Blockchain-based on-chain and off-chain double-trust carbon emission right transaction method and system

Abstract

The application discloses a blockchain-based double-trust on-chain and off-chain carbon emission right transaction method and system, acquires carbon emission data of an enterprise, combines the carbon emission data and carbon dioxide mass concentration as real-time carbon emission data, stores the real-time carbon emission data in a BigchainDB storage network, calculates minute, hour, day, month and year carbon emission of the enterprise according to the real-time carbon emission data and stores the carbon emission, then applies for a trusted carbon emission digital certificate, uploads the enterprise ID, enterprise name, enterprise carbon emission, enterprise trusted carbon emission digital certificate and enterprise credit value to a Fabric alliance chain transaction network, and the enterprise performs carbon emission right transaction on the alliance chain transaction network. The application uses the alliance chain technology to ensure data transparency and non-tamperability in the transaction process, and solves the technical problem that the current carbon emission right transaction system cannot guarantee the authenticity and credibility of carbon emission data.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to a blockchain-based method and system for on-chain and off-chain dual-trust carbon emission rights trading. Background Technology

[0002] Many researchers both domestically and internationally have proposed blockchain-based solutions, but these primarily focus on carbon asset management, trading incentive mechanisms, and carbon emission trading methods. Some researchers have failed to provide specific solutions and architectures, lacking data tracking and measurement of carbon emissions, and thus cannot ensure the authenticity and reliability of carbon emission data provided by off-chain enterprises within the consortium blockchain. Therefore, the authenticity and credibility of enterprise carbon emissions during on-chain carbon emission trading within the consortium blockchain cannot be guaranteed. Summary of the Invention

[0003] To address the issue of data opacity in traditional carbon emission trading systems, the purpose of this invention is to provide a blockchain-based method and system for carbon emission trading that is both on-chain and off-chain trusted, ensuring the authenticity and reliability of corporate carbon emission data during the carbon emission trading process.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A blockchain-based method for trading carbon emission rights with dual on-chain and off-chain trust includes the following steps:

[0006] Obtain the enterprise's carbon emission data and combine the carbon emission data and carbon dioxide mass concentration into a real-time carbon emission data JSON string;

[0007] Encrypt the real-time carbon emission data JSON string and then publish the encrypted data;

[0008] It receives encrypted data, decrypts it, and stores the decrypted real-time carbon emission data in the BigchainDB storage network.

[0009] Calculate and store the company's minute, hour, day, month, and yearly carbon emissions based on the decrypted real-time carbon emission data;

[0010] Enterprises can apply for credible digital carbon emission certificates based on minute, hour, day, month, and year carbon emissions.

[0011] Upload the enterprise ID, enterprise name, enterprise carbon emissions, enterprise trusted carbon emissions digital certificate, and enterprise reputation value to the Fabric consortium blockchain transaction network;

[0012] Based on the data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network, enterprises can trade carbon emission rights on the consortium blockchain trading network.

[0013] Furthermore, carbon emission data of enterprises can be obtained through IoT devices; the carbon emission data of enterprises includes flue gas humidity, flue gas velocity, flue gas static pressure, flue gas temperature and carbon dioxide volume concentration.

[0014] Furthermore, the specific process for decrypting the real-time carbon emission data and storing it on the BigchainDB storage network is as follows:

[0015] 5.1 Upload the decrypted real-time carbon emission data to the BigchainDB storage network. If the data upload is successful, the BigchainDB storage network generates a transaction ID for the real-time carbon emission data. After successful upload, proceed to step 5.3. If the upload fails, proceed to step 5.2.

[0016] 5.2 Store the real-time carbon emission data that failed to upload to the Redis cache, and proceed to step 5.1;

[0017] 5.3 Store the transaction ID of the real-time carbon emission data in the BigchainDB storage network and the timestamp of the acquired carbon emission data into the RealtimeCol of the MongoDB database. If the storage is successful, the real-time carbon emission data storage is completed. If the storage fails, proceed to step 5.4.

[0018] 5.4 Store the failed transaction ID and timestamp to the Redis cache, then proceed to step 5.3.

[0019] Furthermore, the specific process for calculating and storing a company's minute, hourly, daily, monthly, and annual carbon emissions based on the decrypted real-time carbon emission data is as follows:

[0020] Based on the start and end timestamps of the carbon emission data within a minute, obtain all transaction IDs within the time period in RealtimeCol. Access the Bigchain network using multiple threads, and retrieve real-time carbon emission data from the BigchainDB storage network based on the transaction IDs to achieve fuzzy queries on the BigchainDB storage network. Calculate the average flue gas humidity, average flue gas velocity, average flue gas static pressure, average flue gas temperature, average carbon dioxide volume concentration, and average carbon dioxide mass concentration for all real-time carbon emission data within each minute, and store them in the BigchainDB storage network. Then, store the transaction IDs and timestamps of the minute data in MinTimeCol in MongoDB.

[0021] Based on the minute timestamp within the hour, the transaction ID in MinTimeCol is obtained. Multiple threads retrieve minute carbon emission data from the BigchainDB storage network, calculate the company's hourly carbon emissions, upload them to the BigchainDB storage network, and then store the transaction ID and timestamp in HourTimeCol in MongoDB.

[0022] Based on the hourly timestamp of the day, the transaction ID in HourTimeCol is obtained. Multiple threads retrieve hourly carbon emission data from the BigchainDB storage network, calculate the daily carbon emission amount, upload the carbon emission amount and daily timestamp to the BigchainDB storage network, and then store the transaction ID and timestamp in DayTimeCol in MongoDB.

[0023] Based on the daily timestamp within the month, the transaction ID in DayTimeCol is obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network, calculate the monthly carbon emission amount, upload the carbon emission amount and monthly timestamp to BigchainDB, and then store the transaction ID and timestamp in MonthTimeCol in MongoDB.

[0024] Based on the daily timestamps within the year, the transaction ID in DayTimeCol is obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network, calculate the annual carbon emission amount, upload the carbon emission amount and annual timestamp to BigchainDB, and then store the transaction ID and timestamp in YearTimeCol in MongoDB.

[0025] Furthermore, the Fabric consortium blockchain carbon emission rights network includes corporate and government-affiliated exchanges with credible carbon emission records.

[0026] Furthermore, the data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network includes the interaction of enterprise carbon emission data and trusted carbon emission digital certificates.

[0027] Furthermore, the gRPC framework is implemented to realize data interaction between the interactive gateway, the Fabric consortium blockchain transaction network, and the BigchainDB storage network; the transaction methods include two types: one-way auction and fixed-price transaction.

[0028] Furthermore, the reputation score is calculated using the following formula:

[0029] R score =T nums +CE proportion (B.1)

[0030] In the formula:

[0031] R score ---Enterprise reputation score;

[0032] T nums ---A credit score that a company receives based on the number of times it participates in carbon emissions trading;

[0033] CE proportion —A reputation value derived from the company's latest carbon emissions as a percentage of its total allowance.

[0034] Furthermore, the specific process for enterprises to trade carbon emission rights on a consortium blockchain trading network is as follows:

[0035] Enterprises initiate requests to sell or purchase carbon emission rights;

[0036] Based on the credit mechanism, the purchase ratio of enterprises whose carbon emission rights requests for sale or purchase have been approved is determined, and transactions between buyers and sellers are matched, with enterprises with higher credit scores taking priority in transactions.

[0037] A blockchain-based, dual-trust on-chain and off-chain carbon emission trading system, including

[0038] The carbon emission data acquisition module is used to acquire carbon emission data from enterprises and combine the carbon emission data and carbon dioxide mass concentration into a real-time carbon emission data JSON string.

[0039] The encryption module is used to encrypt real-time carbon emission data JSON strings and then publish the encrypted data.

[0040] The decryption module is used to receive encrypted data, decrypt it, and store the decrypted real-time carbon emission data in the BigchainDB storage network.

[0041] The carbon emission calculation module is used to calculate and store the enterprise's carbon emissions in minutes, hours, days, months, and years based on the decrypted real-time carbon emission data.

[0042] The certificate application module is used for enterprises to apply for trusted digital carbon emission certificates based on minute, hour, day, month, and year carbon emission levels.

[0043] The upload module is used to upload the enterprise ID, enterprise name, enterprise carbon emissions, enterprise trusted carbon emissions digital certificate, and enterprise reputation value to the Fabric consortium blockchain transaction network.

[0044] The transaction module is used for data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network, enabling enterprises to trade carbon emission rights on the consortium blockchain transaction network.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] This invention, based on consortium blockchain and BigchainDB technology, ensures the authenticity and reliability of on-chain and off-chain carbon emission data for enterprises during carbon emission trading, solving the problem of unreliable carbon emission data provided by enterprises. Because of the immutable nature of the BigchainDB storage network, data is stored in BigchainDB, carbon emission calculations are performed, and the calculated carbon emissions are stored in BigchainDB, ensuring the authenticity and reliability of the carbon emission data during the calculation process. Identity authentication technology is used to verify the identities of enterprises accessing the consortium blockchain trading network, ensuring the security of the network. Consortium blockchain technology guarantees data transparency and immutability during the trading process. Based on reputation scores, enterprises are incentivized to participate in the carbon emission trading system and to reduce carbon emissions. This invention improves and optimizes existing carbon emission trading methods and has significant practical value.

[0047] Furthermore, by utilizing IoT technology and IoT devices to continuously collect data during the emission process, human intervention can be reduced, and data problems caused by excessive human intervention in the accounting process can be eliminated. Attached Figure Description

[0048] Figure 1 This is an architectural diagram of the method of the present invention.

[0049] Figure 2 This is a system schematic diagram of the present invention. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the entire process and technical details of this application are described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of this application, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0051] See Figure 1 A blockchain-based method for dual-trusted on-chain and off-chain carbon emission rights trading includes the following steps:

[0052] Step 1: Enterprises participating in carbon emission trading must configure IoT devices in accordance with the requirements of the "Technical Specification for Continuous Monitoring of Carbon Dioxide Emissions from Flue Gas of Thermal Power Plants" issued by the National Energy Administration, including humidity testers, flow rate testers, pressure testers, temperature measuring instruments, and carbon dioxide analyzers.

[0053] Step 2: During the enterprise's production process, the IoT devices described in Step 1 are used to collect the enterprise's carbon emission data, including flue gas humidity, flue gas velocity, flue gas static pressure, flue gas temperature, and carbon dioxide volume concentration. The carbon dioxide mass concentration is calculated according to formulas (A.1) and (A.2) provided in the "Technical Specification for Continuous Monitoring of Carbon Dioxide Emissions from Flue Gas of Thermal Power Plants." The collected carbon emission data and carbon dioxide mass concentration are combined into a real-time carbon emission data JSON string.

[0054]

[0055] In the formula:

[0056] c Sn ---Carbon dioxide concentration under standard conditions, expressed in grams per cubic meter (g / m³) 3 );

[0057] c S ----The volumetric concentration of carbon dioxide measured by CO2-CEMS, in grams per cubic meter (g / m³) 3 ).

[0058] Note: In formula (A.1), the mass concentration and volume concentration should be the same on both dry and wet basis.

[0059]

[0060] In the formula:

[0061] c d ---Carbon dioxide concentration on a dry basis under standard conditions, in grams per cubic meter (g / m³) 3 );

[0062] c w ---Wet-based carbon dioxide concentration under standard conditions, in grams per cubic meter (g / m³) 3 );

[0063] X SW ---Moisture content in flue gas.

[0064] Step 3: Encrypt the real-time carbon emission data JSON string using the company's public key to ensure the security and privacy of carbon emission data during the company's production process. Then, publish the encrypted data to the MQTT server via the MQTT (Message Queuing Telemetry Transport) protocol.

[0065] Step 4: Configure the generator set carbon data center storage server within the enterprise and build a BigchainDB (giant chain database) storage network consisting of the generator set carbon data center and the Ministry of Ecology and Environment carbon data center. The BigchainDB storage network provides immutable storage for the data.

[0066] Step 5: The generator set carbon data center server subscribes to the carbon emission data topic, receives the real-time carbon emission data JSON string, decrypts the string using the enterprise private key, and stores the decrypted real-time carbon emission data. The specific process for storing the decrypted real-time carbon emission data is as follows:

[0067] 5.1 The decrypted real-time carbon emission data is uploaded to the BigchainDB storage network via the java-planetmint-driver. If the data upload is successful, the BigchainDB storage network will generate a transaction ID (txid) for the real-time carbon emission data. The transaction ID is a unique identifier for the real-time carbon emission data in the BigchainDB storage network. After successful upload, proceed to step 5.3. If the data upload fails, proceed to step 5.2.

[0068] 5.2 Store the real-time carbon emission data that failed to upload to the Redis cache, and proceed to step 5.1;

[0069] 5.3 Store the transaction ID that uniquely identifies the carbon emission data in the BigchainDB storage network and the timestamp of the carbon emission data collected by the IoT device into the RealtimeCol database of the MongoDB database. If the storage is successful, the real-time carbon emission data storage is completed. If the storage fails, proceed to step 5.4.

[0070] 5.4 Store the failed transaction ID and timestamp to the Redis cache, then proceed to step 5.3.

[0071] Step 6: Calculate and store the enterprise's minute, hour, day, month, and yearly carbon emissions according to the calculation formulas in the "Technical Specification for Continuous Monitoring of Carbon Dioxide Emissions from Flue Gas of Thermal Power Plants". The specific process for calculating and storing minute, hour, day, month, and yearly carbon emissions is as follows:

[0072] 6.1 Minute-by-minute carbon emission data calculation and storage: Based on the start and end timestamps of the carbon emission data within a minute, obtain all transaction IDs for that time period in RealtimeCol. Multi-threaded access to the Bigchain network is used to retrieve real-time carbon emission data from the BigchainDB storage network based on the transaction IDs, enabling fuzzy queries on the BigchainDB storage network. Calculate the average flue gas humidity, average flue gas velocity, average flue gas static pressure, average flue gas temperature, average carbon dioxide volume concentration, and average carbon dioxide mass concentration for all real-time carbon emission data within each minute. Store the average values ​​of these parameters and the minute timestamps in steps 5.1 and 5.2) to the BigchainDB storage network, and then store the transaction IDs and timestamps of the minute data in MinTimeCol in MongoDB.

[0073] 6.2 Hourly Carbon Emission Calculation and Storage: The transaction ID in MinTimeCol is obtained based on the minute timestamp within the hour. Multiple threads retrieve minute-level carbon emission data from the BigchainDB storage network. The hourly carbon emissions for the enterprise are calculated using formulas (A.3) to (A.6). The calculated carbon emissions and hourly timestamp are uploaded to BigchainDB, and then the transaction ID and timestamp are stored in HourTimeCol in MongoDB.

[0074]

[0075] In the formula:

[0076] ---Measure the average velocity of wet flue gas at the cross-section, in meters per second (m / s);

[0077] K V ---Velocity field coefficient;

[0078] ---The average velocity of wet flue gas measured by CMS at the cross-sectional velocity, in meters per second (m / s).

[0079]

[0080] In the formula:

[0081] Q S --- Actual operating conditions for wet flue gas flow rate, in cubic meters per hour (m³ / h) 3 / h);

[0082] A----Measurement of the cross-sectional area, in cubic meters (m²) 3 ).

[0083]

[0084] In the formula:

[0085] Q Sn --Dry flue gas volumetric flow rate under standard conditions, in cubic meters per hour (m³ / h) 3 / h);

[0086] t s --- Flue gas temperature, in degrees Celsius (°C);

[0087] P atm - Atmospheric pressure, the unit is Pascal (Pa);

[0088] P S --- Flue gas static pressure, measured in Pascals (Pa).

[0089] G h =c d ×Q Sn ×10 -6 (A.6)

[0090] In the formula:

[0091] G h --- Flue gas carbon dioxide emission mass flow rate, in tons per hour (t / h).

[0092] 6.3 Daily Carbon Emission Calculation and Storage: Based on the hourly timestamp of the day, the transaction ID in HourTimeCol is obtained. Multiple threads retrieve hourly carbon emission data from the BigchainDB storage network. The daily carbon emission is calculated according to Formula A.7. The calculated carbon emission and daily timestamp are uploaded to BigchainDB, and then the txid and timestamp are stored in DayTimeCol in MongoDB.

[0093]

[0094] In the formula:

[0095] G d ---Daily carbon dioxide emissions, expressed in tons per day (t / d);

[0096] G hi ---The carbon dioxide emissions in the i-th hour of the day, expressed in tons per hour (t / h);

[0097] 6.4 Monthly Carbon Emission Calculation and Storage: Based on the day timestamps within the month, the transaction IDs in DayTimeCol are obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network. Monthly carbon emissions are calculated according to Formula A.8. The calculated carbon emissions and monthly timestamps are uploaded to BigchainDB. Then, the txid and timestamp are stored in MonthTimeCol in MongoDB.

[0098]

[0099] In the formula:

[0100] G m ---Monthly carbon dioxide emissions, in tons per month (t / m³);

[0101] G di ---Carbon dioxide emissions on day i of the month, in tons per day (t / d);

[0102] D m ---Number of days in the month;

[0103] 6.5 Annual Carbon Emission Calculation and Storage: Based on the daily timestamps within the year, the transaction ID in DayTimeCol is obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network. The annual carbon emission is calculated using formula A.9. The calculated carbon emission and annual timestamp are uploaded to BigchainDB, and then the txid and timestamp are stored in YearTimeCol in MongoDB.

[0104]

[0105] In the formula:

[0106] G y ---Annual carbon dioxide emissions, in tons per year (t / a);

[0107] G′ di ---The carbon dioxide emissions on day i of the year, expressed in tons per day (t / d);

[0108] D y ---Number of days in that year

[0109] Step 7: Enterprises that have completed the carbon emission data collection, storage, and calculation processes as required in Steps 1 through 6 can guarantee the authenticity and reliability of their off-chain carbon emission data on the blockchain. Enterprises can apply to the Ministry of Ecology and Environment for a trusted carbon emission digital certificate. The Ministry of Ecology and Environment will review the enterprise's trusted environment; if the review is successful, a trusted carbon emission digital certificate will be issued and stored on the BigchainDB storage network; if the review fails, a trusted carbon emission digital certificate will not be issued. The trusted carbon emission digital certificate is used to prove the authenticity and reliability of the enterprise's off-chain carbon emission data on the blockchain.

[0110] Step 8: Establish a Fabric consortium blockchain carbon emission rights network consisting of companies with credible carbon emission records and government-affiliated exchanges. The government-affiliated exchanges are used to verify the reasonableness of the transaction prices initiated by companies and the number of carbon emission rights sold.

[0111] Step 9: Implement an interactive gateway based on the gRPC (Google-initiated remote procedure call) framework to handle data interaction between the Fabric consortium blockchain transaction network and the BigchainDB storage network, including enterprise carbon emissions and trusted carbon emission digital certificates;

[0112] Step 10: The consortium blockchain carbon emission trading network verifies the identity of enterprises accessing the network. If the verification is successful, the enterprise is allowed to access the network; if the verification fails, the enterprise is not allowed to access the network, thus ensuring the security of the consortium blockchain carbon emission trading network.

[0113] Step 11: The Fabric consortium blockchain carbon emissions trading network provides a new reputation mechanism to incentivize companies to participate in the network and reduce their carbon emissions. This reputation is determined by the number of times a company successfully participates in carbon emissions trading, T. nums And the latest carbon emissions of enterprises as a percentage of total allowances. proportion Determine the corporate reputation value R score The number of times a company successfully participates in carbon emissions trading, T. nums Cumulatively, companies that participate in carbon emissions trading and successfully match their allocations receive an additional 1 credit point, while those that participate but fail to match receive an additional 0.5 credit points. This is used to incentivize companies to participate in carbon emissions trading. The percentage of a company's latest carbon emissions relative to its total allowance is calculated based on its CE rating. proportion Cumulative calculations are performed daily at midnight to determine the percentage of each company's latest carbon emissions relative to the total carbon emission allowance. Companies are then ranked from lowest to highest percentage. The top 25% of companies receive an additional 1 credit point, companies between 25% and 75% receive an additional 0.5 credit points, and the bottom 25% receive an additional 0 credit points, thus incentivizing companies to reduce carbon emissions. Company credit points are calculated using formula (B.1):

[0114] R score =Tnums +CE proportion (B.1)

[0115] In the formula:

[0116] R score ---Enterprise Reputation Value

[0117] T nums ---A company's credit score based on the number of times it participates in carbon emissions trading.

[0118] CE proportion —Reputation score derived from a company's latest carbon emissions as a percentage of its total allowance.

[0119] Step 12: Enterprises on the consortium blockchain trading network obtain a trusted carbon emission digital certificate from BigchainDB through the interactive gateway, and then upload basic enterprise information such as enterprise ID, enterprise name, enterprise carbon emission, enterprise trusted carbon emission digital certificate, and enterprise reputation value to the Fabric consortium blockchain trading network.

[0120] Step 13: The Fabric consortium blockchain carbon emission trading network offers two trading methods for enterprises to choose from: one-way auctions and fixed-price trading.

[0121] Step 14: Enterprises trade carbon emission rights on the consortium blockchain trading network. The specific process is as follows:

[0122] 14.1 Enterprises on the consortium blockchain transaction network obtain their latest carbon emission CEP through an interactive gateway. used Fill in the selling price, selling quantity, company ID, company name, credit score and other information, select the transaction method, and initiate a request to sell carbon emission rights;

[0123] 14.2 Enterprises on the consortium blockchain trading network fill in information such as purchase price, purchase quantity, enterprise ID, enterprise name, and reputation value, select the transaction method, and initiate a request to purchase carbon emission rights;

[0124] 14.3 Government-affiliated exchanges shall formulate transaction review rules and complete request review through smart contracts.

[0125] For both sale and purchase requests, the first step is to verify the company's trusted carbon emission digital certificate. The digital certificate is then verified using the public key of the Ministry of Ecology and Environment. If the verification passes, the review is approved; otherwise, the review is not approved.

[0126] Then, for sales requests, the reasonableness of the sales quantity and sales price is reviewed.

[0127] For purchase requests, the reasonableness of the purchase price is reviewed. The review also checks whether the sales volume is greater than or equal to the company's current available carbon emissions; if so, the review is approved; otherwise, it is rejected. Furthermore, the sales price and purchase price are compared to the previous day's lowest and highest trading prices on the national carbon market; if so, the review is approved; otherwise, it is rejected.

[0128] If all the above checks pass, the sell and buy transaction methods in the smart contract will be invoked, and the sell and buy transactions will be published on the consortium blockchain transaction network to lock the enterprise's sell volume; if any check fails, the enterprise's sell request or buy request will fail.

[0129] 14.4 The purchase ratio of enterprises is determined according to the credit mechanism. For a transaction with one sale request and multiple purchase requests, the credit values ​​of all enterprises that initiate purchase requests are ranked from high to low. The top 25% of enterprises have 100% purchase rights, enterprises with credit values ​​of 25% to 75% have 75% purchase rights, and enterprises with credit values ​​of the bottom 25% have 50% purchase rights.

[0130] 14.5 Match buyers and sellers. Based on whether a company's purchase price meets the transaction requirements, the order of purchases is ranked from most suitable to least suitable. If companies have the same purchase price, the company with the higher reputation score takes priority. Matching is performed according to the purchase order, with the transaction volume being the buyer's purchase quantity multiplied by the purchase rights, until the seller's sales volume reaches zero, at which point the matching ends. * indicates multiplication.

[0131] 14.6 If a seller is successfully matched, the reputation value increases by 1; if a seller is unsuccessful in matching, the locked carbon emissions are released. If a buyer is successfully matched, the reputation value increases by 1; if a buyer is unsuccessful in matching, the reputation value increases by 0.5.

[0132] Step 15: Update the information of companies participating in carbon emission trading in the consortium blockchain trading network, and the transaction ends.

[0133] See Figure 2 A blockchain-based, dual-trust on-chain and off-chain carbon emission trading system includes:

[0134] The carbon emission data acquisition module is used to acquire the carbon emission data of enterprises and combine the carbon emission data and carbon dioxide mass concentration into real-time carbon emission data.

[0135] The encryption module is used to encrypt real-time carbon emission data and then publish the encrypted data.

[0136] The decryption module is used to receive encrypted data, decrypt it, and store the decrypted real-time carbon emission data in the BigchainDB storage network.

[0137] The carbon emission calculation module is used to calculate and store the enterprise's carbon emissions in minutes, hours, days, months, and years based on the decrypted real-time carbon emission data.

[0138] The certificate application module is used for enterprises to apply for trusted digital carbon emission certificates based on minute, hour, day, month, and year carbon emission levels.

[0139] The upload module is used to upload the enterprise ID, enterprise name, enterprise carbon emissions, enterprise trusted carbon emissions digital certificate, and enterprise reputation value to the Fabric consortium blockchain transaction network.

[0140] The transaction module is used for data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network, enabling enterprises to trade carbon emission rights on the consortium blockchain transaction network.

[0141] This invention utilizes IoT devices to continuously collect carbon emission data during enterprise production processes, avoiding data falsification that can occur during manual operations. It leverages the decentralized and tamper-proof characteristics of the BigchainDB storage network to store enterprise carbon emission data, and uses MongoDB to perform fuzzy queries based on timestamp ranges to calculate enterprise carbon emissions. These emissions are then stored on the BigchainDB storage network, ensuring the authenticity, reliability, and traceability of off-chain carbon emission data storage. The invention also utilizes the Ministry of Ecology and Environment's oversight of off-chain enterprise carbon emission data to issue trusted digital carbon emission certificates to enterprises, which serve as credentials for verifying carbon emission data within the consortium blockchain trading network. An interactive gateway is provided to handle data exchange between the BigchainDB storage network and the consortium blockchain trading network.

[0142] To ensure the security of the consortium blockchain trading network, identity authentication is required for enterprises accessing the network. To ensure the legitimacy of consortium blockchain transactions, government-affiliated exchange nodes are added to the network to be responsible for establishing a transaction legitimacy review mechanism. Two trading methods, one-way auction and fixed price, are provided for enterprises to choose from. A new reputation mechanism is proposed to incentivize enterprises to participate in the carbon emission trading system proposed in this invention and to encourage them to reduce carbon emissions.

[0143] Example 1

[0144] A blockchain-based carbon emission trading system with dual on-chain and off-chain trust. Figure 1 This is a system architecture diagram of the present invention. The system consists of an IoT device layer, a BigchainDB data storage and computing layer, and a consortium blockchain carbon emission rights trading layer, from bottom to top. Figure 2The functional block diagram of this invention includes: an IoT device layer comprising a carbon emission data acquisition and calculation module and a carbon emission data encryption module; a BigchainDB data storage and calculation layer comprising a carbon emission data decryption module, a carbon emission data storage module, a carbon emission calculation module, and a trusted carbon emission digital certificate module; an interaction gateway module for data interaction between the BigchainDB data storage and calculation layer and the consortium blockchain carbon emission rights trading layer; and a consortium blockchain carbon emission rights trading layer comprising a carbon emission rights trading module, a government-affiliated exchange supervision module, a reputation mechanism module, and an identity authentication module.

[0145] Taking a one-way bidding process as a specific implementation example, the specific implementation steps of the present invention will be further described in detail. Taking the one-way bidding process between thermal power plants as an example, the specific implementation steps are as follows:

[0146] Step 1: Configure IoT devices in thermal power plants, including flue gas parameter monitoring units and carbon dioxide concentration detection units, specifically including humidity testers, flow rate testers, pressure testers, temperature measuring instruments, and carbon dioxide analyzers;

[0147] Step 2: The thermal power plant builds a BigchainDB storage network consisting of the generator carbon data center and the Ministry of Ecology and Environment carbon data center, and deploys and runs the code for storing and calculating the carbon emissions of the thermal power plant;

[0148] Step 3: The thermal power plant initiates an application to the Ministry of Ecology and Environment to obtain a credible digital certificate for carbon emissions. The Ministry of Ecology and Environment reviews the credibility of the off-chain enterprise carbon emission data on the blockchain. If the review is successful, a digital certificate is issued and uploaded to the BigchainDB storage network. If the review is unsuccessful, no certificate is issued.

[0149] Step 4: Establish a Fabric consortium blockchain carbon emission trading network consisting of 5 thermal power plant nodes and 1 government-affiliated exchange node;

[0150] Step 4: Thermal power plant 1 obtains the latest carbon emissions of the enterprise through the interactive gateway, fills in the sales information, and initiates a sales request; thermal power plants 2, 3, 4, and 5 fill in the purchase price and other information and initiate a purchase request.

[0151] Step 5: The CA institution verifies the identity of the thermal power plant through the Fabric consortium blockchain transaction network. If the verification fails, the transaction will fail; if the verification is successful, the government-affiliated exchange will review the power plant's credible carbon emission digital certificate, the reasonableness of the sales volume and price of power plant 1, and the reasonableness of the purchase prices of power plants 2 through 5. If the review is successful, the sales and purchase information will be published on the Fabric transaction network. The transaction information of power plants that fail the review will not be published on the Fabric transaction network. In this case, both the sales and purchase transactions are assumed to be successful.

[0152] Step 6: Match buyers and sellers in one-way bidding transactions according to the reputation mechanism. Table 1 shows the key fields of the sale information published on the Fabric carbon emission trading network after the thermal power plant 1, which has passed identity verification, initiated a sale transaction request and passed the transaction review. Table 2 shows the key purchase information and purchase right information obtained by sorting by reputation value after the thermal power plants 2 to 5, which initiated purchase requests and passed identity verification during the bidding period, passed the transaction review and published on the Fabric carbon emission trading network.

[0153] Table 1 Key Fields of Seller Information in Fabric Transaction Network

[0154]

[0155] Table 2. Key fields and purchase rights information for buyers in the Fabric transaction network.

[0156]

[0157] In the one-way bidding process, the thermal power plants were first ranked according to the transaction price: HEP3, HEP5, HEP2, and HEP4. The four participating thermal power plants were then ranked according to their credit rating: HEP3, HEP2, HEP5, and HEP4. This credit rating ranking determined the proportion of the purchase rights each thermal power plant had: HEP3 had 100% of the purchase rights, HEP2 and HEP5 had 75% each, and HEP4 had 50%. Table 3 shows the results of the one-way bidding transaction.

[0158] Table 3 Results of One-Way Bidding Transactions

[0159]

[0160] HEP3 had the highest purchase price and 100% purchase rights, therefore its transaction price was 48 yuan, and its transaction volume was 8 tons. HEP5 had the second highest purchase price and 75% purchase rights, therefore it could purchase 4.5 tons. HEP1 sold 10 tons, HEP3 purchased 8 tons, and HEP5's transaction volume was 2 tons. HEP2 and HEP4 failed to participate in this auction. The reputation value of the thermal power plant was determined based on the proportion of its used carbon emissions to its total carbon emissions and the success of its transaction. A successful transaction for HEP3 increased its reputation value by 1, resulting in a final reputation value of 5. A successful transaction for HEP5 increased its reputation value by 1, resulting in a final reputation value of 3. A failed transaction for HEP2 increased its reputation value by 0.5, resulting in a final reputation value of 3.5. A failed transaction for HEP4 increased its reputation value by 0.5, resulting in a final reputation value of 1.5.

[0161] Step 7: Store the data after the thermal power plant transaction to the Fabric transaction network, and the transaction is complete.

[0162] The above implementation examples are one-way bidding transaction examples provided by the present invention, and are not intended to limit the present invention in any way. Under the premise of not exceeding the technical solution described in the claims, enterprises may choose other transaction methods to conduct transactions.

[0163] This invention, based on IoT, consortium blockchain, and BigchainDB technologies, ensures the authenticity and reliability of on-chain and off-chain carbon emission data for enterprises during carbon emission trading, solving the problem of unreliable carbon emission data provided by enterprises. The Ministry of Ecology and Environment issues credible carbon emission data certificates to enterprises, providing authoritative proof of their carbon emission credibility. IoT technology and devices continuously collect data during the emission process, reducing human intervention and eliminating data problems caused by excessive human manipulation in the calculation process. BigchainDB's immutable data characteristic allows for the storage of data generated by IoT devices, followed by carbon emission calculations, ensuring the authenticity and reliability of carbon emission data during calculation. Identity authentication technology verifies the identities of enterprises accessing the consortium blockchain trading network, ensuring its security. Consortium blockchain technology guarantees data transparency and immutability during transactions, and government-affiliated exchange nodes are introduced to formulate transaction review rules, ensuring the rationality of carbon emission trading on the consortium blockchain. Two trading methods are proposed: auction and fixed price. A new reputation mechanism is introduced to incentivize enterprises to participate in this carbon emission trading system and to reduce carbon emissions. This invention improves and optimizes the existing carbon emission trading system, and has certain practical significance and value.

Claims

1. A blockchain-based method for dual-trusted on-chain and off-chain carbon emission rights trading, characterized in that: Includes the following steps: Obtain the enterprise's carbon emission data and combine the carbon emission data and carbon dioxide mass concentration into a real-time carbon emission data JSON string; Encrypt the real-time carbon emission data JSON string and then publish the encrypted data; It receives encrypted data, decrypts it, and stores the decrypted real-time carbon emission data in the BigchainDB storage network. 5.1 Upload the decrypted real-time carbon emission data to the BigchainDB storage network. If the data upload is successful, the BigchainDB storage network generates a transaction ID for the real-time carbon emission data. After successful upload, proceed to step 5.

3. If the upload fails, proceed to step 5.

2. 5.2 Store the real-time carbon emission data that failed to upload to the Redis cache, and proceed to step 5.1; 5.3 Store the transaction ID of the real-time carbon emission data in the BigchainDB storage network and the timestamp of the acquired carbon emission data into the RealtimeCol of the MongoDB database. If the storage is successful, the real-time carbon emission data storage is completed. If the storage fails, proceed to step 5.

4. 5.4 Store the failed transaction ID and timestamp to the Redis cache, then proceed to step 5.3; Calculate and store the company's minute, hour, day, month, and yearly carbon emissions based on the decrypted real-time carbon emission data; Enterprises can apply for credible digital carbon emission certificates based on minute, hour, day, month, and year carbon emissions. Upload the enterprise ID, enterprise name, enterprise carbon emissions, enterprise trusted carbon emissions digital certificate, and enterprise reputation value to the Fabric consortium blockchain transaction network; Based on the data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network, enterprises can trade carbon emission rights on the consortium blockchain trading network.

2. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The company’s carbon emission data is obtained through IoT devices. The company’s carbon emission data includes flue gas humidity, flue gas velocity, flue gas static pressure, flue gas temperature and carbon dioxide volume concentration.

3. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The specific process for calculating and storing a company's minute, hourly, daily, monthly, and annual carbon emissions based on the decrypted real-time carbon emission data is as follows: Based on the start and end timestamps of the carbon emission data within a minute, obtain all transaction IDs within the time period in RealtimeCol, access the Bigchain network through multiple threads, and obtain real-time carbon emission data in the BigchainDB storage network based on the transaction IDs to achieve fuzzy query of the BigchainDB storage network; Calculate the average flue gas humidity, average flue gas velocity, average flue gas static pressure, average flue gas temperature, average carbon dioxide volume concentration, and average carbon dioxide mass concentration for all real-time carbon emission data within each minute, and store them in the BigchainDB storage network. Then, store the transaction ID and timestamp of the minute data in MinTimeCol in MongoDB. Based on the minute timestamp within the hour, the transaction ID in MinTimeCol is obtained. Multiple threads retrieve minute carbon emission data from the BigchainDB storage network, calculate the company's hourly carbon emissions, upload them to the BigchainDB storage network, and then store the transaction ID and timestamp in HourTimeCol in MongoDB. Based on the hourly timestamp of the day, the transaction ID in HourTimeCol is obtained. Multiple threads retrieve hourly carbon emission data from the BigchainDB storage network, calculate the daily carbon emission amount, upload the carbon emission amount and daily timestamp to the BigchainDB storage network, and then store the transaction ID and timestamp in DayTimeCol in MongoDB. Based on the daily timestamp within the month, the transaction ID in DayTimeCol is obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network, calculate the monthly carbon emission amount, upload the carbon emission amount and monthly timestamp to BigchainDB, and then store the transaction ID and timestamp in MonthTimeCol in MongoDB. Based on the daily timestamps within the year, the transaction ID in DayTimeCol is obtained. Multiple threads retrieve daily carbon emission data from the BigchainDB storage network, calculate the annual carbon emission amount, upload the carbon emission amount and annual timestamp to BigchainDB, and then store the transaction ID and timestamp in YearTimeCol in MongoDB.

4. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The Fabric consortium blockchain carbon emissions rights network includes corporate and government-affiliated exchanges with credible carbon emissions records.

5. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The data exchange between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network includes the exchange of enterprise carbon emission data and trusted carbon emission digital certificates.

6. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The gRPC framework is implemented to realize the data interaction between the Fabric consortium blockchain transaction network and the BigchainDB storage network, and the transaction methods include one-way auction and fixed-price transaction.

7. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, Reputation score is calculated using the following formula: In the formula: ---Enterprise reputation score; ---A credit score that a company receives based on the number of times it participates in carbon emissions trading; —A reputation value derived from the company's latest carbon emissions as a percentage of its total allowance.

8. The blockchain-based on-chain and off-chain dual-trust carbon emission rights trading method according to claim 1, characterized in that, The specific process for enterprises to trade carbon emission rights on a consortium blockchain trading network is as follows: Enterprises initiate requests to sell or purchase carbon emission rights; Based on a credit mechanism, the purchase ratio of enterprises whose carbon emission rights requests are approved will be determined. The system matches buyers and sellers, prioritizing transactions for companies with higher credit scores.

9. A blockchain-based carbon emission trading system with dual on-chain and off-chain trust, characterized in that: include The carbon emission data acquisition module is used to acquire carbon emission data from enterprises and combine the carbon emission data and carbon dioxide mass concentration into a real-time carbon emission data JSON string. The encryption module is used to encrypt real-time carbon emission data JSON strings and then publish the encrypted data. The decryption module is used to receive encrypted data, decrypt it, and store the decrypted real-time carbon emission data in the BigchainDB storage network. The decrypted real-time carbon emission data is uploaded to the BigchainDB storage network. If the data upload is successful, the BigchainDB storage network generates a transaction ID for the real-time carbon emission data. After successful upload, step 5.3 is executed. If the upload fails, the real-time carbon emission data that failed to upload is stored in the Redis cache, and the decrypted real-time carbon emission data is uploaded to the BigchainDB storage network again. The transaction ID and timestamp of the real-time carbon emission data obtained from the BigchainDB storage network are stored in the RealtimeCol database of the MongoDB database. If the storage is successful, the real-time carbon emission data storage ends. If the storage fails, the transaction ID and timestamp of the failed storage are stored in the Redis cache, and the transaction ID and timestamp of the real-time carbon emission data obtained from the BigchainDB storage network are stored in the RealtimeCol database of the MongoDB database again. The carbon emission calculation module is used to calculate and store the enterprise's carbon emissions in minutes, hours, days, months, and years based on the decrypted real-time carbon emission data. The certificate application module is used for enterprises to apply for trusted digital carbon emission certificates based on minute, hour, day, month, and year carbon emission levels. The upload module is used to upload the enterprise ID, enterprise name, enterprise carbon emissions, enterprise trusted carbon emissions digital certificate, and enterprise reputation value to the Fabric consortium blockchain transaction network. The transaction module is used for data interaction between the Fabric consortium blockchain carbon emission rights network and the BigchainDB storage network, enabling enterprises to trade carbon emission rights on the consortium blockchain transaction network.

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