Aquatic product carbon footprint tracing and carbon sink authentication system based on block chain

Abstract

The invention, which relates to the technical field of aquaculture carbon footprint management, discloses a block chain-based aquatic product carbon footprint tracing and carbon sink authentication system comprising a data acquisition module, a carbon footprint calculation module, a block chain evidence storage module and an authentication service module. The data acquisition module is provided with an intelligent electric meter, an automatic feeder data interface and a mobile end manual input terminal; the carbon footprint calculation module is provided with an emission factor database, a carbon emission calculation engine and a shellfish and algae carbon sink quantitative model; the block chain evidence storage module is provided with an alliance chain network, a data evidence storage intelligent contract and a traceability query intelligent contract; the authentication service module is provided with a multi-source data cross verification unit and an automatic contract preliminary examination and electronic certificate issuing unit. According to the method, the under-chain data credibility problem is solved through multi-source data cross validation; and quantifying the shell and algae culture carbon sink contribution by adopting a conservative coefficient method. According to the invention, full-life-cycle carbon footprint tracing of aquatic products can be realized, and verifiable carbon label information is provided for consumers.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint management technology in aquaculture, and in particular to a blockchain-based aquatic product carbon footprint traceability and carbon sink certification system. Background Technology

[0002] Aquaculture is a vital global food industry, but it is also a unique sector characterized by both carbon emissions and carbon sequestration. On the one hand, energy consumption, feed production, and cold chain transportation during the aquaculture process generate substantial carbon emissions; on the other hand, farmed species such as shellfish and algae possess significant carbon sequestration capabilities, absorbing and storing carbon dioxide through biological carbon fixation processes. Driven by these dual carbon objectives, establishing a full life-cycle carbon footprint traceability system for aquatic products and providing credible certification of their carbon sequestration contributions has become an urgent need for the industry's green transformation.

[0003] The current management of carbon footprint of aquatic products faces the following technical challenges: First, the data lacks credibility. Carbon emission data recorded by livestock farms themselves lacks third-party verification mechanisms, posing a risk of falsification and concealment, resulting in low public trust in carbon footprint data. Traditional paper records and centralized database storage methods are easily tampered with, failing to meet the stringent requirements for data authenticity in the carbon trading market.

[0004] Second, the traceability chain is broken. Aquatic products go through multiple stages from farming to the table, including farming, processing, storage, transportation, and sales. Data from each stage is scattered across different entities' information systems, creating data silos and making it impossible to build a complete end-to-end carbon footprint profile. The batch splitting and merging during product circulation further increases the difficulty of traceability.

[0005] Third, quantifying carbon sinks is difficult. Shellfish fix calcium carbonate through shell calcification, and algae absorb carbon dioxide through photosynthesis; however, there is a lack of standardized quantification methods and credible verification mechanisms for these carbon sink contributions. Existing carbon footprint assessment standards primarily target carbon emissions, and the methods for quantifying carbon sinks in aquaculture are still inadequate, lacking scientific assessment of computational uncertainties.

[0006] Fourth, carbon labeling lacks credibility. Currently, most carbon labels for aquatic products are self-declared by companies, making it impossible for consumers and regulatory agencies to independently verify the authenticity of the label data. This weakens the market value and guiding role of carbon labels. The carbon sequestration certification process relies on manual review, resulting in low transparency, low efficiency, and high costs.

[0007] The immutability, distributed consensus mechanism, and automatic execution capability of smart contracts in blockchain technology provide a technological foundation for solving the above problems.

[0008] However, existing blockchain traceability systems generally suffer from the problem of "off-chain data credibility," meaning that blockchain can only guarantee that data will not be tampered with after it is uploaded to the chain, but cannot guarantee the authenticity of the data collection before it is uploaded. In addition, existing systems lack dedicated quantitative models and uncertainty assessment methods for carbon sinks in aquaculture.

[0009] To address these issues, there is an urgent need for a blockchain-based carbon footprint traceability and carbon sink certification system for aquatic products. Summary of the Invention

[0010] To address the aforementioned issues, this application proposes a blockchain-based aquatic product carbon footprint traceability and carbon sink certification system, comprising a data acquisition module, a carbon footprint calculation module, a blockchain evidence storage module, and a certification service module.

[0011] The data acquisition module includes energy consumption acquisition units, feed data acquisition units, production data acquisition units, and logistics data acquisition units. Each acquisition unit is connected to the carbon footprint calculation module via a data interface. The carbon footprint calculation module includes an emission factor database, a carbon emission calculation engine, and a carbon sink quantification engine. It is connected to the blockchain evidence storage module via a data interface. The blockchain evidence storage module includes a consortium blockchain network, data evidence storage smart contracts, and traceability query smart contracts. It is connected to the authentication service module via a smart contract call interface. The authentication service module includes a multi-source data cross-validation unit, authentication audit smart contracts, and a certificate issuance unit.

[0012] The data acquisition module is used to collect carbon emission and carbon sink data at each stage of the aquatic product's life cycle. The energy consumption acquisition unit obtains electricity consumption data from aquaculture equipment via smart meters. These smart meters comply with the DL / T 645 communication protocol, have an accuracy class of at least 1.0, and support RS485 or wireless communication. Data acquisition is performed every 15 minutes to every hour. The smart meters are installed in the main distribution cabinet or branch circuits of the aquaculture farm, measuring the electricity consumption of major power-consuming equipment such as aerators, water pumps, and temperature control equipment.

[0013] The feed data acquisition unit includes two data acquisition methods: an automatic feeder data interface and a mobile input terminal. The automatic feeder data interface connects to the automatic feeder control system via an RS485 bus or IoT communication module, acquiring real-time data on the type, weight, and time of each feed feeding, with an accuracy of at least 0.1 kg. The mobile input terminal is a data acquisition application installed on a smartphone. Farmers obtain feed product information by scanning the feed packaging barcode, manually enter the feeding weight, and submit the data.

[0014] The production data collection unit acquires product harvest data via a mobile terminal, collecting parameters including harvest date, total product weight, and the percentage of shell weight or algae dry weight. When receiving the goods, buyers enter the actual weighing weight via a mobile terminal, and the system automatically compares the buyer's data with the data declared by the farmers.

[0015] The logistics data acquisition unit connects to third-party logistics platforms via API to obtain the origin coordinates, destination coordinates, transportation mileage, vehicle type, and transportation time of transportation orders. In cold chain transportation scenarios, the logistics data acquisition unit also acquires temperature curve data from the vehicle-mounted temperature control recorder.

[0016] The carbon footprint calculation module is the core of the system's algorithm, responsible for converting raw collected data into standardized carbon emissions and carbon sinks. The emission factor database uses a relational database storage structure and includes tables for power grid emission factors, feed carbon footprint coefficients, transportation emission factors, and packaging material carbon footprint coefficients. The power grid emission factor table stores the annual average power grid emission factors for each provincial-level administrative region, with data sourced from official data released by the national environmental protection department. The emission factor database supports version management; each update generates a new version number and records the update time, while historical version data is permanently retained.

[0017] The carbon emissions calculation engine employs a life cycle assessment framework, breaking down product carbon emissions into three segments: emissions from the farming stage, emissions from the processing stage, and emissions from the logistics stage. Emissions from the farming stage equal the total electricity consumption during the farming period multiplied by the emission factor of the power grid where the farm is located, plus the total feed consumption multiplied by the carbon footprint coefficient of the corresponding feed type. Emissions from the processing stage equal the electricity consumption during processing multiplied by the emission factor of the power grid where the processing plant is located, plus the amount of packaging materials used multiplied by the carbon footprint coefficient of the corresponding material. Emissions from the logistics stage equal the transportation weight multiplied by the transportation distance multiplied by the transportation emission factor of the corresponding vehicle type.

[0018] The carbon sequestration quantification engine establishes separate carbon sequestration calculation models for shellfish aquaculture and algae aquaculture. The formula for the shellfish carbon sequestration model is: Net carbon sequestration of shellfish equals the dry weight of the shell multiplied by the mass percentage of calcium carbonate multiplied by the mass fraction of carbon, and the effective sequestration factor. The mass percentage of calcium carbonate ranges from 0.90 to 0.95, the mass fraction of carbon is 0.12, and the effective sequestration factor is 0.5 to reflect carbon dioxide release during shell formation and scientific uncertainties. The formula for the algae carbon sequestration model is: Net carbon sequestration of algae equals the dry weight of the algae multiplied by the carbon content percentage, where the carbon content percentage ranges from 0.25 to 0.35 depending on the algae species.

[0019] The uncertainty range output by the carbon footprint calculation module is calculated using the Monte Carlo simulation method. The sources of uncertainty are divided into two categories: data uncertainty and model uncertainty. Data uncertainty stems from measurement errors in the original collected data, while model uncertainty arises from the value ranges of emission factors and carbon sink calculation parameters. The system obtains the probability distribution of the carbon footprint calculation results through more than 1000 random sampling calculations, and uses the 95% confidence interval as the uncertainty range output.

[0020] The blockchain evidence storage module forms the foundation of the system's trust, responsible for storing carbon footprint data, executing business logic, and achieving multi-party consensus. The consortium blockchain network employs a practical Byzantine fault-tolerant consensus mechanism, with network nodes divided into consensus nodes and ordinary nodes. Consensus nodes, operated by certification authorities, industry associations, and government regulatory departments, number at least four and are responsible for transaction verification and block generation. Ordinary nodes, operated by livestock companies, processing companies, and logistics companies, have data upload and query permissions but do not participate in the consensus process.

[0021] The data storage smart contract receives the calculation results from the carbon footprint calculation module, performs format validation and integrity checks on the input data, generates a data digest, and writes it to the blockchain. The data structure includes product batch code, carbon emissions at each stage, carbon sink, net carbon footprint value, data credibility score, calculation engine version number, storage timestamp, and upload node signature. The data digest is generated using the SHA-256 hash algorithm, and the original data is stored in an off-chain distributed database and associated with hash values.

[0022] The traceability query smart contract receives product batch codes as input and retrieves all documented records for each stage of the product's journey from breeding to sales based on these batch codes. For product batches with upstream and downstream connections, the traceability query smart contract recursively retrieves the documented records of related batches, constructing a complete product flow diagram.

[0023] The certification service module automates and makes carbon sink certification transparent. A multi-source data cross-validation unit performs multi-dimensional credibility verification on the data reported by farmers. Energy consumption data verification compares the electricity consumption reported by farmers with the metering data from the power company; a deviation exceeding 5% is marked as abnormal. Feed data verification compares the feed consumption reported by farmers with the sales records of feed suppliers. Production data verification calculates the theoretical production range based on the number of seedlings, the breeding cycle, and feed consumption; a deviation of more than 20% in the actual declared production is marked as abnormal. Purchase data verification compares the shipment weight declared by farmers with the purchase weight entered by the purchaser. The multi-source data cross-validation unit calculates a comprehensive data credibility score based on the verification results.

[0024] The certification audit smart contract receives certification applications submitted by aquaculture enterprises and automatically executes the preliminary review rules: checking the integrity of the data chain, whether the credibility score reaches the threshold, whether the carbon footprint calculation results meet the certification conditions, and whether the applicant enterprise has any historical abnormal records. Applications that pass the preliminary review are pushed to the certification body node for manual review.

[0025] Upon successful review, the certificate issuance unit automatically generates an electronic certification certificate. The certificate includes a unique certificate number, product batch code, name of the aquaculture enterprise, certified net carbon sequestration, name of the certification body, issuance date, and validity period. The certificate is digitally signed using the certification body's private key via ECDSA, and the issued certificate information is written to the blockchain.

[0026] The system also includes a product batch coding system and a consumer query terminal. The product batch coding adopts a hierarchical structure design, with a total code length of 24 digits, consisting of five parts: region code, company code, year code, batch serial number, and check code. The product batch coding supports splitting and merging operations. When splitting, a sub-batch code is generated that inherits all historical data of the parent batch. When merging, a new code is generated and the weight percentage of each raw material batch is recorded.

[0027] The consumer query terminal is a mobile web application or mini-program. Consumers can scan the QR code on the carbon label on the product packaging to query complete carbon footprint information. The query terminal displays four sections: basic product information, detailed carbon footprint information, carbon sink certification information, and data verification information, and provides on-chain data verification functionality.

[0028] In summary, the blockchain-based aquatic product carbon footprint traceability and carbon sink certification system of this invention, compared with traditional technologies, solves the problem of off-chain data credibility through a multi-source data cross-validation mechanism, establishes a carbon sink quantification model for shellfish and algae using a conservative coefficient method and outputs the uncertainty range, and utilizes smart contracts to automate and make the carbon sink certification process transparent. This solves problems in traditional carbon footprint management such as data tampering, broken traceability chains, difficulty in quantifying carbon sinks, and lack of certification transparency. It enables full life-cycle carbon footprint traceability of aquatic products, supports credible carbon sink certification, and provides consumers with verifiable carbon label information.

[0029] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the overall architecture of the blockchain-based aquatic product carbon footprint traceability and carbon sink certification system of the present invention; Figure 2 This is a flowchart of the data acquisition and carbon footprint calculation process for this invention; Figure 3 This is a flowchart of the multi-source data cross-validation process of the present invention; Figure 4 This is a flowchart of the carbon sequestration certification process of this invention; Figure 5 This is a schematic diagram illustrating the splitting and merging of batch codes for the products of this invention; Figure 6 This is a schematic diagram of the consumer QR code query interface of the present invention. Detailed Implementation

[0031] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0032] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0033] Techniques, systems, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the instruction manual.

[0034] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0035] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0036] Example 1 like Figure 1 As shown in the figure, this embodiment describes the overall architecture of a blockchain-based aquatic product carbon footprint traceability and carbon sink certification system.

[0037] The system consists of four parts: a data acquisition module, a carbon footprint calculation module, a blockchain evidence storage module, and an authentication service module. The modules are connected through data interfaces and smart contract call interfaces to form a complete carbon footprint traceability closed loop.

[0038] The data acquisition module is located at the bottom layer of the system and includes an energy consumption acquisition unit, a feed data acquisition unit, a production data acquisition unit, and a logistics data acquisition unit. The energy consumption acquisition unit uses a smart meter compliant with the DL / T 645 communication protocol. The acquired parameters include active energy, reactive energy, and maximum demand. Maximum demand is a standard term in the power industry, referring to the maximum value among all demand values ​​obtained by calculating the average power consumption of users according to a fixed demand cycle within a preset settlement period, expressed in kW. This embodiment uses the Wasion Group DTSD341 model, operating at 380V, with an accuracy class of 1.0, and supporting RS485 communication. The meter is installed at the main incoming line of the farm's power distribution room and on branch circuits of major equipment such as aerators and water pumps, automatically collecting cumulative energy data every 15 minutes. The feed data acquisition unit supports two data acquisition methods: for farms equipped with automatic feeders, it connects to the feeder control system via RS485 bus or IoT communication module to obtain real-time data on feed type, weight, and time for each feeding; for farms without automatic feeders, farmers scan feed packaging barcodes and manually input the feeding weight via a mobile app. The yield acquisition unit acquires harvest data via a mobile app, where farmers input the harvest date, total product weight, shell weight percentage, or algae dry weight percentage. The system supports uploading photos of the harvest site. The logistics data acquisition unit connects to third-party logistics platforms such as SF Express Cold Chain and JD Logistics via API to obtain transportation trajectory, mileage, and vehicle type information.

[0039] The carbon footprint calculation module is deployed on a cloud server; this embodiment uses an Alibaba Cloud ECS instance with an 8-core CPU and 32GB of memory. This module includes an emission factor database, a carbon emission calculation engine, and a carbon sink quantification engine. The emission factor database is stored using MySQL 8.0 and contains emission factors for power grids in various provinces, carbon footprint coefficients for 23 common aquaculture feeds, emission factors for various transportation modes, and carbon footprint coefficients for commonly used packaging materials. The carbon emission calculation engine uses a life cycle assessment framework to calculate and aggregate carbon emissions at the aquaculture, processing, and logistics stages. The carbon sink quantification engine establishes separate calculation models for shellfish and algae. The shellfish carbon sink model is based on shell dry weight, calcium carbonate content, and effective sequestration coefficient, while the algae carbon sink model is based on algae dry weight and carbon content ratio. The system also uses Monte Carlo simulation to output the uncertainty range of the carbon footprint calculation results.

[0040] The blockchain evidence storage module is built on the FISCO BCOS 3.0 consortium blockchain platform, using the PBFT consensus mechanism with a 2-second block generation interval. This embodiment deploys four consensus nodes, operated by the Fujian Provincial Fisheries Technology Extension Station, the Chinese Academy of Fishery Sciences, a third-party certification body, and an industry association, respectively. An additional 12 ordinary nodes are operated by participating aquaculture, processing, and logistics companies. Each node uses the national cryptographic algorithm SM2 for identity authentication. The data storage smart contract receives the carbon footprint calculation results, generates a SHA-256 data digest, and writes it to the blockchain. The original data is stored off-chain in the IPFS distributed storage system and associated with a CID. The traceability query smart contract supports retrieving full-chain carbon footprint data based on product batch codes, supporting both forward and reverse tracing.

[0041] The authentication service module includes a multi-source data cross-validation unit, an authentication audit smart contract, and a certificate issuance unit. The multi-source data cross-validation unit interfaces with the State Grid's electricity consumption information collection system, the ERP systems of major feed suppliers, and the inventory management systems of aquatic product purchasers. It performs cross-validation from four dimensions: energy consumption data, feed data, production data, and purchase data, calculating a comprehensive data credibility score. The authentication audit smart contract, written in Solidity, automatically performs data integrity checks, credibility score checks, authentication condition checks, and enterprise qualification checks. Upon successful review, the certificate issuance unit automatically generates an electronic authentication certificate, which is digitally signed using the SM2 algorithm and then written to the blockchain.

[0042] The system also includes a consumer query terminal, developed as a WeChat mini-program. Consumers can scan the carbon label QR code on the product packaging to query complete carbon footprint information, certification details, and verify the consistency of the data with the blockchain evidence in real time.

[0043] The modules work together to form a complete business loop: farmers apply for product batch codes and store them on the blockchain when stocking seedlings; during the breeding process, each collection unit continuously uploads data; after harvest, the carbon footprint calculation module calculates carbon emissions and carbon sinks; the calculation results are stored on the blockchain; when enterprises apply for carbon sink certification, the system performs cross-validation and preliminary review; after the certification body completes the review, it automatically issues certificates; and consumers can scan the code to query complete information.

[0044] Example 2 like Figure 2 As shown, this embodiment illustrates the data acquisition and carbon footprint calculation process.

[0045] The complete process is divided into a data collection phase and a carbon footprint calculation phase, which are executed sequentially.

[0046] The data acquisition phase includes the following steps: Step S101: Batch Code Generation. When stocking seedlings, farmers initiate a batch code application via a mobile app, inputting basic information such as the species being raised, the number of seedlings, and the coordinates of the breeding area. The system automatically generates a 24-digit product batch code based on the region code, company code, year, and serial number. Immediately after the code is generated, the data storage smart contract is invoked to store the data on the blockchain, recording the code creation time and the creator's identity.

[0047] Step S102: Data collection during the breeding process. During the breeding period, the energy consumption collection unit automatically collects the power consumption data of each circuit every 15 minutes. The data format includes the device code, collection timestamp, and active power reading. The feed data collection unit collects the feed type, feeding weight, and feeding time after each feeding. Farms equipped with automatic feeders will automatically report the data through the feeder control system. Farms without automatic feeders will manually enter the data through the App. The data uploaded by each collection unit is first temporarily stored in the data aggregation server. A daily summary record is generated in batches every morning, and after calculating the data summary, it is uploaded to the blockchain for evidence storage.

[0048] Step S103, Harvest Data Collection. At the end of the aquaculture cycle, farmers enter harvest data through the App, including harvest date, total product weight, shell weight percentage (for shellfish aquaculture) or dry weight percentage (for algae aquaculture), and upload photos of the harvest site as supporting evidence. The system automatically adds time and GPS location watermarks to the photos.

[0049] Step S104, Acquisition Data Collection. When receiving goods, the buyer enters the actual weighing weight through the App. The system automatically links the buyer's data with the data declared by the farmers for subsequent cross-validation.

[0050] Step S105: Processing and Logistics Data Collection. After the product enters the processing stage, the processing plant reports processing energy consumption, water consumption, and packaging material usage. After entering the logistics stage, the system obtains transportation trajectory, mileage, and vehicle type data from the logistics platform via API interface. Data from each stage is linked to the product batch code and stored on the blockchain for verification.

[0051] The carbon footprint calculation phase includes the following steps: Step S201, Data Reading. The carbon footprint calculation module reads all collected data for the target batch from the data aggregation server according to the target batch code, including daily energy consumption data, feed feeding records, harvest data, processing data, and logistics data during the breeding period.

[0052] Step S202: Carbon emission calculation in segments. The carbon emission calculation engine calculates carbon emissions in three segments. Carbon emissions from the farming stage equal the total electricity consumption during the farming period multiplied by the annual grid emission factor of the province where the farming site is located, plus the cumulative value of the weight of each feed feeding multiplied by the carbon footprint coefficient of the corresponding feed type. Carbon emissions from the processing stage equal the electricity consumption during processing multiplied by the grid emission factor of the province where the processing plant is located, plus the amount of packaging materials used multiplied by the carbon footprint coefficient of the corresponding material. Carbon emissions from the logistics stage equal the transportation weight multiplied by the transportation mileage multiplied by the transportation emission factor of the corresponding vehicle type. The total carbon emissions of the product equal the sum of the carbon emissions from these three segments.

[0053] Step S203, carbon sequestration calculation. The carbon sequestration quantification engine calls the corresponding calculation model based on the product type. In this embodiment, oysters are used as an example. The calculation formula is: net carbon sequestration of shellfish equals the dry weight of the shells multiplied by 0.92 multiplied by 0.12 multiplied by 0.5, where the dry weight of the shells equals the total weight of the product multiplied by the shell weight percentage multiplied by 0.85, 0.92 is the mass percentage of calcium carbonate, 0.12 is the mass fraction of carbon, and 0.5 is the effective sequestration coefficient.

[0054] Step S204, Net Carbon Footprint Calculation. The net carbon footprint of a product equals the total carbon emissions of the product minus the carbon sink, divided by the net weight of the product, expressed in kilograms of CO2 equivalent per kilogram of product. If the result is negative, the product is marked as a negative carbon product.

[0055] Step S205, Uncertainty Assessment. The system sets a probability distribution for each input parameter, performs 1000 Monte Carlo random sampling calculations, and outputs the uncertainty range as the 95% confidence interval. The final output format is the center value plus or minus the uncertainty range.

[0056] Step S206, result notarization. The carbon footprint calculation result is called to the blockchain via a data notarization smart contract. The notarized data includes product batch code, carbon emission details at each stage, carbon sink amount, net carbon footprint value, uncertainty range, calculation engine version number, and calculation timestamp.

[0057] In this embodiment, the calculation results for a certain batch of oyster products (batch code 3501-A12345-2024-00000128-79) are as follows: electricity consumption during the aquaculture period was 672 kWh, with a grid emission factor of 0.4251 and energy consumption carbon emissions of 285.7 kg CO2 equivalent; feed consumption was 4850 kg, with a feed carbon footprint coefficient of 1.85, and feed carbon emissions are not included in this batch (already included in the energy consumption of the aquaculture process); processing energy consumption was 98 kWh, and 50 foam boxes (25 kg) of packaging materials were used, resulting in carbon emissions of 41.7 + 18.5 = 60.2 kg CO2 equivalent in the processing stage; the transportation distance was 120 km, with refrigerated trucks transporting 2 tons of goods, resulting in carbon emissions of 28.8 kg CO2 equivalent in the logistics stage; the total carbon emissions were 374.7 kg CO2 equivalent. The total product weight is 2050 kg, with shells accounting for 28% of the weight. The dry weight of the shells is 487.9 kg, and the carbon sink is 26.9 kg CO2 equivalent per kilogram of product… (Calculation error, recalculated): Carbon sink equals 487.9 multiplied by 0.92 multiplied by 0.12 multiplied by 0.5 equals 26.9 kg CO2 equivalent. Net carbon footprint equals 374.7 minus 26.9 equals 347.8 kg CO2 equivalent. Net carbon footprint per unit equals 347.8 divided by 2050 equals 0.170 kg CO2 equivalent per kilogram of product, making it a positive carbon product. To achieve negative carbon, the carbon sink needs to be increased or carbon emissions reduced.

[0058] Example 3 like Figure 3 As shown, this embodiment illustrates the multi-source data cross-validation process.

[0059] Multi-source data cross-validation is the core mechanism for solving the trustworthiness problem of off-chain data. It identifies anomalies and fraudulent activities by comparing similar data from multiple independent data sources. The validation process consists of three stages: data acquisition, four-dimensional validation, and score calculation.

[0060] Data Acquisition Phase: The system obtains verification benchmark data from four external data sources via API interfaces. Energy consumption benchmark data is obtained from the State Grid electricity information collection system, with the interface returning the monthly settlement electricity consumption data from the farm's electricity meter. Feed benchmark data is obtained from the ERP systems of major feed suppliers, with the interface returning detailed historical purchase records from the farmers. Production benchmark data is calculated using the system's built-in production prediction model, with input variables including stocking quantity, number of rearing days, cumulative feed quantity, and average water temperature. Purchase benchmark data is obtained from records entered by purchasers within the system.

[0061] The four-dimensional verification phase will execute the following steps in sequence: Step S301, Energy Consumption Data Verification. Read the monthly cumulative electricity consumption reported by farmers through smart meters, compare it with the electricity consumption settled by the State Grid, and calculate the deviation rate. The deviation rate is equal to the absolute value of the reported electricity consumption minus the State Grid electricity consumption, divided by the State Grid electricity consumption. The judgment rules are as follows: a deviation rate of less than or equal to 5% is considered normal, scoring 30 points; a deviation rate of 5% to 10% is considered slightly abnormal, scoring 20 points; a deviation rate greater than 10% is considered seriously abnormal, scoring 0 points and marked as requiring manual verification.

[0062] Step S302, Feed Data Verification. Read the cumulative feed consumption reported by farmers and compare it with the cumulative purchase volume obtained from suppliers. The judgment rule is: consumption less than or equal to purchase volume is considered normal, scoring 25 points; consumption exceeding 105% of purchase volume is considered abnormal, scoring 0 points. For cases where supplier data cannot be obtained, farmers are required to upload purchase invoices. The system will recognize and compare the invoices using OCR; if the recognition confidence is below 90%, deduct 5 points.

[0063] Step S303, Production Reasonableness Verification. The system calls the production prediction model to calculate the theoretical production range. The model uses multiple linear regression, trained on historical data, and achieves an R-squared of 0.85. The actual production reported by farmers is read, and it is determined whether it falls within the range of 80% to 120% of the theoretical production. The judgment rule is: falling within the range is considered normal, scoring 20 points; exceeding the range is considered abnormal, scoring 5 points.

[0064] Step S304, Verification of Acquisition Data. Read the shipment weight declared by the farmers and the purchase weight entered by the buyers, and calculate the deviation rate. The judgment rules are as follows: a deviation rate of less than or equal to 3% is considered normal and scores 25 points; a deviation rate of 3% to 10% is considered slightly abnormal and scores 15 points; a deviation rate of more than 10% is considered seriously abnormal and scores 0 points and is marked as requiring manual verification.

[0065] Scoring Calculation Stage: The overall credibility score is the sum of the scores in the four dimensions, with a maximum score of 100. A score of 70 or above is required to apply for carbon sequestration certification; scores between 60 and 70 require supplementary materials for re-verification; and scores below 60 result in direct rejection of the certification application.

[0066] In this embodiment, the cross-validation results for a certain batch of oyster products are as follows: reported energy consumption data: 2016 kWh; State Grid settlement value: 1952 kWh; deviation rate: 3.3%, deemed normal, score: 30 points; cumulative feed consumption: 4850 kg; cumulative purchase: 5200 kg; consumption is less than purchase, deemed normal, score: 25 points; theoretical yield range: 1850-2280 kg; actual declared yield: 2050 kg, falling within the range, deemed normal, score: 20 points; shipment weight: 2050 kg; purchase weight: 2015 kg; deviation rate: 1.7%, deemed normal, score: 25 points. The overall credibility score is 100 points, qualifying the product for carbon sequestration certification.

[0067] Example 4 like Figure 4 As shown, this embodiment illustrates the carbon sequestration certification process.

[0068] The carbon sink certification process is divided into four stages: application submission, smart contract preliminary review, certification body review, and certificate issuance. The entire process is recorded on the blockchain to ensure transparency and traceability.

[0069] Application Submission Stage: Aquaculture enterprises submit carbon sink certification applications through the web-based management backend or mobile app. Step S401: Enterprises select the batch code of the product to be certified, choose the certification type (negative carbon product certification requires a negative net carbon footprint, low carbon product certification requires a net carbon footprint lower than the industry benchmark), and fill in the enterprise contact information. Step S402: The system automatically attaches the carbon footprint calculation report, multi-source cross-validation report, and all original data storage certificates for that batch. Step S403: Enterprises upload scanned copies of their business license, aquaculture permit for water areas and tidal flats, and pollution-free production area certification, among other qualification documents. Step S404: After the enterprise confirms submission, the system generates an application number, the application status changes to "Pending Preliminary Review," and the application record is uploaded to the blockchain via a smart contract for storage.

[0070] Smart Contract Preliminary Review Stage: The smart contract automatically executes the preliminary review rules without manual intervention. Step S405: Data Integrity Check. The smart contract iterates through the data storage records for this batch from seedling placement to harvest, confirming that key nodes (seedling registration, harvest registration, and purchase registration) are all documented and that there are no gaps exceeding 7 days. Step S406: Credibility Score Check. The comprehensive score from multi-source cross-validation is read, confirming that it is not lower than the preliminary review threshold of 70 points. Step S407: Certification Condition Check. The carbon footprint calculation result is read. If applying for negative carbon product certification, the net carbon footprint value is confirmed to be negative; if applying for low carbon product certification, the net carbon footprint value is confirmed to be less than 0.5 kg CO2 equivalent per kilogram of product. Step S408: Enterprise Qualification Check. The enterprise's historical certification records are checked, confirming that there are no records of certificates being revoked due to data falsification and that the enterprise is not on a blacklist. Once all four checks are passed, the application status changes to "Pending Review," and the system pushes the review task to the certification body node. If any check fails, the application status changes to "Initial Review Returned," and the system returns the specific reason for the failure. The initial review result is stored on the blockchain as evidence.

[0071] Certification Body Recertification Stage: Step S409: The certification body auditor logs into the certification workbench, views the list of applications awaiting recertification, and clicks to enter the details page. Step S410: The auditor reviews the application materials, including carbon footprint calculation details, cross-validation details, blockchain-based evidence records, and enterprise qualification documents. Raw data can be downloaded for independent accounting and verification. Step S411: If the auditor deems the materials insufficient, they can request supplementary materials. The system notifies the enterprise to supplement the materials, and the supplementary materials are also uploaded to the blockchain for evidence storage. Step S412: If the auditor deems on-site verification necessary, they can initiate a verification application. Verification personnel visit the breeding site to inspect the equipment and records. The verification results are entered into the system using a structured form and uploaded to the blockchain. Step S413: After completing the audit, the auditor fills in the audit comments, selecting "Pass" or "Fail" and explaining the reasons. The audit comments, auditor employee number, and operation time are uploaded to the blockchain for evidence storage.

[0072] Certificate Issuance Stage: Step S414: After the review is passed, the certificate issuance unit automatically generates an electronic certification certificate. The certificate content includes the certificate number (format: CSH-year-6-digit serial number), product batch code, name of the aquaculture enterprise and unified social credit code, product variety, certified net carbon sink or net carbon footprint value, certification type, name of the certification body, auditor number, issuance date, and a validity period of 12 months. Step S415: The certificate is digitally signed using the certification body's SM2 private key, with the signature covering all fields of the certificate content. Step S416: The signed certificate information is written to the blockchain via a smart contract, indexed and associated with the product batch code, and the certificate status is set to "valid". Step S417: The system generates a printable PDF certificate and a QR code electronic certificate for the enterprise to download.

[0073] Certificate lifecycle management: Once a certificate expires, its status automatically changes to "expired," and the enterprise can apply for renewal. If falsified certification data is discovered, the certification body can initiate a revocation process. After multi-signature approval by the consortium blockchain management committee, the certificate status changes to "revoked," and the revocation record is stored on the blockchain as evidence.

[0074] In this embodiment, the entire process of an oyster farming enterprise applying for low-carbon product certification takes 5 working days: Day 1: Application is submitted and passes the initial review via smart contract; Days 2-3: Certification body auditors review the materials; Day 4: Auditors request supplementary calibration certificates for aerator meters, which the enterprise uploads on the same day; Day 5: Auditors pass the final review, and the system automatically issues certificate number CSH-2024-000058.

[0075] Example 5 like Figure 5 As shown in the figure, this embodiment explains the mechanism for splitting and merging product batch codes.

[0076] Product batch codes are the core identifiers of the traceability system, and they need to handle scenarios of product splitting and merging during the actual circulation process.

[0077] Batch coding structure: The total code length is 24 characters, in the format PPPP-EEEEEE-YYYY-SSSSSSSS-CC. PPPP is a 4-digit regional code, using the first four digits of the national standard administrative division code; EEEEEE is a 6-digit enterprise code, taking the 9th to 14th digits of the unified social credit code; YYYY is a 4-digit year; SSSSSSSS is an 8-digit serial number; CC is a 2-digit modulo 97 check digit. Example code: 3501-A12345-2024-00000128-79.

[0078] Batch Splitting Process: When the same batch of farmed products is sold through multiple channels, a splitting operation is performed. Step S501: Farmers select the parent batch code to be split in the App. Step S502: Input the splitting details, including the destination (buyer's name) and weight of each sub-batch. Step S503: The system verifies whether the sum of the split weights equals the total weight of the parent batch, with an allowable error range of ±2%. Step S504: The system generates a code for each sub-batch, in the format of the parent batch code plus a 2-digit splitting sequence number, such as 3501-A12345-2024-00000128-79-01. Step S505: The system calculates the carbon footprint value inherited by each sub-batch according to the weight ratio, using the formula: sub-batch carbon emissions equal parent batch carbon emissions multiplied by the sub-batch weight divided by the total weight of the parent batch; carbon sequestration is calculated similarly. Step S506: The splitting operation record is uploaded to the blockchain for evidence storage, including the parent batch code, a list of sub-batch codes, the splitting ratio, the operation time, and the operator.

[0079] This example illustrates a scenario where a fish farmer harvests 2000 kg of oysters (parent batch 3501-A12345-2024-00000128-79), resulting in carbon emissions of 300 kg CO2 equivalent and carbon sinks of 180 kg CO2 equivalent, leading to a net carbon footprint of 0.06 kg CO2 equivalent per kg of product. The harvest is split into two sub-batches: 1200 kg sold to processing plant A and 800 kg sold to processing plant B. Sub-batchuse-01 inherits 180 kg of carbon emissions and 108 kg of carbon sinks; sub-batchuse-02 inherits 120 kg of carbon emissions and 72 kg of carbon sinks. The net carbon footprint per unit for both sub-batches remains the same as the parent batch, at 0.06.

[0080] Batch Consolidation Process: When a processing plant uses multiple batches of raw materials from livestock farming to process the same batch of product, a consolidation operation is performed. Step S507: The processing plant initiates a consolidation operation in the system, inputting the batch codes and corresponding weights of each raw material. Step S508: The system verifies that each raw material batch is valid and not referenced by other processing batches. Step S509: The system generates a new processing batch code, with the enterprise code being the processing plant code. Step S510: The system establishes the association between the new batch and each raw material batch, recording the weight percentage of each raw material batch. Step S511: The system calculates the inherited raw material carbon footprint based on weight percentage, using the formula: Inherited carbon footprint equals the sum of the unit carbon footprint of each raw material batch multiplied by its corresponding weight percentage. Step S512: New carbon emissions from the processing stage are calculated separately and added to the inherited carbon footprint. Step S513: The consolidation operation record is uploaded to the blockchain for evidence storage.

[0081] This embodiment combines the following scenario: A processing plant purchases three batches of farmed oysters: Batch A weighs 1000 kg with a net carbon footprint of 0.05 per unit; Batch B weighs 1500 kg with a net carbon footprint of 0.08 per unit; and Batch C weighs 500 kg with a net carbon footprint of 0.03 per unit. These are mixed and processed into ready-to-eat oyster products. The inherited raw material carbon footprint is equal to 0.05 multiplied by 1000 divided by 3000 plus 0.08 multiplied by 1500 divided by 3000 plus 0.03 multiplied by 500 divided by 3000, which equals 0.0167 plus 0.04 plus 0.005 equals 0.0617 kg of carbon dioxide equivalent per kilogram of raw material. The processing stage adds 50 kg of carbon dioxide equivalent to the raw material's carbon emissions, producing 2800 kg of finished product. The carbon emission per unit of the processing stage is equal to 50 divided by 2800, which equals 0.018. The final unit net carbon footprint of the processed batch is equal to 0.0617 plus 0.018, which equals 0.0797 kg of carbon dioxide equivalent per kilogram of product.

[0082] Traceability query support: When consumers or regulatory agencies query processing batches, the traceability query smart contract recursively retrieves the associated raw material batches, returns the complete upstream supply chain path, and displays the breeding enterprise, breeding location, carbon footprint data and weight percentage of each raw material batch.

[0083] Example 6 like Figure 6 As shown, this embodiment describes the interface for consumers to scan a code to query information.

[0084] The consumer query terminal is developed in the form of a WeChat mini program. Consumers can scan the carbon label QR code on the product packaging to query the complete carbon footprint information.

[0085] Carbon label QR code specifications: The QR code size is no less than 15 mm x 15 mm, with an error correction level of L. The encoded content is in URL format and includes the product batch code, an 8-digit random query key, and a 16-digit HMAC anti-counterfeiting verification code. The QR code is printed in a prominent position on the product's outer packaging and uses water-resistant ink to ensure it does not fade during cold chain transportation.

[0086] Query Process: Step S601: Consumers use WeChat's scan function to scan the carbon label QR code. Step S602: WeChat recognizes the URL and redirects to the carbon footprint query mini-program. Step S603: The mini-program parses the URL to extract the batch code, query key, and anti-counterfeiting verification code. It performs an HMAC-SHA256 operation on the batch code and query key using the system key, comparing the result with the anti-counterfeiting verification code in the URL. If they do not match, the query is terminated with an invalid QR code message. Step S604: After successful verification, the mini-program sends a query request to the backend server. The backend calls the blockchain traceability query smart contract to obtain all stored data for that batch. Step S605: The mini-program renders the query results page and displays it to the consumer.

[0087] The search results page is divided into four sections: The basic product information section is located at the top of the page, displaying the name of the aquaculture company, the aquaculture location (with map thumbnails indicating the location), the aquaculture species, the start and end dates and days of the aquaculture cycle, and the product specifications and net weight. This example shows: XX Aquaculture Cooperative, XX County, XX Province, XX Sea Area, Pacific oysters, from March 15th to September 28th, 2024 (197 days), 500 grams per bag.

[0088] The carbon footprint details section is located in the center of the page, using visual charts to illustrate its composition. A pie chart shows the percentage of carbon emissions from each stage, with sectors representing five parts: aquaculture energy consumption, feed production, processing energy consumption, packaging materials, and transportation and distribution. Clicking on a sector displays its specific value. The bar chart below shows the total carbon emissions in gray on the left and the carbon credits in green on the right (only displayed for shellfish and algae products). The text at the bottom displays the net carbon footprint value, formatted as "Net carbon footprint per kilogram of product: X.XX kg CO2 equivalent." Negative values ​​are displayed in green and labeled "Negative Carbon Product." The range of uncertainty is also indicated in small gray text.

[0089] The carbon sink certification section is located below the carbon footprint details. If the batch has been certified, the certification certificate card will be displayed, including the certificate number, certification type, certification body name and logo, issuance date, validity period, and current status (valid is shown in green, expired in gray, and revoked in red). Clicking "View Certificate Details" will expand to display the complete certificate information, and clicking "Download Certificate" will save the PDF file. If the batch has not been certified, it will display "This batch of products has not yet applied for carbon sink certification."

[0090] The data verification section is located at the bottom of the page. It displays the blockchain notarization information for this batch of data, including the notarization time, block height, and transaction hash (showing the first 8 and last 8 digits, omitting the middle ones). A "Verify Data Authenticity" button is provided. After the consumer clicks it, the mini-program connects to the blockchain node in real time to read the on-chain notarized data digest, and simultaneously recalculates the SHA-256 digest of the currently displayed data, comparing the two digest values. If the comparison matches, a green checkmark icon and the text "Data verification passed, consistent with blockchain notarization" are displayed; if the comparison does not match, a red warning icon and the text "Data may have been tampered with, please refer to it with caution" are displayed.

[0091] Anomaly monitoring function: The backend system records the timestamp, IP address, and location city of each scan query. When the same batch of codes is queried by more than 3 IPs from different cities within 24 hours, the system triggers an anomaly warning, determining that the carbon label may have been copied and misused, and pushes the warning information to the production enterprise administrator and the market supervision interface.

[0092] In this example, a consumer purchases a bag of oysters at a supermarket. After scanning the carbon label QR code on the packaging, the mini-program completes the query and displays the results page within 2.3 seconds. The consumer learns that the oysters come from a specific sea area, have a 197-day farming cycle, and have a net carbon footprint of 0.06 kg CO2 equivalent per kilogram of product. They have obtained a valid low-carbon product certification issued by the China Quality Certification Center. The consumer clicks the data verification button, and the mini-program displays that the data verification is successful, enhancing the consumer's trust in the product's carbon footprint information.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical methods of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical methods to deviate from the spirit and scope of the technical methods of the present invention.

Claims

1. A blockchain-based aquatic product carbon footprint traceability and carbon sink certification system, characterized in that, It includes a data acquisition module, a carbon footprint calculation module, a blockchain evidence storage module, and an authentication service module; The data acquisition module is used to acquire electricity consumption data of aquaculture equipment, feed consumption data, product harvest data, transportation trajectory and mileage data. The data acquisition module is connected to the carbon footprint calculation module through a data interface. The carbon footprint calculation module includes an emission factor database, a carbon emission calculation engine, and a carbon sink quantification engine. The emission factor database stores grid emission factors, feed carbon footprint coefficients, and transportation emission factors; the carbon emission calculation engine uses the energy consumption data, feed data, and logistics data input from the data acquisition module to call the corresponding grid emission factors, feed carbon footprint coefficients, and transportation emission factors in the emission factor database to calculate the carbon emissions at each stage; the carbon sink quantification engine calculates the carbon sink contribution of shellfish or algae farming based on product type and production data. The carbon footprint calculation module and the blockchain evidence storage module are connected via a data interface; The blockchain evidence storage module includes a consortium blockchain network, data evidence storage smart contracts, and traceability query smart contracts; The consortium blockchain network consists of nodes from breeding enterprises, processing enterprises, logistics enterprises, and certification authorities. The data storage smart contract receives the calculation results output by the carbon footprint calculation module and generates a data digest, which is then written to the blockchain. The calculation results are the full-chain carbon footprint data, including carbon emissions, carbon sinks, net carbon footprint value, and storage data calculated by the carbon footprint calculation module. The traceability query smart contract retrieves and returns the full-chain carbon footprint data based on the product batch code. The blockchain evidence storage module and the authentication service module are connected via a smart contract API call. The authentication service module includes a multi-source data cross-validation unit, an authentication audit smart contract, and a certificate issuance unit; The multi-source data cross-validation unit compares and verifies the data reported by farmers with data from power companies, feed suppliers, and buyers, and outputs a data credibility score. The certification audit smart contract performs an automatic preliminary review based on the data integrity and credibility score, and pushes applications that pass the preliminary review to the certification body node for manual review. After the review is passed, the certificate issuance unit automatically generates an electronic certification certificate containing the product batch code, net carbon sink, certification body signature, and validity period, and writes the certificate information into the blockchain.

2. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 1, characterized in that, The data acquisition module includes an energy consumption acquisition unit, a feed data acquisition unit, a production output acquisition unit, and a logistics data acquisition unit; The energy consumption acquisition unit obtains electricity consumption data of the aquaculture equipment through smart meters; The feed data acquisition unit obtains feed consumption data through the automatic feeder data interface or a mobile input terminal. The production data acquisition unit obtains product harvest data through a mobile input terminal. The logistics data acquisition unit obtains transportation trajectory and mileage data through the logistics platform interface, and each acquisition unit is connected to the carbon footprint calculation module through a data interface.

3. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 2, characterized in that, The energy consumption acquisition unit adopts a smart meter that conforms to the DL / T 645 communication protocol and supports RS485 or wireless communication. The acquisition parameters include active energy, reactive energy and maximum demand. Smart meters are installed in the main power distribution cabinet or branch circuits of the farm to measure the electricity consumption of the main power-consuming equipment. The parameters collected by the smart meter are uploaded to the data aggregation server of the data acquisition module after being accompanied by the device's unique code and the collection timestamp. The data acquisition methods of the feed data acquisition unit include acquisition via the automatic feeder data interface and acquisition via a mobile input terminal. The production data acquisition unit obtains product harvest data through a mobile terminal. The parameters of the product harvest data include harvest date, total product weight, shell weight percentage or algae dry weight percentage. When receiving the goods, the buyer enters the actual weighing weight through the mobile terminal, and the system automatically compares the data entered by the buyer with the data declared by the farmer. The logistics data acquisition unit connects with a third-party logistics platform via an API interface to obtain the origin coordinates, destination coordinates, transportation mileage, vehicle type, and transportation time of transportation orders. For shipments not connected to a logistics platform, the shipper must manually enter the mode of transport, origin and destination, and estimated mileage via a mobile input terminal. In cold chain transportation scenarios, the logistics data acquisition unit simultaneously acquires temperature curve data from the vehicle-mounted temperature control recorder, with a acquisition frequency of no less than once every 5 minutes.

4. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 3, characterized in that, The emission factor database adopts a relational database storage structure and includes a power grid emission factor table, a feed carbon footprint coefficient table, a transportation emission factor table, and a packaging material carbon footprint coefficient table. The power grid emission factor table stores the annual average power grid emission factor for each provincial-level administrative region. The feed carbon footprint coefficient table stores the carbon footprint coefficients of various aquaculture feeds throughout their entire life cycle, categorized by the main raw material components of the feeds; The transport emission factor table stores carbon emission coefficients per unit mileage, categorized by vehicle type and fuel type. The emission factor database supports version management. Each update generates a new version number and records the update time, and historical version data is permanently retained. The carbon emission calculation engine adopts a life cycle assessment framework to decompose product carbon emissions into three parts: emissions from the breeding process, emissions from the processing process, and emissions from the logistics process, and calculates them in segments. Emissions during the aquaculture process equal the total electricity consumption during the aquaculture period multiplied by the power grid emission factor of the aquaculture farm's location, plus the total feed consumption multiplied by the carbon footprint coefficient of the corresponding feed type. Emissions during the processing stage equal the electricity consumption during processing multiplied by the emission factor of the power grid where the processing plant is located, plus the amount of packaging materials used multiplied by the carbon footprint coefficient of the corresponding materials; Emissions in the logistics process are equal to the transport weight multiplied by the transport distance multiplied by the transport emission factor corresponding to the vehicle type; The total carbon emissions of a product equal to the emissions from the farming process plus the emissions from the processing process plus the emissions from the logistics process. The carbon sequestration quantification engine establishes carbon sequestration calculation models for shellfish farming and algae farming respectively; The calculation formula for the shellfish carbon sequestration calculation model is as follows: ； Among them, the mass percentage of calcium carbonate ranges from 0.90 to 0.95, the mass fraction of carbon is 0.12, and the effective sequestration coefficient is 0.5 to reflect the release of carbon dioxide and scientific uncertainty during the shell formation process; The calculation formula for the algal carbon sink calculation model is as follows: ； The carbon content ratio ranges from 0.25 to 0.35 depending on the algae species. The carbon sequestration quantification engine also outputs the uncertainty range of the calculation results. The uncertainty coefficient is determined by combining the confidence score of the original data and the confidence interval of the model parameters. The carbon footprint calculation module ultimately outputs the product's net carbon footprint value, calculated using the following formula: ； The unit of net carbon footprint per unit product is kilograms of carbon dioxide equivalent per kilogram of product; when the carbon sink is greater than the total carbon emissions, the net carbon footprint value is negative, and the system marks the batch of products as a negative carbon product.

5. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 4, characterized in that, The consortium blockchain network adopts a practical Byzantine fault-tolerant consensus mechanism, and the network nodes are divided into two categories: consensus nodes and ordinary nodes. Consensus nodes are operated by certification authorities, industry associations, and government regulatory departments, and there are four or more of them. They are responsible for transaction verification and block generation. Ordinary nodes are operated by breeding enterprises, processing enterprises and logistics enterprises. They have the right to upload data to the blockchain and query data, but do not participate in the consensus process. Each node authenticates its identity using a digital certificate, which is uniformly issued by the consortium blockchain management organization. The certificate content includes the node name, organization code, public key, and validity period. The data storage smart contract enables the on-chain solidification of carbon footprint data; The data storage smart contract receives the calculation results output by the carbon footprint calculation module, performs format verification and integrity checks on the input data, generates a data summary, and writes it to the blockchain. The evidence storage data structure includes product batch code, carbon emissions in the breeding process, carbon emissions in the processing process, carbon emissions in the logistics process, carbon sink, net carbon footprint value, data credibility score, calculation engine version number, evidence storage timestamp, and upload node signature. The data digest is generated using the SHA-256 hash algorithm, and the original data is stored in an off-chain distributed database and associated with the hash value. Each notarization transaction generates a unique transaction hash value as a notarization certificate, and the notarization certificate is indexed and associated with the product batch code. The traceability query smart contract enables end-to-end retrieval of carbon footprint data. It receives product batch codes as input and retrieves all documented records for each stage of the product's lifecycle, from breeding to sales. For product batches with upstream and downstream connections, the smart contract recursively retrieves the documented records of related batches, constructing a complete product flow diagram. The query results include carbon emission details, carbon sink contribution, responsible party, documented time, and documented certificates for each stage. The smart contract supports filtering queries based on time range, stage type, responsible party, and other criteria. The query operation does not generate on-chain transactions; it only reads on-chain data and does not consume consensus resources.

6. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 5, characterized in that, The multi-source data cross-validation unit performs multi-dimensional credibility verification on the data reported by farmers; Energy consumption data verification compares the electricity consumption reported by farmers through smart meters with the settlement data of the power company's metering system. If the deviation rate exceeds 5%, it is marked as abnormal. Feed data verification compares the feed consumption reported by farmers with the delivery records of feed suppliers' sales systems. When the cumulative consumption exceeds the cumulative purchase amount, it is marked as abnormal. Production data verification calculates the theoretical production range based on the number of seedlings, the breeding cycle, and feed consumption. If the actual reported production deviates from the theoretical range by more than 20%, it is marked as abnormal. The acquisition data verification compares the shipment weight declared by farmers with the purchase weight entered by buyers, and marks it as abnormal when the deviation rate exceeds 3%. The multi-source data cross-validation unit calculates a comprehensive data credibility score based on the validation results of each item. The score is based on a 100-point scale, with each validation item calculated according to its weight. Items that pass the validation receive full marks, while outliers are deducted points according to the degree of deviation. The certification review smart contract enables automatic preliminary review of carbon sink certification applications; The certificate issuance unit automatically generates an electronic certificate after the certification body completes the manual review.

7. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 6, characterized in that, The product batch code adopts a hierarchical structure design with a total code length of 24 digits, consisting of five parts: region code, enterprise code, year code, batch serial number, and check code. The region code occupies 4 digits, using the first four digits of the national standard administrative division code to represent the province or municipality; The enterprise code occupies 6 digits, which are the last six digits of the enterprise's unified social credit code; the year code occupies 4 digits, indicating the year of seedling placement; The batch serial number occupies 8 digits and is automatically generated by the system according to the batch order of the enterprise in that year; the check code occupies 2 digits and is generated using the modulo-97 check algorithm to detect errors in encoding transmission and input. The product batch code is generated for the first time during the seedling stage. The generation time is when the farmer submits the seedling record through the mobile terminal. The system automatically assigns the code and puts the code creation record on the blockchain for evidence storage. The evidence storage content includes the code, creation time, creator and farm geographical coordinates. The product batch coding supports splitting and merging operations to adapt to actual production and circulation scenarios. Batch splitting is suitable for scenarios where the same batch of products is sold multiple times or through multiple channels. When splitting, the system generates a sub-batch code, which adds a 2-digit splitting sequence suffix to the parent batch code. The sub-batch code inherits all historical data of the parent batch code and records the splitting ratio. The splitting operation is stored on the blockchain for evidence. Batch merging is suitable for scenarios where multiple batches of raw materials from different farms are processed together. When merging, the system generates a new processing batch code, which is associated with all raw material batch codes and records the weight percentage of each raw material batch. Carbon footprint data is inherited from each raw material batch according to the weight percentage. The merging operation is stored on the blockchain for evidence. The product batch code is linked to the physical product through a carrier identifier. The number of carrier identifiers issued is registered and verified with the actual number of products in the system to prevent over-issuance and theft of identifiers.

8. The blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 7, characterized in that, The uncertainty range output by the carbon footprint calculation module is calculated using a combination of Monte Carlo simulation and analytical transfer method. Uncertainty sources can be categorized into two types: data uncertainty and model uncertainty. Data uncertainty stems from measurement and input errors in the original collected data. The uncertainty coefficient of energy consumption data collected by smart meters is ±2%, the uncertainty coefficient of feed data collected by automatic feeders is ±3%, the uncertainty coefficient of manually entered production data is ±5%, and the uncertainty coefficient of mileage data obtained by logistics platforms is ±10%. The model uncertainty stems from the range of values ​​for emission factors and carbon sink calculation parameters. The uncertainty coefficient for the power grid emission factor is ±5%, the uncertainty coefficient for the feed carbon footprint coefficient is ±15%, and the uncertainty coefficient for the effective sequestration coefficient of shellfish carbon sink is ±20%.

9. A blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 8, characterized in that, The calculation process for carbon footprint uncertainty is as follows: First, a probability distribution is set for each input parameter. The measurement data adopts a normal distribution, while the emission factor and carbon sink coefficient adopt a triangular distribution or a uniform distribution. Then, the Monte Carlo simulation method was used to perform more than 1,000 random sampling calculations to obtain the probability distribution of the carbon footprint calculation results; Finally, the upper and lower boundaries of the 95% confidence interval are taken as the uncertainty range for output. The data uncertainty coefficient is dynamically adjusted according to the data source method; if the same parameter is cross-validated with multiple data sources and the validation is successful, the uncertainty coefficient of that parameter is reduced by 50%. If the data comes solely from manual input and is not verified by other data sources, the uncertainty coefficient of this parameter increases by 50%. The overall credibility score output by the multi-source data cross-validation unit serves as an overall adjustment factor for data uncertainty. For every 10-point decrease in the credibility score, the overall data uncertainty coefficient increases by 10%.

10. A blockchain-based aquatic product carbon footprint traceability and carbon sink certification system according to claim 9, characterized in that, The authentication service module also includes a consumer query terminal and a carbon label generation unit; The carbon label generation unit automatically generates a carbon label after the product has completed certification or carbon footprint calculation. The query terminal displays four sections: basic product information, carbon footprint details, carbon sink certification information, and data verification information. The data verification information section provides on-chain verification functionality. After the user clicks the verification button, the query terminal reads the evidence records from the blockchain in real time and compares the hash value of the on-chain data with the hash value of the displayed data. If the comparison matches, the data verification is successful; if the comparison does not match, the data may have been tampered with, and the user is advised to refer to it with caution. The consumer query terminal supports query record statistics. The system records the number of times each carbon tag is queried, the query time, and the query geographical location. When the same carbon tag is queried by multiple different geographical locations in a short period of time, an abnormal warning is triggered, indicating that the carbon tag may be copied and misused.

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