A Blockchain-Based Multi-Node Carbon Emission Smart Contract Verification Method

Abstract

This invention relates to the field of data processing technology. The blockchain-based multi-node carbon emission smart contract verification method provided by this invention aims to solve the problems of opaque data verification, high trust costs, and difficulty in adapting to policy changes in existing carbon asset trading systems. By constructing a layered blockchain architecture, it achieves distributed storage and dynamic verification of data, utilizes edge computing nodes to collect data, and obtains third-party verification data through an oracle node cluster. A dynamic rule engine monitors data changes in real time, triggering multi-level verification processes to improve data accuracy. Simultaneously, it employs an improved Byzantine fault-tolerant mechanism to achieve consensus and features dynamic policy updates and security enhancements, improving the system's flexibility and security. This method achieves multi-party consensus and real-time verification of carbon emission data, reduces the trust costs of inter-enterprise collaboration, and contributes to the healthy development of the carbon emission trading market.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for verifying multi-node carbon emission smart contracts based on blockchain. Background Technology

[0002] Existing carbon asset trading systems mostly adopt a centralized architecture, relying on technologies such as Robotic Process Automation (RPA), data integration, and machine learning to improve trading efficiency and risk control capabilities. While existing carbon asset trading platforms use blockchain technology for data notarization, their functionality is primarily limited to data storage and traceability, failing to address the shortcomings in real-time verification and trust mechanisms during multi-party collaboration.

[0003] Traditional systems rely on manual or centralized audits of carbon emission data, which are prone to verification delays due to single points of failure and struggle to handle the real-time processing demands of massive amounts of data. Existing technologies lack multi-party consensus mechanisms, and the opaque data verification process leads to high trust costs during inter-enterprise collaboration. While some systems have incorporated blockchain technology, smart contracts are only used for transaction execution and data storage, lacking rules for dynamic multi-node verification of carbon emission data, making them ill-suited to complex policy changes and multi-dimensional verification requirements. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a blockchain-based multi-node carbon emission smart contract verification method. This method addresses the problem that while some systems have incorporated blockchain technology, existing blockchain carbon emission smart contracts are only used for transaction execution and data storage. They lack rules for multi-node dynamic verification of carbon emission data and a cross-verification mechanism for fuel-side accounting data and online monitoring data. Consequently, they are ill-suited to adapt to complex policy changes and the need for automated quality control and verification of multi-dimensional carbon data.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] The present invention provides a blockchain-based multi-node carbon emission smart contract verification method, comprising:

[0007] Step S1: Construct a layered blockchain architecture, which includes a data layer, a verification layer, and a contract layer.

[0008] Step S2: Based on the data layer of the layered blockchain architecture, fuel consumption, test data and online carbon emission monitoring data of the enterprise are collected through edge computing nodes, and the collected data is preprocessed to generate a standardized data packet carrying a hash chain data fingerprint generated by a secure hash algorithm.

[0009] Step S3: Input the standardized data packet generated in step S2 into the oracle node cluster, establish a cross-chain trusted channel with the third-party data source by implementing TLS two-way authentication and zero-knowledge proof verification, obtain the associated data verified by reputation scoring, and compare the consistency of the associated data with the standardized data packet.

[0010] Step S4: When the absolute value of the sudden change in CO2 concentration in the non-start-stop state exceeds 15%, the absolute value of the relative difference between the fuel-side calculated emissions and the emission-side online monitoring data exceeds 10%, or the policy document version is updated;

[0011] If either the production and emission change rates do not match, the main verification contract will initiate a multi-level verification process. Online monitoring data at the emission end includes multi-dimensional carbon data quality control comparison data retrieved from flue CO2 monitoring, drone or satellite imagery, and fuel-based emissions calculations will serve as the basis for carbon emission trading settlement.

[0012] In step S5, the main verification contract distributes verification tasks to the verification layer nodes according to the triggering event type in step S4, and obtains the verification results.

[0013] In step S6, each verification node submits the verification result from step S5 to the verification consensus module. An improved Byzantine fault-tolerant mechanism is used to perform weighted fusion of the verification results. After reaching a consensus, the blockchain data status is updated and a final data verification report is generated. The weight coefficient of the weighted fusion is dynamically adjusted according to the node's historical accuracy and real-time status.

[0014] Furthermore, the layered blockchain architecture of the blockchain-based multi-node carbon emission smart contract verification method of the present invention includes a data layer, a verification layer, and a contract layer, wherein:

[0015] The data layer uses a permissioned blockchain to store and verify metadata, and saves encrypted raw carbon emission data through an off-chain storage system.

[0016] The verification layer deploys a dynamic rule engine and a verification network including regulatory nodes, audit nodes, and industry nodes;

[0017] At the contract layer, the Merkle Patricia Tree strategy library is loaded to verify the main contract and store strategy files.

[0018] Furthermore, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, step S5 involves the main verification contract distributing verification tasks to the verification layer nodes according to the triggering event type in step S4, thereby obtaining the verification result, wherein:

[0019] Assign compliance verification algorithm tasks to regulatory nodes to verify whether carbon emission data complies with preset regulations;

[0020] Assign Monte Carlo simulation verification tasks to audit nodes to generate simulated emission datasets and calculate confidence intervals;

[0021] Assign benchmark comparison tasks to industry nodes, which are used to call the benchmark database to generate industry benchmarking analysis results.

[0022] Furthermore, the blockchain-based multi-node carbon emission smart contract verification method of the present invention also includes:

[0023] Step S7: When the strategy library is updated, the new strategy is simulated and run in the sandbox environment through the governance contract. After successful verification, the Merkle root of the strategy library is updated, and the updated strategy is synchronized to the dynamic rule engine through the contract layer. At the same time, a bichain graph recording the version change relationship is constructed.

[0024] Furthermore, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the off-chain storage system adopts a horizontal sharding and vertical sharding strategy:

[0025] Horizontal sharding distributes data across IPFS node clusters according to industry type;

[0026] Vertically segment and separate basic monitoring data and derived verification indicators into different encrypted databases.

[0027] Furthermore, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the dynamic rule engine includes:

[0028] The rule parser compiles JSON or XML policy files into executable validation logic;

[0029] A difference hash comparator identifies version changes by comparing the hash values ​​of policy files.

[0030] The event correlation analyzer establishes a dynamic mapping relationship between production data, fuel data, and online carbon emission monitoring data.

[0031] Furthermore, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the distribution and verification task in step S5 includes phased processing:

[0032] In the initial verification phase, a pre-compiled contract template is invoked to verify the data threshold with a millisecond-level response; in the intermediate verification phase, a secure multi-party computation protocol is activated to achieve cross-verification of encrypted data among multiple nodes.

[0033] In the deep verification phase, an LSTM prediction model based on a long short-term memory network is loaded. The LSTM prediction model generates an emission trend prediction curve through time window sliding training. Its input features include the company's historical fuel consumption, production volume, and policy adjustment factors. When the relative deviation between the actual emission data and the prediction curve exceeds the threshold ±5%, a manual review instruction is generated.

[0034] Furthermore, the intermediate verification stage of the blockchain-based multi-node carbon emission smart contract verification method of the present invention includes:

[0035] The regulatory node runs a compliance verification algorithm and outputs a regulatory compliance index;

[0036] The audit node assigns Monte Carlo simulation verification tasks and generates a simulated emission dataset through the following steps: Based on the equipment's historical operating data, the operating condition intervals are divided according to the load rate ±5%; within the same operating condition interval, fuel consumption parameters for consecutive time windows are randomly selected.

[0037] Latin hypercube sampling is performed on carbon content parameters and equipment operating status parameters to generate a simulated emission dataset on the order of 10^5. The sampling of fuel consumption parameters, carbon content parameters, and equipment operating status parameters must meet the constraints of operating condition interval division and parameter correlation. The confidence interval of the emission data (confidence level ≥ 95%) is calculated. Industry nodes call the benchmark database to generate industry benchmarking analysis results. The main verification contract performs weighted fusion of the output results of the three types of nodes, and the weight coefficients are dynamically adjusted according to the historical accuracy and real-time status of the nodes.

[0038] Furthermore, the blockchain-based multi-node carbon emission smart contract verification method of the present invention also includes a strategy update process:

[0039] The policy text is parsed using a natural language processing engine to generate validation rules in the form of an abstract syntax tree.

[0040] The contract code structure is optimized using loop unrolling techniques to reduce gas consumption;

[0041] The Merkle root of the effective strategy is written into the main chain block header, and historical version and change impact analysis data are stored in the side chain.

[0042] Furthermore, the blockchain-based multi-node carbon emission smart contract verification method of the present invention further includes: performing attribute-based encryption on standardized data packets, and decrypting them when the access strategy meets the preset standard;

[0043] Employ quantum-resistant digital signature algorithms, including CRYSTALS-Dilithium or ECDSA;

[0044] The threshold signature protocol is executed every 24 hours, rotating the verification node keys in the distributed storage.

[0045] The beneficial effects of this invention are:

[0046] This invention, by constructing a layered blockchain architecture and deploying a dynamic rule engine, enables real-time monitoring of carbon emission data and rapidly triggers multi-level verification processes upon detecting anomalies or policy changes. This improves the real-time updating and accurate verification of carbon emission data, contributing to the fair and efficient operation of the carbon emission trading market. By introducing a layered sampling method with operating condition range constraints, Monte Carlo simulation can effectively distinguish fuel consumption characteristics under different production loads, making the generated simulated emission dataset closer to real-world production scenarios. Compared to traditional random sampling methods, this invention reduces the error rate of the confidence interval for carbon emission calculations, significantly improving the credibility of transaction settlement data.

[0047] This invention introduces a verification network including regulatory nodes, audit nodes, and industry nodes, realizing a consensus mechanism with multi-party participation. This mechanism enhances trust during inter-enterprise collaboration, reduces trust costs, and helps promote the healthy development of the carbon emissions trading market.

[0048] The dynamic rule engine can flexibly respond to policy changes and distribute different verification tasks based on the type of triggering event. This enables the invention to adapt to complex policy environments and meet the needs of multi-dimensional verification, improving the flexibility and adaptability of carbon emission data management.

[0049] By employing edge computing nodes to collect data, using off-chain storage systems to store raw data, and implementing security enhancement measures, this invention improves data processing efficiency and strengthens data security. This helps protect the confidentiality and integrity of carbon emission data, preventing data leakage and tampering.

[0050] In summary, these beneficial effects of the present invention provide strong support for the healthy development of the carbon emission trading market. Attached Figure Description

[0051] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0052] Figure 1 A sequence diagram of the blockchain-based multi-node carbon emission smart contract verification method provided in this embodiment of the invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings.

[0054] To better understand the purpose of this invention, the invention will now be described in further detail.

[0055] like Figure 1 As shown, the blockchain-based multi-node carbon emission smart contract verification method provided by this invention includes:

[0056] Step S1 constructs a layered blockchain architecture, which includes:

[0057] The data layer uses a permissioned blockchain to store and verify metadata, and saves encrypted raw carbon emission data through an off-chain storage system.

[0058] The verification layer deploys a dynamic rule engine and a verification network including regulatory nodes, audit nodes, and industry nodes.

[0059] The contract layer loads the Merkle Patricia Tree strategy library, which stores the main validation contract and strategy files.

[0060] Step S2 collects enterprise-side fuel consumption, test data, and online carbon emission monitoring data through edge computing nodes. The data is then processed sequentially with outlier filtering, format standardization, and time series alignment to generate a standardized data packet carrying a hash chain data fingerprint generated by a secure hash algorithm, including SHA3-256 or SHA-256.

[0061] Step S3 inputs the standardized data packet generated in step S2 into the oracle node cluster, establishes a cross-chain trusted channel with the third-party data source by implementing TLS two-way authentication and zero-knowledge proof verification, obtains the associated data verified by reputation scoring, and compares the consistency of the associated data with the standardized data packet.

[0062] Step S4: The main verification contract initiates a multi-level verification process when any of the following conditions are detected: The absolute value of the CO2 concentration mutation exceeds 15% in non-start / stop conditions; the absolute value of the relative difference between fuel-side calculated emissions and emission-side online monitoring data exceeds 10%; policy document version update; mismatch between production and emission change rates. This triggers the main verification contract to initiate a multi-level verification process.

[0063] Step S5: The main verification contract distributes verification tasks to the verification layer nodes based on the triggering event type from step S4.

[0064] Assign compliance verification algorithm tasks to regulatory nodes to verify whether carbon emission data complies with preset regulations;

[0065] Assign Monte Carlo simulation verification tasks to audit nodes to generate simulated emission datasets and calculate confidence intervals;

[0066] Assign benchmark comparison tasks to industry nodes, which are used to call the benchmark database to generate industry benchmarking analysis results;

[0067] In step S6, each verification node submits the verification result from step S5 to the verification consensus module. The improved Byzantine fault tolerance mechanism (PBFT improved algorithm, which adjusts the fault tolerance threshold through dynamic weights) is used to perform weighted fusion of the verification results. After reaching a consensus, the blockchain data status is updated and a final data verification report is generated. The weight coefficients of the weighted fusion are dynamically adjusted according to the node's historical accuracy and real-time status.

[0068] Step S7: When the strategy library is updated, the new strategy is simulated and run in the sandbox environment through the governance contract. After successful verification, the Merkle root of the strategy library is updated, and the updated strategy is synchronized to the dynamic rule engine through the contract layer. At the same time, a bichain graph recording the version change relationship is constructed.

[0069] Building a layered blockchain architecture:

[0070] Data Layer: Permissioned blockchain storage is used to verify metadata, improving data security and traceability; at the same time, encrypted raw carbon emission data is stored through an off-chain storage system, which protects data privacy and improves data access efficiency.

[0071] Verification Layer: A dynamic rule engine is deployed, which can parse and process carbon emission data in real time, improving data accuracy and compliance. Simultaneously, a verification network including regulatory nodes, audit nodes, and industry nodes forms a multi-party verification mechanism, enhancing the credibility of the verification.

[0072] Contract layer: Loads the main verification contract, which is the core of the verification process and is responsible for triggering and executing verification tasks; at the same time, it stores the Merkle Patricia Tree policy library for policy files.

[0073] Data acquisition and processing:

[0074] By collecting enterprise-level fuel consumption, laboratory data, and online carbon emission monitoring data through edge computing nodes, the low latency and high efficiency of edge computing enable real-time data collection.

[0075] The collected data is processed for outlier filtering, format standardization, and time series alignment.

[0076] Generate a standardized data packet carrying a hash chain data fingerprint generated by a secure hash algorithm, including SHA3-256 or SHA-256, to provide a basis for secure data transmission and verification.

[0077] Data validation:

[0078] Standardized data packets are input into the oracle node cluster, and a cross-chain trusted channel with third-party data sources is established through TLS two-way authentication and zero-knowledge proof verification.

[0079] The dynamic rule engine monitors the verified data in real time. When a specific event is detected (such as a sudden change in CO2 concentration, a policy document version update, or a mismatch between the rate of change of production and emissions), the main verification contract is triggered to start a multi-level verification process.

[0080] The main verification contract distributes verification tasks to the verification layer nodes based on the type of triggering event, including compliance verification, Monte Carlo simulation verification, and benchmark comparison with similar enterprises.

[0081] Consensus and Report Generation:

[0082] Each verification node submits its verification results to the verification consensus module, and a consensus is reached using an improved Byzantine fault-tolerant mechanism.

[0083] Update the blockchain data status and generate a final data verification report to provide a scientific basis for carbon emission management.

[0084] Policy updates and security enhancements:

[0085] When the strategy library is updated, the new strategy is simulated and run in the sandbox environment through the governance contract. After successful verification, the Merkle root of the strategy library is updated.

[0086] Implement security enhancement measures, such as attribute-based encryption, quantum-resistant digital signatures, and periodically rotating verification node keys, to improve data security and resistance to attacks.

[0087] Advantages of the method:

[0088] Improve verification efficiency and accuracy: through real-time monitoring and multi-level verification processes.

[0089] Reduce trust costs: Utilize blockchain technology to establish a multi-party verification mechanism to enhance trust during inter-enterprise collaboration.

[0090] Adapting to policy changes and multi-dimensional verification needs: The dynamic rule engine and strategy update process can flexibly respond to policy changes and multi-dimensional verification needs.

[0091] Enhance data security and resilience: Improve data security and resilience through security enhancement steps to protect the confidentiality and integrity of carbon emission data.

[0092] Specifically, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the off-chain storage system in step S1 adopts a horizontal sharding and vertical sharding strategy:

[0093] Horizontal sharding distributes data across IPFS node clusters according to industry type;

[0094] Vertically segment and separate basic monitoring data and derived verification indicators into different encrypted databases.

[0095] Off-chain storage systems employ horizontal and vertical sharding strategies to optimize data storage and management. The specific implementation methods and advantages of these two sharding strategies are as follows:

[0096] Horizontal partitioning strategy:

[0097] Implementation: The horizontal sharding strategy primarily distributes data across an IPFS (InterPlanetary File System) node cluster based on industry type. This means that data from different industries will be allocated to different IPFS nodes for storage.

[0098] Advantages: This sharding approach facilitates the categorized storage and efficient access of data. By distributing data across different IPFS nodes, data reliability and availability are improved. Even if one node fails, data on other nodes remains intact and available, thus reducing the impact of a single node failure on the entire system.

[0099] Vertical partitioning strategy:

[0100] Implementation method: The vertical sharding strategy separates the basic monitoring data from the derived verification indicators and stores them in different encrypted databases. The basic monitoring data is the raw, unprocessed data, while the derived verification indicators are data calculated based on the basic data using a certain algorithm and are used for verification and validation.

[0101] Advantages: By storing these two types of data separately, data integrity and security can be better protected. Basic monitoring data remains in its original state, facilitating subsequent processing and verification; while derived verification indicators are used for dedicated verification and validation processes, improving the efficiency and accuracy of data processing. Furthermore, using an encrypted database to store data enhances security, preventing unauthorized access and tampering.

[0102] In summary, the horizontal and vertical sharding strategies of the off-chain storage system together constitute an important data storage and management mechanism in this smart contract verification method, providing strong guarantees for the reliability, availability, and security of data.

[0103] Specifically, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the dynamic rule engine in step S4 includes:

[0104] The rule parser compiles JSON / XML format policy files into executable validation logic;

[0105] The rule parser uses the Antlr4 framework to parse JSON / XML policy files, generate an abstract syntax tree (AST), and generate executable verification logic through LLVM intermediate code. The differential hash comparator calculates the hash value of the policy file based on the SHA-256 algorithm and compares it with the historical hash value stored in the blockchain. If they are inconsistent, the version update process is triggered.

[0106] A difference hash comparator identifies version changes by comparing the hash values ​​of policy files.

[0107] The event correlation analyzer establishes a dynamic mapping relationship between production data, fuel data, and online carbon emission monitoring data.

[0108] The dynamic rule engine is a core component responsible for real-time monitoring and processing of multi-source carbon emission data, and triggering corresponding verification processes based on preset rules and strategies. The dynamic rule engine comprises three key components: a rule parser, a differential hash comparator, and an event correlation analyzer. Their respective functions and operating methods are as follows:

[0109] Rule parser:

[0110] Function: Compiles policy files in JSON / XML and other formats into executable validation logic.

[0111] How it works: The strategy file includes various validation rules, threshold settings, and triggering conditions. The rule parser parses these strategy files and transforms them into a form that the dynamic rule engine can understand and execute, thereby improving the validation of data in accordance with preset rules.

[0112] Difference hash comparator:

[0113] Function: Identify version changes by comparing the hash values ​​of policy files.

[0114] How it works: The differential hash comparator calculates the hash value of the current policy file and compares it with the previously stored hash value. If they don't match, it means the policy file has been changed. At this point, the differential hash comparator triggers an update process to ensure the validation logic in the dynamic rule engine is consistent with the latest policy file.

[0115] Event Correlation Analyzer:

[0116] Function: Establish a dynamic mapping relationship between production data, fuel data, and online carbon emission monitoring data.

[0117] How it works: The event correlation analyzer monitors carbon emission data and related production data in real time, and uses data analysis techniques to identify the correlations and trends between these data. When a specific triggering event is detected (such as a sudden change in CO2 concentration, a large difference between data from the fuel end and the online carbon emission monitoring end, a policy document update, or a mismatch between the rate of change of production and emissions), the event correlation analyzer matches these events with preset verification rules and triggers the corresponding verification process, thereby improving the accuracy and compliance of carbon emission data.

[0118] These three components work together to enable the dynamic rules engine to flexibly respond to complex policy changes and multi-dimensional verification needs, providing strong support for the verification and certification of carbon emission data.

[0119] Specifically, in the blockchain-based multi-node carbon emission smart contract verification method of the present invention, the distribution verification task in step S5 includes phased processing:

[0120] In the initial verification stage, the pre-compiled contract template is called to verify the data threshold with a millisecond-level response; e2. In the intermediate verification stage, the secure multi-party computation protocol is activated to achieve cross-verification of encrypted data among multiple nodes;

[0121] In the intermediate verification stage, the secure multi-party computation protocol is activated to achieve cross-verification of encrypted data among multiple nodes;

[0122] In the deep verification phase, an LSTM prediction model is loaded, using the company's historical fuel consumption, production volume, and policy adjustment factors as input features. The model is trained through a time window to generate an emission trend prediction curve. When the relative deviation between the actual emission data and the prediction curve exceeds the threshold (±5%), a manual review instruction is generated.

[0123] The distribution and verification task in step S5 is carefully designed as a phased process to improve the accuracy and compliance of carbon emission data. The three phases are the initial verification phase, the intermediate verification phase, and the deep verification phase, each with different verification responsibilities, working together to complete the distribution and verification task.

[0124] Initial verification stage:

[0125] In this stage, the system invokes the pre-compiled contract template to perform millisecond-level response verification of the data thresholds. This means the system can quickly perform preliminary data screening, rapidly identifying and excluding obviously abnormal or non-compliant data by checking whether it exceeds the preset threshold range. The purpose of this step is to reduce the burden on subsequent verification stages and improve overall verification efficiency.

[0126] Intermediate verification stage:

[0127] The intermediate verification phase is more in-depth and complex. In this phase, the system activates a secure multi-party computation protocol to achieve cross-verification of encrypted data across multiple nodes. This means that regulatory nodes, audit nodes, and industry nodes will jointly participate in the verification process, each performing different verification tasks.

[0128] The regulatory node is responsible for running the compliance verification algorithm and outputting the regulatory compliance index; the audit node executes Monte Carlo simulations to calculate the confidence interval of the emission data and assess the reliability and accuracy of the data; and the industry node calls the benchmark database to generate industry benchmarking analysis results, comparing the data with the industry average.

[0129] The main verification contract performs a weighted fusion of the output results of these three types of nodes. The weight coefficients are dynamically adjusted based on the historical accuracy and real-time status of the nodes to improve the comprehensiveness and accuracy of the verification results.

[0130] The weighting coefficients are calculated as follows:

[0131] W = α⋅Ai + β⋅Si;

[0132] Where Ai is the node's historical accuracy (based on the accuracy of the verification results over the past 30 days), Si is the real-time status score (including indicators such as node online rate and response latency), and α and β are adjustment factors (default values ​​are 0.7 and 0.3, respectively).

[0133] Deep verification phase:

[0134] The deep validation phase further validates and refines the results of the intermediate validation. In this phase, the system loads an LSTM prediction model to perform emission trend analysis, predicting future emission trends and comparing them with actual data.

[0135] The LSTM prediction model is built using the PyTorch framework. The input features include the company's historical fuel consumption (normalized), production (smoothed quarterly), and policy adjustment factors (represented by one-hot encoding) over the past 24 months. The model is trained using a time window sliding method (window size of 12 months and sliding step size of 1 month). The loss function is mean squared error (MSE), and the optimizer is Adam.

[0136] If the emission trend deviates significantly from the prediction model's results, and the deviation exceeds a preset threshold, the system will generate a manual review instruction. This means that for potentially problematic data, the system will require final manual review and confirmation to improve data accuracy and compliance.

[0137] By gradually deepening and refining these three stages, the distribution and verification tasks can improve the accuracy and compliance of carbon emission data, providing strong support for achieving the carbon neutrality goal.

[0138] Specifically, the intermediate verification stage of the blockchain-based multi-node carbon emission smart contract verification method of the present invention includes:

[0139] The regulatory node runs a compliance verification algorithm and outputs a regulatory compliance index;

[0140] The audit node assigns a Monte Carlo simulation verification task, which generates a simulated emission dataset of magnitude 10_x0005_ by randomly sampling fuel consumption parameters, carbon content parameters, and equipment operating status parameters. The sampling of fuel consumption parameters, carbon content parameters, and equipment operating status parameters must meet the constraints of operating condition interval division and parameter correlation. The confidence interval of the emission data (confidence level ≥ 95%) is calculated. The industry node calls the benchmark database to generate industry benchmarking analysis results. The main verification contract performs weighted fusion of the output results of the three types of nodes, and the weight coefficients are dynamically adjusted according to the historical accuracy and real-time status of the nodes.

[0141] During Monte Carlo simulation verification, the audit node employs a stratified random sampling strategy to improve simulation accuracy. Specifically, this includes:

[0142] Operating condition range division: Based on the company's historical equipment operating data, calculate the distribution range of load rate (current power / rated power) and divide the operating condition ranges into ±5% intervals. For example, when the rated power of the equipment is 1000kW, the actual power range corresponding to the load rate range of 85%-90% is 850-900kW.

[0143] Fuel consumption sampling: Within the same operating condition range, extract fuel consumption datasets for continuous time windows (e.g., 1 hour), and select representative samples using Latin hypercube sampling to avoid sampling bias caused by operating condition fluctuations.

[0144] Parameter correlation constraints: Joint sampling is conducted on carbon content parameters and equipment operating status parameters (such as combustion efficiency and flue gas temperature) to ensure that the physical correlation between parameters meets the requirements of actual production processes.

[0145] The intermediate verification phase employed various technical methods to conduct in-depth verification of carbon emission data. Specific details of this phase are as follows:

[0146] Regulatory node tasks:

[0147] The regulatory node is primarily responsible for running the compliance verification algorithm. This algorithm carefully examines carbon emission data to ensure it complies with relevant regulations and policies.

[0148] After the algorithm is executed, the regulatory node will output a regulatory compliance index, which is an important reference indicator for assessing data compliance.

[0149] Audit node tasks:

[0150] The audit node then performs Monte Carlo simulations, a statistical simulation method used to calculate confidence intervals for emissions data.

[0151] By simulating a large number of possible scenarios, audit nodes can assess the reliability and accuracy of data, thereby improving the authenticity of the data.

[0152] Industry node tasks:

[0153] Industry nodes will access the benchmark database to generate industry benchmarking analysis results.

[0154] By comparing a company's current emissions data with that of other companies in the same industry, industry nodes can assess whether the company's current emissions level is within a reasonable range.

[0155] During the intermediate verification phase, these three nodes each undertake different verification tasks, collectively forming a comprehensive and in-depth verification system. To ensure the accuracy and fairness of the verification results, the main verification contract performs weighted fusion processing on the output results of these three types of nodes.

[0156] Weighted fusion process of the main verification contract:

[0157] The main verification contract first receives the output results from regulatory nodes, audit nodes, and industry nodes.

[0158] Next, it dynamically adjusts the weighting coefficients based on the node's historical accuracy and real-time status. This means that the weights are updated in real time according to the actual performance of each node to ensure the fairness and accuracy of the verification results.

[0159] Finally, the main verification contract sums the results from each node according to their respective weight coefficients to arrive at the final verification result. This process not only improves the accuracy and reliability of the verification but also enhances the system's flexibility and adaptability.

[0160] Through the intermediate verification stage and the weighted fusion process of the master verification contract, this method can achieve comprehensive and in-depth verification of carbon emission data, providing strong support for the healthy development of the carbon emission trading market.

[0161] Specifically, the strategy update process in step S7 of the blockchain-based multi-node carbon emission smart contract verification method of the present invention includes:

[0162] The policy text is parsed using a natural language processing engine to generate validation rules in the form of an abstract syntax tree.

[0163] The BERT pre-trained model is used to perform semantic parsing on policy texts, extract key constraints (such as emission limits and monitoring frequency), and generate validation rules in the form of abstract syntax trees (ASTs) through rule template mapping. The template mapping table is stored in the policy library.

[0164] The contract code structure is optimized using loop unrolling techniques to reduce gas consumption;

[0165] The Merkle root of the effective strategy is written into the main chain block header, and historical version and change impact analysis data are stored in the side chain.

[0166] Step S7 specifically includes three steps:

[0167] Strategy rule generation:

[0168] In this step, the system uses a natural language processing engine to parse the policy text. This technology can deeply understand the policy content and transform it into verification rules that computers can understand.

[0169] The parsed validation rules are presented in the form of an abstract syntax tree, providing a clear and structured foundation for subsequent policy execution.

[0170] Contract code optimization:

[0171] To improve the execution efficiency of smart contracts and reduce execution costs, the system uses loop unrolling technology to optimize the contract code structure.

[0172] By reducing unnecessary loops and redundant calculations, this step significantly reduces gas consumption, making the execution of smart contracts on the blockchain more cost-effective.

[0173] Strategy Deployment and Version Management:

[0174] After the policy update is completed, the system will write the Merkle root of the effective policy into the main chain block header.

[0175] Meanwhile, to support version management and change impact analysis, the system stores historical version data and change impact analysis data in a sidechain. This not only preserves the historical record of strategies but also facilitates subsequent version backtracking and change analysis.

[0176] Through the coordinated efforts of these three steps, the policy update process enables dynamic optimization and continuous improvement of the blockchain-based multi-node carbon emission smart contract verification method. This not only enhances the system's flexibility and adaptability.

[0177] Specifically, the blockchain-based multi-node carbon emission smart contract verification method of the present invention further includes a security enhancement step: performing attribute-based encryption on standardized data packets, and decrypting them when the access policy satisfies (regulatory level ≥ 2) ∧ (jurisdiction matching);

[0178] Employ quantum-resistant digital signature algorithms, including CRYSTALS-Dilithium or ECDSA;

[0179] The threshold signature protocol is executed every 24 hours, rotating the verification node keys in the distributed storage.

[0180] Attribute-based encryption (h1) is applied to standardized data packets:

[0181] In this step, the system provides additional security protection for the processed and generated standardized data packets. Specifically, it employs attribute-based encryption, meaning that data access permissions are set based on specific attributes or conditions.

[0182] In this example, data packets will only be decrypted and accessed when the access policy meets the following conditions: "Regulatory level ≥ 2" is defined as having a Level 2 or higher regulatory qualification; and "Jurisdiction matching" is defined as the administrative region of the IP address requesting the data access matching the registered location of the data source company. This encryption method not only enhances data security but also achieves fine-grained access control, ensuring that only users meeting specific conditions can access the data.

[0183] Digital signature algorithms resistant to quantum attacks, including CRYSTALS-Dilithium or ECDSA(h2):

[0184] To address the potential threat posed by future quantum computers, the system employs the CRYSTALS-Dilithium algorithm to generate digital signatures. This algorithm is specifically designed to resist attacks from quantum computers, ensuring that data signatures remain valid and secure even after quantum computing technology matures.

[0185] The threshold signature protocol is executed every 24 hours, rotating the verification node key (h3) in the distributed storage:

[0186] To improve the overall security and resistance to attacks, the system implements a periodic key rotation mechanism. Specifically, a threshold signature protocol is executed every 24 hours to rotate the keys of the verification nodes in the distributed storage.

[0187] Threshold signature protocols are distributed key generation and signature schemes that allow multiple nodes to collaboratively generate and verify keys, thereby improving the robustness and security of the system. By periodically rotating keys, the system can further reduce the risk of key leakage and ensure its continuous secure operation.

[0188] These security enhancements together constitute the security system of the blockchain-based multi-node carbon emission smart contract verification method, aiming to improve the confidentiality, integrity, and availability of data, while enhancing the overall security and resistance to attacks of the system.

[0189] Explanation of technical terms in this invention:

[0190] Blockchain: A distributed ledger technology that enables multi-party trusted collaboration through decentralized and tamper-proof data storage. In this invention, it is used for the distributed storage and verification of carbon emission data.

[0191] Layered blockchain architecture: including data layer, verification layer, and contract layer.

[0192] Data layer: Permissioned chain storage is used to verify metadata, and encrypted raw carbon emission data is stored through an off-chain storage system.

[0193] Verification Layer: Deploys a dynamic rule engine and a verification network consisting of regulatory nodes, audit nodes, and industry nodes, responsible for data verification through multi-party collaboration.

[0194] Contract layer: Loads the Merkle Patricia Tree strategy library, which stores the main verification contract and strategy files, and is used to execute smart contract logic.

[0195] Permissioned Blockchain: A blockchain that allows only authorized nodes to participate.

[0196] Smart contracts: Automatically executed programmatic protocols used to trigger and execute verification tasks, such as main verification contracts and governance contracts.

[0197] Edge computing nodes: Computing devices deployed at the enterprise level to collect fuel consumption, test data, and online carbon emission monitoring data in real time, reducing data transmission latency.

[0198] Secure hash algorithm (SHA3-256 / SHA-256): An encryption algorithm for generating data hash fingerprints.

[0199] Standardized data packets: Data packets that have undergone outlier filtering, format standardization, and time series alignment, and carry hash chain data fingerprints.

[0200] Off-chain storage system: Combining horizontal sharding (distributed storage across IPFS node clusters based on industry type) and vertical sharding (separating basic data and derived metrics to different encrypted databases) to optimize data storage efficiency and security.

[0201] Oracle node cluster: a middleware that connects the blockchain with external data sources, establishing a trusted cross-chain channel through TLS two-way authentication and zero-knowledge proof verification to obtain third-party related data.

[0202] Dynamic rule engine: includes three core components:

[0203] Rule parser: compiles JSON / XML format policy files into executable validation logic.

[0204] Differential hash comparator: Identifies policy version changes by comparing SHA-256 hash values.

[0205] Event Correlation Analyzer: Establishes a dynamic mapping relationship between production data, fuel data, and carbon emission data, triggering a multi-level verification process.

[0206] Initial verification phase: Threshold verification is performed by calling the pre-compiled contract template (millisecond-level response).

[0207] Intermediate verification stage: Activate the Secure Multi-Party Computation Protocol (SMPC) to achieve cross-verification of encrypted data across multiple nodes.

[0208] Deep verification phase: Load the LSTM prediction model, train it with historical data to generate emission trend prediction curves, and trigger manual review when the deviation exceeds the threshold (±5%).

[0209] Improved Byzantine Fault Tolerance Mechanism (PBFT Improved Algorithm): By dynamically adjusting the node weight coefficients (based on historical accuracy and real-time status), the fault tolerance threshold is increased, achieving weighted consensus.

[0210] Monte Carlo simulation: Based on historical equipment operating data, a simulated emission dataset of the order of 10^5 is generated through Latin hypercube sampling, and confidence intervals (confidence level ≥ 95%) are calculated.

[0211] LSTM prediction model: Long Short-Term Memory network, input features include historical fuel consumption, production and policy adjustment factors, and predicts emission trends through sliding time window training.

[0212] Regulatory compliance index: A compliance score output by regulatory nodes, reflecting the degree to which data conforms to preset regulations.

[0213] Industry Benchmark Database: Stores carbon emission data of companies in the same industry, used to generate industry benchmarking analysis results.

[0214] Attribute-based encryption (ABE): An encryption method based on access policies (such as regulatory level ≥2 and jurisdiction matching) to achieve fine-grained data access control.

[0215] Quantum-resistant digital signature algorithms:

[0216] CRYSTALS-Dilithium: A post-quantum cryptographic algorithm that defends against quantum computer attacks.

[0217] ECDSA: Elliptic Curve Digital Signature Algorithm, used for regular data signing.

[0218] Threshold signature protocol: The verification node key is rotated every 24 hours to prevent the risk of key leakage.

[0219] Merkle Patricia Tree policy library: A tree structure for storing policy files, supporting efficient hash verification.

[0220] Governance Contract: Simulates the operation of new policies in a sandbox environment, and updates the Merkle root of the policy library after successful verification.

[0221] Dual-chain graph: A chain structure that records the relationship between strategy version changes. The main chain stores the effective strategies, and the side chain stores historical versions and data on the impact of changes.

[0222] TLS mutual authentication: a transport layer security protocol that ensures mutual authentication between oracle nodes and third-party data sources.

[0223] IPFS (InterPlanetary File System): A distributed storage protocol used for horizontal sharding storage of off-chain data.

[0224] Latin hypercube sampling: a stratified random sampling method that improves the accuracy of Monte Carlo simulations.

[0225] This invention proposes a blockchain-based multi-node carbon emission smart contract verification method, which effectively solves the limitations of smart contracts in carbon emission data verification and enables them to adapt to complex policy changes and multi-dimensional verification needs.

[0226] First, this invention constructs a layered blockchain architecture, including a data layer, a verification layer, and a contract layer. This architecture enables distributed storage and verification of carbon emission data across multiple nodes, improving data reliability and security. The data layer uses a permissioned blockchain to store verification metadata and stores encrypted raw carbon emission data through an off-chain storage system.

[0227] Secondly, this invention utilizes edge computing nodes to collect carbon emission monitoring data from enterprises, and performs outlier filtering, format standardization, and time series alignment on this data to generate standardized data packets carrying hash chain data fingerprints generated by secure hash algorithms, including SHA3-256 or SHA-256. This process improves the accuracy and consistency of the data, providing a reliable foundation for subsequent data verification.

[0228] Regarding data verification, this invention introduces a dynamic rule engine and a multi-level verification process. The dynamic rule engine can monitor carbon emission data in real time and trigger the main verification contract to initiate a multi-level verification process according to preset rules. These verification tasks include compliance verification, Monte Carlo simulation verification, and benchmark comparison with similar enterprises, which can comprehensively cover multiple dimensions of carbon emission data.

[0229] Furthermore, this invention employs an improved Byzantine fault-tolerant mechanism to achieve consensus and update the blockchain data state. This mechanism ensures that verification results remain consistent even in the presence of malicious nodes, further enhancing the system's reliability and security.

[0230] Finally, when the strategy library is updated, this invention simulates the new strategy in a sandbox environment through a governance contract. After successful verification, the Merkle root of the strategy library is updated, and a bichain graph recording the version change relationship is constructed simultaneously.

[0231] In summary, this invention effectively addresses the shortcomings of smart contracts in carbon emission data verification by constructing a layered blockchain architecture, utilizing edge computing and oracle technologies to acquire and verify carbon emission data, deploying a dynamic rule engine and multi-level verification processes, and implementing security enhancement measures. This enables smart contracts to adapt to complex policy changes and multi-dimensional verification needs.

Claims

1. A method for checking a multi-node carbon emission smart contract based on a blockchain, characterized in that, Comprise: Step S1, a layered blockchain architecture is constructed, which comprises a data layer, a verification layer and a contract layer: Step S2, based on the data layer of the layered blockchain architecture, the edge computing node collects enterprise end fuel consumption, test data and carbon emission online monitoring data, and pre-processes the collected data to generate a standardized data packet carrying a hash chain data fingerprint generated by a secure hash algorithm; Step S3, the standardized data packet generated in step S2 is input into a prediction machine node cluster, a cross-chain trusted channel with a third-party data source is established through the implementation of TLS two-way authentication and zero-knowledge proof verification, associated data verified by reputation score is obtained, and the associated data is compared with the standardized data packet for consistency; Step S4, when any one of the following conditions is detected, the main verification contract triggers a multi-level verification process: the absolute value of CO2 concentration mutation under non-start-stop state exceeds 15%, the relative difference absolute value between fuel end accounting emission and emission end online monitoring data exceeds 10%, policy file version update; Production and emission rate do not match Emission end online monitoring data includes flue CO2 monitoring, multi-dimensional carbon data quality control comparison data inverted from unmanned aerial vehicle or satellite image, and fuel end accounting emission as carbon emission right transaction settlement basis Step S5, the main verification contract distributes verification tasks to the verification layer nodes according to the trigger event type in step S4, and obtains the verification result; 2.The blockchain-based multi-node carbon emission smart contract verification method of claim 1, wherein, Step S6, each verification node submits the verification result of step S5 to a verification consensus module, and adopts an improved Byzantine fault tolerance mechanism to weight and fuse the verification result, updates the blockchain data state and generates a final data verification report after reaching a consensus, and the weight coefficient of the weight fusion is dynamically adjusted according to the historical accuracy rate and real-time state of the node. The layered blockchain architecture comprises a data layer, a verification layer and a contract layer, wherein: The data layer stores verification metadata in a permissioned chain, and saves encrypted original carbon emission data through an off-chain storage system; The verification layer deploys a dynamic rule engine and a verification network including supervision nodes, audit nodes and industry nodes; 3.The blockchain-based multi-node carbon emission smart contract verification method of claim 1, wherein, The contract layer loads the main verification contract and the Merkle Patricia Tree strategy library storing the strategy file. Step S5, the main verification contract distributes verification tasks to the verification layer nodes according to the trigger event type in step S4, and obtains the verification result, wherein: The compliance verification algorithm task is assigned to the supervision node to verify whether the carbon emission data meets the preset regulations; The Monte Carlo simulation verification task is assigned to the audit node to generate simulated emission data set and calculate confidence interval; 4.The blockchain-based multi-node carbon emission smart contract verification method of claim 1, wherein, The same enterprise benchmark comparison task is assigned to the industry node to call the benchmark database to generate industry benchmark analysis result. Further comprise: 5.The blockchain-based multi-node carbon emission smart contract verification method of claim 2, wherein, Step S7, when the strategy library is updated, the governance contract simulates the running of the new strategy in a sandbox environment, updates the Merkle root of the strategy library after verification, and synchronizes the updated strategy to the dynamic rule engine through the contract layer, and constructs a double chain graph recording the version change relationship at the same time. The off-chain storage system adopts horizontal and vertical sharding strategy: Horizontal slicing stores data in IPFS node cluster by industry type; Vertical slicing separates basic monitoring data and derived verification indicators to different encrypted databases. 6.The blockchain-based multi-node carbon emission smart contract verification method of claim 2, wherein, The dynamic rule engine includes: Rule parser, which compiles JSON or XML format policy file into executable verification logic; Differential hash comparator, which identifies version changes by comparing policy file hash values; Event correlation analyzer, which establishes dynamic mapping relationship between production data, fuel data and carbon emission online monitoring data.

7. The blockchain-based multi-node carbon emission smart contract verification method of claim 1, wherein, The distribution verification task of step S5 includes phased processing: Primary verification stage, which calls pre-compiled contract template to perform millisecond-level response verification on data threshold; Intermediate verification stage, which activates secure multi-party computing protocol to realize cross-verification of encrypted data among multiple nodes; Deep verification stage, which loads long short-term memory network (LSTM) prediction model, which generates emission trend prediction curve through time window sliding training, and the input features include historical fuel consumption, production and policy adjustment factors of the enterprise. When the relative deviation between actual emission data and prediction curve exceeds the threshold ± 5%, an artificial review instruction is generated. 8.The blockchain-based multi-node carbon emission smart contract verification method of claim 7, wherein, The intermediate verification stage includes: Regulatory node runs compliance verification algorithm to output regulatory compliance index; Audit node assigns Monte Carlo simulation verification task to generate simulated emission data set by the following steps: according to the historical operation data of the equipment, the working condition interval is divided according to the load rate ± 5%; in the same working condition interval, the fuel consumption parameters of the continuous time window are randomly selected; Latin hypercube sampling is performed on the carbon content parameters and equipment operating state parameters to generate 10^5 order of magnitude simulated emission data set, wherein the sampling of fuel consumption parameters, carbon content parameters and equipment operating state parameters needs to meet the working condition interval division and parameter correlation constraints, the confidence interval of emission data is calculated, the industry node calls the benchmark database to generate industry benchmark analysis results, and the main verification contract weights the output results of the three types of nodes, and the weight coefficients are dynamically adjusted according to the historical accuracy and real-time state of the nodes. 9.The blockchain-based multi-node carbon emission smart contract verification method of claim 4, wherein, It also includes a policy update process: Parse policy text through natural language processing engine to generate verification rules in abstract syntax tree form; Apply loop unrolling technique to optimize contract code structure to reduce gas consumption; Write the Merkle root of the effective policy into the main chain block header, and store the historical version and change impact analysis data into the side chain. 10.The blockchain-based multi-node carbon emission smart contract verification method of claim 1, wherein, It also includes attribute-based encryption of standardized data packets, which are decrypted when the access policy meets the preset standard; Adopt quantum attack-resistant digital signature algorithm, including CRYSTALS-Dilithium or ECDSA; Perform threshold signature protocol every 24 hours to rotate the distributed storage verification node key.

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