Carbon credit derivative trading tool based on block chain technology

Abstract

The invention discloses an innovative carbon credit derivative transaction tool based on a block chain technology, relates to the field of carbon transaction and block chain application, and realizes efficient data processing, secure communication and cross-chain interaction by constructing a block chain architecture fusing cross-chain, quantum encryption and edge calculation. Building a dynamic self-adaptive carbon credit derivative digital model by using an artificial intelligence algorithm, and automatically adjusting parameters and evaluating values; a natural language processing technology and an intelligent matching and strategy transaction engine of various transaction strategy templates are integrated, so that intelligent transaction instruction processing and personalized strategy execution are realized; an intelligent risk management and control and compliance supervision system is constructed by means of a machine learning algorithm, risks are monitored in real time and automatically handled, and a distributed data sharing and privacy protection mechanism of technologies such as zero-knowledge proof and homomorphic encryption is adopted to guarantee data security and privacy.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon trading and blockchain, and specifically relates to a carbon credit derivative trading tool based on blockchain technology. Background Art

[0002] The global carbon trading market plays a crucial role in addressing climate change. As an active factor in the market, carbon credit derivatives urgently need to optimize their trading models. The current trading system is plagued by low efficiency. The long intermediate processes lengthen the trading cycle and drive up costs. The phenomenon of information silos is serious, and it is difficult for trading parties to comprehensively understand the information of their counterparts, exacerbating trading risks. The centralized data storage mode is vulnerable to attacks and tampering, and the data security and transparency cannot be guaranteed, severely restricting the healthy development of the carbon trading market. Although there have been some blockchain-based attempts, there are still obvious shortcomings in terms of trading flexibility, refined risk control, and cross-chain collaboration, making it difficult to meet the complex and changing market demands. With the rapid development of emerging technologies such as the Internet of Things and artificial intelligence, new opportunities have been provided for the innovation of carbon credit derivative trading tools, and it is extremely urgent to deeply integrate them to break through the existing dilemmas. Summary of the Invention

[0003] The purpose of the present invention is to provide a carbon credit derivative trading tool based on blockchain technology, comprehensively overcoming the problems of efficiency, security, information asymmetry, etc. in the existing trading mode, so as to promote the carbon trading market to enter a new stage of high efficiency, security, intelligence and high scalability, and contribute to the realization of global carbon emission reduction goals.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A carbon credit derivative trading tool based on blockchain technology, comprising:

[0006] A multi-dimensional integrated blockchain architecture integrating cross-chain, quantum encryption and edge computing, with a data perception layer, a distributed computing layer, a consensus decision-making layer, a smart contract layer and a user interaction layer, capable of realizing efficient data processing, secure communication and cross-chain interaction. Among them, cross-chain technology can interoperate with other different types of blockchain systems, quantum encryption technology ensures the security of data transmission and storage, edge computing improves the transaction processing speed, and the consensus decision-making layer adopts a PoA-DPoS hybrid consensus algorithm;

[0007] A dynamic adaptive carbon credit derivative digital model relying on artificial intelligence algorithms, which can automatically adjust the key parameters and value evaluation of carbon credit derivatives according to multi-source data, and endow it with a multi-dimensional digital identity based on timestamp, geographical location and transaction history. This model uses the results of multi-source data collection and analysis, and automatically updates the value evaluation and attributes of carbon credit derivatives with the help of smart contracts;

[0008] An intelligent matching and strategy trading engine integrating natural language processing technology and various trading strategy templates can achieve intelligent processing of trading instructions and personalized strategy execution. It adopts multiple trading matching dimensions including price, time, risk preference, and counterparty credit. This engine can parse natural language trading instructions and is built-in with big data analysis and machine learning algorithms to optimize trading strategies in real time;

[0009] An intelligent risk control and compliance supervision system constructed by using machine learning algorithms can monitor risks in real time, automatically trigger response measures, and provide customized supervision tools and data interfaces for regulatory agencies. The intelligent risk control system establishes a dynamic risk assessment model through real-time analysis of multi-source data and automatically triggers risk response measures; the compliance supervision system is built-in with a compliance review module to ensure trading compliance;

[0010] A distributed data sharing and privacy protection mechanism adopting zero-knowledge proof and homomorphic encryption technologies, and a data hierarchical authorization access system is established to ensure data security and privacy. This mechanism uses relevant technologies to achieve data verification and interaction, and guarantees the reasonable use of data and privacy protection through the data hierarchical authorization access mechanism.

[0011] Preferably, in the data perception layer of the multi-dimensional fusion blockchain architecture, carbon emission reduction-related data is collected in real time through Internet of Things devices. The distributed computing layer uses edge computing nodes and cloud computing resources to collaboratively process data. The smart contracts deployed in the smart contract layer have self-learning capabilities and can automatically optimize trading strategies according to market dynamics.

[0012] Preferably, when the dynamic adaptive carbon credit derivative digital model deeply analyzes multi-source information such as real-time data of carbon emission reduction projects, market fluctuations, and policy changes, it uses artificial intelligence technologies such as deep learning algorithms and reinforcement learning algorithms to achieve precise adjustment of carbon credit derivative parameters and value assessment.

[0013] Preferably, in the process of trading matching by the intelligent matching and strategy trading engine, a fuzzy matching algorithm is introduced to match and process the inaccurate information in trading instructions, further improving the success rate and efficiency of trading matching.

[0014] Preferably, in the intelligent risk control and compliance supervision system, the risk assessment model uses a risk factor library updated in real time and combines the Monte Carlo simulation method to dynamically quantify and assess market risks, providing a scientific basis for triggering risk response measures.

[0015] Preferably, in the distributed data sharing and privacy protection mechanism, the data hierarchical authorization access system dynamically adjusts users' data access permissions according to factors such as users' trading activity and credit rating to ensure the security and reasonableness of data access.

[0016] Compared with the prior art, the present invention provides a carbon credit derivative trading tool based on blockchain technology, having the following beneficial effects:

[0017] Through a multi-dimensional integrated blockchain architecture that integrates cross-chain technology, quantum encryption technology, and edge computing, the trading process is greatly optimized. Cross-chain interaction breaks the data barrier between different blockchain systems, enabling carbon credit derivatives to be seamlessly connected with assets in other fields, broadening the trading boundaries, enriching the trading scenarios, and significantly enhancing market liquidity. Edge computing pre-positions key computing tasks closer to the data source or the user side, greatly reducing data transmission latency. Trading instructions can be processed and executed almost in real time. Compared with the traditional trading mode, the overall trading efficiency can be increased by several times or even dozens of times, providing strong support for participants to seize the market opportunity.

[0018] The intelligent matching and strategy trading engine introduces multiple trading matching dimensions and natural language processing technology, enabling trading parties to find suitable counterparts more accurately and quickly, reducing the trading search cost. Users only need to describe their trading demands in daily language, and the engine can quickly parse and convert them into executable instructions, greatly simplifying the trading operation process. Even non-professional investors can easily get started, promoting extensive market participation, further enlivening the trading atmosphere, and enhancing the overall trading efficiency of the market.

[0019] Quantum encryption technology builds an impregnable defense line for data transmission and storage, fundamentally resisting potential decryption risks in the era of quantum computing, and ensuring that various sensitive information in the process of carbon credit derivative trading, such as the identities of trading parties, trading amounts, and details of carbon credit assets, will not be stolen or tampered with during network transmission and blockchain ledger storage. Whether facing external hacker attacks or internal data leakage risks, quantum encryption provides unprecedented security protection, allowing market participants to trade with confidence.

[0020] The distributed data sharing and privacy protection mechanism uses cutting-edge technologies such as zero-knowledge proof and homomorphic encryption to protect user privacy while realizing data sharing to meet the market information transparency requirements. Enterprises can prove the compliance and authenticity of data to trading counterparts or regulatory agencies without exposing the core details of their carbon emissions, maintaining business secrets while ensuring the effective conduct of market supervision and trading verification, achieving a perfect balance between data security and information circulation.

[0021] The intelligent risk management and control system built on the basis of machine learning algorithms can monitor various market risks in real time and comprehensively. Through the continuous analysis of massive transaction data, market dynamics and user behavior data, the dynamic risk assessment model accurately captures risk signals, and the timeliness and accuracy of risk warnings have made a qualitative leap compared with traditional risk control methods. Instead of relying on static risk indicators and empirical judgments, it dynamically adjusts risk thresholds according to real-time market changes, provides early insight into potential crises, and provides market participants with sufficient response time windows.

[0022] When a risk event is triggered, the smart contract automatically executes the preset response strategy, such as dynamically adjusting the margin ratio, forced liquidation, limiting trading permissions, etc., realizing the automation and intelligence of risk disposal. This instant response mechanism effectively curbs the spread of risks, avoids individual risk events from triggering systemic financial risks, ensures the stable operation of the carbon credit derivatives market, and safeguards the fundamental interests of the majority of investors.

[0023] The dynamic adaptive carbon credit derivatives digital model uses artificial intelligence algorithms to closely follow the progress of carbon emission reduction projects, market fluctuations, and changes in policy trends, and updates the value assessment and key attributes of carbon credit derivatives in real time. This allows new and complex carbon credit derivatives to emerge and meet the increasingly diverse investment and risk management needs of market participants. For example, with the rapid development of renewable energy technology, the corresponding carbon credit derivatives can quickly adjust valuation and trading rules, stimulate market innovation enthusiasm, promote the continuous evolution of the carbon trading market, and inject a steady stream of financial power into the global carbon emission reduction cause.

[0024] Cross-chain technology has opened up the connection channel between carbon trading and blockchains in multiple fields such as energy, finance, and supply chain, giving rise to innovative cross-industry cooperation models. Carbon credit derivatives can be linked with financial products such as green energy certificates and green bonds to build a new carbon financial ecosystem, expand the depth and breadth of the carbon market, attract more capital inflows, provide strong financial support for achieving carbon peak and carbon neutrality goals, and lead the new trend of global green financial development. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the modules of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The present invention provides a carbon credit derivative trading tool based on blockchain technology as Figure 1 shown, including:

[0028] A multi-dimensional fusion blockchain architecture integrating cross-chain, quantum encryption, and edge computing, with a data perception layer, a distributed computing layer, a consensus decision layer, a smart contract layer, and a user interaction layer, capable of achieving efficient data processing, secure communication, and cross-chain interaction. Among them, cross-chain technology can interoperate with other different types of blockchain systems, quantum encryption technology ensures the security of data transmission and storage, edge computing improves transaction processing speed, and the consensus decision layer adopts a PoA-DPoS hybrid consensus algorithm;

[0029] Build an advanced blockchain infrastructure integrating cross-chain technology, quantum encryption technology, and edge computing. Cross-chain technology allows this trading tool to interoperate with other different types of blockchain systems, such as energy blockchains and financial blockchains, breaking data barriers and expanding the trading scenarios and asset circulation scope of carbon credit derivatives. Quantum encryption technology provides unprecedented security for data transmission and storage, preventing data from being stolen or tampered with and ensuring the absolute security of transaction information. Edge computing sinks some computing tasks to edge nodes close to the data source, greatly improving transaction processing speed and reducing network latency. The blockchain network is divided into a data perception layer, a distributed computing layer, a consensus decision layer, a smart contract layer, and a user interaction layer. The data perception layer collects carbon emission reduction-related data in real time through Internet of Things devices, such as enterprise carbon emission data and carbon sink project monitoring data; the distributed computing layer uses edge computing nodes and cloud computing resources to jointly process massive data; the consensus decision layer adopts an improved Proof of Authority-Delegated Proof of Stake (PoA-DPoS) hybrid consensus algorithm to improve consensus efficiency while ensuring decentralization; the smart contract layer deploys smart contracts with self-learning capabilities that can automatically optimize trading strategies according to market dynamics; the user interaction layer provides a personalized and convenient operation interface for various users.

[0030] A dynamic adaptive carbon credit derivative digital model relying on artificial intelligence algorithms, which can automatically adjust the key parameters and value evaluation of carbon credit derivatives based on multi-source data, and endow it with a multi-dimensional digital identity based on timestamps, geographical locations, and transaction histories. This model uses the results of multi-source data collection and analysis, and automatically updates the value evaluation and attributes of carbon credit derivatives with the help of smart contracts;

[0031] Abandon the traditional fixed digital model of carbon credit derivatives and create a dynamic adaptive digital model. Utilize artificial intelligence algorithms to deeply analyze multi-source information such as real-time data of carbon emission reduction projects, market fluctuations, and policy changes, and automatically adjust the key parameters and value evaluation models of carbon credit derivatives. For example, when a carbon emission reduction project achieves higher emission reduction efficiency due to technological upgrades, the smart contract automatically updates the value evaluation of the associated carbon credit derivatives to more accurately reflect the market value. At the same time, assign a multi-dimensional digital identity based on timestamp and geographical location information to each carbon credit derivative, which not only records its trading history but also tracks the spatio-temporal evolution information of its carbon emission reduction project, enhancing the traceability and authenticity of carbon credit derivatives.

[0032] An intelligent matching and strategy trading engine integrating natural language processing technology and various trading strategy templates can achieve intelligent processing of trading instructions and personalized strategy execution, and adopt multiple trading matching dimensions such as price, time, risk preference, and counterparty credit. This engine can parse natural language trading instructions and is built-in with big data analysis and machine learning algorithms to optimize trading strategies in real time;

[0033] Develop an intelligent matching and strategy trading engine to achieve intelligent processing of trading instructions and personalized trading strategy execution. This engine integrates natural language processing technology, and users can describe their trading intentions through natural language, such as "buy Y quantity of contracts when the carbon futures price reaches X yuan within the next month". The engine automatically parses and converts it into precise trading instructions. At the same time, it is built-in with various trading strategy templates, such as trend following strategy, arbitrage strategy, hedging strategy, etc. Users can choose appropriate strategies according to their own risk preferences and market judgments, or customize strategies. The engine uses big data analysis and machine learning algorithms to monitor market conditions in real time, automatically optimize trading strategies, and improve trading success rate and profit level. In the process of trading matching, in addition to the price priority and time priority principles, introduce multiple matching dimensions such as risk preference matching and counterparty credit matching to screen the most suitable counterparty for users and reduce trading risks.

[0034] An intelligent risk control and compliance supervision system constructed by using machine learning algorithms can monitor risks in real time, automatically trigger response measures, and provide customized supervision tools and data interfaces for regulatory agencies. The intelligent risk control system establishes a dynamic risk assessment model through real-time analysis of multi-source data and automatically triggers risk response measures; the compliance supervision system is built-in with a compliance review module to ensure transaction compliance;

[0035] Build an intelligent risk management and compliance supervision system to achieve real-time monitoring, early warning and intelligent disposal of risks. Use machine learning algorithms to conduct real-time analysis of transaction data, market data, user behavior data, etc., establish a dynamic risk assessment model, and accurately identify potential risks. When the risk indicator reaches the preset threshold, the smart contract automatically triggers risk response measures, such as dynamically adjusting the margin ratio, implementing stop-loss and stop-profit operations, and restricting trading permissions. At the same time, provide regulatory agencies with customized regulatory tools and data interfaces, so that regulatory agencies can obtain panoramic market data in real time and use artificial intelligence to assist regulatory systems to monitor risks and identify violations. The smart contract has a built-in compliance review module, which automatically reviews whether the transaction complies with laws, regulations and regulatory requirements before the transaction is executed to ensure the compliance of the transaction.

[0036] A distributed data sharing and privacy protection mechanism using zero-knowledge proof and homomorphic encryption technology is used, and a hierarchical data authorization access system is established to ensure data security and privacy. This mechanism uses relevant technologies to achieve data verification and interaction, and ensures reasonable use of data and privacy protection through a hierarchical data authorization access mechanism;

[0037] Design a distributed data sharing and privacy protection mechanism to promote efficient sharing of market information while ensuring data security and privacy. Use technologies such as zero-knowledge proof and homomorphic encryption to allow transaction participants to verify and interact with data without leaking sensitive information. For example, when a company shares its carbon emission data, it can prove the authenticity of the data to its counterparty through zero-knowledge proof without disclosing the specific data content. At the same time, using the distributed storage characteristics of blockchain, data is stored in multiple nodes in a dispersed manner to avoid security risks brought about by centralized data storage. Establish a hierarchical data authorization access mechanism to grant different levels of data access rights according to user roles and permissions to ensure the reasonable use of data and privacy protection.

[0038] The data perception layer in the multi-dimensional fusion blockchain architecture collects carbon emission reduction related data in real time through IoT devices. The distributed computing layer uses edge computing nodes and cloud computing resources to collaboratively process data. The smart contracts deployed in the smart contract layer have self-learning capabilities and can automatically optimize trading strategies based on market dynamics.

[0039] The dynamic adaptive carbon credit derivatives digital model uses artificial intelligence technologies such as deep learning algorithms and reinforcement learning algorithms to conduct in-depth analysis of multi-source information such as real-time data on carbon emission reduction projects, market fluctuations, and policy changes, in order to achieve precise adjustments to carbon credit derivatives parameters and value assessments.

[0040] The intelligent matching and strategy trading engine introduces a fuzzy matching algorithm during the transaction matching process to match the imprecise information in the transaction instructions, thereby further improving the success rate and efficiency of transaction matching.

[0041] In the intelligent risk control and compliance supervision system, the risk assessment model uses a real-time updated risk factor library and combines the Monte Carlo simulation method to dynamically quantify and evaluate market risks, providing a scientific basis for triggering risk response measures.

[0042] In the distributed data sharing and privacy protection mechanism, the data hierarchical authorization access system dynamically adjusts users' data access permissions according to factors such as users' trading activity and credit rating to ensure the security and rationality of data access.

[0043] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A carbon credit derivative trading tool based on blockchain technology, characterized in that, Including: A multi-dimensional integrated blockchain architecture that integrates cross-chain, quantum encryption, and edge computing, with a data perception layer, a distributed computing layer, a consensus decision-making layer, a smart contract layer, and a user interaction layer, which can achieve efficient data processing, secure communication, and cross-chain interaction. Among them, cross-chain technology can interoperate with other different types of blockchain systems, quantum encryption technology ensures the security of data transmission and storage, edge computing improves the transaction processing speed, and the consensus decision-making layer adopts a PoA-DPoS hybrid consensus algorithm; A dynamic adaptive digital model of carbon credit derivatives relying on artificial intelligence algorithms, which can automatically adjust the key parameters and value assessment of carbon credit derivatives based on multi-source data, and endow it with multi-dimensional digital identities based on timestamps, geographical locations, and transaction histories. This model uses the results of multi-source data collection and analysis, and automatically updates the value assessment and attributes of carbon credit derivatives through smart contracts; An intelligent matching and strategy trading engine integrating natural language processing technology and various trading strategy templates, which can realize the intelligent processing of trading instructions and the execution of personalized strategies, and adopts multiple trading matching dimensions of price, time, risk preference, and counterparty credit. This engine can parse natural language trading instructions, and is built-in with big data analysis and machine learning algorithms to optimize trading strategies in real time; An intelligent risk management and compliance supervision system built by using machine learning algorithms, which can monitor risks in real time, automatically trigger response measures, and provide customized supervision tools and data interfaces for regulatory agencies. The intelligent risk management system establishes a dynamic risk assessment model through real-time analysis of multi-source data and automatically triggers risk response measures; the compliance supervision system is built-in with a compliance review module to ensure transaction compliance; A distributed data sharing and privacy protection mechanism using zero-knowledge proof and homomorphic encryption technologies, and a data hierarchical authorization access system is established to ensure data security and privacy. This mechanism uses relevant technologies to realize data verification and interaction, and ensures the reasonable use of data and privacy protection through the data hierarchical authorization access mechanism.

2. The carbon credit derivative trading tool based on blockchain technology according to claim 1, wherein: In the data perception layer of the multi-dimensional integrated blockchain architecture, carbon emission reduction-related data is collected in real time through Internet of Things devices. The distributed computing layer uses edge computing nodes and cloud computing resources to jointly process data. The smart contracts deployed in the smart contract layer have self-learning capabilities and can automatically optimize trading strategies according to market dynamics.

3. The carbon credit derivative trading tool based on blockchain technology according to claim 1, wherein: When the dynamic adaptive digital model of carbon credit derivatives deeply analyzes multi-source information such as real-time data of carbon emission reduction projects, market fluctuations, and policy changes, it uses artificial intelligence technologies such as deep learning algorithms and reinforcement learning algorithms to achieve precise adjustment of the parameters and value assessment of carbon credit derivatives.

4. The carbon credit derivative trading tool based on blockchain technology according to claim 1, characterized in that: In the process of trading matching, the intelligent matching and strategy trading engine introduces a fuzzy matching algorithm to match and process the imprecise information in trading instructions, further improving the success rate and efficiency of trading matching.

5. The carbon credit derivative trading tool based on blockchain technology according to claim 1, wherein: In the intelligent risk management and compliance supervision system, the risk assessment model uses a risk factor library that is updated in real time, combined with the Monte Carlo simulation method, to dynamically quantify and assess market risks, providing a scientific basis for triggering risk response measures.

6. The carbon credit derivative trading tool based on blockchain technology according to claim 1, wherein: In the distributed data sharing and privacy protection mechanism, the data hierarchical authorization access system dynamically adjusts the data access permissions of users according to factors such as the transaction activity and credit rating of users, ensuring the security and rationality of data access.

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