Enterprise carbon emission management system based on block chain technology

Abstract

The invention discloses an enterprise carbon emission management system based on a block chain technology. The enterprise carbon emission management system comprises a carbon emission data storage module, a carbon emission quota distribution module and a carbon emission cross-regional transaction module. The method has the advantages that the data processing efficiency and the intelligent analysis capability of the system under different time dimensions are enhanced through time weighting, dynamic compression and environmental data embedding on the carbon data storage and management level. The intelligence, the flexibility and the fairness of enterprise carbon quota allocation are ensured by utilizing a dynamic quota allocation algorithm through adaptive learning, game theory optimization and a market feedback mechanism, and the method adapts to continuously changing carbon markets and enterprise behaviors. A cross-regional carbon transaction mechanism is established, and through automatic execution of an inter-chain interoperation protocol and an intelligent contract, the current situation of isolation of carbon transactions in different regions is broken, and global carbon transaction market integration is realized. The mechanism improves the transaction transparency and the market efficiency, and promotes the development and standardization of the carbon transaction market.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission management and carbon trading technology, and is particularly applicable to an enterprise carbon emission management system based on blockchain technology. Background Art

[0002] Existing carbon emission management and trading systems are mostly based on linear data storage and static quota allocation, which makes it difficult to cope with the complex carbon emission data tracking, real-time quota adjustment and cross-regional carbon trading needs. At the same time, the regional fragmentation of the carbon trading market and the volatility of carbon prices limit the ability of enterprises to trade flexibly across markets.

[0003] Blockchain technology is decentralized, tamper-proof, and transparent, making it a powerful tool for solving transparency and trust issues in carbon trading. However, existing blockchain technology still has shortcomings in processing historical carbon emission data, real-time adjustment of quota allocation, and cross-regional interoperability of regional carbon trading. Therefore, an innovative carbon management system is needed to meet the challenges of existing technologies. Summary of the invention

[0004] The purpose of the present invention is to provide an enterprise carbon emission management system based on blockchain technology, aiming to solve the efficiency and transparency problems of enterprise carbon emission recording, tracing, quota management and cross-regional carbon trading.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The enterprise carbon emission management system based on blockchain technology described in the present invention includes a carbon emission data storage module, a carbon emission quota allocation module, and a carbon emission cross-regional trading module; The carbon emission data storage module stores carbon emission data and weighted carbon emission data of different time periods in time series by establishing a carbon balance tree; The carbon emission quota allocation module dynamically allocates carbon quotas to enterprises based on the enterprise's future carbon emissions and corporate credit rating; when multiple enterprises apply for carbon quotas, the carbon emission quotas between enterprises are automatically balanced based on the game theory model; The cross-regional carbon emission trading module realizes carbon trading between different regions through blockchain and smart contracts.

[0006] Furthermore, the carbon balance tree includes weight variables at different time levels based on the traditional tree structure; each data node of the carbon balance tree includes a timestamp, carbon emissions and weights at different time levels; and weighted carbon emission data for different time periods are calculated based on the weights at different time levels.

[0007] Furthermore, the carbon emission data storage module calculates the weighted total carbon emission within the target time period according to the weights of different time levels by recursively facilitating the carbon balance tree.

[0008] Furthermore, the carbon balance tree also includes environmental data variables that affect carbon emission data, so that each data node also includes environmental data.

[0009] Furthermore, the carbon emission data storage module can automatically compress redundant data nodes and merge similar time periods.

[0010] Furthermore, based on the historical carbon emission data of the enterprise, a machine learning model is used to predict the future carbon emissions of the enterprise; based on the future carbon emissions of the enterprise and the corporate credit rating, the carbon emission quota of the enterprise is adjusted based on an adaptive learning algorithm.

[0011] Furthermore, the game theory model takes corporate carbon emission demand, corporate credit rating and carbon trading market supply and demand relationship as decision factors.

[0012] Furthermore, the carbon emission quota allocation module can dynamically adjust the total carbon emission amount according to the supply and demand relationship of the carbon trading market and the carbon trading price; and warn enterprises whose carbon emissions exceed the quota.

[0013] Furthermore, the carbon trading includes that the seller creates a transaction request containing the buyer's information, carbon emissions and price. After the buyer agrees to the transaction and pays, the smart contract executes the transaction and records it on the blockchain.

[0014] The advantage of the present invention is that it enhances the data processing efficiency and intelligent analysis capabilities of the system in different time dimensions through time weighting, dynamic compression and environmental data embedding at the carbon data storage and management level. The dynamic quota allocation algorithm is used through adaptive learning, game theory optimization and market feedback mechanism to ensure the intelligence, flexibility and fairness of corporate carbon quota allocation, and adapt to the ever-changing carbon market and corporate behavior. A cross-regional carbon trading mechanism has been established, and through the inter-chain interoperability protocol and the automated execution of smart contracts, the isolated status of carbon trading in different regions has been broken, and the global carbon trading market integration has been realized. This mechanism improves transaction transparency and market efficiency, and promotes the development and standardization of the carbon trading market. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the enterprise carbon emission management system based on blockchain technology described in the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0017] The enterprise carbon emission management system based on blockchain technology described in the present invention is as follows: Figure 1 As shown, it includes a carbon emission data storage module, a carbon emission quota allocation module, and a carbon emission cross-regional trading module.

[0018] The carbon emission data storage module stores carbon emission data and weighted carbon emission data of different time periods in time series by establishing a carbon balance tree.

[0019] The carbon balance tree is based on the traditional tree structure and introduces the concept of multi-layer time-weighted tree (MTWT) to increase the dimension and flexibility of its data management.

[0020] When creating a carbon balance tree object, the root node of the tree is initialized to be empty. At the same time, the weight variables of different time levels are initialized to assign different weights to carbon emission data when subsequent data is inserted. When an enterprise generates new carbon emissions, the system generates a new node with a timestamp, carbon emissions, and weights of different time levels. Each data node of the carbon balance tree includes a timestamp, carbon emissions, and weights of different time levels; weighted carbon emission data for different time periods are calculated based on the weights of the different time levels. For example, the carbon emissions in the last month and the average carbon emissions in the past 6 months. Newer carbon emission data can be assigned higher weights, while the weights of older data gradually decrease. In this way, when the system summarizes and audits carbon emission data, it can focus on recent emission data, improve the accuracy of real-time decision-making, and enable the enterprise carbon emission management system to quickly respond to different needs in different application scenarios.

[0021] The location of the carbon balance tree is determined by the timestamp of the enterprise's carbon emissions data. The earlier timestamp is inserted into the left subtree, and the later timestamp is inserted into the right subtree. In this way, the time order of the data is guaranteed. The enterprise carbon emissions management system will recursively traverse the carbon balance tree and calculate the total weighted carbon emissions within the target time period.

[0022] In addition, the carbon balance tree also includes environmental data variables that affect carbon emission data, such as climate conditions, policy changes, energy price fluctuations, etc., so that each data node also includes environmental data, providing contextual information for carbon emission data, which helps to analyze the impact of different environmental variables on carbon emission behavior.

[0023] The carbon emission data storage module can be dynamically expanded or compressed as the amount of data increases or decreases. When the amount of carbon emission data of an enterprise is very large, the system can automatically compress the data nodes of certain time periods, merge redundant nodes, and reduce storage space. In specific query scenarios, the system can decompress the nodes of the relevant time period and restore the complete data. For older data, the carbon emission data storage module can merge multiple adjacent nodes into one node, and retain the total emissions before the merger and the time span after the merger. For example, the carbon emission data for the past three years is compressed into one node, and the total emissions and average emission rate are marked.

[0024] The carbon emission quota allocation module dynamically allocates carbon quotas to enterprises based on their future carbon emissions and credit ratings; when multiple enterprises apply for carbon quotas, the carbon emission quotas between enterprises are automatically balanced based on a game theory model.

[0025] By collecting the corporate carbon credit rating and historical carbon emission data of enterprises, and using machine learning algorithms such as linear regression, the model is trained to predict the future carbon emissions of enterprises. Based on the predicted future carbon emissions and corporate carbon credit rating, an adaptive learning algorithm is used to adjust the carbon emission quota of enterprises through adaptive learning factors, so as to dynamically allocate carbon quotas for enterprises and make the allocation results more accurate and personalized.

[0026] When multiple companies apply for carbon quotas at the same time, the quota allocation will be optimized based on the game theory model. The company's carbon emission demand, corporate credit rating and carbon trading market supply and demand relationship are used as decision factors in the game theory model to generate a balanced allocation strategy to automatically balance the carbon emission quotas among companies.

[0027] The carbon emission quota allocation module of the present invention can also dynamically adjust the total carbon emission amount according to the supply and demand relationship of the carbon trading market and the carbon trading price. It can also monitor the carbon emission behavior of enterprises in real time. When it is found that the carbon emission of an enterprise exceeds the carbon quota, an early warning is automatically issued. Through the strategy of game theory, the fairness of quota allocation and the rational use of market resources are ensured, and the imbalance of resource allocation caused by unreasonable competition among enterprises is avoided.

[0028] The cross-regional carbon emission trading module realizes carbon trading between different regions through blockchain and smart contracts.

[0029] Currently, most carbon trading markets are at the regional or national level, and the carbon trading mechanisms between regions are often incompatible, which limits the flexibility of enterprises to conduct carbon trading in multiple markets.

[0030] The cross-regional carbon emission trading module of the present invention uses the Inter-Blockchain Communication Protocol (IBC) to realize data sharing and trading operations between different blockchain networks. In this way, enterprises can flexibly participate in multiple carbon trading markets, improving the market liquidity and efficiency of carbon trading.

[0031] At the same time, compared with traditional carbon trading, traditional carbon trading often requires supervision and arbitration by third-party institutions, and there are information asymmetry and trust issues. The carbon emission cross-regional trading module of the present invention realizes the automation and transparent management of transactions through blockchain smart contracts. The carbon emission cross-regional trading module of the present invention creates a new smart contract for each carbon quota transaction. The contract includes information such as the addresses of the two parties to the transaction, the number of carbon quotas, and the transaction price. The two parties to the transaction confirm the basic information of the transaction through the contract. The specific information of each transaction is stored in a mapping table, allowing the transaction content to be quickly found and verified when subsequent transactions are executed. When the seller wants to sell carbon quotas, a new transaction request is created and the transaction content (including the buyer, the number of quotas, and the price) is submitted to the carbon emission cross-regional trading module of the present invention. The carbon emission cross-regional trading module of the present invention generates a unique transaction ID for the transaction and stores the transaction details. When the buyer agrees to the transaction and pays the corresponding fees, the smart contract automatically executes the transaction. The contract first checks whether the buyer pays enough transaction amount, then transfers the money to the seller, and completes the transfer of carbon quotas. All transaction processes and results will be permanently recorded on the blockchain to ensure that the entire transaction process is transparent and open, making it easier for companies and regulators to audit and track.

[0032] Through the decentralized nature of blockchain, this invention eliminates the single management agency in traditional carbon trading and enhances the transparency of the trading process. All transaction records are publicly available, allowing market participants to understand the overall situation and make more informed decisions. This improvement in the trust mechanism can effectively attract more companies to participate in the carbon trading market and expand the market scale.

Claims

1. An enterprise carbon emission management system based on blockchain technology, characterized in that: Including carbon emission data storage module, carbon emission quota allocation module, and carbon emission cross-regional trading module; The carbon emission data storage module stores carbon emission data and weighted carbon emission data of different time periods in time series by establishing a carbon balance tree; The carbon emission quota allocation module dynamically allocates carbon quotas to enterprises based on their future carbon emissions and credit ratings; When multiple companies apply for carbon quotas, the carbon emission quotas between companies are automatically balanced based on the game theory model; The cross-regional carbon emission trading module realizes carbon trading between different regions through blockchain and smart contracts.

2. According to claim 1, the enterprise carbon emission management system based on blockchain technology is characterized by: The carbon balance tree includes weight variables at different time levels based on the traditional tree structure; each data node of the carbon balance tree includes a timestamp, carbon emissions and weights at different time levels; and weighted carbon emission data for different time periods are calculated based on the weights at different time levels.

3. According to claim 2, the enterprise carbon emission management system based on blockchain technology is characterized by: The carbon emission data storage module calculates the weighted total carbon emission within the target time period according to the weights of different time levels by recursively facilitating the carbon balance tree.

4. According to claim 2, the enterprise carbon emission management system based on blockchain technology is characterized by: The carbon balance tree also includes environmental data variables that affect carbon emission data, so that each data node also includes environmental data.

5. According to claim 1, the enterprise carbon emission management system based on blockchain technology is characterized by: The carbon emission data storage module can automatically compress redundant data nodes and merge similar time periods.

6. According to claim 1, the enterprise carbon emission management system based on blockchain technology is characterized by: Based on the company's historical carbon emissions data, a machine learning model is used to predict the company's future carbon emissions; based on the company's future carbon emissions and corporate credit rating, the company's carbon emissions quota is adjusted based on an adaptive learning algorithm.

7. According to claim 1, the enterprise carbon emission management system based on blockchain technology is characterized by: The game theory model takes corporate carbon emission demand, corporate credit rating and carbon trading market supply and demand relationship as decision factors.

8. According to claim 1, the enterprise carbon emission management system based on blockchain technology is characterized by: The carbon emission quota allocation module can dynamically adjust the total carbon emission amount according to the supply and demand relationship of the carbon trading market and the carbon trading price; and warn enterprises whose carbon emissions exceed the quota.

9. The enterprise carbon emission management system based on blockchain technology according to claim 1 is characterized by: The carbon trading includes the seller creating a transaction request containing the buyer's information, carbon emissions and price. After the buyer agrees to the transaction and pays, the smart contract executes the transaction and records it on the blockchain.

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