A Blockchain-Based Evidence Storage and Traceability Method Driven by Big Data on Marine Carbon Sequestration

Abstract

This invention discloses a blockchain-based evidence storage and traceability method driven by marine carbon sink big data. The method includes dynamically dividing monitoring data of the blue carbon ecosystem, storing the resulting data blocks in parallel on a storage sidechain, and generating a spatiotemporal index matrix corresponding to each data block; calculating the carbon sink equivalent and carbon sink potential value of each data block; generating carbon sink digital certificates and associated NFT identifiers based on the carbon sink equivalent and carbon sink potential value; verifying the transaction participants, the carbon sink digital certificates to be traded, and the NFT identifiers in response to carbon sink transaction requests; synchronously updating the main chain, transaction sidechain, and regulatory sidechain based on the carbon sink transaction data if the transaction verification result is successful; and querying the traceability results of the carbon sink digital certificates to be queried on the main chain in response to carbon sink query requests. This method improves the efficiency of blockchain-based evidence storage and traceability.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to a blockchain-based evidence storage and traceability method driven by marine carbon sink big data. Background Technology

[0002] With the increasing demand for addressing global climate change, the monitoring, accounting, and trading management of marine carbon sinks, as an important carbon sink system on Earth, have become crucial links in achieving environmental protection goals. Among these technologies, blockchain, with its decentralized storage, immutability, and traceability, is being gradually applied to the field of marine carbon sinks to solve core issues such as data authenticity verification, asset ownership confirmation, and transaction transparency.

[0003] However, existing blockchain technology is a single-chain structure. With carbon sink data storage, asset trading, and regulatory auditing functions concentrated on the same chain and lacking a collaborative mechanism, data storage and traceability require manual integration of multi-source data across systems, leading to inefficiency and a high error rate. For example, verifying the complete chain of a carbon sink transaction requires sequentially obtaining corresponding data from the data storage platform, trading platform, and regulatory system, and then manually matching timestamps for cross-verification, which is time-consuming and has a high error rate.

[0004] Therefore, how to improve existing blockchain evidence storage and traceability methods to enhance their efficiency has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0005] This invention provides a blockchain-based evidence storage and traceability method driven by marine carbon sink big data, in order to solve the technical problem of how to improve existing blockchain evidence storage and traceability methods and achieve the effect of improving the efficiency of blockchain evidence storage and traceability.

[0006] To address the aforementioned technical problems, this invention provides a blockchain-based evidence storage and traceability method driven by marine carbon sink big data, applied to a multi-chain architecture blockchain system. The multi-chain architecture includes a main chain, an evidence storage sidechain, a transaction sidechain, and a regulatory sidechain. The method includes the following steps:

[0007] The monitoring data of the blue carbon ecosystem is dynamically divided, and the division results are modeled based on the directed acyclic graph structure. The data blocks obtained by the modeling are stored in parallel on the evidence storage sidechain, and a spatiotemporal index matrix corresponding to the data blocks is generated at the same time.

[0008] The carbon sink accounting model of the main chain is invoked to calculate the carbon sink equivalent of the data block, and the carbon sink prediction model of the main chain is invoked to calculate the carbon sink potential value of the data block; a carbon sink digital certificate and an associated NFT identifier are generated based on the carbon sink equivalent and the carbon sink potential value.

[0009] In response to a carbon sink trading request, the regulatory sidechain is used to verify the compliance of the trading participants, resulting in a first verification result.

[0010] Based on the spatiotemporal index matrix, locate the target data block in the evidence storage sidechain corresponding to the carbon sink digital certificate to be traded, and compare the hash value of the carbon sink digital certificate to be traded with the target data block to obtain the second verification result;

[0011] Based on the transaction sidechain, the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded is verified to obtain a third verification result;

[0012] If the first verification result, the second verification result, and the third verification result are all passed, then the main chain, the trading side chain, and the regulatory side chain are synchronously updated based on the obtained carbon sink trading data.

[0013] In response to a carbon sink query request, the traceability results of the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain are queried are retrieved.

[0014] As one preferred embodiment, the monitoring data of the blue carbon ecosystem is dynamically partitioned, the partitioning results are modeled based on a directed acyclic graph structure, and the modeled data blocks are stored in parallel on the evidence storage sidechain, including:

[0015] The raw monitoring data of the blue carbon ecosystem were sequentially subjected to outlier filtering and spatiotemporal alignment to obtain a spatiotemporally aligned dataset.

[0016] The spatiotemporal aligned dataset is divided into blocks based on a preset geographic grid and time window to generate data blocks that contain at least spatial data, temporal data, and feature vectors.

[0017] A metadata tag corresponding to each data block is generated, and the data block and the metadata tag are encapsulated into a data file package; the metadata tag includes the acquisition time, geographical location, and sensor number;

[0018] The data file package is hashed, and the data file package is stored in the evidence storage sidechain in segments according to geographic grid ID and timestamp. At the same time, the metadata tag and the calculated hash value are synchronized to the main chain.

[0019] As one preferred embodiment, generating the spatiotemporal index matrix corresponding to the data block includes:

[0020] The geographic grid ID and timestamp of each data block are associated with the storage address of the corresponding data block in the evidence storage side chain to generate an index unit containing spatial coordinates, time tags and data pointers.

[0021] Based on the spatial adjacency of geographic grids and the order of timestamps, all the index units are arranged into a spatiotemporal two-dimensional matrix structure, wherein the row dimension of the two-dimensional matrix structure is the geographic grid ID and the column dimension is the timestamp sequence.

[0022] The hash value verification field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain the spatiotemporal index matrix; the hash value verification field is generated by combining the geographic grid ID, timestamp and data block content through hash calculation, and is used to verify the consistency between the index unit and the data block.

[0023] As one preferred embodiment, the step of calling the carbon sequestration accounting model of the main chain to calculate the carbon sequestration equivalent of the data block includes:

[0024] Extract monitoring data from the data block, the monitoring data including biomass data, environmental data and meteorological data;

[0025] The data fluctuation coefficient is calculated based on the biomass data, the environmental data, and the meteorological data, and one or more carbon sink accounting sub-models that match the fluctuation coefficient are selected, wherein the carbon sink accounting sub-model includes at least a static accounting sub-model and a dynamic accounting sub-model.

[0026] The historical calculation results of the selected carbon sink accounting sub-models are compared with the historical measurement results, and the weight coefficients of each carbon sink accounting sub-model in the carbon sink equivalent calculation are obtained based on the comparison results.

[0027] The monitoring data is input into the carbon sink accounting sub-model obtained through filtering to obtain an intermediate calculated value of carbon sink equivalent. The intermediate calculated value of carbon sink equivalent is then weighted based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

[0028] As one preferred embodiment, the step of calling the main chain's carbon sink prediction model to calculate the carbon sink potential value of the data block includes:

[0029] An initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system are constructed, the federated learning framework including multiple participants;

[0030] Based on the historical monitoring data and corresponding historical carbon sequestration potential values ​​of each of the participating parties, several training datasets are constructed; each training dataset is preprocessed, and each preprocessed training dataset is input into the initial carbon sequestration prediction model for training to obtain the trained first model parameters.

[0031] Each of the first model parameters is uploaded to the main chain, and the first model parameters are processed based on a preset aggregation strategy to obtain the second model parameters; the initial carbon sink prediction model is updated based on the second model parameters to obtain the trained carbon sink prediction model.

[0032] The monitoring data in the data block is input into the pre-built carbon sink prediction model to obtain the carbon sink potential value corresponding to the monitoring data.

[0033] As one preferred embodiment, the generation of carbon sink digital certificates based on the carbon sink equivalent and the carbon sink potential value, and the associated NFT identifier with the carbon sink digital certificate, includes:

[0034] The carbon sequestration equivalent and the carbon sequestration potential value are standardized, and the standardized carbon sequestration equivalent and carbon sequestration potential value are integrated with the corresponding metadata tags, carbon sequestration accounting model identifiers, and carbon sequestration prediction model parameter fingerprints to generate basic integrated data.

[0035] Obtain the original data file package corresponding to the basic integrated data in the evidence storage sidechain, calculate the hash value of the data packet based on the first hash algorithm, wherein the hash value of the data packet is consistent with the hash value of the original data file package stored on the sidechain;

[0036] The global hash value is obtained by calculating the hash value of the data packet, the corresponding metadata tag, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint based on the second hash algorithm.

[0037] Based on the global hash value associated with the carbon sink equivalent, carbon sink potential value, metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint in the basic integrated data, the carbon sink digital certificate is generated, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

[0038] As one preferred embodiment, the step of locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage sidechain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the hash value of the target data block to obtain a second verification result includes:

[0039] Obtain the metadata tags, data file package hash value, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint associated with the digital carbon sink certificate to be traded;

[0040] Based on the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage sidechain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block;

[0041] The first hash value of the data file package is calculated based on the first hash algorithm. If the first hash value is inconsistent with the hash value of the data file package, the verification is deemed to have failed.

[0042] Otherwise, the second hash value of the metadata tag, the data file package hash value, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint is calculated based on the second hash algorithm;

[0043] If the second hash value is inconsistent with the global hash value of the carbon sink digital certificate to be traded, the verification is deemed to have failed; otherwise, the verification is deemed to have passed, and the second verification result is generated.

[0044] As one preferred embodiment, the step of verifying the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded based on the transaction sidechain to obtain a third verification result includes:

[0045] Based on the first NFT identifier corresponding to the carbon sink digital certificate to be traded, extract the ownership address, effective time, expiration time and transaction records corresponding to the first NFT identifier on the transaction sidechain;

[0046] The ownership address is compared with the transaction initiator address to generate a first comparison result; the current transaction time is compared with the effective time and the expiration time to obtain a second comparison result; the regulatory record corresponding to the first NFT identifier is extracted from the regulatory sidechain, and the transaction record is compared with the regulatory record to obtain a third comparison result;

[0047] A third verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

[0048] As one preferred embodiment, the synchronous update of the main chain, the trading sidechain, and the regulatory sidechain based on the acquired carbon sink trading data includes:

[0049] Based on the carbon sink transaction data, the ownership of the carbon sink to be traded is transferred, and the generated ownership change record is stored on the main chain. The carbon sink transaction data includes, but is not limited to, the addresses of the two parties to the transaction, the transaction quantity, the transaction price, and the transaction time.

[0050] The transaction information is integrated, and a hash calculation is performed on the integrated transaction information. The generated unique transaction hash value is stored on the transaction sidechain, and the compliance verification result is recorded on the regulatory sidechain.

[0051] As one preferred embodiment, the query results for the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain include:

[0052] Based on the certificate identifier or spatiotemporal index condition in the carbon sink digital certificate to be queried, the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate are obtained from the main chain;

[0053] Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage sidechain is located using the spatiotemporal index matrix to obtain the original monitoring data file package;

[0054] Query the historical data of the associated NFT identifier in the transaction sidechain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the regulatory sidechain;

[0055] Based on the original monitoring data file package, the historical data of the transfer, and the compliance verification record, the traceability result of the carbon sink digital certificate to be queried is generated.

[0056] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0057] 1) This invention achieves functional decoupling and efficient collaboration through a multi-chain architecture: the main chain is responsible for the storage of core assets and models, the storage sidechain uses a DAG structure to store spatiotemporal sharded data in parallel, and the transaction sidechain and regulatory sidechain handle asset transfer and compliance verification respectively, completely solving the functional redundancy problem of traditional single chains. Among them, the spatiotemporal index matrix constructs a three-dimensional retrieval system of "geographic grid ID + timestamp + data pointer", which improves data positioning efficiency by more than 90% and supports second-level retrieval of the original data block of the storage sidechain during transaction verification; the cross-chain synchronization mechanism realizes the full automation of the accounting-rights confirmation-transaction-regulation process, eliminating the need for manual integration of multi-source data, and reducing the carbon sink transaction traceability time from 4-6 hours in the traditional solution to less than 10 minutes.

[0058] 2) This invention constructs a three-dimensional trusted verification and precise traceability system: pre-compliant verification of the regulatory sidechain, dual hash comparison of the evidence storage sidechain, and NFT contract status verification of the transaction sidechain form a triple protection; through a synchronous update mechanism, it ensures that the ownership information of the main chain, the circulation records of the transaction sidechain, and the compliance verification results of the regulatory sidechain are consistent in real time, avoiding cross-chain state conflicts; relying on the association between the spatiotemporal index matrix and the NFT identifier, it can aggregate data from the three chains with one click to generate a full-link traceability report including the data source, accounting model, and transaction compliance, realizing full-process transparency of carbon sink assets from collection to transaction, and providing an efficient and reliable technical infrastructure for the market-oriented operation of the blue carbon ecosystem. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating a blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks in one embodiment of the present invention.

[0060] Figure 2 This is a schematic diagram of the architecture of a multi-chain structure in one embodiment of the present invention;

[0061] Among them, 11 is the main chain; 12 is the evidence storage side chain; 13 is the transaction side chain; and 14 is the regulatory side chain. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0063] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0064] In the description of this invention, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to communication within two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0065] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] One embodiment of the present invention provides a blockchain-based evidence storage and traceability method driven by big data on marine carbon sequestration. For details, please refer to [link to specific documentation]. Figure 1 , Figure 1 The diagram shown illustrates a blockchain-based evidence storage and traceability method driven by marine carbon sink big data, as one embodiment of the present invention. This method is applied to a multi-chain architecture blockchain system, which includes a main chain, an evidence storage sidechain, a transaction sidechain, and a regulatory sidechain. The method includes steps S1-S7:

[0067] S1: Dynamically divide the monitoring data of the blue carbon ecosystem, model the division results based on the directed acyclic graph structure, store the modeled data blocks in parallel on the evidence storage sidechain, and generate a spatiotemporal index matrix corresponding to the data blocks.

[0068] S2: Call the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block, and call the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block; generate a carbon sink digital certificate and an associated NFT identifier with the carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value.

[0069] S3: In response to a carbon sink trading request, the compliance of the trading participants is verified based on the regulatory sidechain to obtain the first verification result;

[0070] S4: Based on the spatiotemporal index matrix, locate the target data block in the evidence storage sidechain corresponding to the carbon sink digital certificate to be traded, and compare the hash value of the carbon sink digital certificate to be traded with the target data block to obtain the second verification result;

[0071] S5: Based on the transaction sidechain, verify the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded, and obtain the third verification result;

[0072] S6: If the first verification result, the second verification result, and the third verification result are all passed, then the main chain, the trading side chain, and the regulatory side chain are synchronously updated based on the obtained carbon sink trading data;

[0073] S7: In response to a carbon sink query request, query the traceability results of the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain.

[0074] Specifically, this invention utilizes blockchain technology to construct a marine carbon sink management system that is efficient in evidence storage, accurate in accounting, reliable in transactions, and allows for real-time monitoring. This provides technical support for achieving environmental protection goals and also offers a referable solution for big data evidence storage and asset trading in other fields. First, the technical terms mentioned in this invention will be explained.

[0075] Blue carbon ecosystems: These are marine ecosystems with strong carbon sequestration capabilities, such as mangroves, salt marshes, and seagrass beds, which capture and store carbon dioxide through photosynthesis.

[0076] Directed Acyclic Graph (DAG): A data structure consisting of nodes and directed edges, with no cyclic paths. In this scheme, it is used to model monitoring data blocks, supporting parallel storage and efficient retrieval.

[0077] Spatiotemporal index matrix: a three-dimensional index structure that uses "geographic grid ID + timestamp + data pointer" to associate data blocks in the evidence storage sidechain, achieving second-level data positioning;

[0078] Carbon sink equivalent: A quantitative indicator that measures the ability of a marine ecosystem to absorb and store carbon dioxide, calculated using a carbon sink accounting model;

[0079] Carbon sink potential: A projected value for future carbon sink capacity based on historical data and predictive models, used to assess the sustainability of blue carbon ecosystems;

[0080] NFT Identifier: Non-fungible token identifier, used to uniquely identify digital carbon sink certificates, supporting asset ownership confirmation and trading.

[0081] Hash value comparison: A method to verify the integrity and authenticity of data by comparing the hash values ​​of the data. If the hash values ​​are inconsistent, it indicates that the data may have been tampered with.

[0082] Federated learning framework: a distributed machine learning approach that allows participants to collaboratively train models without sharing the original data, thus protecting data privacy.

[0083] Compliance verification: A preliminary step in carbon trading, this step verifies the qualifications, authority, and compliance of trading participants based on a regulatory sidechain. For example, it checks whether participants have carbon trading qualifications and whether they comply with environmental policies, generating a "pass / fail" first verification result to ensure the legality and compliance of trading entities and lay the foundation for subsequent transaction verification.

[0084] Main Chain: The main chain undertakes the core functions of model notarization, smart contracts, and global index. Model notarization includes storing the code and parameter fingerprints of carbon sink accounting models and prediction models to ensure algorithm traceability. Smart contracts include core contracts for deploying carbon sink digital certificate generation and NFT ownership transfer, triggering sidechain operations through cross-chain calls. Global Index: Maintains key information such as hash values ​​and metadata tags of each sidechain to form a unified carbon sink asset ledger. The main chain typically adopts a consortium blockchain consensus mechanism to ensure the consistency and security of core data.

[0085] Evidence storage sidechain: Specifically designed for the structured storage of blue carbon monitoring data, including DAG data modeling, hash verification, and spatiotemporal index matrix.

[0086] Trading sidechain: Manages the entire trading process of carbon sink assets, including NFT identification management, smart contract execution, and high-frequency trading processing.

[0087] Regulatory sidechain: Building a firewall for real-time supervision and auditing, including compliance verification, end-to-end traceability, and intelligent early warning.

[0088] like Figure 2 The diagram illustrates a multi-chain architecture provided by an embodiment of the present invention, comprising a main chain 11, a notarization sidechain 12, a transaction sidechain 13, and a regulatory sidechain 14. This multi-chain architecture, through a modular design where the main chain coordinates core logic, the notarization sidechain specializes in data storage and indexing, the transaction sidechain handles asset transfers, and the regulatory sidechain independently performs compliance audits, achieves separation of responsibilities and cross-chain collaboration. This improves data notarization efficiency, transaction processing speed, and regulatory real-time performance, while a triple verification mechanism ensures data authenticity, contract validity, and compliance. Ultimately, it forms a closed-loop system with end-to-end traceability, providing efficient, secure, and regulatory-compliant technical support for the accurate accounting, reliable trading, and standardized management of marine carbon sinks.

[0089] Preferably, in one embodiment of the present invention, the dynamic partitioning of monitoring data of the blue carbon ecosystem, modeling the partitioning results based on a directed acyclic graph structure, and storing the modeled data blocks in parallel on the evidence storage sidechain includes:

[0090] The raw monitoring data of the blue carbon ecosystem were sequentially subjected to outlier filtering and spatiotemporal alignment to obtain a spatiotemporally aligned dataset.

[0091] The spatiotemporal aligned dataset is divided into blocks based on a preset geographic grid and time window to generate data blocks that contain at least spatial data, temporal data, and feature vectors.

[0092] A metadata tag corresponding to each data block is generated, and the data block and the metadata tag are encapsulated into a data file package; the metadata tag includes the acquisition time, geographical location, and sensor number;

[0093] The data file package is hashed, and the data file package is stored in the evidence storage sidechain in segments according to geographic grid ID and timestamp. At the same time, the metadata tag and the calculated hash value are synchronized to the main chain.

[0094] It should be noted that this embodiment aims to preprocess and structure the raw monitoring data of the blue carbon ecosystem to solve the problems of data heterogeneity, spatiotemporal inconsistency and storage efficiency, and to provide a reliable data foundation for subsequent carbon sink accounting, transaction verification and full-chain traceability.

[0095] Specifically, this embodiment first performs outlier detection on the raw monitoring data, using statistical methods or machine learning algorithms to identify and remove data points that significantly deviate from normal values. Then, spatiotemporal alignment is performed, specifically including unifying the sampling frequency for data with inconsistent timestamps through interpolation; and matching data to corresponding grid cells based on a geographic information system to address spatial location deviations, thereby generating a "spatiotemporally aligned dataset" containing a unified time series and spatial coordinates.

[0096] Based on a preset geographic grid and time window, the spatiotemporally aligned dataset is divided into blocks. The monitored sea area is divided into M×N grids, each grid is assigned a unique ID, a preset geographic grid is generated, and a time window is set at 24 hours.

[0097] Each data block contains three types of information: spatial data, temporal data, and feature vectors. Spatial data includes the geographic grid ID and latitude and longitude range; temporal data includes the start and end timestamps of the time window; and feature vectors include key monitoring parameters within that spatiotemporal range.

[0098] Metadata tags are generated for each data block, including collection time, geographical location, and sensor number, to trace the background of data collection. The data blocks and metadata tags are encapsulated into a data file package. A hash calculation is performed on the file package to obtain a unique hash value, which serves as the digital fingerprint of the data. Any data tampering will result in a change in the hash value.

[0099] Finally, the data file package is fragmented and stored on the evidence storage sidechain according to the geographic grid ID and timestamp. The spatiotemporal dependencies between blocks are modeled using a DAG structure, supporting parallel storage and efficient querying. At the same time, metadata tags and hash values ​​are synchronized to the main chain, providing data indexes and integrity proofs for the main chain to call the carbon sink model.

[0100] A Directed Acyclic Graph (DAG) is an acyclic directed graph composed of nodes and directed edges. Nodes represent data units, and edges represent dependencies or temporal relationships between nodes, and there are no cycles. Unlike the chain structure of traditional blockchains, DAGs support parallel data processing. Each node can be independently verified and linked to multiple predecessor nodes, making it suitable for high-frequency data writing and complex dependency modeling.

[0101] Preferably, in one embodiment of the present invention, generating the spatiotemporal index matrix corresponding to the data block includes:

[0102] The geographic grid ID and timestamp of each data block are associated with the storage address of the corresponding data block in the evidence storage side chain to generate an index unit containing spatial coordinates, time tags and data pointers.

[0103] Based on the spatial adjacency of geographic grids and the order of timestamps, all the index units are arranged into a spatiotemporal two-dimensional matrix structure, wherein the row dimension of the two-dimensional matrix structure is the geographic grid ID and the column dimension is the timestamp sequence.

[0104] The hash value verification field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain the spatiotemporal index matrix; the hash value verification field is generated by combining the geographic grid ID, timestamp and data block content through hash calculation, and is used to verify the consistency between the index unit and the data block.

[0105] It should be noted that this embodiment aims to construct an efficient spatiotemporal data indexing system to solve the problems of rapid spatiotemporal dimension retrieval and data consistency verification of blue carbon monitoring data.

[0106] Specifically, the blue carbon monitoring area is first gridded, for example, by using equidistant latitude and longitude grids or adaptive grids, assigning a unique geographic grid ID to each grid, and generating a timestamp sequence according to a preset time window.

[0107] When a data block is stored on the evidence storage sidechain, its geographic grid ID, timestamp, and storage address in the DAG are extracted to generate an index unit, including the grid ID, timestamp, and storage address. For example, the index unit of a mangrove monitoring data block is (G_01, 2023100100, 0x5a3c2f...), indicating that the data corresponds to the monitoring result of grid G_01 at 00:00 on October 1, 2023, and is stored in the DAG node.

[0108] Sort all geographic grid IDs by spatial adjacency to form a matrix row list; arrange timestamps by chronological order to form a matrix column list. Fill the matrix cells with index cells according to the grid ID → timestamp mapping. For example, the index cell for grid G_01 at 00:00 is filled into the matrix (row = G_01, column = 00:00), forming a complete spatiotemporal two-dimensional matrix structure. This arrangement ensures that historical data for the same grid is continuous in the column direction, and multi-grid data at the same time point are parallel in the row direction, facilitating batch queries by region + time range.

[0109] For each index unit, the geographic grid ID, timestamp, and data block content are combined, and the hash value is calculated using the first hash algorithm to generate a verification field Hash, which includes the grid ID, timestamp, and data content.

[0110] When it is necessary to verify the consistency between the index unit and the data block, the combined hash value of the data block is recalculated and compared with the verification field stored in the index matrix. If they match, it proves that the data has not been tampered with and the index points to the correct value; if they do not match, it indicates that the index or data is abnormal. This mechanism ensures the reliability of the entire process from index locating to the data block, avoiding verification failures caused by incorrect storage addresses or data tampering.

[0111] Preferably, in one embodiment of the present invention, the step of calling the carbon sequestration accounting model of the main chain to calculate the carbon sequestration equivalent of the data block includes:

[0112] Extract monitoring data from the data block, the monitoring data including biomass data, environmental data and meteorological data;

[0113] The data fluctuation coefficient is calculated based on the biomass data, the environmental data, and the meteorological data, and one or more carbon sink accounting sub-models that match the fluctuation coefficient are selected, wherein the carbon sink accounting sub-model includes at least a static accounting sub-model and a dynamic accounting sub-model.

[0114] The historical calculation results of the selected carbon sink accounting sub-models are compared with the historical measurement results, and the weight coefficients of each carbon sink accounting sub-model in the carbon sink equivalent calculation are obtained based on the comparison results.

[0115] The monitoring data is input into the carbon sink accounting sub-model obtained through filtering to obtain an intermediate calculated value of carbon sink equivalent. The intermediate calculated value of carbon sink equivalent is then weighted based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

[0116] This embodiment addresses the problems of poor adaptability and insufficient real-time performance of single models in traditional carbon sequestration. Specifically, firstly, the technical terms used in this embodiment will be explained.

[0117] Data volatility coefficient: an indicator that measures the magnitude of change in monitoring data over time and is used to assess the stability of an ecosystem. For example, drastic fluctuations may indicate that the ecosystem is disturbed.

[0118] Static accounting sub-model: A carbon sink calculation model based on fixed parameters and empirical formulas, suitable for long-term average carbon sink estimation of stable ecosystems;

[0119] Dynamic accounting sub-model: A carbon sink calculation model that considers real-time changes in environmental variables. It is constructed through machine learning or system dynamics methods and is suitable for rapidly changing ecological scenarios.

[0120] Weighting coefficients: The contribution ratio of each sub-model in the final carbon sink equivalent calculation, which are dynamically adjusted based on the results of historical data verification.

[0121] Three types of monitoring data were parsed from the data blocks of the evidence storage sidechain, including biomass data, such as aboveground biomass of mangroves (unit: tons / hectare) and vegetation cover (percentage); environmental data, such as bottom seawater salinity (‰) and dissolved oxygen concentration (mg / L); and meteorological data, such as average daily temperature (°C) and annual cumulative rainfall (mm).

[0122] The obtained multi-source heterogeneous data are normalized to eliminate the influence of dimensions. The standard deviation or coefficient of variation is calculated based on recent monitoring data to assess the degree of data fluctuation; the recent period can be the past month. For example, the fluctuation coefficient of biomass data can be expressed as: Fluctuation coefficient = (Standard deviation / Mean) × 100%.

[0123] Data is categorized into different types based on volatility coefficients, such as stable (volatility coefficient < 10%), volatile (volatility coefficient < 30%), and volatile (volatility coefficient ≥ 30%).

[0124] Recent monitoring data was selected, and carbon sink equivalents were calculated using each sub-model. These values ​​were then compared with measured values ​​from the same period to calculate the average relative error of each sub-model. The measured values ​​from the same period included real carbon sink data obtained through remote sensing inversion or field sampling. Weighting coefficients were determined based on the reciprocal of the average relative error; models with smaller errors had higher weights.

[0125] The current monitoring data is input into the selected sub-models to obtain intermediate calculated values, and the final carbon sink equivalent is calculated based on the weighting coefficients.

[0126] Preferably, in one embodiment of the present invention, the step of calling the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block includes:

[0127] An initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system are constructed, the federated learning framework including multiple participants;

[0128] Based on the historical monitoring data and corresponding historical carbon sequestration potential values ​​of each of the participating parties, several training datasets are constructed; each training dataset is preprocessed, and each preprocessed training dataset is input into the initial carbon sequestration prediction model for training to obtain the trained first model parameters.

[0129] Each of the first model parameters is uploaded to the main chain, and the first model parameters are processed based on a preset aggregation strategy to obtain the second model parameters; the initial carbon sink prediction model is updated based on the second model parameters to obtain the trained carbon sink prediction model.

[0130] The monitoring data in the data block is input into the pre-built carbon sink prediction model to obtain the carbon sink potential value corresponding to the monitoring data.

[0131] It should be noted that this embodiment is used to resolve the conflict between data silos and privacy protection in traditional carbon sink forecasting. LSTM (Long Short-Term Memory) is a special type of recurrent neural network that uses a gating mechanism to address the long-sequence dependency problem of traditional RNNs, making it suitable for processing blue carbon monitoring data with time-series characteristics.

[0132] Specifically, a three-layer LSTM network is constructed. The input layer receives multi-dimensional monitoring data, the hidden layer captures long-term dependencies in the time series through gating units, and the output layer predicts future carbon sequestration potential. A horizontal federated learning architecture is adopted, with participants including marine monitoring agencies and research institutes in coastal provinces. The framework includes:

[0133] Parameter server (main chain): responsible for aggregating model parameters uploaded by all participants;

[0134] Client (Participant): Stores historical monitoring data locally, performs model training, and uploads parameters;

[0135] Secure communication protocol: Homomorphic encryption or differential privacy technology is used to ensure privacy during parameter transmission.

[0136] Each participating party sequentially performs missing value imputation, normalization, and time-series windowing on the local historical monitoring data. Missing value imputation includes filling in missing data points using linear interpolation or random forest methods; normalization includes scaling all feature values ​​to the [0,1] interval to eliminate the influence of units; and time-series windowing includes dividing the data into training samples according to time windows. Each sample contains input features, namely historical monitoring data and labels, namely the corresponding carbon sequestration potential value.

[0137] Each participant independently trains an LSTM model using the preprocessed data, optimizes the parameters through backpropagation, and obtains the locally optimal first model parameters. An early stopping strategy is employed during training to prevent overfitting. Each participant encrypts the first model parameters and uploads them to the main chain, which verifies the legitimacy of the parameter source through digital signatures.

[0138] After the legality verification is passed, the first model parameters are aggregated based on a preset strategy, which includes a data-weighted average. The aggregated second model parameters are then synchronized to all participants to update the initial carbon sink prediction model, forming a globally optimal carbon sink prediction model.

[0139] The monitoring data from the current data block is input into the trained carbon sink prediction model. The model outputs carbon sink potential values ​​at different future time scales, considering carbon sink change trends under different climate change scenarios. It also outputs prediction confidence intervals to assess the reliability of the prediction results.

[0140] Preferably, in one embodiment of the present invention, the step of generating a carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value, and the associated NFT identifier with the carbon sink digital certificate, includes:

[0141] The carbon sequestration equivalent and the carbon sequestration potential value are standardized, and the standardized carbon sequestration equivalent and carbon sequestration potential value are integrated with the corresponding metadata tags, carbon sequestration accounting model identifiers, and carbon sequestration prediction model parameter fingerprints to generate basic integrated data.

[0142] Obtain the original data file package corresponding to the basic integrated data in the evidence storage sidechain, calculate the hash value of the data packet based on the first hash algorithm, wherein the hash value of the data packet is consistent with the hash value of the original data file package stored on the sidechain;

[0143] The global hash value is obtained by calculating the hash value of the data packet, the corresponding metadata tag, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint based on the second hash algorithm.

[0144] Based on the global hash value associated with the carbon sink equivalent, carbon sink potential value, metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint in the basic integrated data, the carbon sink digital certificate is generated, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

[0145] First, the technical terms used in this embodiment will be explained.

[0146] Standardization: The process of converting carbon sink equivalents and carbon sink potential values ​​with different dimensions into a unified scale, such as normalizing to the [0,1] interval, to eliminate the impact of dimensional differences on subsequent calculations.

[0147] First hash algorithm: An algorithm that performs hash calculations on the original data file package to generate a unique digital fingerprint for the data, which can be SHA-256; Second hash algorithm: An algorithm that performs secondary hashing on the integrated data to ensure the uniqueness of the global hash value, which can be Keccak-256; Parameter fingerprint: The encrypted hash value of the model parameters, used to verify the consistency and immutability of the model training process; Model identifier: A string that uniquely identifies the carbon sequestration accounting model, used to record the source and version of the accounting method.

[0148] Specifically, the original calculation results are converted to values ​​in the [0,1] interval using Min-Max normalization, where the original calculation results include carbon sink equivalent and carbon sink potential values. The standardized carbon sink data is then integrated with metadata, which includes metadata tags, model identifiers, and parameter fingerprints.

[0149] The original data file package corresponding to the time and space is retrieved from the evidence storage sidechain, the hash value is calculated using the first hash algorithm, and the hash value is verified to be consistent with the hash value recorded in the evidence storage sidechain to ensure that the data has not been tampered with.

[0150] After combining the data packet hash value with the metadata tag, model identifier, and parameter fingerprint, a second hash algorithm is used to calculate the global hash value. The global hash value serves as a unique identifier for the carbon sink digital certificate, ensuring data integrity and traceability of origin.

[0151] The global hash value is associated with the underlying integrated data (carbon equivalent, carbon sequestration potential, etc.) to form an immutable digital record, as shown in the following structure:

[0152] {"Voucher ID":"0x5a3c2f...","Carbon Sequestration Equivalent":0.75,"Carbon Sequestration Potential Value":0.82,"Timestamp":"2023-10-01T12:00:00Z","Geographic Coordinates":"118.5°E,38.2°N","Accounting Model":"V2.3","Prediction Model":"LSTM_2023Q3","Data Packet Hash":"0x3b7d1e..."}

[0153] An NFT associated with this digital credential is created through a smart contract on the main chain. The NFT must contain at least the following attributes:

[0154] Unique Identifier: An ERC-721 standard token ID generated based on a global hash value;

[0155] Metadata link: Points to the IPFS address or blockchain storage location of the carbon sink digital certificate;

[0156] Dynamic attributes: carbon sequestration equivalent, carbon sequestration potential value;

[0157] Access control: Set the issuer, owner, transaction permissions, etc.

[0158] Preferably, in one embodiment of the present invention, the step of locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage sidechain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the hash value of the target data block to obtain a second verification result includes:

[0159] Obtain the metadata tags, data file package hash value, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint associated with the digital carbon sink certificate to be traded;

[0160] Based on the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage sidechain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block;

[0161] The first hash value of the data file package is calculated based on the first hash algorithm. If the first hash value is inconsistent with the hash value of the data file package, the verification is deemed to have failed.

[0162] Otherwise, the second hash value of the metadata tag, the data file package hash value, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint is calculated based on the second hash algorithm;

[0163] If the second hash value is inconsistent with the global hash value of the carbon sink digital certificate to be traded, the verification is deemed to have failed; otherwise, the verification is deemed to have passed, and the second verification result is generated.

[0164] Specifically, metadata tags, data file package hash values, and model identifiers and parameter fingerprints are extracted from the carbon sink digital certificates to be traded. Based on the geographic grid ID and timestamp in the metadata tag, the corresponding cell is located in the spatiotemporal index matrix. For example, the row with geographic grid ID G_01 is found, and the column with timestamp 20231001 is located; the storage address of the data block is then extracted from the cell.

[0165] Using the API of the evidence storage sidechain, the original data file package in the target data block is retrieved based on the storage address. The same first hash algorithm used when the data was uploaded to the blockchain is used to recalculate the hash value of the retrieved data file package. This recalculated hash value is then compared with the hash value of the data file package recorded in the carbon sink digital certificate. If they do not match, it indicates that the data may have been tampered with or the index may be incorrect, and the verification fails. If they match, the next verification step is performed.

[0166] The second hash value is calculated based on the combination of metadata tag, data file package hash value, accounting model identifier and prediction model parameter fingerprint. The calculated second hash value is compared with the global hash value of carbon sink digital certificate. If they are inconsistent, it means that the certificate's metadata, model information or data association has been tampered with and the verification fails. If they are consistent, the verification passes and a second verification result is generated.

[0167] Preferably, in one embodiment of the present invention, the step of verifying the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded based on the transaction sidechain to obtain a third verification result includes:

[0168] Based on the first NFT identifier corresponding to the carbon sink digital certificate to be traded, extract the ownership address, effective time, expiration time and transaction records corresponding to the first NFT identifier on the transaction sidechain;

[0169] The ownership address is compared with the transaction initiator address to generate a first comparison result; the current transaction time is compared with the effective time and the expiration time to obtain a second comparison result; the regulatory record corresponding to the first NFT identifier is extracted from the regulatory sidechain, and the transaction record is compared with the regulatory record to obtain a third comparison result;

[0170] A third verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

[0171] First, the technical terms used in this embodiment will be explained.

[0172] Ownership Address: Records the blockchain address of the current legal holder of the NFT, similar to a "certificate of ownership" for digital assets; Effective Date: The point at which the NFT begins to have trading or usage rights; Expiration Date: The point at which the NFT's rights expire; Transaction Records: The NFT's circulation history stored in the trading sidechain, including information such as transaction time, addresses of both parties, and transaction quantity; Regulatory Records: Compliance records associated with the NFT in the regulatory sidechain, such as transaction qualification review results and carbon credit additionality certification.

[0173] Specifically, the associated NFT identifier is obtained from the carbon sink digital certificate to be traded. This identifier is automatically created by the smart contract when the carbon sink digital certificate is generated. Through the smart contract interface of the trading sidechain, the information corresponding to the NFT identifier can be queried, including the ownership address, effective time and expiration time, and transaction records.

[0174] Perform a consistency check between the transaction initiator's address (such as 0xAddr_User submitted by the user) and the NFT ownership address (0xAddr1): if they match, generate the first comparison result of ownership matching; if they do not match, generate the first comparison result of ownership mismatch, which may indicate an unauthorized transaction or address forgery.

[0175] The current transaction time must meet the following conditions: effective time ≤ current time ≤ expiration time. If the condition is met, a second comparison result with a valid time will be generated; if the condition is not met, a second comparison result with an invalid time will be generated.

[0176] The regulatory records associated with the NFT identifier are obtained from the regulatory sidechain via a cross-chain protocol. These records include: compliance certification status; transaction limits; and historical regulatory anomalies. The transaction records are then checked against the compliance requirements in the regulatory records, such as whether the transaction quantity exceeds the limit or whether the transacting parties are on the regulatory whitelist. If they match, a third-party comparison result for regulatory compliance is generated; if they do not match, a third-party comparison result for regulatory violation is generated.

[0177] The third verification result is considered successful only if the first, second, and third comparison results are all successful; if any one of the comparison results is unsuccessful, the third verification result is considered unsuccessful, and the smart contract will automatically terminate the transaction.

[0178] Table 1 shows the data comparison table of the triple verification mechanism provided by the present invention.

[0179] Table 1

[0180]

[0181] Preferably, in one embodiment of the present invention, the step of synchronously updating the main chain, the trading side chain, and the regulatory side chain based on the acquired carbon sink trading data includes:

[0182] Based on the carbon sink transaction data, the ownership of the carbon sink to be traded is transferred, and the generated ownership change record is stored on the main chain. The carbon sink transaction data includes, but is not limited to, the addresses of the two parties to the transaction, the transaction quantity, the transaction price, and the transaction time.

[0183] The transaction information is integrated, and a hash calculation is performed on the integrated transaction information. The generated unique transaction hash value is stored on the transaction sidechain, and the compliance verification result is recorded on the regulatory sidechain.

[0184] Specifically, once all three verifications (S3-S5) pass, the smart contract on the transaction sidechain automatically executes the NFT ownership transfer: removing the seller's address from the NFT's ownership address and writing the buyer's address; updating the NFT's dynamic attributes such as the number of transactions and the latest transaction time.

[0185] Generate ownership change records and upload them to the main chain. The record content includes:

[0186] {"Transaction ID":"TX_20240513_001","Seller Address":"0xSeller","Buyer Address":"0xBuyer","Transfer Quantity":50 tons of carbon credits,"Transfer Time":"2024-05-13T15:30:00Z","Associated NFT":"0x7b2a1f..."}

[0187] The main chain adopts a consortium blockchain consensus mechanism to ensure the immutability of ownership change records and network-wide consensus.

[0188] The transaction sidechain collects information throughout the entire transaction process, including basic information such as the addresses of both parties, transaction quantity, price, and transaction time; credential information such as the carbon credit digital certificate ID and associated NFT identifier; and verification information such as the first / second / third verification results. The integrated transaction information is hashed using the SHA-256 algorithm to obtain a unique transaction hash value. The transaction hash value and transaction details (with sensitive information removed) are stored in the transaction sidechain's blocks, supporting quick querying of transaction records using the hash value.

[0189] The smart contract on the regulatory sidechain automatically generates compliance verification results based on preset rules. Verification content includes: the qualifications of the transacting parties, the legality of the carbon sink source, and whether the transaction quantity exceeds limits. Output: compliant or non-compliant. If non-compliant, the violation type is recorded, such as expired qualifications or excessive transactions. The compliance verification results are linked with the transaction hash value and NFT identifier for evidence storage, forming a regulatory audit log.

[0190] After the main chain completes the ownership transfer, it sends update instructions to the transaction sidechain and the regulatory sidechain via a cross-chain messaging protocol, ensuring the atomicity of the three-chain operations. If any chain fails to update, all operations are rolled back. The transaction timestamps recorded by the three chains are accurate to the second, and time consistency is ensured through a blockchain consensus mechanism to avoid inconsistencies caused by time deviations. The ownership transfer records of the main chain, the transaction hash values ​​of the transaction sidechain, and the compliance records of the regulatory sidechain are bidirectionally linked through transaction IDs and NFT identifiers, forming a mutually verifiable closed loop.

[0191] Preferably, in one embodiment of the present invention, the querying of the traceability results of the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain includes:

[0192] Based on the certificate identifier or spatiotemporal index condition in the carbon sink digital certificate to be queried, the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate are obtained from the main chain;

[0193] Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage sidechain is located using the spatiotemporal index matrix to obtain the original monitoring data file package;

[0194] Query the historical data of the associated NFT identifier in the transaction sidechain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the regulatory sidechain;

[0195] Based on the original monitoring data file package, the historical data of the transfer, and the compliance verification record, the traceability result of the carbon sink digital certificate to be queried is generated.

[0196] Specifically, users can input the identifier of the carbon sink digital certificate to be queried, such as the global hash value, through the front-end interface, or initiate a tracing request based on spatiotemporal index conditions. The main chain smart contract retrieves the target certificate according to the query conditions and returns the core information: global hash value, metadata tag, and associated NFT identifier.

[0197] Based on the geographic grid ID and timestamp in the metadata tag, locate the corresponding cell in the spatiotemporal index matrix: extract the target data block based on the storage address through the API interface of the evidence storage sidechain, and parse out the original monitoring data file package; query the transaction sidechain by associating NFT identifiers to obtain ownership change records, contract status and transaction details; query the regulatory sidechain by global hash value or NFT identifier to obtain compliance certification results, transaction regulatory logs and regulatory policy matching degree.

[0198] The data returned by the three chains is standardized and arranged in chronological order, forming a coherent timeline to facilitate viewing the status changes at different stages.

[0199] The integrated traceability results include the following:

[0200] {"Basic Voucher Information":{"Voucher ID":"0x5a3c2f...","Carbon Sequestration Equivalent":13.1 tons / hectare / year,"Carbon Sequestration Potential Value":15.3±1.2 tons / hectare / year},"Data Source Traceability":{"Monitoring Time":"2023-10-01T12:00:00Z","Geographic Location":"118.5°E,38.2°N (G_01 grid)","Raw Data":"Water temperature 25℃, Salinity 32‰, Vegetation Coverage 85%...","Sensor Number":"S_001 (CNAS Certified)"},"Transaction Flow Trajectory":{"Historical Transactions":[{"Transaction Time"]} ":"2023-10-15T14:30:00Z","Transaction Parties":"0xSeller→0xBuyer","Transaction Quantity":50 tons,"Transaction Price":1000 yuan / ton}],"Current Ownership":"0xBuyer (Effective Date: 2023-10-01, Expiry Date: 2030-12-31)"},"Regulatory Compliance Certificate":{"Accounting Model Compliance":"Passed IPCC Mangrove Carbon Sequestration Methodology Certification (V2.3)","Transaction Compliance Status":"All transactions have passed regulatory sidechain verification","Regulatory Agency":"A certain research institute (Recordation Number: 2023001)"}}.

[0201] Finally, by comparing the combined hash value of the global hash value and the three-chain data, it is ensured that the traceability results have not been tampered with, and a PDF report or API interface data with a timestamp and blockchain signature is generated for users or regulatory systems to call.

[0202] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0203] 1) This invention achieves functional decoupling and efficient collaboration through a multi-chain architecture: the main chain is responsible for the storage of core assets and models, the storage sidechain uses a DAG structure to store spatiotemporal sharded data in parallel, and the transaction sidechain and regulatory sidechain handle asset transfer and compliance verification respectively, completely solving the functional redundancy problem of traditional single chains. Among them, the spatiotemporal index matrix constructs a three-dimensional retrieval system of "geographic grid ID + timestamp + data pointer", which improves data positioning efficiency by more than 90% and supports second-level retrieval of the original data block of the storage sidechain during transaction verification; the cross-chain synchronization mechanism realizes the full automation of the accounting-rights confirmation-transaction-regulation process, eliminating the need for manual integration of multi-source data, and reducing the carbon sink transaction traceability time from 4-6 hours in the traditional solution to less than 10 minutes.

[0204] 2) This invention constructs a three-dimensional trusted verification and precise traceability system: pre-compliant verification of the regulatory sidechain, dual hash comparison of the evidence storage sidechain, and NFT contract status verification of the transaction sidechain form a triple protection; through a synchronous update mechanism, it ensures that the ownership information of the main chain, the circulation records of the transaction sidechain, and the compliance verification results of the regulatory sidechain are consistent in real time, avoiding cross-chain state conflicts; relying on the association between the spatiotemporal index matrix and the NFT identifier, it can aggregate data from the three chains with one click to generate a full-link traceability report including the data source, accounting model, and transaction compliance, realizing full-process transparency of carbon sink assets from collection to transaction, and providing an efficient and reliable technical infrastructure for the market-oriented operation of the blue carbon ecosystem.

[0205] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A blockchain-based evidence storage and traceability method driven by big data on marine carbon sequestration, characterized in that, A blockchain system applied to a multi-chain architecture, wherein the multi-chain architecture includes a main chain, a notarization sidechain, a transaction sidechain, and a regulatory sidechain, the method comprising the following steps: The monitoring data of the blue carbon ecosystem is dynamically divided, and the division results are modeled based on the directed acyclic graph structure. The data blocks obtained by the modeling are stored in parallel on the evidence storage sidechain, and a spatiotemporal index matrix corresponding to the data blocks is generated at the same time. The carbon sink accounting model of the main chain is invoked to calculate the carbon sink equivalent of the data block, and the carbon sink prediction model of the main chain is invoked to calculate the carbon sink potential value of the data block; a carbon sink digital certificate and an associated NFT identifier are generated based on the carbon sink equivalent and the carbon sink potential value. In response to a carbon sink trading request, the regulatory sidechain is used to verify the compliance of the trading participants, resulting in a first verification result. Based on the spatiotemporal index matrix, locate the target data block in the evidence storage sidechain corresponding to the carbon sink digital certificate to be traded, and compare the hash value of the carbon sink digital certificate to be traded with the target data block to obtain the second verification result; Based on the transaction sidechain, the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded is verified to obtain a third verification result; If the first verification result, the second verification result, and the third verification result are all passed, then the main chain, the trading side chain, and the regulatory side chain are synchronously updated based on the obtained carbon sink trading data. In response to a carbon sink query request, query the traceability results of the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain; The process of dynamically partitioning the monitoring data of the blue carbon ecosystem, modeling the partitioning results based on a directed acyclic graph structure, and storing the modeled data blocks in parallel on the evidence storage sidechain includes: The raw monitoring data of the blue carbon ecosystem were sequentially subjected to outlier filtering and spatiotemporal alignment to obtain a spatiotemporally aligned dataset. The spatiotemporal aligned dataset is divided into blocks based on a preset geographic grid and time window to generate data blocks that contain at least spatial data, temporal data, and feature vectors. A metadata tag corresponding to each data block is generated, and the data block and the metadata tag are encapsulated into a data file package; the metadata tag includes the acquisition time, geographical location, and sensor number; The data file package is hashed, and the data file package is stored in the evidence storage sidechain in segments according to geographic grid ID and timestamp. At the same time, the metadata tag and the calculated hash value are synchronized to the main chain. The step of generating the spatiotemporal index matrix corresponding to the data block includes: The geographic grid ID and timestamp of each data block are associated with the storage address of the corresponding data block in the evidence storage side chain to generate an index unit containing spatial coordinates, time tags and data pointers. Based on the spatial adjacency of geographic grids and the order of timestamps, all the index units are arranged into a spatiotemporal two-dimensional matrix structure, wherein the row dimension of the two-dimensional matrix structure is the geographic grid ID and the column dimension is the timestamp sequence. The hash value verification field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain the spatiotemporal index matrix; the hash value verification field is generated by combining the geographic grid ID, timestamp and data block content through hash calculation, and is used to verify the consistency between the index unit and the data block.

2. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The step of calling the carbon sequestration accounting model of the main chain to calculate the carbon sequestration equivalent of the data block includes: Extract monitoring data from the data block, the monitoring data including biomass data, environmental data and meteorological data; The data fluctuation coefficient is calculated based on the biomass data, the environmental data, and the meteorological data, and one or more carbon sink accounting sub-models that match the fluctuation coefficient are selected, wherein the carbon sink accounting sub-model includes at least a static accounting sub-model and a dynamic accounting sub-model. The historical calculation results of the selected carbon sink accounting sub-models are compared with the historical measurement results, and the weight coefficients of each carbon sink accounting sub-model in the carbon sink equivalent calculation are obtained based on the comparison results. The monitoring data is input into the carbon sink accounting sub-model obtained through filtering to obtain an intermediate calculated value of carbon sink equivalent. The intermediate calculated value of carbon sink equivalent is then weighted based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

3. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The step of calling the main chain's carbon sink prediction model to calculate the carbon sink potential value of the data block includes: An initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system are constructed, the federated learning framework including multiple participants; Based on the historical monitoring data and corresponding historical carbon sequestration potential values ​​of each of the aforementioned participants, several training datasets are constructed. Each training dataset is preprocessed, and each preprocessed training dataset is input into the initial carbon sink prediction model for training to obtain the trained first model parameters. Each of the first model parameters is uploaded to the main chain, and the first model parameters are processed based on a preset aggregation strategy to obtain the second model parameters; the initial carbon sink prediction model is updated based on the second model parameters to obtain the trained carbon sink prediction model. The monitoring data in the data block is input into the pre-built carbon sink prediction model to obtain the carbon sink potential value corresponding to the monitoring data.

4. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The generation of carbon sink digital certificates based on the carbon sink equivalent and the carbon sink potential value, and the associated NFT identifier with the carbon sink digital certificate, includes: The carbon sequestration equivalent and the carbon sequestration potential value are standardized, and the standardized carbon sequestration equivalent and carbon sequestration potential value are integrated with the corresponding metadata tags, carbon sequestration accounting model identifiers, and carbon sequestration prediction model parameter fingerprints to generate basic integrated data. Obtain the original data file package corresponding to the basic integrated data in the evidence storage sidechain, calculate the hash value of the data packet based on the first hash algorithm, wherein the hash value of the data packet is consistent with the hash value of the original data file package stored on the sidechain; The global hash value is obtained by calculating the hash value of the data packet, the corresponding metadata tag, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint based on the second hash algorithm. Based on the global hash value associated with the carbon sink equivalent, carbon sink potential value, metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint in the basic integrated data, the carbon sink digital certificate is generated, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

5. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The step of locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage sidechain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the hash value of the target data block to obtain a second verification result includes: Obtain the metadata tags, data file package hash value, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint associated with the digital carbon sink certificate to be traded; Based on the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage sidechain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block; The first hash value of the data file package is calculated based on the first hash algorithm. If the first hash value is inconsistent with the hash value of the data file package, the verification is deemed to have failed. Otherwise, the second hash value of the metadata tag, the data file package hash value, the carbon sink accounting model identifier, and the carbon sink prediction model parameter fingerprint is calculated based on the second hash algorithm; If the second hash value is inconsistent with the global hash value of the carbon sink digital certificate to be traded, the verification is deemed to have failed; otherwise, the verification is deemed to have passed, and the second verification result is generated.

6. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The third verification result, obtained by verifying the contract status of the corresponding NFT identifier of the carbon sink digital certificate to be traded based on the transaction sidechain, includes: Based on the first NFT identifier corresponding to the carbon sink digital certificate to be traded, extract the ownership address, effective time, expiration time and transaction records corresponding to the first NFT identifier on the transaction sidechain; The ownership address is compared with the transaction initiator address to generate a first comparison result; the current transaction time is compared with the effective time and the expiration time to obtain a second comparison result; the regulatory record corresponding to the first NFT identifier is extracted from the regulatory sidechain, and the transaction record is compared with the regulatory record to obtain a third comparison result; A third verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

7. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The process of synchronizing and updating the main chain, the trading sidechain, and the regulatory sidechain based on the acquired carbon sink trading data includes: Based on the carbon sink transaction data, the ownership of the carbon sink to be traded is transferred, and the generated ownership change record is stored on the main chain. The carbon sink transaction data includes, but is not limited to, the addresses of the two parties to the transaction, the transaction quantity, the transaction price, and the transaction time. The transaction information is integrated, and a hash calculation is performed on the integrated transaction information. The generated unique transaction hash value is stored on the transaction sidechain, and the compliance verification result is recorded on the regulatory sidechain.

8. The blockchain-based evidence storage and traceability method driven by big data of marine carbon sinks as described in claim 1, characterized in that, The query results for the evidence storage sidechain, the transaction sidechain, and the regulatory sidechain corresponding to the carbon sink digital certificate to be queried on the main chain include: Based on the certificate identifier or spatiotemporal index condition in the carbon sink digital certificate to be queried, the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate are obtained from the main chain; Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage sidechain is located using the spatiotemporal index matrix to obtain the original monitoring data file package; Query the historical data of the associated NFT identifier in the transaction sidechain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the regulatory sidechain; Based on the original monitoring data file package, the historical data of the transfer, and the compliance verification record, the traceability result of the carbon sink digital certificate to be queried is generated.

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