Block chain evidence tracing method based on ocean carbon sink big data driving

Abstract

The invention discloses a block chain evidence storage tracing method based on ocean carbon sink big data driving, which comprises the following steps: dynamically dividing monitoring data of a blue carbon ecological system, storing data blocks obtained by division in an evidence storage side chain in parallel, and generating a spatial-temporal index matrix corresponding to the data blocks; calculating a carbon sink equivalent and a carbon sink potential value of the data block; generating a carbon sink digital certificate and an associated NFT identifier of the carbon sink digital certificate according to the carbon sink equivalent and the carbon sink potential value; in response to the carbon sink transaction request, performing transaction verification on the transaction participant, the to-be-transacted carbon sink digital certificate and the NFT identifier; if the transaction verification result is passed, synchronously updating the main chain, the transaction side chain and the supervision side chain based on the carbon sink transaction data; and in response to the carbon sink query request, querying the to-be-queried carbon sink digital certificate traceability result of the main chain, so that the block chain certificate traceability efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of big data processing technology, and in particular to a blockchain evidence storage and tracing method driven by ocean carbon sink big data. Background Art

[0002] As the need to address global climate change intensifies, the monitoring, accounting, and trading management of ocean carbon sinks, a vital carbon sink system on Earth, have become crucial for achieving environmental protection goals. Blockchain technology, with its decentralized, tamper-proof, and traceable nature, is increasingly being applied to the ocean carbon sink sector, addressing key issues such as data authenticity verification, asset ownership confirmation, and transaction transparency.

[0003] However, existing blockchain technology utilizes a single-chain structure, centralizing carbon sink data storage, asset trading, and regulatory audit functions within a single chain and lacking a coordinated mechanism. This necessitates manual integration of multi-source data across multiple systems for data storage and traceability, resulting in inefficiencies and error-prone processes. For example, verifying the complete chain of events for a carbon sink transaction requires sequentially obtaining data from the data storage platform, trading platform, and regulatory system, and cross-validating the data through manual timestamp matching. This is time-consuming and prone to errors.

[0004] It can be seen that how to improve the existing blockchain evidence tracing method to improve the efficiency of blockchain evidence tracing has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a blockchain evidence storage and tracing method driven by ocean carbon sink big data to solve the technical problem of how to improve the existing blockchain evidence storage and tracing method, thereby achieving the effect of improving the efficiency of blockchain evidence storage and tracing.

[0006] To address the above technical issues, an embodiment of the present invention provides a blockchain evidence storage and traceability method driven by ocean carbon sink big data, which is applied to a blockchain system with a multi-chain architecture. The multi-chain architecture includes a main chain, an evidence storage side chain, a transaction side chain, and a supervision side chain. The method includes the following steps: Dynamically partition the monitoring data of the blue carbon ecosystem, model the partitioning results based on a directed acyclic graph structure, store the modeled data blocks in parallel on the evidence storage side chain, and generate a spatiotemporal index matrix corresponding to the data blocks; Calling the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block, and calling the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block; generating a carbon sink digital certificate and an NFT identifier associated with the carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value; In response to a carbon offset transaction request, performing compliance verification on a transaction participant based on the regulatory side chain to obtain a first verification result; Locating a target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain 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; Performing contract status verification on the NFT identifier corresponding to the carbon sink digital certificate to be traded based on the transaction side chain 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 transaction side chain, and the supervision side chain are synchronously updated based on the acquired carbon sink transaction data; In response to the carbon sink query request, the traceability results of the certificate storage side chain, the transaction side chain and the supervision side chain corresponding to the carbon sink digital certificate to be queried on the main chain are queried.

[0007] As one of the preferred solutions, the monitoring data of the blue carbon ecosystem is dynamically divided, the division results are modeled based on a directed acyclic graph structure, and the data blocks obtained by the modeling are stored in parallel on the evidence storage side chain, including: The original monitoring data of blue carbon ecosystems were sequentially processed by outlier filtering and spatiotemporal alignment to obtain a spatiotemporal aligned dataset; Dividing the spatiotemporal aligned dataset into blocks based on a preset geographic grid and a time window to generate data blocks containing at least spatial data, temporal data, and feature vectors; Generating a metadata tag corresponding to each of the data blocks, and encapsulating the data blocks and the metadata tags into a data file package; the metadata tags include a collection time, a geographic location, and a sensor number; Perform hash calculation on the data file package, store the data file package in the evidence storage side chain according to the geographic grid ID and timestamp, and synchronize the metadata tag and the calculated hash value to the main chain.

[0008] As one preferred solution, generating a spatiotemporal index matrix corresponding to the data block includes: Associating the geographic grid ID and timestamp of each data block with the storage address of the corresponding data block in the evidence storage sidechain to generate an index unit containing spatial coordinates, time tags, and data pointers; According to the spatial adjacent relationship of the geographic grids and the order of the timestamps, all the index units are organized 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 check field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain a spatiotemporal index matrix; the hash value check field is generated by performing a combined hash calculation on the geographic grid ID, timestamp and data block content, and is used to verify the consistency between the index unit and the data block.

[0009] As one preferred solution, the step of calling the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block includes: extracting monitoring data from the data block, wherein the monitoring data includes biomass data, environmental data, and meteorological data; Calculating a data fluctuation coefficient based on the biomass data, the environmental data, and the meteorological data, and selecting one or more carbon sink operator sub-models that match the fluctuation coefficient, wherein the carbon sink operator sub-models include at least a static operator sub-model and a dynamic operator sub-model; Comparing the historical calculation results of the screened carbon sink operator models with the historical measurement results, and obtaining the weight coefficient of each carbon sink operator model in the carbon sink equivalent calculation based on the comparison results; The monitoring data is input into the carbon sink accounting sub-model obtained by screening to obtain an intermediate calculated value of the carbon sink equivalent, and the intermediate calculated value of the carbon sink equivalent is weightedly calculated based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

[0010] As one preferred solution, 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: Constructing an initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system, wherein the federated learning framework includes multiple participants; constructing a plurality of quantitative training data sets based on the historical monitoring data and corresponding historical carbon sink potential values ​​of each of the participants; preprocessing each of the training data sets, and inputting each of the preprocessed training data sets into the initial carbon sink prediction model for training to obtain trained first model parameters; Uploading each of the first model parameters to the main chain, processing the first model parameters based on a preset aggregation strategy to obtain second model parameters; updating the initial carbon sink prediction model 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.

[0011] As one preferred solution, the generation of a carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value, and an NFT identifier associated with the carbon sink digital certificate, includes: Standardizing the carbon sink equivalent and the carbon sink potential value, and integrating the standardized carbon sink equivalent and the carbon sink potential value with corresponding metadata tags, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints to generate basic integrated data; Obtaining an original data file package corresponding to the basic integrated data in the evidence storage side chain, and performing a calculation on the original data file package based on a first hash algorithm to obtain a data packet hash value, wherein the data packet hash value is consistent with the hash value of the original data file package stored on the side chain; Calculating the data packet hash value and the corresponding metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint based on a second hash algorithm to obtain a global hash value; Based on the global hash value, the carbon sink equivalent, the carbon sink potential value, the metadata tag, the carbon sink accounting model identifier and the carbon sink prediction model parameter fingerprint in the basic integrated data are associated to generate the carbon sink digital certificate, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

[0012] As one preferred solution, locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the target data block to obtain a second verification result includes: Obtaining metadata tags, data file package hash values, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints associated with the carbon sink digital certificate to be traded; According to the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage side chain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block; Calculating a first hash value of the data file package based on a first hash algorithm, and determining that the verification fails if the first hash value is inconsistent with the hash value of the data file package; Otherwise, a 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 a 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 determined to be unsuccessful; otherwise, the verification is passed to generate the second verification result.

[0013] As one of the preferred solutions, the contract status verification of the NFT identifier corresponding to the carbon sink digital certificate to be traded is performed based on the transaction side chain to obtain a third verification result, including: 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 record corresponding to the first NFT identifier on the transaction side chain; Compare the ownership address with the transaction initiator address to generate a first comparison result; compare the current transaction time with the effective time and the expiration time respectively to obtain a second comparison result; extract the supervision record corresponding to the first NFT identifier on the supervision side chain, and compare the transaction record with the supervision 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.

[0014] As one preferred solution, the synchronous updating of the main chain, the transaction side chain and the supervision side chain based on the acquired carbon sink transaction data includes: Transferring the ownership of the carbon sinks to be traded based on the carbon sink transaction data, and storing the generated ownership change record in the main chain, wherein 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 hashed, and the generated unique transaction hash value is stored in the transaction side chain, while the compliance verification result is recorded in the regulatory side chain.

[0015] As one preferred solution, the query traceability results of the certificate storage side chain, the transaction side chain, and the supervision side chain 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, obtain the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate from the main chain; Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage side chain is located using the spatiotemporal index matrix to obtain the original monitoring data file package; Query the historical data of the circulation of the associated NFT identifier in the transaction side chain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the supervision side chain; Based on the original monitoring data file package, the circulation history data and the compliance verification record, a traceability result of the carbon sink digital certificate to be queried is generated.

[0016] Compared with the prior art, the embodiments of the present invention have the following advantages: 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 parallelly store spatiotemporal data, and the transaction sidechain and regulatory sidechain respectively handle asset transfer and compliance verification, completely eliminating the functional redundancy problem of traditional single chains. Specifically, the spatiotemporal index matrix constructs a three-dimensional retrieval system of "geographic grid ID + timestamp + data pointer", improving data location efficiency by over 90%, enabling instant retrieval of raw data blocks from the storage sidechain during transaction verification. A cross-chain synchronization mechanism automates the entire process of accounting, title confirmation, transaction, and supervision, eliminating the need for manual integration of multi-source data. This reduces the time required to trace carbon offset transactions from the traditional 4-6 hours to less than 10 minutes.

[0017] 2) The present invention constructs a three-dimensional trusted verification and precise traceability system: pre-compliance verification on the regulatory side chain, double hash comparison on the evidence side chain, and NFT contract status verification on the transaction side chain form triple protection; through the synchronous update mechanism, it ensures that the main chain ownership information, transaction side chain flow records, and regulatory side chain compliance verification results are consistent in real time to avoid cross-chain status conflicts; relying on the association between the spatiotemporal index matrix and the NFT identifier, the three-chain data can be aggregated with one click to generate a full-link traceability report including data source-accounting model-transaction compliance, realizing the transparency of the entire process 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a blockchain evidence storage and traceability method driven by ocean carbon sink big data in one embodiment of the present invention; Figure 2 This is a schematic diagram of the architecture of a multi-chain structure in one embodiment of the present invention; Among them, 11 is the main chain; 12 is the evidence storage side chain; 13 is the transaction side chain; 14 is the supervision side chain. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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 ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] In the description of the present invention, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0021] In the description of the present invention, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" 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, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0023] One embodiment of the present invention provides a blockchain evidence storage and traceability method based on ocean carbon sink big data. For details, please refer to Figure 1 , Figure 1 The figure shows a flow chart of a blockchain evidence storage and traceability method based on ocean carbon sink big data driven in one embodiment of the present invention, which is applied to a blockchain system with a multi-chain architecture. The multi-chain architecture includes a main chain, an evidence storage side chain, a transaction side chain, and a supervision side chain, and includes steps S1-S7: S1: Dynamically partition the monitoring data of the blue carbon ecosystem, model the partitioning results based on a directed acyclic graph structure, store the modeled data blocks in parallel on the evidence storage side chain, and generate a spatiotemporal index matrix corresponding to the data blocks; S2: Calling the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block, and calling the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block; generating a carbon sink digital certificate and an NFT identifier associated with the carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value; S3: In response to the carbon offset transaction request, performing compliance verification on the transaction participants based on the regulatory side chain to obtain a first verification result; S4: Locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain 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; S5: Verify the contract status of the NFT identifier corresponding to the carbon sink digital certificate to be traded based on the transaction side chain to obtain a third verification result; S6: If the first verification result, the second verification result, and the third verification result are all passed, then the main chain, the transaction side chain, and the supervision side chain are synchronously updated based on the acquired carbon sink transaction data; S7: In response to the carbon sink query request, query the traceability results of the certificate storage side chain, the transaction side chain and the supervision side chain corresponding to the carbon sink digital certificate to be queried on the main chain.

[0024] Specifically, this invention uses blockchain technology to build an ocean carbon sink management system that features efficient evidence storage, accurate accounting, reliable transactions, and real-time supervision. This system provides technical support for achieving environmental protection goals and offers a solution that can be used as a reference for big data evidence storage and asset transactions in other fields. First, let's explain the technical terms used in this invention.

[0025] Blue carbon ecosystems: These are ecosystems in the ocean that have strong carbon sink capabilities, such as mangroves, salt marshes, and seagrass beds, which capture and store carbon dioxide through photosynthesis. Directed Acyclic Graph (DAG): A data structure consisting of nodes and directed edges with no cyclic paths. In this solution, it is used to model monitoring data blocks, supporting parallel storage and efficient retrieval. Spatiotemporal index matrix: A three-dimensional index structure that links data blocks in the evidence storage sidechain through "geographic grid ID + timestamp + data pointer" to achieve data positioning in seconds; Carbon sink equivalent: a quantitative indicator that measures the ability of marine ecosystems to absorb and store carbon dioxide, calculated using a carbon sink accounting model; Carbon sink potential: An estimate of future carbon sink capacity based on historical data and forecast models, used to assess the sustainability of blue carbon ecosystems; NFT identifier: a non-fungible token identifier used to uniquely identify carbon sink digital certificates, supporting asset ownership confirmation and transaction circulation; Hash value comparison: A method of verifying data integrity and authenticity by comparing the hash value of the data. If the hash values ​​are inconsistent, it indicates that the data may have been tampered with; Federated learning framework: A distributed machine learning method that allows participants to collaboratively train models without sharing original data, protecting data privacy.

[0026] Compliance verification: A pre-transaction step in carbon offset trading. This involves verifying the qualifications, permissions, and transaction compliance of transaction participants based on the regulatory side chain. For example, it verifies whether the participants have the qualifications for carbon offset trading and whether they comply with environmental protection policy requirements. This generates a first verification result of "pass / fail" to ensure that the transaction entities are legal and compliant, laying the foundation for subsequent transaction verification.

[0027] Main chain: The main chain undertakes the core functions of model notarization, smart contracts and global indexing. Model notarization includes storing the code and parameter fingerprints of the carbon sink accounting model and prediction model to ensure the traceability of the algorithm. Smart contracts include deploying core contracts such as carbon sink digital certificate generation and NFT ownership transfer, and triggering side chain operations through cross-chain calls; Global indexing: Maintaining key information such as hash values ​​and metadata tags of each side chain to form a unified carbon sink asset ledger; The main chain usually adopts a consortium chain consensus mechanism to ensure the consistency and security of core data.

[0028] Evidence storage side chain: Specialized in processing structured storage of blue carbon monitoring data, including DAG data modeling, hash verification and spatiotemporal index matrix.

[0029] Transaction side chain: Manage the entire transaction process of carbon sink assets, including NFT identification management, smart contract execution and high-frequency transaction processing.

[0030] Regulatory side chain: Build a firewall for real-time supervision and auditing, including compliance verification, full-link traceability and intelligent early warning.

[0031] like Figure 2 The figure shows a schematic diagram of the architecture of a multi-chain structure provided by an embodiment of the present invention, which includes a main chain 11, a storage side chain 12, a transaction side chain 13, and a supervision side chain 14. This multi-chain architecture achieves separation of duties and cross-chain collaboration through a modular design in which the main chain coordinates core logic, the storage side chain specializes in data storage and indexing, the transaction side chain handles asset circulation, and the supervision side chain independently performs compliance audits. This not only improves the efficiency of data storage, transaction processing speed, and real-time supervision, but also ensures data authenticity, contract validity, and compliance through a triple verification mechanism, ultimately forming a closed-loop system with full-chain traceability, providing efficient, secure, and regulatory-compliant technical support for the accurate accounting, trusted transactions, and standardized management of marine carbon sinks.

[0032] Preferably, in one embodiment of the present invention, the monitoring data of the blue carbon ecosystem is dynamically divided, the division results are modeled based on a directed acyclic graph structure, and the data blocks obtained by the modeling are stored in parallel in the evidence storage side chain, including: The original monitoring data of blue carbon ecosystems were sequentially processed by outlier filtering and spatiotemporal alignment to obtain a spatiotemporal aligned dataset; Dividing the spatiotemporal aligned dataset into blocks based on a preset geographic grid and a time window to generate data blocks containing at least spatial data, temporal data, and feature vectors; Generating a metadata tag corresponding to each of the data blocks, and encapsulating the data blocks and the metadata tags into a data file package; the metadata tags include a collection time, a geographic location, and a sensor number; Perform hash calculation on the data file package, store the data file package in the evidence storage side chain according to the geographic grid ID and timestamp, and synchronize the metadata tag and the calculated hash value to the main chain.

[0033] It should be noted that this embodiment aims to preprocess and structure the original monitoring data of the blue carbon ecosystem, solve the problems of data heterogeneity, spatiotemporal inconsistency and storage efficiency, and provide a reliable data foundation for subsequent carbon sink accounting, transaction verification and full-link traceability.

[0034] 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. It then performs spatiotemporal alignment. Specifically, for data with inconsistent timestamps, the sampling frequency is unified through interpolation. For spatial position deviations, the data is matched to corresponding grid cells using a geographic information system (GIS) to generate a "spatiotemporally aligned dataset" containing unified time series and spatial coordinates.

[0035] The spatiotemporally aligned dataset is partitioned based on a preset geographic grid and time window. The monitored sea area is divided into M×N grids, each grid is assigned a unique ID, and a preset geographic grid is generated. A 24-hour time window is used.

[0036] Each data block contains three types of information: spatial data, temporal data, and feature vectors. Spatial data includes the geographic grid ID, longitude and latitude range, etc.; temporal data includes the start and end timestamps of the time window; and feature vectors include key monitoring parameters within the time and space range.

[0037] A metadata tag is generated for each data block, including the acquisition time, geographic location, and sensor number, to trace the data collection context. The data block and metadata tag are packaged into a data file package, and the file package is hashed to obtain a unique hash value. This hash value serves as the digital fingerprint of the data, and any data tampering will cause the hash value to change.

[0038] Finally, the data file packages are sharded and stored on the evidence storage sidechain by geographic grid ID and timestamp. The DAG structure is used to model the spatiotemporal dependencies between blocks, supporting parallel storage and efficient querying. At the same time, metadata tags and hash values ​​are synchronized to the main chain, providing data indexing and integrity proof for the main chain to call the carbon sequestration model.

[0039] A Directed Acyclic Graph (DAG) is a non-cyclic, directed graph consisting of nodes and directed edges. Nodes represent data units, while edges represent dependencies or temporal relationships between nodes, without loops. Unlike the chain-like structure of traditional blockchains, DAG supports parallel data processing. Each node can independently verify and link to multiple predecessor nodes, making it suitable for high-frequency data writing and complex dependency modeling.

[0040] Preferably, in one embodiment of the present invention, generating a spatiotemporal index matrix corresponding to the data block includes: Associating the geographic grid ID and timestamp of each data block with the storage address of the corresponding data block in the evidence storage sidechain to generate an index unit containing spatial coordinates, time tags, and data pointers; According to the spatial adjacent relationship of the geographic grids and the order of the timestamps, all the index units are organized 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 check field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain a spatiotemporal index matrix; the hash value check field is generated by performing a combined hash calculation on the geographic grid ID, timestamp and data block content, and is used to verify the consistency between the index unit and the data block.

[0041] It should be noted that this embodiment aims to build an efficient spatiotemporal data indexing system to solve the problems of rapid retrieval of spatiotemporal dimensions and data consistency verification of blue carbon monitoring data.

[0042] Specifically, the blue carbon monitoring area is first gridded, for example, 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.

[0043] When a data block is stored on the proof-keeping sidechain, its geographic grid ID, timestamp, and the block's 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 results of the G_01 grid at 0:00 on October 1, 2023, and is stored in the DAG node.

[0044] All geographic grid IDs are sorted by spatial proximity to form a list of matrix rows. Timestamps are arranged in chronological order to form a list of matrix columns. Index cells are populated into matrix cells according to the mapping relationship between grid ID and timestamp. For example, the index cell for grid G_01 at 00:00 is populated into the matrix at (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 columns and data for multiple grids at the same time point is parallel in the rows, facilitating batch queries based on region and time range.

[0045] The geographic grid ID, timestamp, and data block content corresponding to each index unit are combined, and a hash value is calculated using a first hash algorithm to generate a check field Hash, which includes the grid ID, timestamp, and data content.

[0046] When the consistency between the index unit and the data block needs to be verified, the combined hash value of the data block is recalculated and compared with the checksum field stored in the index matrix. If they are consistent, it proves that the data has not been tampered with and the index is pointing correctly; if they are inconsistent, the index or data is considered abnormal. This mechanism ensures the trustworthiness of the entire process from index positioning to data block, avoiding verification failures caused by storage address errors or data tampering.

[0047] Preferably, in one embodiment of the present invention, the calling of the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block includes: extracting monitoring data from the data block, wherein the monitoring data includes biomass data, environmental data, and meteorological data; Calculating a data fluctuation coefficient based on the biomass data, the environmental data, and the meteorological data, and selecting one or more carbon sink operator sub-models that match the fluctuation coefficient, wherein the carbon sink operator sub-models include at least a static operator sub-model and a dynamic operator sub-model; Comparing the historical calculation results of the screened carbon sink operator models with the historical measurement results, and obtaining the weight coefficient of each carbon sink operator model in the carbon sink equivalent calculation based on the comparison results; The monitoring data is input into the carbon sink accounting sub-model obtained by screening to obtain an intermediate calculated value of the carbon sink equivalent, and the intermediate calculated value of the carbon sink equivalent is weightedly calculated based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

[0048] This embodiment solves the problems of poor adaptability and insufficient real-time performance of a single model in traditional carbon sink accounting. Specifically, first, the professional terms in this embodiment are explained.

[0049] Data volatility coefficient: an indicator that measures the magnitude of changes in monitoring data over a time series, used to assess ecosystem stability. Sharp fluctuations may indicate ecosystem disturbance. Static operator model: a carbon sink calculation model based on fixed parameters and empirical formulas, suitable for estimating the long-term average carbon sink in stable ecosystems; Dynamic operator model: A carbon sink calculation model that takes into account real-time changes in environmental variables. It is constructed through machine learning or system dynamics methods and is suitable for rapidly changing ecological scenarios. Weight coefficient: The contribution ratio of each sub-model in the final carbon sink equivalent calculation, which is dynamically adjusted based on the results of historical data verification.

[0050] Three types of monitoring data are parsed from the data blocks of the evidence storage side chain, including biomass data, such as mangrove aboveground biomass (unit: tons / hectare) and vegetation coverage (percentage); environmental data, such as bottom seawater salinity (‰) and dissolved oxygen concentration (mg / L); and meteorological data, such as average daily temperature (℃) and annual cumulative rainfall (mm).

[0051] Normalize the resulting multi-source, heterogeneous data to eliminate dimensionality effects. Calculate the standard deviation or coefficient of variation based on recent monitoring data to assess data fluctuations. The recent period can be the past month. For example, the coefficient of variation for biomass data can be expressed as: coefficient of variation = (standard deviation / mean) × 100%.

[0052] The data are divided into different types according to the volatility coefficient, such as stable type: volatility coefficient <10%; volatility type: 10%≤volatility coefficient <30%; drastic change type: volatility coefficient ≥30%.

[0053] Recent monitoring data was selected and carbon sink equivalents were calculated using each sub-model. These were then compared with the measured values ​​from the same period to calculate the average relative error for each sub-model. The measured values ​​from the same period included actual carbon sink data obtained through remote sensing inversion or field sampling. The weight coefficient was determined based on the inverse of the average relative error, with models with smaller errors receiving higher weights.

[0054] The current monitoring data are input into the selected sub-models respectively to obtain the intermediate calculated values, and the final carbon sink equivalent is calculated based on the weight coefficient.

[0055] Preferably, in one embodiment of the present invention, the calling of the carbon sequestration prediction model of the main chain to calculate the carbon sequestration potential value of the data block includes: Constructing an initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system, wherein the federated learning framework includes multiple participants; constructing a plurality of quantitative training data sets based on the historical monitoring data and corresponding historical carbon sink potential values ​​of each of the participants; preprocessing each of the training data sets, and inputting each of the preprocessed training data sets into the initial carbon sink prediction model for training to obtain trained first model parameters; Uploading each of the first model parameters to the main chain, processing the first model parameters based on a preset aggregation strategy to obtain second model parameters; updating the initial carbon sink prediction model 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.

[0056] It should be noted that this embodiment addresses 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 issues of traditional RNNs, making it suitable for processing blue carbon monitoring data with time series characteristics.

[0057] Specifically, a three-layer LSTM network is constructed. The input layer receives multi-dimensional monitoring data, the hidden layer captures long-term dependencies in time series through gated units, and the output layer predicts future carbon sequestration potential. A horizontal federated learning architecture is used, and participants include marine monitoring agencies and research institutes in coastal provinces. The framework includes: Parameter server (main chain): responsible for aggregating model parameters uploaded by each participant; Client (participant): Locally stores historical monitoring data, performs model training, and uploads parameters; Secure communication protocol: Use homomorphic encryption or differential privacy technology to ensure privacy during parameter transmission.

[0058] Each participant sequentially performs missing value filling, normalization, and time series windowing on local historical monitoring data. Missing value filling involves using linear interpolation or random forest methods to complete missing data points; normalization involves scaling all feature values ​​to the [0, 1] range to eliminate dimensionality effects; and time series windowing involves dividing the data into training samples based on time windows. Each sample contains input features (i.e., historical monitoring data) and a label (i.e., the corresponding carbon sink potential value).

[0059] Each participant independently trains an LSTM model using preprocessed data, optimizing parameters through a backpropagation algorithm to obtain 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.

[0060] After passing the legality verification, the first model parameters are aggregated based on a preset strategy, which includes a weighted average based on data volume. The aggregated second model parameters are synchronized with all participating parties, and the initial carbon sink prediction model is updated to form a globally optimal carbon sink prediction model.

[0061] The monitoring data in the current data block is fed into a trained carbon sink prediction model. The model outputs carbon sink potential values ​​at different future timescales, taking into account trends under different climate change scenarios. It also outputs prediction confidence intervals to assess the reliability of the prediction results.

[0062] Preferably, in one embodiment of the present invention, the generating of the carbon sink digital certificate according to the carbon sink equivalent and the carbon sink potential value, and the NFT identifier associated with the carbon sink digital certificate, includes: Standardizing the carbon sink equivalent and the carbon sink potential value, and integrating the standardized carbon sink equivalent and the carbon sink potential value with corresponding metadata tags, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints to generate basic integrated data; Obtaining an original data file package corresponding to the basic integrated data in the evidence storage side chain, and performing a calculation on the original data file package based on a first hash algorithm to obtain a data packet hash value, wherein the data packet hash value is consistent with the hash value of the original data file package stored on the side chain; Calculating the data packet hash value and the corresponding metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint based on a second hash algorithm to obtain a global hash value; Based on the global hash value, the carbon sink equivalent, the carbon sink potential value, the metadata tag, the carbon sink accounting model identifier and the carbon sink prediction model parameter fingerprint in the basic integrated data are associated to generate the carbon sink digital certificate, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

[0063] First, the professional terms in this embodiment are explained.

[0064] Standardization: The process of converting carbon sink equivalents and carbon sink potential values ​​of different dimensions into a unified scale, such as normalizing them to the interval [0,1], to eliminate the impact of dimensional differences on subsequent calculations.

[0065] First hash algorithm: an algorithm that performs hash calculations on the original data file package to generate a unique digital fingerprint of 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: an encrypted hash value of the model parameters, used to verify the consistency and non-tamperability of the model training process; model identifier: a string that uniquely identifies the carbon sink accounting model, used to record the source and version of the accounting method.

[0066] Specifically, the original calculation results, including carbon sink equivalent and carbon sink potential values, are converted to values ​​in the [0, 1] interval through Min-Max normalization. The standardized carbon sink data are then integrated with metadata, including metadata tags, model identifiers, and parameter fingerprints.

[0067] Retrieve the original data file package of the corresponding time and space from the evidence storage side chain, calculate the hash value using the first hash algorithm, and verify whether the hash value is consistent with the hash value recorded in the evidence storage side chain to ensure that the data has not been tampered with.

[0068] 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 the unique identifier of the carbon sink digital certificate to ensure data integrity and source traceability.

[0069] The global hash value is associated with the basic integrated data (carbon sink equivalent, carbon sink potential value, etc.) to form an unalterable digital record with the following structure: {"Certificate ID":"0x5a3c2f...","Carbon Sink Equivalent":0.75,"Carbon Sink Potential":0.82,"Timestamp":"2023-10-01T12:00:00Z","Geographic Coordinates":"118.5°E,38.2°N","Calculation Model":"V2.3","Prediction Model":"LSTM_2023Q3","Data Packet Hash":"0x3b7d1e..."}.

[0070] The NFT associated with the digital certificate is created through a smart contract on the main chain. The NFT contains at least the following properties: Unique identifier: ERC-721 standard token ID generated based on the global hash value; Metadata link: points to the IPFS address or blockchain storage location of the carbon sink digital certificate; Dynamic attributes: carbon sink equivalent, carbon sink potential value; Permission control: set issuer, owner, transaction permissions, etc.

[0071] Preferably, in one embodiment of the present invention, locating the target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the target data block to obtain a second verification result includes: Obtaining metadata tags, data file package hash values, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints associated with the carbon sink digital certificate to be traded; According to the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage side chain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block; Calculating a first hash value of the data file package based on a first hash algorithm, and determining that the verification fails if the first hash value is inconsistent with the hash value of the data file package; Otherwise, a 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 a 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 determined to be unsuccessful; otherwise, the verification is passed to generate the second verification result.

[0072] Specifically, the metadata tag, data file package hash value, model identifier, and parameter fingerprint are extracted from the carbon sink digital certificate to be traded. Based on the geographic grid ID and timestamp in the metadata tag, the corresponding cell in the spatiotemporal index matrix is ​​located. For example, the row with the geographic grid ID G_01 and the column with the timestamp 20231001 are found, and the storage address of the data block is extracted from the cell.

[0073] Through the API of the evidence storage side chain, the original data file package in the target data block is obtained according to the storage address. Using the same first hash algorithm as when the data was uploaded to the chain, the hash value of the retrieved data file package is recalculated and compared with the hash value of the data file package recorded in the carbon sink digital certificate. If they are inconsistent, it means that the data may have been tampered with or the index is incorrect, and the verification fails. If they are consistent, the next step of verification is entered.

[0074] Based on the second hash algorithm, a second hash value of the metadata tag, data file package hash value, accounting model identifier and prediction model parameter fingerprint is calculated, and the calculated second hash value is compared with the global hash value of the carbon sink digital certificate: if they are inconsistent, it means that the metadata, model information or data association relationship of the certificate has been tampered with, and the verification fails; if they are consistent, the verification passes, and a second verification result is generated.

[0075] Preferably, in one embodiment of the present invention, the contract status verification of the corresponding NFT identifier of the carbon sink digital certificate to be traded based on the transaction side chain is performed to obtain a third verification result, including: 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 record corresponding to the first NFT identifier on the transaction side chain; Compare the ownership address with the transaction initiator address to generate a first comparison result; compare the current transaction time with the effective time and the expiration time respectively to obtain a second comparison result; extract the supervision record corresponding to the first NFT identifier on the supervision side chain, and compare the transaction record with the supervision 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.

[0076] First, the professional terms in this embodiment are explained.

[0077] Ownership address: records the blockchain address of the current legal holder of the NFT, similar to the "ownership certificate" of digital assets; effective time: the time point when the NFT begins to have trading or usage permissions; expiration time: the time point when the NFT permissions expire; transaction records: the NFT circulation history stored in the transaction side chain, including transaction time, addresses of both parties to the transaction, transaction quantity and other information; regulatory records: compliance records associated with the NFT in the regulatory side chain, such as transaction qualification review results, carbon sink additionality certification, etc.

[0078] Specifically, the NFT identifier associated with the carbon sink digital certificate to be traded is obtained. This identifier is automatically created by the smart contract when the carbon sink digital certificate is generated. Through the smart contract interface of the transaction side chain, the information corresponding to the NFT identifier, including the ownership address, effective and expiration dates, and transaction records, is queried.

[0079] The transaction initiator address (such as 0xAddr_User submitted by the user) and the NFT ownership address (0xAddr1) are checked for consistency: if they are consistent, the first comparison result of ownership matching is generated; if they are inconsistent, the first comparison result of ownership mismatch is generated, which may indicate unauthorized transactions or address forgery.

[0080] The current transaction time must meet the following conditions: effective time ≤ current time ≤ expiration time. If the conditions are met, a second comparison result with a valid time will be generated; if the conditions are not met, a second comparison result with an invalid time will be generated.

[0081] Obtain regulatory records associated with the NFT identifier from the regulatory sidechain through a cross-chain protocol, such as compliance certification status, transaction limits, and historical regulatory anomaly records. Check whether the transaction records are consistent with the compliance requirements in the regulatory records, such as whether the transaction amount exceeds the limit and whether the transacting party is on the regulatory whitelist. If consistent, generate a third-party comparison result indicating regulatory compliance; if inconsistent, generate a third-party comparison result indicating regulatory violation.

[0082] The third verification result is considered passed only when the first, second, and third comparison results are all passed; if any comparison result is not passed, the third verification result is considered failed, and the smart contract is triggered to automatically terminate the transaction.

[0083] As shown in Table 1, it is a data comparison table of the triple verification mechanism provided by the present invention.

[0084] Table 1

[0085] Preferably, in one embodiment of the present invention, the synchronous updating of the main chain, the transaction side chain and the supervision side chain based on the acquired carbon sink transaction data includes: Transferring the ownership of the carbon sinks to be traded based on the carbon sink transaction data, and storing the generated ownership change record in the main chain, wherein 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 hashed, and the generated unique transaction hash value is stored in the transaction side chain, while the compliance verification result is recorded in the regulatory side chain.

[0086] Specifically, when the triple verification (S3-S5) is passed, the smart contract of the transaction side chain automatically executes the NFT ownership transfer: the seller's address is removed from the NFT's ownership address and written to the buyer's address; the dynamic attributes of the NFT such as the number of transactions and the latest transaction time are updated.

[0087] Generate ownership change records and upload them to the main chain. The record content includes: {"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..."} The main chain adopts the alliance chain consensus mechanism to ensure that the ownership change records cannot be tampered with and reach consensus across the entire network.

[0088] The transaction sidechain collects information from 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 sink digital certificate ID and associated NFT identifier; and verification information such as the first, second, and third verification results. The integrated transaction information is hashed using the SHA-256 algorithm to generate a unique transaction hash value. The transaction hash value and transaction details (without sensitive information) are stored in a block on the transaction sidechain, allowing for quick query of transaction records using the hash value.

[0089] The smart contract on the regulatory sidechain automatically generates compliance verification results based on pre-set rules. Verification covers aspects such as the qualifications of the trading parties, the legality of the carbon sink source, and whether the transaction quantity exceeds the limit. The output is either compliant or non-compliant. If non-compliant, the violation type, such as expired qualifications or excessive trading, is recorded. The compliance verification results are linked to the transaction hash value and NFT identifier to form a regulatory audit log.

[0090] After completing the ownership change, the main chain sends update instructions to the transaction sidechain and the regulatory sidechain via a cross-chain messaging protocol, ensuring the atomicity of operations on all three chains. If any chain fails to update, all three chains are rolled back. Transaction timestamps recorded on the three chains are accurate to the second, and a blockchain consensus mechanism ensures time consistency, preventing record inconsistencies caused by time skew. The main chain's ownership change records, the transaction hash values ​​on the transaction sidechain, and the regulatory sidechain's compliance records are bidirectionally linked through transaction IDs and NFT identifiers, forming a mutually verifiable closed loop.

[0091] Preferably, in one embodiment of the present invention, the querying of the traceability results of the certificate storage side chain, the transaction side chain and the supervision side chain corresponding to the carbon sink digital certificate to be queried on the main chain includes: Based on the certificate identifier or spatiotemporal index condition in the carbon sink digital certificate to be queried, obtain the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate from the main chain; Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage side chain is located using the spatiotemporal index matrix to obtain the original monitoring data file package; Query the historical data of the circulation of the associated NFT identifier in the transaction side chain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the supervision side chain; Based on the original monitoring data file package, the circulation history data and the compliance verification record, a traceability result of the carbon sink digital certificate to be queried is generated.

[0092] Specifically, users enter the carbon sink digital certificate identifier to be queried, such as the global hash value, through the front-end interface, or initiate a traceability request using spatiotemporal index conditions. The main chain smart contract retrieves the target certificate based on the query conditions and returns core information: the global hash value, metadata tag, and associated NFT identifier.

[0093] According to the geographic grid ID and timestamp in the metadata tag, the corresponding cell is located in the spatiotemporal index matrix: through the API interface of the evidence side chain, the target data block is extracted according to the storage address, and the original monitoring data file package is parsed; the transaction side chain is queried through the associated NFT identifier to obtain the ownership change record, contract status and transaction details; the supervision side chain is queried through the global hash value or NFT identifier to obtain the compliance certification results, transaction supervision log and supervision policy matching degree.

[0094] The data returned by the three chains is standardized, and the original monitoring data, transaction records, and compliance records are arranged in chronological order to form a coherent timeline, making it easier to view status changes at different stages.

[0095] The integrated traceability results include the following: {"Basic information of certificate":{"Certificate ID":"0x5a3c2f...","Carbon sink equivalent":13.1 tons / hectare / year,"Carbon sink potential value":15.3±1.2 tons / hectare / year},"Data source tracing":{"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 track":{"Historical transactions":[{"Transaction time ":"2023-10-15T14:30:00Z","Transaction Parties":"0xSeller→0xBuyer","Transaction Quantity":50 tons,"Transaction Price":1,000 yuan / ton}],"Current Ownership":"0xBuyer (Effective Date: 2023-10-01, Expiration Date: 2030-12-31)"},"Regulatory Compliance Certificate":{"Accounting Model Compliance":"Passed the IPCC Mangrove Carbon Sink Accounting Methodology Certification (V2.3)","Transaction Compliance Status":"All previous transactions have passed the regulatory side chain verification","Regulatory Agency":"A certain research institute (Registration No.: 2023001)"}}.

[0096] Finally, by comparing the global hash value with the combined hash value of the three-chain data, it is ensured that the traceability result has 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.

[0097] Compared with the prior art, the embodiments of the present invention have the following advantages: 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 parallelly store spatiotemporal data, and the transaction sidechain and regulatory sidechain respectively handle asset transfer and compliance verification, completely eliminating the functional redundancy problem of traditional single chains. Specifically, the spatiotemporal index matrix constructs a three-dimensional retrieval system of "geographic grid ID + timestamp + data pointer", improving data location efficiency by over 90%, enabling instant retrieval of raw data blocks from the storage sidechain during transaction verification. A cross-chain synchronization mechanism automates the entire process of accounting, title confirmation, transaction, and supervision, eliminating the need for manual integration of multi-source data. This reduces the time required to trace carbon offset transactions from the traditional 4-6 hours to less than 10 minutes.

[0098] 2) The present invention constructs a three-dimensional trusted verification and precise traceability system: pre-compliance verification on the regulatory side chain, double hash comparison on the evidence side chain, and NFT contract status verification on the transaction side chain form triple protection; through the synchronous update mechanism, it ensures that the main chain ownership information, transaction side chain flow records, and regulatory side chain compliance verification results are consistent in real time to avoid cross-chain status conflicts; relying on the association between the spatiotemporal index matrix and the NFT identifier, the three-chain data can be aggregated with one click to generate a full-link traceability report including data source-accounting model-transaction compliance, realizing the transparency of the entire process 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.

[0099] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A blockchain evidence storage and traceability method based on ocean carbon sink big data, characterized by: A blockchain system applied to a multi-chain architecture, wherein the multi-chain architecture includes a main chain, a storage side chain, a transaction side chain, and a supervision side chain, and the method includes the following steps: Dynamically partition the monitoring data of the blue carbon ecosystem, model the partitioning results based on a directed acyclic graph structure, store the modeled data blocks in parallel on the evidence storage side chain, and generate a spatiotemporal index matrix corresponding to the data blocks; Calling the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block, and calling the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block; generating a carbon sink digital certificate and an NFT identifier associated with the carbon sink digital certificate based on the carbon sink equivalent and the carbon sink potential value; In response to a carbon offset transaction request, performing compliance verification on a transaction participant based on the regulatory side chain to obtain a first verification result; Locating a target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain 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; Performing contract status verification on the NFT identifier corresponding to the carbon sink digital certificate to be traded based on the transaction side chain 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 transaction side chain, and the supervision side chain are synchronously updated based on the acquired carbon sink transaction data; In response to the carbon sink query request, the traceability results of the certificate storage side chain, the transaction side chain and the supervision side chain corresponding to the carbon sink digital certificate to be queried on the main chain are queried.

2. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The dynamic division of the blue carbon ecosystem monitoring data, modeling of the division results based on a directed acyclic graph structure, and parallel storage of the modeled data blocks in the evidence storage side chain include: The original monitoring data of blue carbon ecosystems were sequentially processed by outlier filtering and spatiotemporal alignment to obtain a spatiotemporal aligned dataset; Dividing the spatiotemporal aligned dataset into blocks based on a preset geographic grid and a time window to generate data blocks containing at least spatial data, temporal data, and feature vectors; Generating a metadata tag corresponding to each of the data blocks, and encapsulating the data blocks and the metadata tags into a data file package; the metadata tags include a collection time, a geographic location, and a sensor number; Perform hash calculation on the data file package, store the data file package in the evidence storage side chain according to the geographic grid ID and timestamp, and synchronize the metadata tag and the calculated hash value to the main chain.

3. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 2 is characterized in that: Generating a spatiotemporal index matrix corresponding to the data block includes: Associating the geographic grid ID and timestamp of each data block with the storage address of the corresponding data block in the evidence storage sidechain to generate an index unit containing spatial coordinates, time tags, and data pointers; According to the spatial adjacent relationship of the geographic grids and the order of the timestamps, all the index units are organized 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 check field of the data block is embedded in the spatiotemporal two-dimensional matrix structure to obtain a spatiotemporal index matrix; the hash value check field is generated by performing a combined hash calculation on the geographic grid ID, timestamp and data block content, and is used to verify the consistency between the index unit and the data block.

4. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The calling of the carbon sink accounting model of the main chain to calculate the carbon sink equivalent of the data block includes: extracting monitoring data from the data block, wherein the monitoring data includes biomass data, environmental data, and meteorological data; Calculating a data fluctuation coefficient based on the biomass data, the environmental data, and the meteorological data, and selecting one or more carbon sink operator sub-models that match the fluctuation coefficient, wherein the carbon sink operator sub-models include at least a static operator sub-model and a dynamic operator sub-model; Comparing the historical calculation results of the screened carbon sink operator models with the historical measurement results, and obtaining the weight coefficient of each carbon sink operator model in the carbon sink equivalent calculation based on the comparison results; The monitoring data is input into the carbon sink accounting sub-model obtained by screening to obtain an intermediate calculated value of the carbon sink equivalent, and the intermediate calculated value of the carbon sink equivalent is weightedly calculated based on the weight coefficient to obtain the carbon sink equivalent corresponding to the monitoring data.

5. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The calling of the carbon sink prediction model of the main chain to calculate the carbon sink potential value of the data block includes: Constructing an initial carbon sink prediction model based on LSTM and a federated learning framework based on the blockchain system, wherein the federated learning framework includes multiple participants; Constructing a number of quantitative training data sets based on the historical monitoring data and corresponding historical carbon sequestration potential values ​​of each of the participants; Preprocessing each of the training data sets, and inputting each of the preprocessed training data sets into the initial carbon sink prediction model for training to obtain trained first model parameters; Uploading each of the first model parameters to the main chain, processing the first model parameters based on a preset aggregation strategy to obtain second model parameters; updating the initial carbon sink prediction model 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.

6. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The generating of the carbon sink digital certificate according to the carbon sink equivalent and the carbon sink potential value, and the NFT identifier associated with the carbon sink digital certificate, includes: Standardizing the carbon sink equivalent and the carbon sink potential value, and integrating the standardized carbon sink equivalent and the carbon sink potential value with corresponding metadata tags, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints to generate basic integrated data; Obtaining an original data file package corresponding to the basic integrated data in the evidence storage side chain, and performing a calculation on the original data file package based on a first hash algorithm to obtain a data packet hash value, wherein the data packet hash value is consistent with the hash value of the original data file package stored on the side chain; Calculating the data packet hash value and the corresponding metadata tag, carbon sink accounting model identifier, and carbon sink prediction model parameter fingerprint based on a second hash algorithm to obtain a global hash value; Based on the global hash value, the carbon sink equivalent, the carbon sink potential value, the metadata tag, the carbon sink accounting model identifier and the carbon sink prediction model parameter fingerprint in the basic integrated data are associated to generate the carbon sink digital certificate, and an NFT identifier associated with the carbon sink digital certificate is generated through a smart contract.

7. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The step of locating a target data block corresponding to the carbon sink digital certificate to be traded in the evidence storage side chain based on the spatiotemporal index matrix, and comparing the hash value of the carbon sink digital certificate to be traded with the target data block to obtain a second verification result includes: Obtaining metadata tags, data file package hash values, carbon sink accounting model identifiers, and carbon sink prediction model parameter fingerprints associated with the carbon sink digital certificate to be traded; According to the geographic grid ID and timestamp in the metadata tag, locate the corresponding target data block in the evidence storage side chain based on the spatiotemporal index matrix, and extract the data file package stored in the target data block; Calculating a first hash value of the data file package based on a first hash algorithm, and determining that the verification fails if the first hash value is inconsistent with the hash value of the data file package; Otherwise, a 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 a 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 determined to be unsuccessful; otherwise, the verification is passed to generate the second verification result.

8. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The contract status verification of the corresponding NFT identifier of the carbon sink digital certificate to be traded based on the transaction side chain is performed to obtain a third verification result, including: 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 record corresponding to the first NFT identifier on the transaction side chain; Compare the ownership address with the transaction initiator address to generate a first comparison result; compare the current transaction time with the effective time and the expiration time respectively to obtain a second comparison result; extract the supervision record corresponding to the first NFT identifier on the supervision side chain, and compare the transaction record with the supervision 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.

9. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The synchronous updating of the main chain, the transaction side chain and the supervision side chain based on the acquired carbon sink transaction data includes: Transferring the ownership of the carbon sinks to be traded based on the carbon sink transaction data, and storing the generated ownership change record in the main chain, wherein 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 hashed, and the generated unique transaction hash value is stored in the transaction side chain, while the compliance verification result is recorded in the regulatory side chain.

10. The blockchain evidence storage and traceability method based on ocean carbon sink big data as claimed in claim 1 is characterized in that: The tracing results of querying the certificate storage side chain, the transaction side chain, and the supervision side chain 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, obtain the global hash value, metadata tag and associated NFT identifier corresponding to the carbon sink digital certificate from the main chain; Based on the geographic grid ID and timestamp in the metadata tag, the target data block in the evidence storage side chain is located using the spatiotemporal index matrix to obtain the original monitoring data file package; Query the historical data of the circulation of the associated NFT identifier in the transaction side chain, and obtain the compliance verification record associated with the carbon sink digital certificate to be queried in the supervision side chain; Based on the original monitoring data file package, the circulation history data and the compliance verification record, a traceability result of the carbon sink digital certificate to be queried is generated.

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