Carbon sink project management method

Abstract

The application relates to the technical field of carbon sink management, and discloses a carbon sink project management method, which comprises the following steps: a whole life cycle of a carbon sink project is split into a plurality of standardized nodes, and closed-loop data flow conversion between the standardized nodes is carried out through a distributed message queue; a carbon sink accounting model is selected according to data of the closed-loop data flow conversion; the carbon sink accounting model is dynamically optimized according to real-time environmental data of a target region, so that carbon storage accounting results are obtained; credible evidence is stored, and a task contract is executed based on a trigger condition; parameters of the carbon sink accounting model are trained according to historical accounting data and actual monitoring data which have been stored as evidence; early warning is triggered according to a node data threshold, an accounting result deviation or a task contract execution state, and the state migration of associated nodes is controlled according to the early warning result. The application realizes intelligent collaborative management of the whole process of a carbon sink project.

Description

Technical Field

[0001] This invention relates to the field of carbon sink management technology, and more specifically, to a carbon sink project management method. Background Technology

[0002] With the continuous improvement of the national voluntary greenhouse gas emission reduction trading market, the development scale of forestry carbon sink projects, such as afforestation carbon sinks and forest management carbon sinks, is gradually expanding. Carbon sink projects typically involve multiple stages, including planning and design, project implementation, monitoring and accounting, verification and certification, trading and settlement, and post-maintenance. They are characterized by long implementation cycles, multiple participating entities, and complex data sources, which places high demands on the collaborative management of the entire project process, accurate calculation of carbon sinks, and reliable data traceability.

[0003] In existing carbon sink project management methods, each stage uses independent information management tools or business systems. There is a lack of a unified data flow mechanism between project planning data, monitoring data, accounting data, and transaction data, making it difficult to effectively correlate data across different stages. Some data still relies on manual collection, processing, and transmission, which can easily lead to data lag or error accumulation, impacting project management efficiency. Furthermore, existing carbon sink accounting methods calculate carbon reserves based on preset regional parameters and fixed accounting models, making it difficult to dynamically adjust to real-time environmental changes, regional differences, and long-term monitoring data during project implementation. This results in insufficient adaptability of the accounting model, and as the project cycle extends, discrepancies may arise between the accounting results and actual carbon reserves. Key data in existing carbon sink projects are typically managed using centralized storage methods. Project process data, accounting results, and transaction records lack a reliable end-to-end evidence storage mechanism, making it difficult to effectively track data sources, modification processes, and status changes. This leads to insufficient data credibility and low traceability efficiency during project verification, transaction auditing, and subsequent model optimization.

[0004] Therefore, it is necessary to design a carbon sequestration project management method to address the problems existing in current technologies. Summary of the Invention

[0005] In view of this, the present invention proposes a carbon sink project management method, which aims to solve the above-mentioned problems.

[0006] This invention proposes a carbon sink project management method, comprising: The entire lifecycle of a carbon sequestration project is divided into several standardized nodes. A state machine is configured for each standardized node, and closed-loop data flow between the standardized nodes is carried out through a distributed message queue. Based on the source node output data of the closed-loop data flow, real-time data from third-party systems, and historical project data, a carbon sink accounting model is selected from the model library; the source node output data is the data processed and output by the standardized nodes, including tree species type, average growth cycle, initial value of carbon conversion rate, forest density, and diameter at breast height measurement data; the real-time data from third-party systems is the data obtained in real time from external systems, including daily rainfall, accumulated temperature data, soil organic matter content data, terrain slope, and slope aspect data; Based on real-time environmental data of the target area, a regional correction factor is determined, the carbon sink accounting model is dynamically optimized, and the carbon storage accounting result is obtained based on the optimized carbon sink accounting model. The source node output data and the carbon storage calculation results are used to establish a reliable record, and the task contract is executed based on the triggering conditions. Based on the historical accounting data and actual monitoring data that have been stored, the data are input into the dynamic optimization process to train the parameters of the carbon sink accounting model. Early warnings are triggered based on node data thresholds, accounting result deviations, or task contract execution status, and the state transition of associated nodes is controlled based on the results of the early warnings.

[0007] Furthermore, the closed-loop data flow includes: When the source node outputs data, it attaches metadata tags, which include source node identifier, source stage, data type, data version number, integrity check code, and timestamp. Based on the metadata tags, flow rules are matched to determine the target node. According to the stage and node attributes in the metadata tags, the data is allocated to the corresponding partition of the distributed message queue. After receiving the data, the target node performs integrity verification. If the verification passes, the node status is updated; if the verification fails, a retry is triggered. If the number of retries exceeds the maximum limit, the node status is changed to abnormal freeze.

[0008] Furthermore, the state machine of the standardized node includes states such as pending startup, in execution, data pending verification, completed, and abnormally frozen. A data dependency graph between each standardized node is pre-configured, and the data dependency graph is a directed acyclic graph; when the state of any source node changes to abnormal freeze, all downstream nodes of the source node are suspended according to the data dependency graph; when the source node recovers to normal, the state of the downstream nodes is restored to pending startup.

[0009] Furthermore, when selecting a carbon sequestration accounting model, the following should be considered: A multi-level weighted matching rule is used for matching, which includes a basic attribute layer, a scene adaptation layer, and a precision requirement layer. The basic attribute layer matches based on tree species type and regional climate zone, the scene adaptation layer matches based on project scene tags, and the precision requirement layer matches based on the error range requirements for the purpose of carbon sink data.

[0010] Furthermore, when selecting a carbon sequestration accounting model, the following factors are also considered: Candidate models are selected from the model library using a decision tree; the historical fit of each candidate model is determined based on historical project data of the same tree species in the same region; the final score of each candidate model is determined based on the decision tree hierarchy deviation and the historical fit, and the candidate model with the highest score is selected as the carbon sink accounting model.

[0011] Furthermore, when dynamically optimizing the carbon sink accounting model, the method of determining the regional correction factor includes: Acquire real-time rainfall data P, accumulated temperature data T, and soil organic matter content data S for the target area, and obtain the corresponding baseline values ​​P0, T0, and S0 for the same area; calculate the relative deviation factors P / P0, T / T0, and S / S0 respectively; perform a weighted summation of the relative deviation factors to obtain the dimensionless regional correction factor R = w_P·(P / P0) + w_T·(T / T0) + w_S·(S / S0); where w_P, w_T, and w_S are weight coefficients and satisfy w_P + w_T + w_S = 1; multiply the regional correction factor R by the basic carbon conversion rate parameter C_base of the carbon sink accounting model to obtain the actual carbon conversion rate C_actual = C_base × R.

[0012] Furthermore, the dynamic optimization also includes: The predicted carbon storage of the carbon sink accounting model is compared with the actual monitoring data to obtain the deviation rate; when the deviation rate exceeds the deviation tolerance, the key parameters of the carbon sink accounting model are adjusted through optimization algorithms; when the historical data accumulation of the target area exceeds the data volume threshold, the parameters of the target area are trained.

[0013] Furthermore, when performing trusted notarization based on the source node output data and the carbon storage calculation results, and executing the task contract based on trigger conditions, the process includes: The source node output data and the carbon storage calculation results are appended with digital signatures, timestamps, and integrity verification codes, and stored in a trusted data warehouse; the triggering conditions include time node triggering, data threshold triggering, and node state change triggering. The triggering condition is obtained, and the operation permission is verified according to the triggering condition. If the verification is successful, the binding action is executed and an execution log is generated for evidence storage. If the execution fails, the task is retried. If the retry fails, the task is frozen and an alarm is pushed.

[0014] Furthermore, the parameter training for the carbon sink accounting model includes: Acquire newly added valid monitoring data events in the trusted evidence; when the newly added valid monitoring data events include actual monitoring data of the same tree species in the same area as the current project, use them as incremental samples; compare the model prediction values ​​of the incremental samples with the actual monitoring data, and when the deviation exceeds the calibration tolerance, perform parameter training of the carbon sink accounting model.

[0015] Furthermore, it also includes: configuring operation permissions and data access scope for different roles in each of the standardized nodes, wherein the operation permissions are bound to the state machine, and the same role has different operation permissions in different states; synchronizing the flow status of the standardized nodes and the state machine change records to the trusted storage in real time, and executing the planned tasks bound to the current node according to the changes in the flow status.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: the established standardized data pipeline enables model decisions to be based on complete information throughout the entire process rather than isolated parameters; dynamic calibration continuously improves the accuracy of accounting results; credible evidence provides a reliable calibration benchmark; feedback allows the model to continuously evolve with data accumulation; and status and early warning information drive the entire closed-loop automatic operation. Based on the above collaborative mechanism, this invention eliminates process fragmentation, significantly reduces carbon sink accounting errors, greatly reduces data traceability and verification costs, and enables real-time capture and automatic handling of progress anomalies, achieving intelligent, precise, and reliable management of the entire carbon sink project process. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a carbon sink project management method provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figure 1 As shown, a carbon sequestration project management method is proposed, including: S100: The entire life cycle of a carbon sink project is divided into several standardized nodes, a state machine is configured for each standardized node, and closed-loop data flow between standardized nodes is carried out through a distributed message queue. S200: Select a carbon sink accounting model from the model library based on the source node output data of the closed-loop data flow, real-time data from third-party systems, and historical project data; S300: Based on real-time environmental data of the target area, determine the regional correction factor, dynamically optimize the carbon sink accounting model, and obtain the carbon storage accounting result based on the optimized carbon sink accounting model; S400: Performs trusted notarization based on source node output data and carbon storage calculation results, and executes task contracts based on trigger conditions; S500: Based on the historical accounting data that has been stored and the actual monitoring data, the parameters of the carbon sink accounting model are trained by inputting them into the dynamic optimization process. S600: Triggers early warnings based on node data thresholds, accounting result deviations, or task contract execution status, and controls the state migration of associated nodes based on the early warning results.

[0020] In step S100, the entire lifecycle of a carbon sequestration project is divided into six core phases: planning, implementation, accounting, trading, maintenance, and termination, comprising fifteen standardized nodes. Each standardized node has clearly defined data input and output items. The planning phase includes the site selection assessment node, the tree species suitability analysis node, and the approval and filing node. The site selection assessment node requires input of forest soil type (e.g., red soil, yellow soil) and ecological protection red line data, both of which must meet preset indicator conditions (e.g., soil organic matter content not less than 2.5%, forest boundary distance not less than 500 meters from the ecological protection red line, slope not greater than 25 degrees) to output a site selection feasibility conclusion. The tree species suitability analysis node requires input of regional climate data (average annual temperature, precipitation) and tree species growth characteristic data (fast-growing or slow-growing, shade-loving or sun-loving), and outputs the average growth cycle (years) and the initial carbon conversion rate (tons of carbon per acre per year). The approval and filing node requires input of the site feasibility report and the tree species suitability analysis results, and outputs a project filing notice. The implementation phase includes planting acceptance, initial tending, and mid-term monitoring. The planting acceptance phase requires input of seedling survival rate (percentage) and seedling specifications (average diameter at breast height, plant height), and outputs a planting acceptance certificate. The initial tending phase requires input of tending time (year, month, day) and fertilizer application amount (kg per acre), and outputs an initial tending report. The mid-term monitoring phase requires input of forest density (trees per acre) and diameter at breast height measurement data (cm), and outputs a mid-term monitoring report. The accounting phase includes quarterly carbon storage measurement and annual verification. The quarterly carbon storage measurement phase requires input of mid-term monitoring data and accounting model parameters, and outputs a quarterly carbon storage report (including carbon storage tons and the calculation process). The annual verification phase requires input of the quarterly carbon storage report and on-site verification data, and outputs an annual verification opinion. The trading phase includes carbon sink listing, contract signing, and delivery. The carbon sink listing phase requires input of annual verification opinions and carbon sink calculation results, and outputs a listing application form. The contract signing stage requires inputting the listing results and buyer qualification certificates, and outputting the carbon sink trading contract. The delivery stage requires inputting the carbon sink trading contract and carbon sink certificate, and outputting a delivery completion certificate. The maintenance phase includes regular tending and disaster recovery stages. The regular tending stage requires inputting the tending plan (frequency and content) and forest growth status data, and outputting regular tending records. The disaster recovery stage requires inputting the disaster type (typhoon, fire, pests and diseases) and damaged area (acres), and outputting a disaster recovery report. The termination stage includes project liquidation and archiving stages. The project liquidation stage requires inputting carbon sink trading details and maintenance records, and outputting a project liquidation report. The archiving stage requires inputting the full-cycle report and contract documents, and outputting a project archive package.

[0021] Each standardized node is configured with a state machine, which includes at least five states: Pending Start, Executing, Data Pending Verification, Completed, and Abnormal Freeze. The Pending Start state indicates that the node has not yet started execution, waiting for prerequisites to be met, i.e., the preceding node has completed and the data has been delivered. The Executing state indicates that the node is performing data processing, including data entry, third-party data acquisition, and accounting calculations. The Data Pending Verification state indicates that the node's output data has been generated, awaiting integrity and correctness verification. The Completed state indicates that the node's data verification has passed, the process has ended normally, and downstream nodes can be triggered to start. The Abnormal Freeze state indicates that an anomaly has occurred in the node's execution or data verification, such as missing data, data exceeding thresholds, or no response from external systems; the process is paused, requiring manual intervention or automatic remediation. The state transition conditions for each node are predefined during node configuration. For example, the conditions for the planting acceptance node to transition from Pending Start to Executing are: both preceding nodes (tree species suitability analysis node and approval / filing node) have been changed to the Completed state, and their output data (tree species growth cycle, initial carbon conversion rate value, and filing notification) have been successfully pushed to the input queue of this node.

[0022] Standardized data flow between nodes is achieved through a distributed message queue; this embodiment uses Apache Kafka as the distributed message queue. Closed-loop data flow is implemented through four stages: data encapsulation and tagging, flow rule matching, partitioned routing execution, and feedback verification, ensuring accurate and efficient data flow.

[0023] During the data encapsulation and tagging stage, when each node completes execution and generates output data, the system automatically encapsulates the data and generates metadata tags. These tags include the following fields: source node identifier (e.g., NODE_PLAN_02), source stage (e.g., planning stage), data type (e.g., tree species parameters, monitoring data, calculation results), data version number, integrity check code (e.g., MD5 value), and timestamp (accurate to milliseconds). For example, when the tree species suitability analysis node in the planning stage outputs data on the average growth cycle of Chinese fir (15 years) and the initial carbon conversion rate of 0.8 tons of carbon per acre per year, the generated metadata tag would be "Planning Stage - Tree Species Suitability Analysis - Chinese Fir - Growth Cycle 15 Years - Carbon Conversion Rate 0.8 - Check Code A1B2C3D4 - Timestamp 20260601143025". This tag standardization ensures that the subsequent rule engine can accurately identify and route the data.

[0024] In the data flow rule matching stage, the distributed message queue has a built-in rule engine. This engine reads key fields from metadata tags and matches them against a pre-defined data flow rule table to determine the target node. The data flow rule table adopts a mapping structure of "data type + source stage → target node list". For example, tree growth cycle data (source stage is the planning stage) is only pushed to the carbon sink model parameter library in the accounting stage; forest density data (source stage is the implementation stage) is pushed to both the quarterly carbon storage measurement node in the accounting stage (as a calculation parameter) and the early warning node in the maintenance stage (for monitoring abnormal growth). The data flow rules support visual configuration, allowing project owners to customize or adjust the data flow path according to actual management needs.

[0025] During the partitioned routing execution phase, data is allocated to the "phase-node" two-level partitions of the distributed message queue based on the phase and node attributes in the metadata tags. The first-level partitions are divided by phase (planning, implementation, accounting, transaction, maintenance, termination), and the second-level partitions are divided by node. For example, planning phase data enters the planning first-level partition, and tree species suitability analysis data further enters the planning_02 second-level partition under this first-level partition; mid-term monitoring data in the implementation phase enters the implementation_03 second-level partition. Target nodes only subscribe to their corresponding second-level partitions, avoiding data redundancy and invalid data processing overhead caused by full subscription.

[0026] During the feedback verification phase, the target node retrieves data from the queue and performs real-time integrity verification. Verification includes: whether required fields are complete (e.g., planting acceptance data must include two core pieces of information: seedling survival rate and seedling specifications), whether the data format is correct (e.g., survival rate should be a value between zero and one hundred), and whether the checksum matches. If the verification passes, the target node's status changes from pending to executing and data processing begins. If the verification fails, the system automatically triggers a retry push and alarm mechanism: sending a retransmission request to the source node and pushing an alarm to the data entry role, while recording the number of retries. If retries fail more than three times, the node's status is changed to abnormal freeze, blocking subsequent processing to prevent the spread of erroneous data.

[0027] This method also includes a node dependency management step, which is configured once before the entire method runs and continues to apply throughout step S100. Specifically, a data dependency graph between standardized nodes is pre-configured. This graph is a directed acyclic graph (DAG), where nodes are standardized nodes and edges represent data dependencies. For example, the planting acceptance node depends on the output data (tree growth cycle, initial carbon conversion rate) of the tree species suitability analysis node and the output data (site feasibility conclusion) of the site selection assessment node. Therefore, the graph contains two directed edges: "tree species suitability analysis → planting acceptance" and "site selection assessment → planting acceptance". Based on this data dependency graph, the system automatically generates the flow rules for each node, which is the core basis of the aforementioned flow rule table.

[0028] When the state machine of any source node changes to an abnormal frozen state, the system retrieves all downstream nodes that depend on that source node based on the data dependency graph and synchronously changes the state machines of these downstream nodes to a paused waiting state. This paused waiting state is a special state between "pending startup" and "execution," indicating that although the node meets the startup conditions, the upstream data it depends on is abnormal. For example, if a mid-term monitoring node enters an abnormal freeze due to data verification failure, downstream nodes such as the quarterly carbon storage metering node, which depend on the output data of the mid-term monitoring node, will be synchronously placed in a paused waiting state. The downstream nodes will automatically return to the "pending startup" state until the source node recovers (either manually unfreezes the node after troubleshooting by maintenance personnel or automatically recovers). This cascading control mechanism avoids the risk of downstream nodes using incorrect data for calculations due to upstream data anomalies, ensuring the reliability of the entire data link.

[0029] It's important to note that in conventional computer or process management technologies, directed acyclic graphs (DAGs) are typically used only for topology sorting or task scheduling, addressing only the execution order. However, in this solution, DAGs are endowed with unique functions of business semantic binding and dynamic risk transmission control. The specific differences are as follows: First, the DAG in this solution is not merely an execution order graph, but also an automatic generator of data flow rules. Conventional usage requires manual definition of the execution order between tasks, while in this solution, the system automatically generates a flow rule table based on the edges (data dependencies) of the DAG. Specifically, the system automatically identifies the edge "tree species suitability analysis → planting acceptance" and automatically configures data routing mapping in the message queue's rule engine, eliminating the need for manually writing complex forwarding logic. Second, the DAG in this solution possesses dynamic fault tolerance capabilities for anomaly backpropagation and cascading freezes. In conventional usage, if an upstream task fails, downstream tasks typically only passively report an error or wait for a timeout. However, in this solution, when the system detects that a node in a directed acyclic graph (DAG) has become abnormally frozen, the system dynamically traverses the DAG in reverse, synchronously setting all downstream subgraph nodes that directly or indirectly depend on that node to a paused waiting state. This is an anti-pollution mechanism not found in conventional DAG schedulers, managing not only the execution order but also the reliability of data links. Thirdly, this solution deeply integrates the static dependencies of the DAG with the real-time states of the node state machines, making state change events a new driving force for the flow of the DAG, achieving state-driven dynamic flow control, whereas conventional DAGs only focus on the static structure.

[0030] In step S200, multi-dimensional data needs to be accessed first. These data sources include output data from upstream nodes and real-time data from external systems. Specifically, this method supports the access of three types of data. The first type is node output data, such as tree species type, average growth cycle, and initial carbon conversion rate output from the tree species suitability analysis node during the planning phase, and forest density and diameter at breast height (DBH) measurement data output from the mid-term monitoring node during the implementation phase. This type of data is automatically delivered via a distributed message queue, requiring no manual transcription. The second type is third-party system data, obtained in real-time through standardized API connections to external systems. For example, connecting to a meteorological platform to obtain daily rainfall and accumulated temperature data, connecting to a soil monitoring network to obtain soil organic matter content data, and connecting to a GIS system to obtain topographic slope and aspect data. The third type is historical project data. The system reads historical project parameters for the same region and tree species from a trusted historical database or trusted evidence storage system, such as the actual carbon conversion rate, biomass expansion factor, and measured carbon sink values ​​for Chinese fir projects in South China over the past five years. This data will serve as the benchmark for subsequent model selection and calibration.

[0031] After integrating multi-dimensional data, this method employs a multi-level weighted matching rule to initially screen suitable carbon sequestration models from the model library. The model library includes various types, such as IPCC refined models (classified by vegetation type), regionally corrected models (e.g., models specified in the Chinese Forestry Carbon Sequestration Methodology), and machine learning prediction models (regression or time-series prediction models trained on historical data). The multi-level weighted matching rule comprises three levels: a basic attribute layer, a scenario adaptation layer, and a precision requirement layer. The weights of each level are set according to their impact on the accounting results.

[0032] The basic attribute layer has a weighting of 45% to 55%. This layer matches tree species (such as Chinese fir, Masson pine, and eucalyptus) and regional climate zones (such as the humid and hot region of South China, the cold temperate region of Northeast China, and the arid region of Northwest China). It serves as the underlying constraint for carbon sequestration and directly determines the core parameters of the model (such as growth rate, carbon conversion rate baseline, and biomass expansion factor). The matching accuracy of this layer has an irreplaceable impact on the accounting results, hence its weighting is set to the maximum. For example, the Southern Chinese fir project prioritizes matching the fast-growing forest carbon sequestration model, while the Northeast larch project prioritizes matching the coniferous forest carbon sequestration model.

[0033] The scenario adaptation layer has a weighting of 25% to 35%. This layer matches projects based on their specific scenarios, including but not limited to whether the project has undergone disaster recovery or is an artificial forest. Scenario factors are localized modifications to basic attributes, affecting the fluctuation range of carbon sequestration, and have a moderate weighting. For example, projects that have experienced typhoon disasters need to be overlaid with a post-disaster carbon sequestration correction model, and artificial economic forest projects need to be overlaid with an economic forest carbon allocation model.

[0034] The accuracy requirement layer accounts for 15% to 25% of the weight. This layer matches the error range required by the end use of the carbon sink data, determining the computational complexity and goodness of fit of the model. For example, carbon trading projects require an error accuracy of ±3%, so a computationally intensive but highly accurate machine learning prediction model is preferred; routine monitoring projects allow for an error accuracy of ±5%, so a computationally efficient IPCC refinement model is preferred.

[0035] To ensure that the weight allocation adapts to the actual project scenario, this method also includes a dynamic weight linkage step between layers. Specifically, the scenario feature vector of the current project (including fields such as disaster markers, artificial forest markers, and management intensity) is acquired in real time. When a preset strong interference factor is detected in the scenario adaptation layer (e.g., the project is marked as post-typhoon restoration or non-artificial forest), the system determines that the interference of this scenario factor on the calculation results has exceeded the normal level. At this time, the lower limit of the weight ratio of the scenario adaptation layer is automatically increased (e.g., from 25% to 35%), and the upper limit of the weight ratio of the accuracy requirement layer is simultaneously decreased (e.g., from 25% to 15%), while the weight of the basic attribute layer remains unchanged. This dynamic linkage mechanism ensures that when there is significant scenario interference, the model selects to adapt to the scenario features first, avoiding the selection of a model that is not applicable to the current actual scenario due to a one-sided pursuit of theoretical accuracy.

[0036] After multi-level weighted matching is completed, the specific model screening and decision-making stage begins. This method employs a hybrid mechanism combining decision tree initial screening and historical data weighted voting. In the decision tree initial screening stage, a decision tree is constructed with "regional climate zone—tree species type—project scenario" as the main decision nodes. The root node of the decision tree is the regional climate zone (e.g., South China humid and hot, Central China subtropical, Northeast cold temperate, Northwest arid, Southwest plateau), the second layer is the tree species type (coniferous, broadleaf, fast-growing, slow-growing), and the third layer is the project scenario (planted forest, natural forest, disaster restoration). Two to three candidate models are selected from the model library along the decision tree path. For example, for the South China humid and hot climate, Chinese fir (fast-growing coniferous), and plantation project, the initial screening along the decision tree path "South China humid and hot → fast-growing coniferous → plantation" yields two candidate models: a fast-growing forest carbon sink model and an IPCC refined model (Chinese fir version).

[0037] In the historical data voting stage, historical project data for the same region and tree species (read from a trusted historical database) is used to calculate the historical fit for each candidate model. The historical fit is calculated as the reciprocal of the deviation rate between the model's predicted carbon storage and the actual measured value; the smaller the deviation rate, the higher the fit. The fit is normalized and used as the voting weight. For example, the historical fit of the fast-growing forest carbon sink model in the South China Chinese fir project is 95%, and its voting weight is set to 0.6; the historical fit of the IPCC refined model (Chinese fir version) is 85%, and its voting weight is set to 0.4.

[0038] In the model score calculation and initial selection stage, the final score of each candidate model is calculated using the formula S = W1 multiplied by (1 - D) + W2 multiplied by H. Here, S is the final model score; W1 and W2 are configurable weight coefficients, both defaulting to 0.5. W1 ranges from 0.4 to 0.6, and W2 is equal to 1 - W1. For scenarios with high accuracy requirements (such as carbon trading accounting), W2 can be increased to 0.6; for efficiency-priority scenarios (such as daily monitoring), W1 can be increased to 0.6. D is the decision tree level deviation, referring to the deviation of key parameters between the candidate model and the optimal path model of the decision tree. The formula is D = the absolute value of the difference between the key parameter values ​​of the candidate model and the optimal path model, divided by the key parameter value of the optimal path model. Key parameters include carbon conversion rate and tree growth cycle coefficient. The D value ranges from 0 to 0.1; when D is greater than 0.1, the candidate model is automatically eliminated. H is the historical fit, ranging from 0 to 1. The model with the highest S value is selected as the initial accounting model. In this embodiment, "optimal path model" refers to the carbon sink accounting model corresponding to the leaf node reached by matching the decision tree layer by layer according to the project characteristics (tree species type, project scenario) starting from the root node (regional climate zone) for the current project. "Key parameter values ​​of the optimal path model" refers to the core parameter values ​​pre-configured in the optimal path model, including but not limited to carbon conversion rate parameters and tree species growth cycle coefficient.

[0039] When the absolute value of the difference between the final model scores of the top two candidate models is less than a preset arbitration threshold (set to 0.05 in this embodiment), it indicates that the two models are very close in theoretical scores, and it is difficult to make a reliable decision based solely on the scores. At this point, a simulation comparison arbitration mode is activated: carbon storage is calculated using the top two candidate models for the current project data, and the calculation results are quickly compared with the actual monitoring values ​​from the previous period. The calculation deviation rate is calculated, and the model with the smaller deviation is selected as the final initial accounting model. This arbitration mechanism effectively solves the problem of difficulty in making decisions when model scores are close.

[0040] This method also includes dynamic weight adjustment and convergence determination steps. It monitors in real-time the deviation of environmental variables (key environmental variables in this embodiment include annual average rainfall, annual accumulated temperature, and average temperature) from the historical averages of the same tree species in the same region over the past five years. The criterion for determining significant deviation is: the absolute value of the deviation between the actual value of the environmental variable and the historical average exceeds a first preset threshold of 20%, and the difference is verified to be statistically significant by a t-test (setting the degrees of freedom to be four, p < 0.05). When a significant deviation is determined, the environmentally sensitive parameters of the candidate models (e.g., growth rate coefficient, carbon conversion rate adjustment coefficient) are fine-tuned. The magnitude of the fine-tuning is piecewise mapped to the degree of deviation: when the deviation is 20% to 30%, the environmentally sensitive parameters are fine-tuned according to the first proportion (±5% in this embodiment); when the deviation is greater than 30%, they are fine-tuned according to the second proportion (±10% in this embodiment). After fine-tuning, the voting weights and final scores of each candidate model are recalculated. To ensure the stability of the adjustment process, this method also includes a convergence determination step: after fine-tuning, the model score is recalculated. If the fluctuation of the recalculated model score compared with that before fine-tuning exceeds a preset stability threshold (set to ±3% in this embodiment), a second fine-tuning is triggered until the fluctuation converges to within the preset stability threshold, ensuring that the model adapts to sudden environmental changes while maintaining decision stability.

[0041] In step S300, the role of the regional correction factor is to quantify the actual impact of regional ecological characteristics on carbon conversion rate and dynamically inject it into the model to achieve regional adaptation. First, real-time rainfall data P (unit: mm / year), accumulated temperature data T (unit: ℃·day), and soil organic matter content data S (unit: %) for the target region are obtained. The historical average values ​​for the same period over the past 10 years for the same region are read as benchmark values ​​P0, T0, and S0 (if it is a newly developed area and there is no historical data, the national standard reference value for the same climate zone or the recommended value for the corresponding climate zone in GB / T 17297-1998 is used). Each real-time data is divided by the corresponding benchmark value to obtain three dimensionless relative deviation factors: P / P0, T / T0, and S / S0. These three relative deviation factors reflect the degree of deviation of the current rainfall, accumulated temperature, and soil organic matter from the benchmark state (greater than 1 indicates better than the benchmark, less than 1 indicates worse than the benchmark). The three dimensionless relative deviation factors mentioned above are integrated into a comprehensive regional correction factor R by weighted summation. The calculation formula is: R = w_P · (P / P0) + w_T · (T / T0) + w_S · (S / S0), where w_P, w_T, and w_S are the integration weights of each factor, satisfying w_P + w_T + w_S = 1. The specific value is based on the measured data of carbon sink projects in the target area over the past five years. The contribution coefficients of each factor to the carbon conversion rate are obtained through multiple linear regression analysis, and the significance is verified by F test (p < 0.05). This regional correction factor R is a dimensionless value, and its physical meaning is the comprehensive deviation of the current ecological conditions of the target area from the baseline state. The closer the R value is to 1, the more consistent it is with the baseline state. An R value greater than 1 indicates that the comprehensive conditions are conducive to carbon sinking, and an R value less than 1 indicates that the comprehensive conditions are unfavorable to carbon sinking.

[0042] Taking a project in the hot and humid region of South China as an example, let's assume the real-time rainfall P = 1520 mm / year, and the baseline rainfall for the same region over the past 10 years P0 = 1400 mm / year. Then the relative deviation factor for rainfall P / P0 ≈ 1.086. Let the real-time accumulated temperature T = 6200 ℃·day, and the baseline accumulated temperature T0 = 5800 ℃·day. Then the relative deviation factor for accumulated temperature T / T0 ≈ 1.069. Let the real-time soil organic matter content S = 2.8%, and the baseline soil organic matter S0 = 2.5%. Then the relative deviation factor for soil organic matter S / S0 = 1.120. The weights determined through multiple linear regression are w_P = 0.3, w_T = 0.4, and w_S = 0.3 (each weight must satisfy the condition that the sum of the three is 1; specific values ​​can be refitted based on historical data from different regions). Substituting the values ​​into the formula, the regional correction factor is calculated as: R = 0.3 × 1.086 + 0.4 × 1.069 + 0.3 × 1.120 = 1.090. This result indicates that the current regional ecological conditions are approximately 9.0% better than the baseline state, which is conducive to carbon sequestration. If the baseline carbon conversion rate for this region is C_base = 0.8 tC / (mu·year), then the actual carbon conversion rate after adjustment by the regional correction factor is C_actual = C_base × R = 0.8 × 1.090 = 0.872 tC / (mu·year). Based on this actual carbon conversion rate, combined with parameters such as forest area, forest age structure, and biomass expansion factor in the project area, the final carbon storage calculation result is calculated according to the selected carbon sequestration accounting model. By injecting the regional correction factor, the carbon sequestration accounting model can dynamically adjust the carbon conversion rate parameters according to the real-time ecological conditions of different regions, achieving regional adaptation.

[0043] This method also includes a weight adaptive adjustment step. The time-series collection variances of rainfall P, accumulated temperature T, and soil organic matter content S within the current calculation period are acquired in real time. When the collection variance of a data source exceeds a preset fluctuation threshold (set to 1.5 times the historical average variance of the data source in this embodiment), it indicates that the data source has recently experienced abnormal fluctuations, and the reliability of its relative deviation factors (P / P0, T / T0, or S / S0) has decreased. The system automatically reduces the fusion weight of this data source (by 30% in this embodiment) and redistributes the reduced weight according to the contribution ratio of the other two data sources in the current weight system, ensuring that the sum of the three weights is always 1. For example, if the monthly rainfall data fluctuates drastically (variance exceeds the threshold), the rainfall weight w_P is reduced from 0.3 to 0.21. The reduced weight of 0.09 is redistributed according to the ratio of accumulated temperature weight (0.4) and soil organic matter weight (0.3) (i.e., 0.4:0.3). After the adjustment, the accumulated temperature weight w_T is approximately 0.351, and the soil organic matter weight w_S is approximately 0.259, with the total weight remaining at 1. After the weight adjustment, the regional correction factor R is recalculated according to the updated weights.

[0044] This method also includes a multi-source data conflict arbitration step. When the deviation between the real-time value of any data source and the reference value range mapped by the other data sources exceeds a preset conflict threshold (set to ±30% in this embodiment), a cross-validation process is triggered. Specifically, the system independently maps the reference rainfall range (based on historical regression relationships) using accumulated temperature and soil organic matter data. If the real-time rainfall data deviates from the reference ranges of both sources simultaneously, the data source is determined to be abnormal (possibly due to sensor malfunction or data transmission error). The system automatically removes the abnormal data source and recalculates the regional correction factor R based on the relative deviation factors of the remaining two data sources (weights are normalized and redistributed according to the original weight ratios of the remaining data sources to ensure that the sum of the weights is 1). If only one data source remains after removal, the long-term historical mean of that data source is used as its relative deviation factor (i.e., the factor is set to 1) to continue calculating the R value. Through adaptive weight adjustment and multi-source data conflict arbitration, the calculation result of the regional correction factor can maintain reliability when the data source is abnormal.

[0045] The incremental calibration mechanism complements the dynamic injection of regional correction factors, focusing on iterative optimization of the model using actual monitoring feedback. The predicted carbon storage of the carbon sink accounting model is compared with actual monitoring values ​​periodically (quarterly in this embodiment). Actual monitoring values ​​are obtained through a combination of remote sensing technology (such as vegetation cover inversion from Landsat satellite imagery) and field sampling (measured diameter at breast height and tree height in sample plots). The comparison result is the deviation rate, calculated as the absolute value of the difference between the predicted and measured values ​​divided by the measured value and multiplied by 100%. If the deviation rate exceeds a preset tolerance (±5% in this embodiment), the key parameters of the model are fine-tuned using the gradient descent algorithm. The objective function of the gradient descent algorithm is to minimize the sum of squared prediction errors. The parameters to be fine-tuned include the biomass expansion factor (BEF), carbon conversion rate, and timber density. The algorithm iteration stops when the error change rate is less than 0.1% or the number of iterations reaches 100.

[0046] When the accumulated historical data for a certain region / tree species dimension exceeds a preset data threshold (set to one hundred valid samples in this embodiment, where valid samples refer to sample records containing complete input parameters and measured verification values), the system automatically triggers incremental training of the region-specific model. Incremental training employs online learning algorithms (such as stochastic gradient descent SGD), updating the existing model parameters using new samples instead of training from scratch, thereby reducing computational overhead.

[0047] In step S400, a trusted evidence storage mechanism is employed to protect critical data. Specifically, when each source node outputs data and carbon storage calculation results are generated, the system attaches a digital signature, a timestamp, and an integrity check code to them and stores them in a trusted data warehouse. The digital signature is signed with the private key of the data generation role, ensuring the authenticity and non-repudiation of the data source; the timestamp is provided by the National Time Service Center or a trusted time source, ensuring the accuracy of the data generation time; the integrity check code uses a SHA-256 hash value, ensuring that the data has not been tampered with during transmission and storage. Simultaneously, the system records audit logs for each data access, modification, and deletion operation, and the logs themselves are also attached with digital signatures and timestamps. The aforementioned trusted data is stored in a trusted data warehouse, which can be implemented using a centralized database with complete audit logs, or using distributed ledger or blockchain technology. Once the data is stored, any modification will generate a new version record and retain historical versions, ensuring traceability throughout the entire process. It should be noted that on-chain evidence storage is only one preferred implementation method and not a necessary condition for implementing trusted evidence storage in this method.

[0048] The data stored in the trusted data warehouse falls into three categories. The first category is the output of the source nodes, such as planting acceptance reports, mid-term monitoring reports, and project settlement reports. The second category is carbon storage accounting results, such as quarterly carbon storage reports (including snapshots of input parameters, model version numbers, regional correction factor values, intermediate calculated values, and final results) and annual verification opinions. The third category is transaction records, such as carbon sink listing certificates, carbon sink trading contract summaries, and fund flow summaries.

[0049] Based on credible evidence storage, this method executes task contracts according to trigger conditions, enabling cross-stage and cross-system task execution. Trigger conditions include three categories: time node triggers, data threshold triggers, and node status change triggers. Time node triggers are based on set time nodes or time intervals; for example, the 180th day after the completion of the planting acceptance node in the implementation phase triggers the mid-term tending inspection task in the maintenance phase. Data threshold triggers are based on node data thresholds or data status; for example, when the quarterly carbon storage measurement result in the accounting phase is lower than a set threshold (e.g., 50 tons per hectare), a replanting reminder task is triggered. Node status change triggers are based on node state machine change events or external system events; for example, when the status of the quarterly carbon storage measurement node changes from "in execution" to "completed," a data archiving task is triggered; when a verification approval signal from an external verification agency is received, a listing application task is triggered.

[0050] The execution process of the task contract includes the following steps. First, the triggering conditions are acquired in real time through the event listening module, including internal system data (node ​​flow results, node state machine change events, and time nodes) and external system events. Second, operation permissions are verified based on the triggering conditions. The monitored data is compared with the set threshold, and the current triggering role is verified to have the permission to execute the task. The task types and permission ranges that different roles can trigger are configured through a permission matrix. After successful verification, binding actions are executed (such as freezing the subsequent permissions of incomplete nodes or pushing data to the trading platform), and an execution log containing fields such as execution time, triggering conditions, and execution operations is generated and stored along with a digital signature and timestamp. After execution, the execution result is pushed to the associated nodes. If execution fails (such as external system interface timeout), a retry is performed. In this embodiment, the retry strategy is to retry once every two hours, with a maximum of three retries. Normal operation resumes after a successful retry. If a retry fails, the task is frozen and an alarm is pushed.

[0051] In step S500, the system sets up a data listener to continuously monitor for newly added valid monitoring data events in the trusted data warehouse. Whenever new valid monitoring data is stored, the listener performs the following operations.

[0052] In the data extraction and verification stage, newly added monitoring data records are extracted from a trusted data warehouse, and the validity of their digital signatures and timestamps is verified to confirm the authenticity of the data source and that it has not been tampered with. Extracted fields include monitoring time, monitoring area, tree species type, measured carbon storage value, measured average diameter at breast height (DBH), measured forest density, and monitoring method.

[0053] In the same region and same tree species filtering step, the extracted data is compared with the current project, and only sample data that meets the conditions of the same region and same tree species are retained, while irrelevant data is automatically filtered out.

[0054] In the incremental sample collection and deviation comparison triggering stage, the filtered valid samples are collected into the historical dataset of the region-tree species dimension and used as incremental samples. Subsequently, the deviation comparison step of the incremental calibration mechanism is triggered, comparing the predicted carbon storage value of the currently selected carbon sink accounting model corresponding to the incremental sample with the actual monitoring data recorded in the incremental sample, and calculating the deviation rate.

[0055] In the calibration triggering and parameter training phase, when the deviation exceeds the calibration tolerance (set to ±5% in this embodiment), parameter training of the carbon sink accounting model is initiated, triggering the parameter fine-tuning process (executed according to the gradient descent method in step S300). Simultaneously, when the accumulated total number of samples in the historical dataset for that region-tree species dimension exceeds the data volume threshold (set to one hundred valid samples in this embodiment), parameter training of the region-specific model is performed. Incremental training employs online learning algorithms (such as stochastic gradient descent SGD), updating the existing model parameters using newly added samples instead of training from scratch, thereby reducing computational overhead.

[0056] Because the stored evidence data used is protected by digital signatures and timestamps, and its historical versions are traceable, using it as a benchmark for model calibration has higher credibility and trustworthiness compared to traditional reliance on historical data in unprotected centralized databases. Furthermore, since calibration is triggered in real-time, it eliminates the need for manual report review and initiation, significantly improving the timeliness of model optimization.

[0057] In step S600, whenever the state machine of any standardized node changes (e.g., from executing to data pending verification, from data pending verification to completed, or from completed to abnormally frozen), the system immediately generates a state change record. This state change record includes the following fields: node identifier, node name, state before change, state after change, change timestamp, operator identity, and reason for change. This state change record, after being appended with a digital signature and timestamp, is stored in a trusted data warehouse, ensuring that every change in the node's state is traceable and auditable.

[0058] While recording status changes, the system determines whether the changed status meets the triggering conditions of the planned task and executes the planned task bound to the current node based on the change in status. For example, when the status of the planting acceptance node changes from "data pending verification" to "completed," a time-based planned task of "starting mid-term tending inspection in 180 days" is triggered. When the status of the quarterly carbon storage measurement node changes from "in execution" to "data pending verification," a "data integrity verification" task is triggered. When the status of any node changes to "abnormal freeze," an "abnormal notification and investigation" task is triggered.

[0059] This method also includes a permission configuration step. Operational permissions and data access scopes are configured for different roles in each standardized node, constructing a role-node permission matrix to clearly define the operational permissions and data access boundaries of each role in each node. In this embodiment, the core configuration of the permission matrix is ​​as follows: the project owner has data entry permissions during the planning, implementation, and maintenance phases, and data viewing permissions throughout all phases of the entire lifecycle; the verification agency only has permission to modify parameters and submit verification opinions during the accounting phase, and only data viewing permissions during other phases; the regulatory department has full data viewing permissions throughout all phases of the entire lifecycle and can initiate audit traceability; the carbon trading platform only has data entry and viewing permissions during the trading phase, and no operational permissions during other phases. The permission matrix supports dynamic adjustment, and permission change records are stored with digital signatures and timestamps appended as evidence.

[0060] Operational permissions are bound to a state machine, with the same role possessing different operational permissions in different states. For example, when a node is in the "pending startup" state, the project team has the permission to start node tasks; when the node is in the "running" state, they have the permission to perform data entry and correction tasks; when the node is in the "completed" state, they only have data viewing permissions; and when the node is in the "abnormally frozen" state, they have the permission to trigger anomaly investigation tasks. Regulatory authorities and project teams both have the permission to trigger anomaly investigation tasks when a node is in the "abnormally frozen" state, while verification agencies and trading platforms do not have this permission.

[0061] This method also includes a dynamic early warning step. Early warnings are triggered based on node data thresholds, deviations in accounting results, or the execution status of task contracts. Warning types include progress warnings and accounting warnings. Progress warnings address deviations between the actual completion time and the planned completion time of nodes. For example, if a nurturing node is delayed by more than fifteen days, the system will push a warning to the project manager via SMS and in-system messaging, linking it to impact predictions. The time threshold can be set according to the regional ecological characteristics. Accounting warnings address deviations between carbon storage accounting results and historical data for the same period. For example, if the carbon storage growth rate is less than 20% of the historical average, the system will analyze possible causes and recommend matching solutions from the remedial solution knowledge base. Warning levels are divided into three levels: general, important, and urgent. General warnings are only pushed to the project manager, important warnings are pushed to the project manager and the verification agency, and urgent warnings are pushed to all roles with an attached risk handling time limit.

[0062] When an alert is triggered, the system invokes the corresponding emergency response task. For example, when a progress alert is triggered due to a delay of more than fifteen days in a nurturing node, the system invokes the "Nurturing Remediation" emergency response task. This task performs the following operations: pushes a detailed remediation task list to the project manager; synchronizes the alert information and remediation task status with the regulatory authorities; and records the remediation execution time limit. The execution status of this emergency response task is fed back to the node mapping linkage module in real time to control the state transition of related nodes: if the remediation is completed, the suspended related node is restored to the pending state; if the remediation timeout occurs, the related node remains in the suspended waiting state and the alert level is upgraded.

[0063] It should be noted that trusted evidence storage can be implemented using blockchain technology. Specifically, a consortium blockchain architecture (such as Hyperledger Fabric or FISCO BCOS) is adopted, with nodes including project teams, verification agencies, carbon trading platforms, and regulatory authorities. Each node jointly maintains a distributed ledger. Data evidence storage achieves immutability by submitting data hash values ​​and metadata to the consortium blockchain, which, after consensus, writes them into blocks. Historical monitoring data of the same region and tree species is extracted from the blockchain as calibration samples by monitoring new block events. Node state change records are recorded on the blockchain in the form of transactions, achieving distributed consensus and permanent traceability of state changes. On-chain evidence storage is only one preferred implementation method and is not a necessary condition for achieving trusted evidence storage using this method.

[0064] In summary, the seven steps in this embodiment are not independent modules, but are closely linked according to a progressive logic of data flow, model decision-making, dynamic optimization, reliable evidence storage, feedback calibration, status linkage, and early warning response. A standardized data pipeline is established to ensure complete and continuous data transmission to subsequent nodes. The transmitted complete multi-dimensional data is used for intelligent model selection. The model is dynamically optimized and carbon storage is calculated through regional correction factors and incremental calibration. The source node output data and carbon storage calculation results are reliably stored, and cross-stage action triggering is achieved through a task contract execution mechanism. The stored reliable historical data is used as a calibration mechanism to feed back new samples, forming a positive cycle of evidence storage, feedback, and optimization. Node status is stored in real time and drives task linkage. Refined access control and early warning linkage are achieved using status information and task execution status. Together, these constitute a complete closed-loop technical chain, realizing intelligent collaborative management of the entire carbon sink project process.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A carbon sequestration project management method, characterized in that, include: The entire lifecycle of a carbon sequestration project is divided into several standardized nodes. A state machine is configured for each standardized node, and closed-loop data flow between the standardized nodes is carried out through a distributed message queue. Based on the source node output data of the closed-loop data flow, real-time data from third-party systems, and historical project data, a carbon sink accounting model is selected from the model library; the source node output data is the data processed and output by the standardized nodes, including tree species type, average growth cycle, initial value of carbon conversion rate, forest density, and diameter at breast height measurement data; the real-time data from third-party systems is the data obtained in real time from external systems, including daily rainfall, accumulated temperature data, soil organic matter content data, terrain slope, and slope aspect data; Based on real-time environmental data of the target area, a regional correction factor is determined, the carbon sink accounting model is dynamically optimized, and the carbon storage accounting result is obtained based on the optimized carbon sink accounting model. The source node output data and the carbon storage calculation results are used to establish a reliable record, and the task contract is executed based on the triggering conditions. Based on the historical accounting data and actual monitoring data that have been stored, the data are input into the dynamic optimization process to train the parameters of the carbon sink accounting model. Early warnings are triggered based on node data thresholds, accounting result deviations, or task contract execution status, and the state transition of associated nodes is controlled based on the results of the early warnings. Dynamic optimization of the carbon sink accounting model includes: Acquire real-time rainfall data P, accumulated temperature data T, and soil organic matter content data S for the target area, and obtain the corresponding baseline values ​​P0, T0, and S0 for the same area; calculate the relative deviation factors P / P0, T / T0, and S / S0 respectively; perform a weighted summation of the relative deviation factors to obtain the dimensionless regional correction factor R = w_P·(P / P0) + w_T·(T / T0) + w_S·(S / S0); where w_P, w_T, and w_S are weight coefficients and satisfy w_P + w_T + w_S = 1; multiply the regional correction factor R by the basic carbon conversion rate parameter C_base of the carbon sink accounting model to obtain the actual carbon conversion rate C_actual = C_base × R.

2. The carbon sequestration project management method according to claim 1, characterized in that, The closed-loop data flow includes: When the source node outputs data, it attaches metadata tags, which include source node identifier, source stage, data type, data version number, integrity check code, and timestamp. Based on the metadata tags, flow rules are matched to determine the target node. According to the stage and node attributes in the metadata tags, the data is allocated to the corresponding partition of the distributed message queue. After receiving the data, the target node performs integrity verification. If the verification passes, the node status is updated; if the verification fails, a retry is triggered. If the number of retries exceeds the maximum limit, the node status is changed to abnormal freeze.

3. The carbon sequestration project management method according to claim 2, characterized in that, The state machine of the standardized node includes states such as pending startup, in execution, data pending verification, completed, and abnormally frozen. A data dependency graph between each standardized node is pre-configured, and the data dependency graph is a directed acyclic graph; when the state of any source node changes to abnormal freeze, all downstream nodes of the source node are suspended according to the data dependency graph; When the source node returns to normal, the downstream node's state returns to pending startup.

4. The carbon sequestration project management method according to claim 3, characterized in that, When selecting a carbon sequestration accounting model, the following should be included: A multi-level weighted matching rule is used for matching, which includes a basic attribute layer, a scene adaptation layer, and a precision requirement layer. The basic attribute layer matches based on tree species type and regional climate zone, the scene adaptation layer matches based on project scene tags, and the precision requirement layer matches based on the error range requirements for the purpose of carbon sink data.

5. The carbon sequestration project management method according to claim 4, characterized in that, When selecting a carbon sequestration accounting model, the following are also included: Candidate models are selected from the model library using a decision tree; the historical fit of each candidate model is determined based on historical project data of the same tree species in the same region; the final score of each candidate model is determined based on the decision tree hierarchy deviation and the historical fit, and the candidate model with the highest score is selected as the carbon sink accounting model.

6. The carbon sequestration project management method according to claim 5, characterized in that, The dynamic optimization also includes: The predicted carbon storage of the carbon sink accounting model is compared with the actual monitoring data to obtain the deviation rate; when the deviation rate exceeds the deviation tolerance, the key parameters of the carbon sink accounting model are adjusted through optimization algorithms; when the historical data accumulation of the target area exceeds the data volume threshold, the parameters of the target area are trained.

7. The carbon sequestration project management method according to claim 6, characterized in that, When performing trusted notarization based on the source node output data and the carbon storage calculation results, and executing the task contract based on trigger conditions, the process includes: The source node output data and the carbon storage calculation results are appended with digital signatures, timestamps, and integrity verification codes, and stored in a trusted data warehouse; the triggering conditions include time node triggering, data threshold triggering, and node state change triggering. The triggering condition is obtained, and the operation permission is verified according to the triggering condition. If the verification is successful, the binding action is executed and an execution log is generated for evidence storage. If the execution fails, the task is retried. If the retry fails, the task is frozen and an alarm is pushed.

8. The carbon sequestration project management method according to claim 7, characterized in that, Training the parameters of the carbon sink accounting model includes: Acquire newly added valid monitoring data events in the trusted evidence; when the newly added valid monitoring data events include actual monitoring data of the same tree species in the same area as the current project, use them as incremental samples; compare the model prediction values ​​of the incremental samples with the actual monitoring data, and when the deviation exceeds the calibration tolerance, perform parameter training of the carbon sink accounting model.

9. The carbon sequestration project management method according to claim 8, characterized in that, Also includes: Different roles are configured with operation permissions and data access scope in each of the standardized nodes. The operation permissions are bound to the state machine. The same role has different operation permissions in different states. The flow status and state machine change records of the standardized nodes are synchronized to the trusted evidence storage in real time, and the planned tasks bound to the current node are executed according to the changes in the flow status.

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