider, which can slide to select any time window, automatically filter the NFT carbon credit tokens that are on-chain within this time range, and update the display area; Differentiate different industry tokens with different layers, icons, or colors; For example, blue for manufacturing, red for energy, green for agriculture, etc., and support layer switching and aggregated display. Convert the carbon emissions, carbon entropy index, and current estimated market value into visual features; Users can hover over any token identification point, and an on-chain metadata information box will automatically pop up to display the NFT carbon credit token.

[0038] The preset carbon emission intensity threshold is set by the staff. By collecting different carbon emission intensities, take the average value of multiple carbon emission intensities as the preset carbon emission intensity threshold; Similarly, set the preset carbon emission difference threshold and the preset deviation degree threshold.

[0039] In this embodiment, a carbon emission intensity threshold adjustment formula based on the difference between the current region and the average carbon emission intensity in the double-chain hybrid blockchain architecture is used to achieve dynamic adjustment of the preset threshold, fully reflecting the real-time differences in carbon emission intensity in different regions, effectively improving the recognition accuracy of abnormal carbon emission behaviors, and reducing misjudgments. A freezing probability function is adopted to calculate the probability of an account being frozen according to the magnitude of the carbon emission intensity deviation and the system tolerance range, making the freezing decision more flexible and scientific, avoiding over-freezing caused by a single threshold trigger, and helping to protect the rights and interests of compliant users.

[0040] Design a freezing status determination logic function to divide account freezing into three levels: warning, restriction, and freezing, and take different levels of restriction measures for different risk levels to improve the refinement and humanization levels of system governance. Through dynamic adjustment and phased response mechanisms, reduce the mis-triggering of smart contracts caused by data anomalies, improve the reliability and rationality of the automatic execution of smart contracts, maintain the trust of all parties in the system, and enhance the fairness and transparency of carbon emission supervision.

[0041] Embodiment 2 Please refer to Figure 3 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. A blockchain-based comprehensive carbon emission reduction measurement system is provided, including: A carbon intelligent acquisition gateway module, which establishes a hierarchical data acquisition architecture, introduces a carbon emission description language, collects and structurally unifies internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data; builds an intelligent oracle network, dynamically accesses external data sources, and aggregates multi-source heterogeneous data using the BFT consensus mechanism to form a basic trusted carbon data set; A consensus blockchain network module, which constructs a double-chain hybrid blockchain architecture including a main chain and a side chain, combines a carbon right pledge mechanism, and automatically calculates the carbon emission intensity based on the basic trusted carbon data set; marks the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high-carbon emission behavior data and triggers an account freezing operation; A trusted enhanced certification module, which uses a differential verification mechanism to identify changes in carbon emissions based on high-carbon emission behavior data, and combines the carbon flow topology structure generated by the product structure BOM table to track abnormal behaviors; performs trusted certification processing on high-carbon emission behavior data to obtain trusted certification data; A dynamic carbon asset protocol module, which maps the trusted certification data to NFT carbon credit tokens, constructs a three-dimensional carbon credit identifier, and forms tradable carbon assets; introduces mainstream market price information, constructs a hierarchical carbon token liquidity pool through an automated market-making mechanism, and discovers the liquidity and value of carbon assets; A carbon flow visualization center module, which constructs a four-dimensional carbon sand table integrating time, space, industry, and value dimensions, and performs four-dimensional visualization display of NFT carbon credit tokens.

[0042] Since the electronic device introduced in this embodiment is the electronic device adopted for implementing the carbon emission reduction comprehensive measurement method based on blockchain in the embodiments of the present application, based on the carbon emission reduction comprehensive measurement method based on blockchain introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device adopted for the carbon emission reduction comprehensive measurement method based on blockchain in the embodiments of the present application, it falls within the scope protected by the present application.

[0043] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0044] The above is only the preferred implementation manner of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for ordinary technical users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A comprehensive carbon emission reduction measurement method based on blockchain, characterized in that Including: S1. Establish a hierarchical data collection architecture, introduce a carbon emission description language, collect and structurally unify internal heterogeneous data through a protocol converter, and obtain multi-source heterogeneous data; Build an intelligent oracle network, dynamically access external data sources, and aggregate multi-source heterogeneous data using the BFT consensus mechanism to form a basic trusted carbon dataset; S2. Construct a double-chain hybrid blockchain architecture including a main chain and a side chain, combine a carbon right pledge mechanism, and automatically calculate the carbon emission intensity based on the basic trusted carbon dataset; Mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high-carbon emission behavior data, and trigger an account freezing operation; S3. Based on the high-carbon emission behavior data, use a differential verification mechanism to identify changes in carbon emissions, and combine the carbon flow topology structure generated by the product structure BOM table to track abnormal behaviors; Perform trusted certification processing on the high-carbon emission behavior data to obtain trusted certification data; S4. Map the trusted certification data to NFT carbon credit tokens, construct a three-dimensional carbon credit identifier, and form tradable carbon assets; Introduce mainstream market price information, construct a hierarchical carbon token liquidity pool through an automated market-making mechanism, and discover the liquidity and value of carbon assets; S5. Construct a four-dimensional carbon sand table integrating the dimensions of time, space, industry, and value, and perform four-dimensional visualization display on the NFT carbon credit tokens.

2. The integrated carbon emission reduction measurement method based on blockchain according to claim 1, wherein The method for obtaining the multi-source heterogeneous data includes: Based on the source type of carbon emission data, divide the carbon emission data sources within the enterprise into levels, establish a hierarchical data collection architecture, and collect the internal heterogeneous data related to enterprise carbon emissions through the hierarchical data collection architecture; The hierarchical data collection architecture includes a device perception layer, a business system layer, a monitoring and control layer, and an edge computing layer; Introduce a carbon emission description language including field definition, context binding, and emission factor mapping, perform semantic modeling on the collected internal heterogeneous data, and represent it as a unified carbon emission data field; Map the carbon emission data field to a data structure in CEDL format through a protocol converter, and then output unified structured multi-source heterogeneous data.

3. The integrated carbon emission reduction measurement method based on blockchain according to claim 2, wherein The method for obtaining the basic trusted carbon dataset includes: Build an intelligent oracle network by deploying n oracle nodes with independent data collection and verification capabilities; Use the oracle nodes to access external data sources, collect external carbon-related data, perform consistency verification on the external carbon-related data through the BFT consensus mechanism, and retain the same external carbon-related data verified and submitted by more than two-thirds of the oracle nodes to form basic carbon data units with credibility and timestamp markings; Aggregate the basic carbon data units with the multi-source heterogeneous data to form a basic trusted carbon dataset.

4. The integrated 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 double-chain structure is adopted to construct a double-chain hybrid blockchain architecture including a main chain and a side chain. The main chain is used for the on-chain storage and certification of the basic trusted carbon data set, and the side chain is used to execute the carbon emission intensity calculation and the carbon right pledge mechanism; a carbon right pledge unit is deployed on the main chain to manage the enterprise's pledged tokens and account status; an exception handling unit is deployed on the side chain to identify high carbon emission intensity and trigger the account freezing mechanism; Based on the carbon rights pledge rules, an enterprise needs to pledge tokens anchored to carbon quotas The number of pledged tokens increases dynamically with the enterprise's historical number of violations; calculate the number of pledged tokens based on the benchmark quantity and the historical number of violations, and store the corresponding amount of pledged tokens in the carbon rights pledge unit; Deploy and run smart contracts in the side chain, and automatically execute the carbon emission intensity calculation logic by running the smart contracts; divide the carbon emissions within a unit time by the carbon output within the same time to calculate the carbon emission intensity.

5. A comprehensive carbon emission reduction measurement method based on blockchain according to claim 4, characterized in that, The method for triggering the account freezing operation includes: By comparing the carbon emission intensity with the preset carbon emission intensity threshold, mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high carbon emission behavior data; dynamically adjust the preset carbon emission intensity threshold through the carbon emission intensity threshold adjustment formula; Establish a freezing probability function, and adopt a step-by-step response strategy for the account at different risk levels; use a phased freezing mechanism to construct a freezing state determination logic function, and disassemble the account freezing operation into a warning state, a restricted state, and a freezing state; and trigger the account freezing operation through the freezing state determination logic function.

6. The integrated carbon emission reduction measurement method based on blockchain according to claim 5, wherein The method for tracking abnormal behaviors includes: Compare the historical carbon emission records before and after marking the high carbon emission behavior data, perform sliding difference analysis according to the preset time window, use the first-order difference method to calculate the difference in carbon emissions within a unit time, and compare it with the preset carbon emission difference threshold. If the carbon emission difference is greater than the preset carbon emission difference threshold, it is judged 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, and construct a carbon flow topology structure map based on the components and process flows in the bill of materials. The carbon flow topology structure map is a directed graph including m carbon source nodes, carbon emission paths, and emission factors; Use the carbon flow topology structure map to locate the key nodes in the carbon emission path and extract the carbon source nodes related to the high carbon emission behavior data; based on the high carbon emission behavior data, construct a time series prediction model, and use a long short-term memory network to predict the trend of the carbon emissions of the carbon source nodes to obtain the predicted carbon emissions; Compare the actual carbon emissions of the carbon source nodes with the predicted carbon emissions and calculate the deviation degree; if the deviation degree is greater than the preset deviation degree threshold, mark the carbon source node as an abnormal behavior point; collect all the abnormal behavior points among the carbon source nodes related to the high carbon emission behavior data for abnormal behavior tracking.

7. A comprehensive carbon emission reduction measurement method based on blockchain according to claim 6, characterized in that The method for obtaining the trusted certification data includes: For the currently marked high carbon emission behavior data, trace back a preset time length; extract the time series corresponding to the high carbon emission behavior data, and the time series includes carbon emissions, power consumption, process section production capacity utilization rate, and external temperature, to form a multi-dimensional carbon emission behavior sequence matrix; Discretize the time series, construct the probability distribution function of emission values, and calculate the information entropy value of the time series using the Shannon entropy formula ; Define the reference interval of the information entropy value as , and generate the carbon entropy index through the normalization formula; Define the carbon entropy index judgment threshold. If the carbon entropy index is less than or equal to the carbon entropy index judgment threshold, it means that the abnormal behavior is within the acceptable fluctuation range and the certification passes; if the carbon entropy index is greater than the carbon entropy index judgment threshold, it means that the abnormal behavior is within the unacceptable fluctuation range and the certification fails; Combine the high-carbon emission behavior data with the corresponding carbon entropy index, carbon flow topological structure map, and basic credible carbon data corresponding to the carbon emission intensity less than or equal to the preset carbon emission intensity threshold to form a set of credible certification data.

8. A comprehensive carbon emission reduction measurement method based on blockchain according to claim 7, characterized in that, The method for obtaining the three-dimensional carbon credit identifier includes: Convert the credible certification data according to the ERC-1155 standard, and generate NFT carbon credit tokens by automatically calling the NFT minting contract; Upload the NFT carbon credit tokens to the blockchain and generate a chain-of-rights confirmation certificate; Construct a three-dimensional carbon credit identifier based on the credibility dimension, industry dimension, and time-space dimension, embed the three-dimensional identifier information as extended metadata into the NFT carbon credit tokens, and read it through a preset query interface on the chain.

9. A comprehensive carbon emission reduction measurement method based on blockchain according to claim 8, characterized in that, The method for obtaining the four-dimensional carbon sand table includes: Introduce time dimension tags, space dimension tags, industry dimension tags, and value dimension tags into the metadata of NFT carbon credit tokens; The time dimension tags include the occurrence time, certification time, and token on-chain time of the carbon emission reduction behavior; The space dimension tags include the location coordinates of the certification 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. Use the WebGL three-dimensional modeling engine, combined with the on-chain metadata interface of the NFT carbon credit token, to perform graphic rendering of the four-dimensional carbon sand table; Use the two-dimensional map as the base map, map the space dimension tags of the NFT token to the corresponding longitude and latitude positions, and present them as carbon points in the four-dimensional carbon sand table. Construct a time axis slider, which can slide to select any time window, automatically filter the NFT carbon credit tokens on the chain within this time range, and update the display area; Distinguish different industry tokens with different layers, icons, or colors; Convert carbon emissions, carbon entropy index, and current estimated market value into visual features.

10. A blockchain-based comprehensive carbon emission reduction measurement system for implementing the blockchain-based comprehensive carbon emission reduction measurement method according to any one of claims 1 to 9, characterized in that, Include: Carbon intelligent collection gateway module, establish a hierarchical data collection architecture, introduce carbon emission description language, collect and structurally unify internal heterogeneous data through a protocol converter to obtain multi-source heterogeneous data; Build an intelligent oracle network, dynamically access external data sources, and aggregate multi-source heterogeneous data using the BFT consensus mechanism to form a basic credible carbon data set. Consensus blockchain network module, construct a double-chain hybrid blockchain architecture including a main chain and a side chain, combine the carbon rights pledge mechanism, and automatically calculate the carbon emission intensity based on the basic credible carbon data set; Mark the data with a carbon emission intensity greater than the preset carbon emission intensity threshold as high-carbon emission behavior data and trigger the account freezing operation. Credible enhanced certification module, based on the high-carbon emission behavior data, use the differential verification mechanism to identify changes in carbon emissions, and combine the carbon flow topological structure generated by the product structure BOM table to track abnormal behaviors; Perform credible certification processing on the high-carbon emission behavior data to obtain credible certification data. The Dynamic Carbon Asset Protocol Module maps trusted certification data into NFT carbon credit tokens, constructs a three-dimensional carbon credit identifier, and forms tradable carbon assets; introduces mainstream market price information, constructs a hierarchical carbon token liquidity pool through an automated market-making mechanism, and conducts liquidity and value discovery of carbon assets; The Carbon Flow Visualization Central Module constructs a four-dimensional carbon sand table integrating time, space, industry, and value dimensions, and conducts four-dimensional visualization of NFT carbon credit tokens.

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