Carbon credit automatic trading system based on intelligent contract

Abstract

The invention relates to the technical field of carbon credit transaction, in particular to a carbon credit automatic transaction system based on an intelligent contract, which comprises an intelligent contract self-adaptive updating management module, a carbon credit evaluation module, an intelligent contract module, a transaction matching module and a user interface module, the intelligent contract self-adaptive updating management module is responsible for continuous monitoring, verification and updating of the intelligent contract; the carbon credit evaluation module evaluates the submitted emission reduction project; the intelligent contract module generates an intelligent contract required for executing a carbon credit transaction; the transaction matchmaking module optimizes the transaction process; and the transaction recording module records detailed information of each carbon credit transaction on the block chain. According to the invention, current business requirements can be met, and long-term foresight and adaptability are realized, so that the carbon credit trading platform can effectively cope with development and change of the future market.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon credit trading, and particularly to an automated carbon credit trading system based on smart contracts. Background Art

[0002] With the increasingly serious problems of global warming and climate change, countries and regions have taken measures to reduce greenhouse gas emissions. As a market-based environmental protection measure, carbon credit trading allows enterprises or individuals that emit greenhouse gases to offset their emissions by purchasing carbon credits, while also motivating the development of emission reduction projects. The effective implementation of carbon credit trading requires an efficient, transparent, and fair trading platform to ensure the smooth progress of transactions and the fairness of the market.

[0003] Traditional carbon credit trading systems mostly rely on centralized management and manual operations, which not only increase the complexity and cost of transactions, but may also lead to problems such as low trading efficiency, information opacity, and difficult auditing. With the development of blockchain technology and smart contracts, an automated carbon credit trading system built based on these new technologies provides a brand-new solution. This system can achieve automated transactions, immutable data, and traceability throughout the process, greatly improving the efficiency and transparency of carbon credit trading, reducing transaction costs, and enhancing market trust. However, to build an automated carbon credit trading system that can adapt to market demand changes, meet relevant regulatory requirements, and maintain system stability and security still faces many challenges. How to ensure that smart contracts are updated in a timely manner to adapt to new market demands and regulatory changes, how to accurately evaluate carbon credit amounts, and how to efficiently match the orders of buyers and sellers are all key issues that need to be solved. Summary of the Invention

[0004] Based on the above objectives, the present invention provides an automated carbon credit trading system based on smart contracts.

[0005] The automated carbon credit trading system based on smart contracts includes a smart contract adaptive update management module, a carbon credit assessment module, a smart contract module, a trading matching module, a trading record module, and a user interface module, where; The smart contract adaptive update management module is responsible for the continuous monitoring, verification, and update of smart contracts. The smart contract adaptive update management module is responsible for verifying whether the smart contracts meet the current and future carbon credit trading requirements, maintaining the forward-looking and adaptability of transactions; The carbon credit assessment module, with the support of the smart contract adaptive update management module, evaluates the submitted emission reduction projects and determines the carbon credit amounts; The smart contract module generates smart contracts required for executing carbon credit transactions based on the carbon credit assessment results; The transaction matching module automatically matches buyers and sellers according to the parameters of the smart contract and market demand, optimizing the transaction process; The transaction record module records the detailed information of each carbon credit transaction on the blockchain, ensuring the transparency and traceability of transactions; The user interface module submits emission reduction project information, initiates transaction requests, and views transaction status and market dynamics in real time through the user interface module.

[0006] Furthermore, the smart contract adaptive update management module includes: Continuous monitoring and verification function: The smart contract adaptive update management module is responsible for continuously monitoring and verifying all smart contracts; Version compatibility check: Before any update operation, by introducing a version compatibility check mechanism, ensure the compatibility of the new version of the smart contract with the existing environment; Multi-signature approval process: Implement a multi-signature approval process during the update process, requiring participants (such as developers, auditors, system administrators, etc.) to jointly approve the update of the contract; Automatic rollback mechanism: When it is found that the updated smart contract has problems or does not meet the expected effect, automatically start the rollback mechanism to restore the contract to the previous stable version, ensuring the continuous and stable operation of transactions; Forward-looking demand verification: The smart contract adaptive update management module is responsible for verifying whether the smart contract meets the current and predicted future carbon credit trading needs. By analyzing market trends, technological developments, and regulatory changes, predict future demand changes, guide the optimization and update of the smart contract, and maintain the forward-looking and adaptability of transactions.

[0007] Furthermore, the continuous monitoring and verification function includes: Advanced efficiency evaluation: In order to accurately evaluate the execution efficiency of the smart contract in carbon credit trading, introduce transaction throughput and response time as measurement criteria to adapt to the high-frequency trading environment. The calculation formula is: ; ; Among them, represents the number of transactions successfully processed within the time period , is the length of the observation period, represents the th transaction response time; Comprehensive Security Analysis: To improve the accuracy of security analysis, an anomaly detection mechanism based on the behavior pattern of smart contracts is introduced. The security risk of smart contracts is quantified by calculating the anomaly score AnomalyScore, and the calculation formula is: AnomalyScore ; where represents the number of times the behavior of the smart contract deviates from the normal pattern during the observation period, is the total number of behaviors of the smart contract within the same time period; Dynamic Compliance Verification: By calculating the compliance index ComplianceIndex, not only the current regulations and market demands are compared, but also future changes are predicted to ensure that the smart contract meets the compliance requirements. The calculation formula is: ComplianceIndex ; where is the compliance score for evaluating the smart contract based on the current regulations and market demands, is the score for evaluating the future compliance requirements of the smart contract.

[0008] Furthermore, the version compatibility check includes: Contract Dependency Relationship Evaluation: Analyze the dependency relationships between smart contracts to ensure that new version contracts do not break the dependency chain between existing contracts. Use the depth-first search DFS algorithm to represent the dependency relationships between contracts, and calculate the connectivity of the dependency graph to evaluate the stability of the dependency relationships. Based on the contract dependency graph , is the set of nodes (representing smart contracts), is the set of edges (representing the dependency relationships between contracts), and the connectivity score is calculated as: ; where is the number of nodes (including itself) reached through DFS starting from the target contract node, is the total number of nodes in the graph; Data Structure Compatibility Analysis: Analyze the changes in data structures in new and old version smart contracts, and use the structure similarity algorithm to calculate the compatibility index of data structures. Suppose the data structure sets of two smart contracts A and B are respectively and , and the compatibility index is calculated as: ; where represents the number of intersection elements of the sets and , that is, the elements shared by the two data structures, represents the set and the number of elements in the union, that is, the total number of elements in the two data structures; Interactive interface consistency verification: Verify whether the interactive interfaces of the new version of the contract remain consistent, evaluated by the matching degree of interface signatures. Let the sets of interface signatures compared between smart contracts A and B be and respectively. The matching degree of interface signatures is determined by calculating the number of matching signatures in the two sets. The calculation formula for the matching degree is: ; where is the number of matching signatures in and , and are the total numbers of signatures in sets and respectively.

[0009] Furthermore, the multi-signature approval process includes: Define the approval personnel list: Determine the personnel who need to participate in the approval; Set the signature threshold: Set the signature threshold , indicating the minimum number of signatures required for the contract update operation; Initiate an update request: When the contract needs to be updated, initiate an update request and notify all approval personnel; Collect signatures: Each approval personnel reviews the update request and signs the request; Verify signatures and count: Collect all signatures, verify their validity, and count the number of valid signatures. The calculation formula for the multi-signature approval process is: ; ; where is the total number of valid signatures collected, is the signature of the th approval personnel. If the signature is valid, then , otherwise is a boolean value indicating whether the approval condition is met.

[0010] Furthermore, the automatic rollback mechanism includes: Establish performance and security baselines: Define a set of performance indicators KPIs and record the KPIs of the contract version before the update as the baseline ; Monitor the performance after the update: After updating the smart contract, monitor the performance of the same set of KPIs in real time ; Calculation performance deviation: For each KPI, calculate the deviation ratio DeviationRatio , and the calculation formula is: DeviationRatio ; Judge whether to trigger rollback: Set the tolerance threshold Threshold For each KPI, if the DeviationRatio of the KPI exceeds the corresponding Threshold , then set the rollback trigger flag RollbackFlag to True, which is expressed as: RollbackFlag DeviationRatio Threshold ; Execute automatic rollback: If RollbackFlag = True, automatically roll back the smart contract to the corresponding contract version.

[0011] Furthermore, the forward-looking requirement verification includes: Integrate dynamic data sources: Integrate multiple dynamic data sources, including real-time carbon credit market data, regulatory dynamics, and technology development reports, to obtain more comprehensive data support; Data analysis technology: Use principal component analysis (PCA) to analyze and understand the information in the data sources, and extract key factors that affect future carbon credit demand; Build a composite prediction model: Build a composite prediction model that comprehensively considers multiple indicators. The model is expressed as: ; Wherein, is the predicted future carbon credit trading demand volume, is an indicator representing the market trend, is an indicator representing technology development, is an indicator representing regulatory impact, is the intercept term, , , are the coefficients corresponding to the market trend indicator, technology development indicator, and regulatory impact indicator, is the error term; Train the model and evaluate: Use historical data to train the composite prediction model, regularly adjust and optimize the model parameters to adapt to market changes, apply the trained model to predict future carbon credit trading demand, and evaluate the adaptability of the existing smart contract according to the prediction results. According to the evaluation results, guide the optimization and update of the smart contract.

[0012] Furthermore, the carbon credit assessment module includes: Data reception: The carbon credit assessment module receives relevant data of emission reduction projects, including project type, emission reduction forecast, project implementation area, and relevant environmental impact reports; Preliminary analysis: Conduct preliminary analysis of the received data to assess its completeness and accuracy; Emission reduction accounting: Calculate the greenhouse gas emission reduction brought about by the emission reduction project; Carbon credit quota estimation: Estimate the corresponding carbon credit quota based on the accounting results; Smart contract adaptive update management module support: The smart contract adaptive update management module provides the latest market data and regulatory information, and supports the carbon credit assessment module to make adjustments; Results release: The assessment results and determined carbon credits are recorded in the smart contract.

[0013] Furthermore, the transaction matching module includes: Collecting requirements: The transaction matching module collects and updates the transaction requirements of buyers and sellers in the market, including the buyer's purchase price, quantity requirements and preference conditions, and the seller's selling price, available quantity and quality standards; Parameter definition and acquisition: Through the smart contract adaptive update management module, the transaction matching module obtains the latest market data and regulatory information, and defines the parameters of transaction matching, including price priority, time priority, quantity matching and quality matching; Matching algorithm execution: Implementing an automated matching algorithm, using transaction matching parameters and collected demand data for calculation; Optimize transaction matching: Optimization strategies include price priority, time priority and best match; Confirm and execute the transaction: If the buyer and seller are successfully matched, the transaction matching module will automatically confirm and execute the transaction through the smart contract. Both parties will receive a notification of successful matching and complete the transaction in accordance with the terms of the smart contract.

[0014] Beneficial effects of the present invention: The present invention enhances the adaptability to relevant regulations, market and environmental changes through the smart contract adaptive update management module. It is not only responsible for continuous monitoring and verification of smart contracts to ensure that they comply with the latest regulations and market demands at any time, but also through the forward-looking demand verification function, analyzes market trends, technological development and regulatory change factors, and predicts future demand changes. This dynamic adaptation and prediction capability ensures that the trading system can not only meet current business needs, but also has long-term foresight and adaptability, so that the carbon credit trading platform can effectively respond to future market developments and changes.

[0015] In the present invention, an automated matching algorithm automatically matches buyers and sellers according to the parameters of the smart contract and market demand, greatly optimizing the trading process. It not only ensures the efficient progress of transactions, reduces waiting time, but also guarantees the fairness of transactions. Through a transparent price setting and priority queue mechanism, it ensures that the buyer with the highest bid and the seller with the lowest asking price can conduct transactions preferentially. In addition, the optimization strategy of the trading matching module also takes into account first-in-time and best match, further enhancing the market liquidity and trading matching efficiency.

[0016] In the present invention, by recording the detailed information of each carbon credit transaction on the blockchain, the transparency and traceability of transactions are greatly enhanced. The application of blockchain technology not only provides an immutable and easily verifiable transaction record for both trading parties, but also provides a tool for regulatory agencies to monitor and audit in real time, helping to maintain the integrity and credibility of the carbon credit market. At the same time, the design of the user interface module ensures the direct embodiment of the user experience, providing an intuitive and friendly operation interface, enabling users to easily submit emission reduction project information, initiate transaction requests, and view transaction status and market dynamics in real time, further enhancing the user-friendliness of the system and the convenience of transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of the system function modules of an embodiment of the present invention; Figure 2 It is a schematic diagram of the smart contract adaptive update management module of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further elaborates on the present invention in conjunction with specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] like Figure 1-2 As shown, the carbon credit automated trading system based on smart contracts includes a smart contract adaptive update management module, a carbon credit assessment module, a smart contract module, a transaction matching module, a transaction record module and a user interface module, wherein; The smart contract adaptive update management module is responsible for the continuous monitoring, verification and update of smart contracts to adapt to relevant regulations, market and environmental changes. The smart contract adaptive update management module is responsible for verifying whether the smart contract meets the current and future carbon credit trading needs and maintains the forward-looking and adaptable nature of the transaction; With the support of the smart contract adaptive update management module, the carbon credit assessment module assesses the submitted emission reduction projects and determines the carbon credit quota. During the assessment process, the carbon credit assessment module uses environmental impact analysis tools and third-party verification reports to ensure the accuracy and fairness of the assessment results; The smart contract module generates the smart contracts required to execute carbon credit transactions based on the carbon credit assessment results. The smart contract module uses the guidance obtained from the smart contract adaptive update management module to ensure that all contracts are up to date and fully comply with current regulations and market needs; The transaction matching module automatically matches buyers and sellers according to the parameters of the smart contract and market demand, optimizing the transaction process. The transaction matching module ensures the efficiency and fairness of the transaction, but also relies on the accuracy and update of the smart contract module; The transaction record module records the detailed information of each carbon credit transaction on the blockchain to ensure the transparency and traceability of the transaction. The work of the transaction record module depends on the results of the transaction matching module and is also based on the results of the smart contract execution. The user interface module submits emission reduction project information, initiates transaction requests, and views transaction status and market dynamics in real time through the user interface module. The user interface module is a direct manifestation of the user experience, and its design and function updates also require the support of the smart contract adaptive update management module to ensure that the information and functions provided are synchronized with other parts of the system; Through the organic combination and mutual cooperation of the modules, not only can an efficient and transparent carbon credit trading platform be provided, but also through the innovative introduction of the smart contract adaptive update management module, it is ensured that the trading system can continuously adapt to the changing relevant regulations and market demands, while maintaining consistency and foresight with user needs.

[0022] The smart contract adaptive update management module includes: Continuous monitoring and verification function: The smart contract adaptive update management module is responsible for continuously monitoring and verifying all smart contracts to ensure that their execution and functions continuously comply with relevant regulations, market demands, and environmental changes; Version compatibility check: Before any update operation, by introducing a version compatibility check mechanism, ensure the compatibility of the new version of the smart contract with the existing environment, and avoid the updated contract affecting normal operation; Multi-signature approval process: Implement a multi-signature approval process during the update process, requiring participants (such as developers, auditors, system administrators, etc.) to jointly approve the update of the contract, ensure the necessity and security of the update, and increase the transparency and trust of the update process; Automatic rollback mechanism: When it is found that there are problems or the expected effects are not met in the updated smart contract, automatically start the rollback mechanism to restore the contract to the previous stable version, ensure the continuous and stable operation of the transaction, and avoid failures caused by update errors; Forward-looking requirement verification: The smart contract adaptive update management module is responsible for verifying whether the smart contract meets the current and predicted future carbon credit trading requirements. By analyzing market trends, technological developments, and regulatory changes, predict future demand changes, guide the optimization and update of the smart contract, and maintain the foresight and long-term adaptability of the transaction; Through the above mechanisms, the smart contract adaptive update management module ensures that the smart contracts in the carbon credit automated trading system can flexibly respond to various external changes, continuously meet business needs, and maintain the stability and security of the system.

[0023] The continuous monitoring and verification function includes: Advanced efficiency assessment: In order to accurately evaluate the execution efficiency of smart contracts in carbon credit trading, introduce transaction throughput and response time as the measurement criteria to adapt to the high-frequency trading environment, and the calculation formula is: ; ; Among them, represents the number of transactions successfully processed during the time period , is the length of the observation period, in seconds, represents the response time of the th transaction; Comprehensive security analysis: To improve the accuracy of security analysis, an anomaly detection mechanism based on the behavior pattern of smart contracts is introduced. The security risk of smart contracts is quantified by calculating the anomaly score AnomalyScore. The calculation formula is: AnomalyScore ; Among them, represents the number of times the behavior of the smart contract deviates from the normal pattern during the observation period, is the total number of behaviors of the smart contract within the same time period; Dynamic compliance verification: By calculating the compliance index ComplianceIndex, not only the current regulations and market demands are compared, but also future changes are predicted to ensure that smart contracts comply with compliance requirements in the long term. The calculation formula is: ComplianceIndex ; Among them, is the compliance score for evaluating the smart contract based on the current regulations and market demands, is the score for evaluating the possible future compliance requirements of the smart contract; Using a linear regression model as a prediction tool, the compliance score at a future time point is predicted. The linear regression model is expressed as: ; Among them, are model parameters learned from training data, , , are features extracted from the data, such as historical compliance score trends, regulation change frequencies, market demand changes, etc., is the error term, representing the difference between the model prediction and the actual value; By introducing transaction throughput and response time to evaluate the contract execution efficiency, adopting an anomaly detection mechanism based on the behavior pattern of smart contracts to improve the accuracy of security analysis, and implementing dynamic compliance verification to adapt to changes in regulations and market demands, the smart contracts in the carbon credit automated trading system are ensured to be continuously optimized and adaptable in terms of execution efficiency, security, and compliance. It is applicable to the rapidly changing and highly regulated carbon credit trading market, which helps to improve the overall performance of the system and user trust.

[0024] Version compatibility check includes: Contract dependency evaluation: Analyze the dependency relationships between smart contracts to ensure that the new version of the contract does not break the dependency chain between existing contracts. Use the depth-first search (DFS) algorithm to represent the dependency relationships between contracts, calculate the connectivity of the dependency graph to evaluate the stability of the dependency relationships, and based on the contract dependency graph , is the set of nodes (representing smart contracts), is the set of edges (representing the dependency relationships between contracts), and the connectivity score is calculated as follows: ; where, is the number of nodes (including itself) reached by DFS starting from the target contract node, is the total number of nodes in the graph; Data structure compatibility analysis: Analyze the changes in data structures in the new and old versions of smart contracts, and use the structure similarity algorithm to calculate the compatibility index of data structures. Suppose the data structure sets of two smart contracts A and B are and , and the of the compatibility index is calculated as follows: ; where, represents the number of intersection elements of the sets and , that is, the elements common to the two data structures, represents the number of union elements of the sets and , that is, the total number of elements of the two data structures; Interaction interface consistency verification: Verify whether the interaction interfaces of the new version of the contract remain consistent, and evaluate by the matching degree of interface signatures. Suppose the sets of interface signatures compared between smart contracts A and B are and , and the matching degree of the interface signatures is determined by calculating the number of matching signatures in the two sets. The calculation formula of the matching degree is: ; where, is in and the number of matching signatures in and are respectively the total number of signatures in sets and The denominator is the total number of signatures in the two sets minus the number of matching signatures, representing all unique signatures considered between the two contracts; In a carbon credit automated trading system, smart contracts are the core for handling key logics such as transactions, evaluations, and matchmaking. By implementing the above version compatibility check mechanism, the system can maintain the stability of the system and the integrity of data when updating smart contracts, avoid compatibility issues introduced by updates, ensure the smoothness of the trading process and the accuracy of data. This not only improves the maintainability and reliability of the system, but also guarantees the security and efficiency of user transactions.

[0025] The multi-signature approval process includes: Define the approval personnel list: Determine the personnel who need to participate in the approval, such as system administrators, contract developers, compliance reviewers, and user representatives, etc.; Set the signature threshold: Set the signature threshold , indicating the minimum number of signatures required for a contract update operation. Only when the number of collected signatures reaches or exceeds the threshold can the update operation proceed; Initiate an update request: When a contract needs to be updated, initiate an update request and notify all approval personnel; Collect signatures: Each approval personnel reviews the update request and signs the request; Verify signatures and count: Collect all signatures, verify their validity, and count the number of valid signatures. The calculation formula for the multi-signature approval process is: ; ; Among them, is the total number of valid signatures collected, is the signature of the th approval personnel. If the signature is valid, then , otherwise is a boolean value indicating whether the approval condition is met. If the number of collected valid signatures reaches or exceeds the threshold , then is true (True), and the update operation can be executed; otherwise, it is false (False), and the update operation cannot proceed; In a carbon credit automated trading system based on smart contracts, implementing a multi-signature approval process can ensure that every update to the smart contract undergoes extensive review and approval, thereby reducing security risks and compliance issues caused by improper updates. Especially in a complex and sensitive field like carbon credit trading, joint approval from multiple parties can enhance the transparency of the system and user trust, promoting the healthy development of the carbon credit market.

[0026] The automatic rollback mechanism includes: Establish performance and security baselines: Define a set of performance indicators KPIs, such as transaction throughput (TPS), execution delay (Delay), etc., and record the KPIs of the contract version before the update as the baseline ; Monitor the performance after the update: After updating the smart contract, monitor the performance of the same set of KPIs in real-time ; Calculate the performance deviation: For each KPI, calculate the deviation ratio DeviationRatio , and the calculation formula is: DeviationRatio ; Determine whether to trigger rollback: Set the tolerance threshold Threshold For each KPI, if the DeviationRatio of the KPI exceeds the corresponding Threshold , then set the rollback trigger flag RollbackFlag to True, which is expressed as: RollbackFlag DeviationRatio Threshold ; Execute automatic rollback: If RollbackFlag = True, then automatically roll back the smart contract to the corresponding contract version.

[0027] Forward-looking requirement verification includes: Integrate dynamic data sources: Integrate multiple dynamic data sources, which include real-time carbon credit market data, regulatory dynamics, and technology development reports to obtain more comprehensive data support; Data analysis techniques: Use principal component analysis (PCA) to analyze and understand the information in the data sources, and extract key factors that have an impact on future carbon credit demand; PCA reduces the dimension by calculating the eigenvalues and eigenvectors of the covariance matrix of the data and selecting the most important several eigenvectors. Suppose there are samples, and each sample has For [number of variables] variables, the data matrix is , and PCA aims to find a set of orthogonal bases such that the projections of the data on these bases have the maximum variance. The calculation formula is: ; ; ; where is the centered data matrix, is the covariance matrix, and by solving the eigenvalues and eigenvectors of the covariance matrix ([ function) to perform ; Construct a composite prediction model: Construct a composite prediction model that comprehensively considers multiple indicators. The model is expressed as: ; where is the predicted future demand for carbon credit trading, is an indicator representing the market trend, is an indicator representing the technological development, is an indicator representing the regulatory impact, is the intercept term, , , are the coefficients corresponding to the market trend indicator, technological development indicator, and regulatory impact indicator, is the error term, representing random errors or influencing factors not captured by the model; Train the model and evaluate: Use historical data to train the composite prediction model, regularly adjust and optimize the model parameters to adapt to market changes, apply the trained model to predict the future demand for carbon credit trading, and evaluate the adaptability of the existing smart contracts based on the prediction results. According to the evaluation results, guide the optimization and update of the smart contracts to ensure that they can meet future market demands; In the carbon credit automated trading system, due to the complexity and variability of the carbon market, relying solely on a single prediction model may not be able to accurately capture all the dynamics of future demand. By integrating multiple data sources and adopting a composite prediction model, it is possible to more comprehensively understand the market and environmental change trends, more accurately predict the future demand for carbon credit trading, enabling smart contracts to timely reflect market changes. Through continuous optimization and update, maintain the long-term competitiveness and adaptability of the system, and provide users with continuous and stable carbon credit trading services.

[0028] The carbon credit assessment module includes: Data reception: The carbon credit assessment module receives relevant data of emission reduction projects, including project type, emission reduction forecast, project implementation area, and relevant environmental impact reports; Preliminary analysis: Conduct preliminary analysis of the received data to assess its completeness and accuracy; Emission reduction accounting: Calculate the greenhouse gas emission reduction brought about by the emission reduction project; Emission reduction accounting includes: Determine baseline emissions: ; in, represents the baseline annual emissions, that is, the expected emissions if no emission reduction projects are implemented, is the amount of project activity (e.g. electricity generation, the unit can be MWh), is the emission factor for the old technology (e.g., the emission factor for coal-fired power plants, which can be in units of ); Calculate actual project emissions: ; in, represents the emissions after the implementation of the emission reduction project, is the emission factor of the new technology (e.g. the emission factor of wind power generation, which is theoretically 0); Calculation of emission reductions: ; in, Represents the amount of greenhouse gas emissions reduction achieved through the implementation of emission reduction projects; Carbon credit quota estimation: Estimate the corresponding carbon credit quota based on the accounting results; The process of carbon credit estimation includes: Calculating greenhouse gas emission reductions ; Determine the carbon credit conversion rate: Determine the conversion rate of emission reductions to carbon credits, which is usually determined by the relevant environmental protection agency or the rules of the carbon trading market; Estimated carbon credits: ; in, is the calculated greenhouse gas emission reduction, is the number of carbon credits per ton of CO2 emissions reduction (for example, 1 ton of CO2 may be equivalent to 1 carbon credit), is the estimated carbon credit amount, which indicates the total amount of carbon credits that this emission reduction project can generate; Smart contract adaptive update management module support: The smart contract adaptive update management module provides the latest market data and regulatory information, and supports the carbon credit assessment module to make adjustments; Result Release: Record the evaluation results and the determined carbon credits in the smart contract to ensure the transparency of the evaluation process and the traceability of the results; The carbon credit evaluation module not only provides a scientific, transparent, and fair evaluation mechanism but also ensures that the carbon credit trading system can quickly adapt to external changes, enhancing the flexibility of the system and the responsiveness of the market. This dynamic evaluation and update process is crucial for supporting the healthy development of the carbon market and improving the efficiency of carbon trading.

[0029] The transaction matching module includes: Collect Requirements: The transaction matching module collects and updates the transaction requirements of buyers and sellers in the market, including the purchase price, quantity requirements, and preference conditions of buyers, as well as the selling price, available quantity, and quality standards of sellers; Parameter Definition and Acquisition: Through the smart contract adaptive update management module, the transaction matching module obtains the latest market data and regulatory information and defines the parameters for transaction matching, including price priority, time priority, quantity matching degree, and quality matching degree; Execute Matching Algorithm: Implement an automated matching algorithm and calculate using the parameters for transaction matching and the collected requirement data; Optimize Transaction Matching: The optimization strategies include price priority, time priority, and best match. Price priority ensures that the buyer with the highest bid and the seller with the lowest asking price are matched first. Time priority ensures that the buyer and seller who submit their requirements earliest are matched first. Best match ensures the highest matching degree in terms of quantity and quality between buyers and sellers; Confirm and Execute Transaction: If the buyer and seller are successfully matched, the transaction matching module will automatically confirm and execute the transaction through the smart contract. Both parties to the transaction will receive a notice of successful matching and complete the transaction according to the terms of the smart contract; The automated matching algorithm is as follows: Establish a Priority Queue: The buy order queue is sorted in descending order of price. If the prices are the same, it is sorted in ascending order of submission time. The sell order queue is sorted in ascending order of price. If the prices are the same, it is sorted in ascending order of submission time; Process New Orders: When there are new buy or sell orders, place them in the corresponding priority queue according to the buy or sell order price and timestamp; Match Orders: Check the first position (highest bid) of the buy order queue and the first position (lowest asking price) of the sell order queue to determine if there are matching conditions. If there are matching conditions (the buy order price is not lower than the sell order price), execute the transaction; Determine the Transaction Price: The transaction price is determined based on the prices of the buy and sell orders; Update the Order Book and Market Information: After the transaction is completed, update the order status of both buyers and sellers and the market information; The expression form is as follows: There is an array of buy order prices and the sell order price array , and their corresponding quantity arrays and , the matching and execution of the transaction are described by the following formula: Calculation formula for the matching condition: , if is true, it means that the buy order and sell order at the head of the queue can be matched; Calculation formula for the transaction price: ; Calculation formula for the trading volume: , the trading volume depends on the minimum quantity available for trading in the buy order and sell order; The transaction matching module allows the system to dynamically adjust its internal algorithms and parameters to adapt to market and regulatory changes, ensuring the liquidity and efficiency of the carbon credit market, while providing a transparent and fair trading environment for both buyers and sellers. Through such a mechanism, the smart contract can not only optimize the trading process based on real-time data, but also ensure the long-term adaptability and sustainable development of the system.

[0030] Those of ordinary skill in the art should understand that: the discussion of any embodiment above is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above, which are not provided in detail for the sake of brevity.

[0031] The present invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An automated carbon credit trading system based on smart contracts, characterized in that It includes smart contract adaptive update management module, carbon credit assessment module, smart contract module, transaction matching module, transaction record module and user interface module, among which; The smart contract adaptive update management module is responsible for the continuous monitoring, verification and update of the smart contract. The smart contract adaptive update management module is responsible for verifying whether the smart contract meets the current and future carbon credit trading needs and maintains the forward-looking and adaptable nature of the transaction; The carbon credit assessment module, with the support of the smart contract adaptive update management module, assesses the submitted emission reduction projects and determines the carbon credit quota; The smart contract module generates a smart contract required to execute carbon credit transactions based on the carbon credit assessment results; The transaction matching module automatically matches buyers and sellers according to the parameters of the smart contract and market demand, optimizing the transaction process; The transaction record module records the detailed information of each carbon credit transaction on the blockchain to ensure the transparency and traceability of the transaction; The user interface module submits emission reduction project information, initiates transaction requests, and checks transaction status and market dynamics in real time through the user interface module.

2. The carbon credit automated trading system based on smart contract according to claim 1, wherein The smart contract adaptive update management module includes: Continuous monitoring and verification function: The smart contract adaptive update management module is responsible for continuous monitoring and verification of all smart contracts; Version compatibility check: Before any update operation, a version compatibility check mechanism is introduced to ensure the compatibility of the new version of the smart contract with the existing environment; Multi-signature approval process: A multi-signature approval process is implemented during the update process, requiring participants to jointly approve the contract update; Automatic rollback mechanism: When problems are found in the updated smart contract or it does not meet the expected effect, the rollback mechanism will be automatically activated to restore the contract to the last stable version to ensure continuous and stable operation of transactions; Forward-looking demand verification: The smart contract adaptive update management module is responsible for verifying whether the smart contract meets the current and predicted future carbon credit trading needs. By analyzing market trends, technological development and regulatory change factors, it predicts future demand changes, guides the optimization and update of smart contracts, and maintains the foresight and adaptability of transactions.

3. The carbon credit automated trading system based on smart contract according to claim 2, characterized in that, The continuous monitoring and verification functions include: Advanced Efficiency Evaluation: To accurately evaluate the execution efficiency of smart contracts in carbon credit trading, trading throughput and response time are introduced as measurement criteria to adapt to the high-frequency trading environment. The calculation formula is: ； ； Among them, represents the number of transactions successfully processed during the time period , is the length of the observation period, represents the response time of the th transaction; Comprehensive Security Analysis: To improve the accuracy of security analysis, an anomaly detection mechanism based on the behavior pattern of smart contracts is introduced. The security risk of smart contracts is quantified by calculating the anomaly score AnomalyScore, and the calculation formula is: AnomalyScore ; Among them, represents the number of times the behavior of the smart contract deviates from the normal mode during the observation period, is the total number of behaviors of the smart contract within the same time period; Dynamic compliance verification: By calculating the compliance index ComplianceIndex, it not only compares current regulations and market demands but also predicts future changes to ensure that smart contracts meet compliance requirements. The calculation formula is: ComplianceIndex ; Among them, is a compliance score for evaluating smart contracts based on current regulations and market demands, is a score for evaluating future compliance requirements of smart contracts.

4. The carbon credit automated trading system based on smart contract according to claim 3, wherein, The version compatibility check includes: Contract Dependency Relationship Evaluation: Analyze the dependency relationships between smart contracts to ensure that new versions of contracts do not break the dependency chains between existing contracts. Use the depth-first search (DFS) algorithm to represent the dependency relationships between contracts, calculate the connectivity of the dependency graph to evaluate the stability of the dependency relationships, and based on the contract dependency graph , is the set of nodes, is the set of edges, and the connectivity score is calculated as follows: ； Among them, is the number of nodes reached by DFS starting from the target contract node, is the total number of nodes in the graph; Data Structure Compatibility Analysis: Analyze the changes in data structures in smart contracts of new and old versions, and use a structural similarity algorithm to calculate the compatibility index of data structures. Let the data structure sets of two smart contracts A and B be respectively and , and the calculation formula for the compatibility index is: ; Among them, represents the number of intersection elements of the set and That is, the elements shared by the two data structures, represents the number of union elements of the set and That is, the total number of elements of the two data structures; Interactive Interface Consistency Verification: Verify whether the interactive interfaces of the new version of the contract remain consistent, and evaluate through the matching degree of interface signatures. Suppose the sets of interface signatures compared between smart contracts A and B are respectively and . The matching degree of interface signatures is determined by calculating the number of matching signatures in the two sets. The calculation formula for the matching degree is: ; Among them, is the number of signatures matched in and , and are the total numbers of signatures in the sets and respectively.

5. The carbon credit automated trading system based on smart contract according to claim 4, characterized in that, The multi-signature approval process includes: Define the list of approvers: determine the personnel who need to participate in the approval; Set signature threshold: Set the signature threshold , indicating the minimum number of signatures required for a contract update operation; Initiate an update request: When a contract needs to be updated, initiate an update request and notify all approvers; Collect signatures: Each approver reviews the update request and signs the request; Verify signatures and statistics: Collect all signatures, verify validity, and count the number of valid signatures. The calculation formula for the multi-signature approval process is: ； ； Among them, is the total number of valid signatures collected, is the signature of the th approver. If the signature is valid, then , otherwise is a boolean value indicating whether the approval condition is met.

6. The carbon credit automated trading system based on smart contracts according to claim 5, characterized in that The automatic rollback mechanism includes: Establish performance and security baselines: Define a set of performance indicators KPIs, and record the KPIs of the contract version before the update as the baseline ; Monitor the updated performance: After updating the smart contract, monitor the performance of the same set of KPIs in real time ; Calculation performance deviation: For each KPI, calculate the deviation ratio DeviationRatio , and the calculation formula is: DeviationRatio ; Determine whether to trigger rollback: Set the tolerance threshold Threshold For each KPI, if the DeviationRatio of the KPI exceeds the corresponding Threshold , then set the rollback trigger flag RollbackFlag to True, which is expressed as: RollbackFlag DeviationRatio Threshold ; Execute automatic rollback: If RollbackFlag = True, automatically roll back the smart contract to the corresponding contract version.

7. The automated carbon credit trading system based on smart contract according to claim 6, characterized in that The forward-looking requirements verification includes: Integrate dynamic data sources: Integrate multiple dynamic data sources, including real-time carbon credit market data, regulatory dynamics, and technology development reports, to obtain more comprehensive data support; Data analysis techniques: principal component analysis is used to analyze and understand the information in the data source and extract key factors that affect future carbon credit demand; Construct a composite prediction model: Construct a composite prediction model that comprehensively considers multiple indicators. The model is expressed as: ； Among them, is the predicted future demand for carbon credit trading, is an indicator representing the market trend, is an indicator representing the technological development, is an indicator representing the regulatory impact, is the intercept term, , , are the coefficients corresponding to the market trend indicator, the technological development indicator, and the regulatory impact indicator, is the error term; Model training and evaluation: Use historical data to train composite prediction models, regularly adjust and optimize model parameters to adapt to market changes, apply trained models to predict future carbon credit trading demand, and evaluate the adaptability of existing smart contracts based on the prediction results. Based on the evaluation results, guide the optimization and update of smart contracts.

8. The carbon credit automated trading system based on a smart contract according to claim 7, wherein The carbon credit assessment module includes: Data reception: The carbon credit assessment module receives relevant data of emission reduction projects, including project type, emission reduction forecast, project implementation area, and relevant environmental impact reports; Preliminary analysis: Conduct preliminary analysis of the received data to assess its completeness and accuracy; Emission reduction accounting: Calculate the greenhouse gas emission reduction brought about by the emission reduction project; Carbon credit quota estimation: Estimate the corresponding carbon credit quota based on the accounting results; Smart contract adaptive update management module support: The smart contract adaptive update management module provides the latest market data and regulatory information, and supports the carbon credit assessment module to make adjustments; Results release: The assessment results and determined carbon credits are recorded in the smart contract.

9. The automated carbon credit trading system based on smart contract according to claim 8, characterized in that The transaction matching module includes: Collecting requirements: The transaction matching module collects and updates the transaction requirements of buyers and sellers in the market, including the buyer's purchase price, quantity requirements and preference conditions, and the seller's selling price, available quantity and quality standards; Parameter definition and acquisition: Through the smart contract adaptive update management module, the transaction matching module obtains the latest market data and regulatory information, and defines the parameters of transaction matching, including price priority, time priority, quantity matching and quality matching; Matching algorithm execution: Implementing an automated matching algorithm, using transaction matching parameters and collected demand data for calculation; Optimize transaction matching: Optimization strategies include price priority, time priority and best match; Confirm and execute the transaction: If the buyer and seller are successfully matched, the transaction matching module will automatically confirm and execute the transaction through the smart contract. Both parties will receive a notification of successful matching and complete the transaction in accordance with the terms of the smart contract.

Images