Wetland Carbon Flux Assessment and Management System Based on 3D Digital Twin

Abstract

This invention relates to the field of wetland carbon assessment technology and discloses a wetland carbon flux assessment and management system based on three-dimensional digital twins. The system includes modules for three-dimensional modeling, flux monitoring, model optimization, and twin updating. The three-dimensional modeling module integrates wetland geospatial coordinates, ecological parameter sets, elevation, vegetation cover density, and soil type stratification information to construct a three-dimensional terrain grid model and generate a geotagged three-dimensional digital twin. The flux monitoring module collects data such as atmospheric temperature gradients based on real-time environmental parameters, extracts carbon flux change characteristics through time series analysis, and outputs dynamic distribution state values. The model optimization module uses an adaptive evolutionary algorithm to adjust parameter matching, iteratively updates constraints, and generates an optimized parameter set. The twin updating module identifies abnormal grid cells, corrects attributes and calibrates topological relationships based on measured data, and outputs an updated assessment model.

Description

Technical Field

[0001] This invention relates to the field of wetland carbon assessment technology, specifically to a wetland carbon flux assessment and management system based on three-dimensional digital twins. Background Technology

[0002] Wetlands, as important carbon reservoirs, play an irreplaceable role in the global carbon cycle, and changes in their carbon flux directly affect regional and even global ecological balance and climate stability. Currently, wetland carbon flux assessment and management mainly rely on a combination of traditional field observations and model simulations. Field observations typically involve setting up monitoring stations to collect relevant parameters such as atmospheric, vegetation, and soil data to estimate carbon flux. However, this approach has significant limitations. The deployment of monitoring stations is often constrained by geographical conditions and cost, making it difficult to achieve comprehensive coverage of the entire wetland ecosystem. This results in data that is localized and one-sided, failing to accurately reflect the spatial heterogeneity of wetland carbon flux.

[0003] Model simulation is another commonly used approach. Existing models are mostly built on two-dimensional planes, neglecting the three-dimensional complexity of wetland topography and the vertical stratification of ecological parameters. For example, changes in wetland elevation affect water level distribution, thereby altering vegetation growth and soil respiration. Two-dimensional models cannot accurately depict the impact of these three-dimensional spatial relationships on carbon flux. Furthermore, the parameter adjustments in existing models largely rely on empirical values ​​or static settings, making dynamic optimization based on real-time monitoring data difficult, resulting in insufficient timeliness and accuracy.

[0004] With the intensification of global climate change, wetland ecosystems face numerous uncertainties such as frequent water level fluctuations and changes in vegetation types. Traditional assessment methods struggle to respond quickly to these dynamic changes and cannot provide accurate and timely decision support for wetland carbon management. Furthermore, assessing wetland carbon flux involves the integration of multi-source data, including geospatial information, ecological parameters, and environmental factors. Existing technologies lack effective means for data integration and analysis, resulting in low data utilization and affecting the reliability of assessment results. Summary of the Invention

[0005] The purpose of this invention is to provide a wetland carbon flux assessment and management system based on three-dimensional digital twins to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a wetland carbon flux assessment and management system based on three-dimensional digital twins, the system comprising:

[0007] The 3D modeling module integrates elevation data, vegetation cover density, and soil type stratification information based on wetland geospatial coordinates and ecological parameter sets to construct a multi-resolution 3D wetland topographic mesh model and generate a geotagged 3D wetland digital twin.

[0008] Based on the real-time environmental parameters of the three-dimensional wetland digital twin, the flux monitoring module collects dynamic data on atmospheric temperature gradient, water level fluctuation frequency and light radiation intensity, extracts carbon flux change characteristics through time series analysis, and outputs dynamic distribution status values ​​of carbon flux.

[0009] Based on the dynamic distribution state value of carbon flux, the model optimization module uses an adaptive evolutionary algorithm to adjust the matching degree between the vegetation growth rate parameter and the soil respiration coefficient of the three-dimensional wetland digital twin, iteratively updates the model parameter constraints, and generates an optimized three-dimensional twin parameter set.

[0010] The twin update module identifies abnormal grid cells in the 3D wetland digital twin based on the optimized 3D twin parameter set, corrects the grid cell attributes by comparing with measured carbon flux data, recalibrates the 3D spatial topology, and outputs an updated wetland carbon flux assessment model.

[0011] Preferably, the three-dimensional wetland digital twin includes a set of geospatial grid coordinate parameters and a set of ecological stratification attribute parameters; the dynamic distribution state value of carbon flux includes a set of temperature response coefficients, a set of water level correlation parameters, and a set of light radiation influencing factors; the optimized three-dimensional twin parameter set includes a set of vegetation cover correction parameters and a set of soil carbon release adjustment coefficients; and the updated wetland carbon flux assessment model includes a set of grid cell calibration parameters and a set of spatial topological relationship verification parameters.

[0012] Preferably, the 3D modeling module includes:

[0013] The spatial data processing submodule receives wetland boundary coordinates and satellite remote sensing images, segments multispectral band data, extracts vegetation index distribution maps and soil moisture contour lines, and generates a wetland ecological stratification feature dataset.

[0014] The mesh construction submodule, based on the wetland ecological stratification feature dataset, divides the three-dimensional spatial voxel units, associates the elevation value, vegetation cover and soil type code of each voxel unit, and establishes the initial three-dimensional mesh topology.

[0015] The attribute fusion submodule integrates the initial 3D mesh topology with real-time sensor network data, labels the temperature conductivity coefficient and water level permeability of each voxel unit, and generates a geotagged 3D wetland digital twin.

[0016] Preferably, the flux monitoring module includes:

[0017] The dynamic acquisition submodule connects to the environmental sensor nodes of the three-dimensional wetland digital twin, captures soil temperature change curves at different depths, water surface carbon dioxide concentration gradients, and vegetation canopy light absorption rates, and generates the original carbon flux time series dataset.

[0018] The feature extraction submodule analyzes the original carbon flux time series dataset, calculates the sensitivity index of diurnal temperature difference to carbon release rate, statistically analyzes the carbon absorption fluctuation amplitude within the water level change cycle, and outputs a carbon flux change feature vector.

[0019] The state generation submodule integrates the carbon flux change feature vector with the spatial location information of the three-dimensional wetland digital twin, maps the carbon flux value to the corresponding three-dimensional grid cell, and generates the dynamic distribution state value of carbon flux.

[0020] Preferably, the model optimization module includes:

[0021] The parameter matching submodule analyzes the abnormal fluctuation data in the dynamic distribution state value of carbon flux, compares it with the preset vegetation photosynthetic efficiency threshold of the three-dimensional wetland digital twin, and identifies the parameter matching deviation area.

[0022] The evolutionary optimization submodule applies a population variation strategy in the parameter matching deviation region to adjust the correlation weight between soil microbial activity coefficient and vegetation root carbon storage capacity, and generates a set of candidate parameter optimization schemes.

[0023] The constraint iteration submodule verifies the impact of the candidate parameter optimization scheme set on the connectivity of the 3D wetland digital twin mesh, selects parameter combinations that satisfy the carbon flux balance condition, and generates the optimized 3D twin parameter set.

[0024] Preferably, the twin update module includes:

[0025] The anomaly detection submodule traverses the optimized 3D twin parameter set, locates grid cells where the difference rate between the simulated and measured carbon flux values ​​exceeds a threshold, and marks the spatial location coordinates and deviation type.

[0026] The attribute correction submodule injects the measured gas concentration data from the ground monitoring station based on the marked spatial location coordinates, overwriting the simulated carbon flux attribute value of the corresponding grid cell;

[0027] The topology calibration submodule recalculates the hydrological connectivity index and carbon diffusion path weights of the corrected grid cells and adjacent cells, and outputs an updated wetland carbon flux assessment model.

[0028] Preferably, the system further includes a carbon component analysis module, which performs the following operations:

[0029] Receive the grid cell attribute set output by the updated wetland carbon flux assessment model, and separate the data of dissolved organic carbon, methane emissions and plant biomass carbon storage.

[0030] Multi-source data fusion technology was used to integrate the data on dissolved organic carbon, methane emissions, and plant biomass carbon storage to construct a spatial distribution heat map of carbon components.

[0031] By linking the spatial distribution heat map of carbon components with hydrological path data from a 3D wetland digital twin, a set of optimized parameters for carbon transport pathways is generated.

[0032] Preferably, the system further includes an exception response module, which performs the following operations:

[0033] Monitor abrupt event data in the dynamic distribution state value of carbon flux to identify carbon release peaks triggered by a sudden rise in temperature or a sharp drop in water level;

[0034] The historical state records of the updated wetland carbon flux assessment model are invoked to match carbon flux recovery paths under similar environmental conditions;

[0035] Generate a three-dimensional mesh cell carbon flux suppression scheme for mutation events and output an abnormal event control instruction set.

[0036] Preferably, the system further includes a flux prediction module, which performs the following operations:

[0037] By integrating the updated wetland carbon flux assessment model with meteorological forecast data, the impact of future rainfall intensity on soil carbon dissolution rate is simulated.

[0038] The spatiotemporal convolution algorithm is used to extrapolate the carbon absorption trend during the vegetation growth cycle and generate a prediction curve of carbon sink capacity changes.

[0039] By integrating the predicted carbon sequestration capacity change curve with the topographic water storage capacity parameters of the three-dimensional wetland digital twin, a long-term carbon flux evolution map is output.

[0040] Preferably, the system further includes a management decision module, which performs the following operations:

[0041] Receive the coordinate set of carbon sink saturation regions from the long-term carbon flux evolution map;

[0042] By comparing historical wetland restoration project data, vegetation configuration schemes that improve carbon storage efficiency were selected;

[0043] Generate a list of human intervention measures for three-dimensional grid cells, and drive the ecological restoration equipment to execute operation instructions.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] By constructing a multi-resolution 3D wetland topographic mesh model using a 3D modeling module, a geotagged digital twin is generated, which can fully present the three-dimensional topographic features of the wetland as well as the layered information of vegetation and soil. This 3D presentation method breaks through the limitations of traditional 2D models in spatial representation, and can meticulously reflect the water level distribution, vegetation growth differences, and soil characteristics in different elevation areas, making the spatial dimension of carbon flux assessment more comprehensive and avoiding assessment bias caused by ignoring 3D features.

[0046] The flux monitoring module collects dynamic data such as atmospheric temperature gradient, water level fluctuation frequency, and solar radiation intensity based on real-time environmental parameters from a 3D digital twin. It then extracts carbon flux variation characteristics through time series analysis. This combination of real-time monitoring and dynamic analysis can capture subtle changes in wetland carbon flux over time, reflecting the immediate impact of fluctuations in different environmental factors on carbon flux. Compared to traditional methods relying on fixed-period sampling, this approach better reflects the dynamic characteristics of carbon flux.

[0047] The model optimization module employs an adaptive evolutionary algorithm to adjust the matching degree between vegetation growth rate parameters and soil respiration coefficient based on the dynamic distribution state of carbon flux, and iteratively updates the model parameter constraints. This parameter optimization method eliminates the reliance on empirical values, allowing model parameters to remain synchronized with the actual ecological conditions of wetlands. This makes the model's simulation of carbon flux more closely resemble reality and reduces errors caused by unreasonable parameter settings.

[0048] The twin update module identifies anomalous grid cells based on the optimized parameter set, corrects attributes by combining measured data, and recalibrates the three-dimensional spatial topology. This process continuously eliminates deviations between the model and the actual wetland, ensuring that the updated assessment model consistently reflects the true state of the wetland and making the assessment results more valuable. Simultaneously, the entire system achieves the fusion and dynamic processing of multi-source data, improving data utilization efficiency and making the assessment and management of wetland carbon flux more targeted and adaptable, better addressing the complex changes in wetland ecosystems. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the working principle of the wetland carbon flux assessment and management system based on three-dimensional digital twins as described in this invention.

[0050] Figure 2 A flowchart illustrating the association between a 3D twin and a parameter set;

[0051] Figure 3 A flowchart for the 3D modeling module;

[0052] Figure 4 A flowchart for the model optimization module;

[0053] Figure 5 This is a flowchart of the carbon component analysis module. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 This invention provides a wetland carbon flux assessment and management system based on three-dimensional digital twins, the system comprising:

[0056] Dynamic simulation and precise management of wetland carbon cycle are achieved through multi-module collaboration. The 3D modeling module, based on wetland geospatial coordinates and ecological parameter sets, integrates elevation data, vegetation cover density, and soil type stratification information to construct a multi-resolution 3D wetland topographic mesh model, generating a geotagged 3D wetland digital twin. The flux monitoring module collects real-time dynamic data on atmospheric temperature gradient, water level fluctuation frequency, and solar radiation intensity, extracts carbon flux change characteristics through time series analysis, and outputs dynamic carbon flux distribution values. The model optimization module uses an adaptive evolutionary algorithm to adjust the matching degree between vegetation growth rate parameters and soil respiration coefficient, iteratively updates model parameter constraints, and generates an optimized 3D twin parameter set. The twin update module identifies abnormal mesh cells, compares them with measured data to correct attributes, recalibrates spatial topological relationships, and outputs an updated wetland carbon flux assessment model.

[0057] Example 1: See Figure 2The construction of a 3D wetland digital twin is based on a geospatial grid coordinate parameter set and an ecological stratification attribute parameter set. The geospatial grid coordinate parameter set includes the latitude, longitude, elevation, and spatial resolution information of each grid cell. This data was acquired through high-precision satellite remote sensing imagery and ground mapping technology, ensuring the spatial accuracy of the model is consistent with the actual wetland topography. The grid cells are divided using a regular voxel structure, and the side length of each voxel cell can be adjusted according to actual needs to adapt to the carbon flux analysis requirements at different scales. The ecological stratification attribute parameter set includes vegetation type coding, soil organic matter content, and hydrological permeability coefficient. These parameters are determined comprehensively through multispectral remote sensing inversion, soil sampling analysis, and hydrological monitoring data. The vegetation type coding adopts an international standard classification system to ensure that the carbon cycle characteristics of different wetland vegetation can be accurately mapped to the model. Soil organic matter content data was obtained through laboratory measurements and spatial interpolation using near-infrared spectroscopy to form a continuous soil carbon storage distribution map. The hydrological permeability coefficient was calculated through field infiltration tests and groundwater flow simulations, used to describe the migration process of water in the soil and its impact on carbon release.

[0058] The dynamic distribution of carbon flux comprises a set of temperature response coefficients, a set of water level-related parameters, and a set of light radiation influencing factors. The temperature response coefficient set was established through long-term monitoring of the relationship between soil temperature and carbon dioxide emission rates at different depths, using a nonlinear regression model to describe the regulatory effect of temperature on microbial decomposition activity. The water level-related parameter set quantifies the coupling effect between wetland water level fluctuations and methane emissions; data are derived from a water level sensor network and static box methane flux observations. The light radiation influencing factors set is based on photosynthetically active radiation absorptivity data from the vegetation canopy, combined with a leaf-scale photosynthesis model, to calculate the carbon assimilation efficiency of vegetation under different light conditions. These datasets are integrated using time-series analysis methods to form a dynamic distribution of carbon flux reflecting its spatiotemporal variations.

[0059] The optimized 3D twin parameter set includes a vegetation cover correction parameter set and a soil carbon release adjustment coefficient set. The vegetation cover correction parameter set optimizes the model's simulation accuracy of vegetation carbon uptake by assimilating remotely sensed leaf area index and measured ground root distribution data. The soil carbon release adjustment coefficient set, based on soil respiration observation data, corrects the functional relationship between microbial decomposition rate and temperature sensitivity, enabling the model to more accurately reflect carbon release dynamics under different environmental conditions. The parameter optimization process employs an adaptive evolutionary algorithm, iteratively adjusting model parameters to gradually improve the match between the output results and measured carbon flux data.

[0060] The updated wetland carbon flux assessment model includes a grid cell calibration parameter set and a spatial topology verification parameter set. The grid cell calibration parameter set stores deviation compensation coefficients between measured and simulated values. These coefficients are calculated by comparing the model output with surface flux observation data and are used to correct systematic errors in specific regions of the model. The spatial topology verification parameter set records the recalculated weights of carbon diffusion pathways between grids. The data is derived from hydrological connectivity analysis and carbon isotope tracing techniques and is used to describe carbon migration pathways in the wetland system and their impact on the overall carbon balance.

[0061] The operation of a 3D wetland digital twin relies on the real-time fusion and dynamic updating of multi-source data. The geospatial grid coordinate parameter set is updated periodically with satellite remote sensing data to reflect dynamic changes in wetland topography, such as sediment deposition or erosion processes. The ecological stratification attribute parameter set is updated based on periodic field surveys and automatic sensor network data, ensuring that temporal changes in vegetation growth status and soil carbon storage are promptly incorporated into the model. The calculation of the dynamic distribution state value of carbon flux employs sliding time window analysis, combined with real-time environmental monitoring data, to generate a dynamic output reflecting the current carbon cycle status.

[0062] The model optimization module continuously compares simulation results with measured data to identify areas of parameter mismatch and uses an evolutionary algorithm to adjust key parameters. The optimization process considers the interactions of different environmental factors, such as the synergistic effect of temperature and humidity on soil respiration, and the coupling effect of light and nutrients on vegetation photosynthesis. The optimized parameter set is updated to a 3D wetland digital twin through gridded calculations to ensure that the model's dynamic response capability remains consistent with actual ecological processes.

[0063] The twin update module uses anomaly detection algorithms to locate areas of significant discrepancy between simulated and measured carbon flux values, and then injects ground monitoring data to correct the model output. The correction process not only adjusts the attribute values ​​of the target grid cells but also recalculates their hydrological connectivity index and carbon diffusion path weights with adjacent cells to maintain spatial consistency of the model. The updated wetland carbon flux assessment model output includes a calibrated carbon flux distribution map and a spatial topology verification report, providing dynamic decision support for wetland carbon management.

[0064] The system employs a distributed architecture for its data flow, ensuring efficient processing and real-time analysis of massive amounts of environmental monitoring data. The mesh construction submodule of the 3D modeling module supports multi-scale spatial partitioning, allowing for flexible adjustment of model resolution according to research needs. The feature extraction submodule of the flux monitoring module utilizes signal processing techniques to separate noise from raw sensor data and extract effective carbon flux variation features. The constraint iteration submodule of the model optimization module accelerates the parameter selection process through parallel computing, meeting the timeliness requirements of large-scale wetland carbon flux simulation.

[0065] The visualization interface of the 3D wetland digital twin integrates Geographic Information System (GIS) technology, supporting multi-dimensional display and analysis of the spatiotemporal distribution of carbon flux. Users can interactively query carbon cycle parameters for specific grid cells or compare carbon flux trends at different time points. The system's output data format is compatible with mainstream ecological models, facilitating data exchange and joint analysis with other carbon cycle research tools.

[0066] The implementation of wetland carbon flux assessment and management systems relies on the collaborative application of interdisciplinary technologies. Remote sensing technology provides large-scale land cover and ecological parameter inversion data, sensor networks enable high spatiotemporal resolution environmental monitoring, and computational models integrate multi-source information and simulate carbon cycle processes. The modular design of the system allows for customization of functions according to the specific characteristics of wetlands, such as adding salinity-related carbon release factors for coastal wetlands, or incorporating freeze-thaw cycle parameters for soil respiration regulation in alpine wetlands.

[0067] The long-term operation of the system requires the establishment of standardized data quality control processes, including sensor calibration, remote sensing data preprocessing, and model parameter sensitivity analysis. Regular maintenance ensures the stable operation of hardware equipment, while software algorithm updates integrate the latest carbon cycle research findings, such as novel greenhouse gas monitoring technologies or improved ecological process models. The validation of the 3D wetland digital twin uses independent observation datasets, and cross-validation methods are employed to evaluate the model's applicability and robustness under different environmental conditions.

[0068] Example 2: See Figure 3 The spatial data processing submodule of the 3D modeling module receives wetland boundary coordinate sets and multispectral satellite remote sensing images to conduct basic data collection on wetland ecological characteristics. After radiometric and atmospheric correction preprocessing, the satellite remote sensing data is used to extract the normalized vegetation index (NVI) using band arithmetic techniques. This NVI reflects vegetation growth and cover density. Soil moisture data is obtained through a combination of thermal infrared and visible light bands, and combined with microwave remote sensing inversion results to form a soil moisture contour map. These remote sensing-derived data are spatially registered and accuracy verified with ground-measured sample plot data, ultimately generating a wetland ecological stratification feature dataset containing vegetation index distribution, soil moisture gradient, and surface temperature field. The dataset is divided into three layers according to the wetland's vertical structure: surface vegetation biomass data records the leaf area index and biomass carbon density of different plant communities; middle soil carbon storage data includes parameters such as organic carbon content, bulk density, and porosity; and bottom groundwater parameters cover groundwater level fluctuation range, hydraulic conductivity, and hydrochemical characteristics.

[0069] The grid construction submodule, based on a wetland ecological stratification feature dataset, uses a spatial discretization method to divide the study area into regular three-dimensional voxel units. The spatial resolution of each voxel unit is set according to the required research accuracy, typically 0.5-5 meters horizontally and 10-30 cm vertically. The elevation values ​​of the voxel units are obtained through airborne lidar measurements, combined with ground control point calibration to form a digital elevation model. Vegetation cover data is derived from object-oriented classification results of high-resolution aerial imagery, with accuracy controlled by ground quadrat surveys. Soil type coding refers to the international soil classification system, and the classification of each soil layer is determined based on field profile surveys and laboratory analysis. These spatial attribute data are linked to each voxel unit through a topology construction algorithm, forming an initial three-dimensional grid model with a hierarchical structure. The grid topology is stored using a semi-edge data structure, recording the connection relationships between each voxel unit and its adjacent units, establishing a computational foundation for subsequent spatial analysis of carbon flux.

[0070] The attribute fusion submodule performs spatiotemporal matching between the initial 3D mesh topology and dynamic monitoring data collected by a real-time sensor network. IoT sensor nodes distributed throughout the wetland continuously record parameters such as soil temperature profiles, pore water pressure, and carbon dioxide concentration. This real-time data is aggregated to a data center via a wireless transmission network, and after time synchronization and outlier removal, it is spatially correlated with the 3D mesh model. Each voxel is assigned corresponding environmental parameters based on its spatial location, including physical properties such as temperature conductivity, water permeability, and gas diffusion rate. The temperature conductivity is determined through thermal response experiments, describing the efficiency of heat transfer in the soil-vegetation system; water permeability is calculated based on Darcy's law, reflecting the ability of water to migrate in the porous medium; and the gas diffusion rate takes into account the influence of soil texture and moisture content. These dynamic attributes, combined with the static mesh structure, ultimately generate a geotagged 3D wetland digital twin with spatiotemporal continuity.

[0071] The dynamic acquisition submodule of the flux monitoring module acquires key parameters of the carbon cycle process through a distributed sensor network. The atmospheric boundary layer monitoring tower is equipped with a three-dimensional ultrasonic anemometer and an infrared gas analyzer to measure near-surface turbulent exchange and carbon dioxide flux. Water monitoring buoys carry multi-parameter water quality probes to record indicators such as dissolved oxygen, pH, and dissolved organic carbon concentration. A soil profile sensor array is deployed at 10-cm intervals to monitor temperature, humidity, and carbon dioxide concentration gradients at different depths. A vegetation canopy spectrometer periodically collects leaf reflectance spectra to calculate photosynthetically active radiation absorptivity and light energy utilization efficiency. These heterogeneous monitoring devices achieve data synchronization through a unified timestamp system, forming a raw carbon flux dataset with strict temporal correlation. The data acquisition frequency is set according to the rate of process change; a high-frequency sampling of 10Hz is used for rapidly changing atmospheric turbulence, while an hourly recording mode is used for slowly changing soil respiration.

[0072] The feature extraction submodule performs multi-scale analysis on the raw carbon flux time-series data. For atmospheric turbulence data, the eddy covariance method is used to calculate the instantaneous values ​​of sensible heat flux, latent heat flux, and carbon dioxide flux. Water carbon flux data is processed using the mass balance method, combined with flow velocity measurements to calculate the transport flux of each carbon component. Soil respiration data is decomposed into temperature-driven and humidity-influencing terms, and an empirical model based on environmental factors is established. Vegetation photosynthesis data is analyzed using light response curves to determine characteristic parameters such as the maximum photosynthetic rate and light compensation point. These analyses employ a sliding time window technique to identify the diurnal rhythm and seasonal trends of carbon flux changes. For anomalous climate events such as heavy rainfall or drought, a specialized event detection algorithm is used to capture abrupt changes in the carbon cycle. The analysis results are output in the form of feature vectors, including quantitative indicators such as diurnal temperature range sensitivity index, water level periodic fluctuation amplitude, and light intensity response slope.

[0073] The state generation submodule integrates the extracted carbon flux variation features with the spatial structure of the 3D wetland digital twin. Feature vectors are located to corresponding grid cells based on their spatial origin, establishing an explicit spatial relationship between carbon flux and environmental driving factors. For gaps between monitoring points, spatial interpolation methods are used to expand the feature distribution, considering the spatial heterogeneity of topography, vegetation, and soil type. The interpolation process incorporates geostatistical methods to calculate the spatial autocorrelation range and anisotropy of the feature variables. The final generated dynamic distribution state values ​​of carbon flux are stored in the form of grid attributes, with each voxel cell containing its current flux value, trend, and main environmental impact factors. These state values ​​are managed through a time-series database, supporting analysis functions such as historical retrospective and trend prediction.

[0074] The collaborative operation of the 3D modeling module and the flux monitoring module enables the digital representation of wetland carbon cycle processes. The ecological stratification features provided by the spatial data processing submodule establish a structural framework for carbon flux analysis, the 3D topological relationships formed by the grid construction submodule provide a computational basis for the spatial distribution of fluxes, and the real-time monitoring data integrated by the attribute fusion submodule endows the model with dynamic response capabilities. The flux monitoring module captures key carbon cycle processes through a multi-source sensor network, feature extraction algorithms reveal environmental driving mechanisms, and the state generation process realizes the mapping and transformation of monitoring data into a 3D model. This complete technological chain, from static structure to dynamic processes, constitutes the core methodology for wetland carbon flux assessment and management.

[0075] The system's implementation relies on the integrated application of several key technologies. Remote sensing image processing technology enables the rapid acquisition of large-scale ecological parameters; Internet of Things (IoT) technology supports the stable operation of the distributed monitoring network; 3D modeling technology constructs the spatial framework of a digital twin; and spatiotemporal data analysis technology reveals the changing patterns of the carbon cycle. The organic integration of these technologies breaks through the spatiotemporal limitations of traditional wetland research, making dynamic monitoring of carbon flux across all elements and processes possible. The system design considers the complexity of wetland ecosystems, adapting to the research needs of different types of wetlands through a modular architecture, such as adding tidal influence factors for salt marshes or freeze-thaw process parameters for peat wetlands.

[0076] Data quality control is implemented throughout all stages of system operation. Remote sensing data undergoes geometrical calibration and radiometric consistency checks, sensor data is periodically calibrated and cross-validated, and model parameters undergo sensitivity analysis and uncertainty assessment. System maintenance includes routine inspections and updates of hardware equipment and iterative optimization of software algorithms to ensure the continuity of long-term monitoring and the reliability of data products. Validation of the 3D wetland digital twin uses independent observation datasets, evaluating the model's representational capabilities through spatial cross-validation and time-series validation. The user interface provides flexible data visualization and analysis tools, supporting researchers in understanding wetland carbon cycle processes from different perspectives and providing technical support for scientific research and applied decision-making.

[0077] Example 3: See Figure 4The parameter matching submodule of the model optimization module identifies deviation regions between the simulation results and measured data of the 3D wetland digital twin by analyzing the spatiotemporal variation characteristics of the dynamic distribution state values ​​of carbon flux. This process first establishes the spatial correspondence between carbon flux observation points and model grid cells, using the nearest neighbor interpolation method to map discrete monitoring data to a continuous 3D grid. For each matched grid cell, the relative deviation between its simulated and measured values ​​is calculated; this index reflects the model's representational ability under specific environmental conditions. Deviation analysis considers variation characteristics at different time scales, including seasonal trends, diurnal rhythms, and short-term fluctuations. The threshold for vegetation photosynthetic efficiency is set with reference to plant physiological research, distinguishing the light energy conversion characteristics of C3 and C4 plants, and is locally adjusted based on the maximum net photosynthetic rate measured in the field. The identification of parameter matching deviation regions uses a spatial clustering algorithm to merge adjacent grid cells with similar deviation characteristics into the same optimization region, providing a spatial framework for subsequent targeted parameter adjustments.

[0078] The evolutionary optimization submodule implements adaptive parameter adjustment strategies in identified parameter matching deviation regions. The optimization of soil microbial activity coefficients is based on microbial growth kinetics, considering multiple environmental constraints such as temperature, humidity, and substrate availability. The adjustment of vegetation root carbon storage capacity links root distribution depth with carbon allocation strategies, distinguishing resource acquisition patterns for different plant life forms. An improved genetic algorithm is introduced into the parameter optimization process; its core iterative formula is:

[0079] ;

[0080] in, represents the parameter set for generation t, which includes soil microbial activity coefficient and root carbon storage capacity variables to be optimized. As a mutation operator, it maintains population diversity by introducing random perturbations into the parameter space; The representative reference parameter set is derived from observations of typical wetland ecosystems reported in the literature; It is a fitness function that quantifies the degree to which the combination of parameters improves the accuracy of carbon flux simulation; The learning rate coefficient controls the step size for parameter updates.

[0081] Learning rate coefficient The value ranges from 0.1 to 0.3, with specific values ​​for different wetland types: reed wetlands =0.2 (calibrated based on measured data from wetlands in Yancheng, Jiangsu Province in 2023); Mangrove wetlands =0.15 (referencing the recommended value for tropical wetlands in the "Handbook of Wetland Carbon Cycle Model Parameters"). Basis for determination: Through 500 iterations of testing, this range balances parameter update speed and model stability.

[0082] fitness function The calculation parameters and formulas are as follows: The weighting coefficient =0.6 (carbon flux simulation bias weight). =0.4 (grid connectivity weight); deviation threshold ≤5% (allowable deviation between simulated and measured values). ≥0.8 (Minimum threshold for grid connectivity). Source: Based on monitoring data from the Yangtze River Estuary wetlands in 2022-2023.

[0083] Reference parameter set The baseline value for soil microbial activity coefficient is 1.2-1.8 (unit: g C·kg). -1 ・d -1 (Source: Soil sampling analysis from the Sanjiang Plain wetlands); Vegetation root carbon storage capacity: 3.5-5.0 (unit: kg C·m³) -2 (Based on vegetation quadrat survey data from Taihu Lake wetland).

[0084] During algorithm execution, parameter combinations with high fitness are retained and participate in the next generation of evolution, while poorly performing combinations are gradually eliminated. The generation of the candidate parameter optimization scheme set adopts a parallel computing architecture, simultaneously evaluating the performance of multiple parameter combinations to improve optimization efficiency.

[0085] The constraint iteration submodule verifies the physical rationality and ecological consistency of candidate parameter schemes generated by evolutionary optimization. Grid connectivity verification is achieved by calculating the hydrological path integrity index, which reflects the continuity of the water cycle process after parameter adjustment. The carbon flux balance condition is verified using the principle of mass conservation, comparing whether the difference between carbon input and output fluxes in each grid cell is within an acceptable range. The parameter selection process establishes a multi-objective optimization framework, considering three dimensions: improving simulation accuracy, maintaining computational stability, and ensuring the rationality of ecological processes. By setting constraint thresholds, parameter combinations that improve local fit but disrupt the overall model consistency are excluded. The final selected set of optimized parameters undergoes sensitivity analysis to determine the allowable fluctuation range of each parameter, providing a basis for uncertainty assessment in model application.

[0086] The anomaly detection submodule of the twin update module uses a statistical process control-based method to identify grid cells requiring correction. The difference rate calculation between simulated and measured carbon flux values ​​employs standardization to eliminate the influence of dimensional differences between different carbon components. Threshold settings reference the natural fluctuation range of long-term monitoring data, typically considering deviations exceeding two standard deviations as significant anomalies. Spatial coordinate recording combines local and global coordinate systems, preserving both the relative positions of grid cells in the 3D model and their absolute locations in geographic space. A deviation type classification system distinguishes between systematic and random deviations; the former reflects model structural defects, while the latter mainly stems from measurement noise or transient disturbances. Accurate identification of deviation types provides a basis for selecting subsequent correction strategies.

[0087] The attribute correction submodule implements a grid cell attribute update method based on data assimilation. After quality control, the measured data from ground monitoring stations are optimally interpolated and fused with the model output. The spatial representativeness of gas concentration data is determined through semi-variogram analysis, considering the influence of monitoring point density and spatial autocorrelation range. The correction process employs a stepwise update strategy, first adjusting key parameters that significantly affect carbon flux, and then gradually extending to secondary variables. For components that are difficult to measure directly, such as dissolved organic carbon, stable isotope tracer data are introduced as indirect constraints. The corrected grid cell attribute values ​​are managed through a version control system, retaining a complete modification history for traceability analysis. The correction operation not only changes the attributes of the target cell but also triggers the recalculation of associated parameters in adjacent cells, maintaining spatial continuity.

[0088] The topology calibration submodule handles spatial relationship adjustments required by attribute corrections. The hydrological connectivity index is recalculated based on an improved D8 algorithm, considering the combined effects of topographic slope, soil permeability, and vegetation water-blocking effects. The carbon diffusion path weights are updated using a landscape resistance model to quantify the carbon transport impedance of different media types. The calibration process employs an iterative relaxation method, first fixing known and accurate grid cell attributes and then gradually optimizing areas with higher uncertainty. Verification of spatial topological relationships is achieved by comparing the spatial autocorrelation characteristics of carbon fluxes before and after correction, ensuring the adjusted model maintains a reasonable spatial pattern. The final output updated wetland carbon flux assessment model includes a complete record of parameter revisions and topology change descriptions, supporting transparency and repeatability in model application.

[0089] The coordinated implementation of model optimization and digital twin update processes forms a closed-loop feedback system. The diagnostic results of the parameter matching submodule provide goal guidance for evolutionary optimization, while constraint iteration ensures the rationality of the optimization direction. Anomaly detection locates the specific spatial position of model defects, and attribute correction directly improves local representation capabilities. Topology calibration maintains the coordination of spatial relationships from a system-wide perspective. This iterative optimization mechanism, from local to global and then back to local feedback, enables the 3D wetland digital twin to continuously self-improve. Optimization experience accumulated during system operation is stored in a case library, providing a reference paradigm for the construction of similar wetland models.

[0090] At the technical implementation level, the model optimization module adopts a microservice architecture, with each submodule capable of independent deployment and expansion. The evolutionary optimization algorithm supports GPU-accelerated computation to handle search problems in high-dimensional parameter spaces. The twin update module incorporates blockchain technology to ensure the immutability and auditability of the data correction process. A 3D visualization engine renders the differences between the model before and after optimization in real time, assisting researchers in understanding the spatial effects of parameter adjustments. The system interface design follows the Open Geospatial Consortium standard, supporting interoperability with other environmental models.

[0091] Quality control measures are implemented throughout the entire process. A global sensitivity analysis is conducted before parameter optimization to identify key parameters to be tuned. Diversity monitoring indicators are set during the evolutionary process to prevent premature convergence. Constraint checks utilize third-party validation datasets to avoid overfitting. Anomaly detection algorithms are tested on synthetic data to evaluate their false positive and false negative rates. Impact assessments are performed before attribute correction operations to predict their potential impact on downstream analyses. Topology calibration results are evaluated for their improvement effect using cross-validation. These measures collectively ensure the reliability and robustness of model optimization and updates.

[0092] Parameter validity verification data: Test data table (Table 1), which explains the simulation accuracy corresponding to the parameter values.

[0093] Table 1. Correspondence between parameter combinations and simulation accuracy:

[0094]

[0095] Example 4: See Figure 5The carbon component analysis module receives the updated grid cell attribute set output by the wetland carbon flux assessment model and conducts analysis of multi-component carbon cycle processes. Taking the Yangtze River Estuary coastal wetland as an example, the system isolates three key carbon components: dissolved organic carbon (DOC) mainly originates from the decomposition of plant residues and tidal input; methane emissions (CH4) are closely related to sulfate-reducing bacteria activity; and plant biomass carbon storage reflects the carbon sequestration capacity of the reed community. The module uses a fusion method of high-resolution mass spectrometry data and gas chromatography monitoring results to establish the spatial distribution characteristics of each carbon component. Horizontally, DOC concentration shows a gradient pattern decreasing from sea to land; vertically, CH4 emission hotspots are concentrated in the anaerobic layer at a depth of 30-50 cm; and plant carbon storage shows a distinct patchy distribution, highly correlated with vegetation community structure.

[0096] When integrating carbon composition information using multi-source data fusion technology, priority rules are set to handle the weight allocation of data from different sources. Laboratory measurement data has the highest precision weight, in-situ sensor monitoring data provides the advantage of temporal resolution, and remote sensing inversion results contribute to spatial continuity. The fusion process uses a confidence-weighted algorithm, considering the spatiotemporal representativeness and measurement error range of each data source. Taking a block in Chongming Dongtan Wetland as an example, the generation process of the carbon composition spatial distribution heat map includes the following steps: First, the 1km×1km study area is divided into 100 10m×10m grid cells; then, the measured data from three sampling points in each cell, five sets of continuous sensor monitoring records, and vegetation indices from the Sentinel-2 satellite are integrated; finally, a carbon composition distribution map with a resolution of 0.5 meters is generated through spatial interpolation. The color gradation of the heat map is set according to international standards, with DOC concentration using a blue gradient, CH4 flux using a red hue, and plant carbon storage represented by a green tone.

[0097] When correlating the spatial distribution heatmap of carbon components with hydrological pathway data from a 3D wetland digital twin, a coupling method based on spatial overlay analysis is established. The hydrological pathway data includes tidal channel network vector maps, groundwater flow direction grids, and pore water movement simulation results. The system automatically identifies the spatial intersection between high-value areas of each carbon component and the hydrological pathway, calculating the potential flux of carbon transport. In the case of the Nanhui Beach in the Yangtze River Estuary, the high DOC concentration area highly overlaps with the tidal pathway, indicating that tidal driving is the main driving force for dissolved carbon migration; CH4 emission hotspots are distributed in stagnation zones with slower groundwater flow, reflecting the promoting effect of anaerobic environment on methanogenesis. The generation of the carbon transport pathway optimization parameter set includes the following key indicators: tidal throughput index, groundwater residence time, and carbon component diffusion rate. These parameters provide a quantitative basis for wetland carbon management.

[0098] The anomaly response module's sudden event monitoring function is configured for three typical scenarios: a sudden temperature rise scenario with a trigger threshold of over 8°C within 24 hours; a sharp drop in water level with a warning line defined as a daily water level drop greater than 15 cm; and extreme weather scenarios, which are linked to wind speed and rainfall intensity during typhoons. The module scans the real-time data stream using a sliding time window algorithm, and automatically activates the emergency response procedure when it detects an environmental anomaly exceeding the threshold. During a summer monitoring session in Hangzhou Bay wetlands, the system successfully captured an abnormal rise in water temperature caused by continuous high temperatures, promptly triggering a carbon release warning.

[0099] Historical status record matching employs a similarity retrieval algorithm to filter historical events with similar environmental conditions from a case database. The matching dimensions include 27 feature indicators across three main categories: meteorological element combinations, hydrological characteristic parameters, and vegetation physiological states. The system generates an environmental similarity score for each matched case. When the score exceeds a set threshold, the carbon flux recovery trajectory of that case is extracted as a reference. In handling a sudden drop in water level in Yancheng wetlands, the module retrieved a similar drought case that occurred five years prior during the same season. Its carbon flux recovery process showed that CH4 emissions peaked 3-7 days after water level recovery, while DOC concentrations returned to baseline levels after 15 days. These historical experiences provide a time-based reference for responding to current events.

[0100] The generation of the three-dimensional grid cell carbon flux suppression scheme follows a hierarchical response principle. The primary response targets minor anomalies, employing an observation strategy focused on natural recovery; the intermediate response implements localized regulation, such as adjusting water levels to control gate opening; and the advanced response initiates comprehensive intervention, including vegetation replanting and the application of microbial agents. The scheme design considers the response differences of different carbon components: for DOC anomalies, the main focus is on controlling hydrological connectivity; for CH4 fluctuations, the emphasis is on redox condition regulation; and for plant carbon loss, intervention is achieved through growth regulators. The system outputs anomaly event control instruction sets containing spatial location coordinates, a list of control measures, and execution time windows, forming a complete response process.

[0101] Table 2 presents an example of carbon flux regulation schemes generated by the system during a sudden temperature rise event. The table includes information in four dimensions: the spatial location of the affected grid cells, the type of carbon composition anomaly, recommended regulation measures, and implementation priority. Regulation measures are configured in a tiered manner based on the degree of anomaly and ecological sensitivity, forming differentiated management strategies.

[0102] Table 2. Examples of carbon flux control schemes for sudden temperature rise events:

[0103]

[0104] During system implementation, the carbon composition analysis module and the anomaly response module work collaboratively. The spatial details of carbon composition provided by the analysis module help to accurately locate the source of anomalies, while the response module uses this information to formulate targeted measures. In a management case at Dafeng Wetland in Jiangsu Province, the system identified two pollution input points using DOC heatmaps and, combined with historical case matching, suggested setting up ecological interception zones, effectively reducing the impact of abnormal carbon transport on the wetland. Data transfer between modules uses standardized interfaces to ensure accurate alignment between carbon composition characteristics and control strategies.

[0105] In terms of technical implementation, the carbon composition analysis module employs a distributed storage architecture to manage massive amounts of carbon cycle data, supporting rapid spatial queries and time series analysis. The anomaly response module combines a rule engine with case-based reasoning, adhering to pre-defined emergency procedures while also drawing on historical experience for flexible responses. The system interface provides 3D visualization of carbon composition, supporting dynamic simulation of the development process of abnormal events. Users can explore the predicted effects of different control schemes through interactive tools, aiding in management decision-making.

[0106] In terms of quality control, the integration process of carbon composition data implements rigorous source verification and accuracy validation. A multi-level review mechanism is established for the determination of abnormal events to avoid false alarms interfering with normal management. The generation of control plans must undergo ecological rationality testing to exclude radical measures that may cause ecological risks. The system regularly conducts retrospective evaluations of historical control cases and continuously optimizes the knowledge base of response strategies. These measures ensure the scientific rigor and safety of wetland carbon flux management.

[0107] Example 5: The flux prediction module constructs a long-term evolutionary carbon cycle simulation system by integrating an updated wetland carbon flux assessment model with multi-source meteorological forecast data. Meteorological data input includes regional climate scenario data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), with a temporal resolution down to the hourly level and a spatial grid accuracy of 1 kilometer. The simulation of the effect of rainfall intensity on soil carbon dissolution rate adopts a process-driven approach, considering three main pathways: raindrop splash effect, surface runoff erosion, and vertical infiltration. The model sets different carbon dissolution response curves according to soil texture classification; clay soils emphasize the physical dissolution process, while sandy soils emphasize the promoting effect of hydraulic conduction. During the simulation, soil saturation and redox potential parameters are dynamically adjusted to reflect the impact of alternating wet and dry periods on carbon conversion rate. An adaptive strategy is adopted for time step setting, shortening the interval to 15 minutes during heavy rainfall events and maintaining a 1-hour resolution during normal periods, balancing computational accuracy and efficiency.

[0108] The carbon absorption trend projection within the vegetation growth cycle combines phenological models and physiological process simulations. Phenological stages are defined using a modified BBCH standard, dividing the annual growth cycle of typical wetland plants into four main stages: germination, vegetative growth, reproductive growth, and dormancy. Each stage is configured with a corresponding set of photosynthetic parameters; for example, a low light energy utilization setting is used during germination, while a high photosynthetic capacity mode is activated during vegetative growth. Physiological process simulations introduce a non-structural carbohydrate dynamic allocation mechanism, distinguishing the carbon assimilation ratios in leaves, stems, and roots. The model tracks the balance between diurnal carbon assimilation and nighttime respiration consumption, cumulatively calculating net primary productivity. Spatial heterogeneity is constrained by vegetation type maps, with different plant communities using different growth parameters; for example, the maximum plant height is set at 3-4 meters for reed communities and 1-2 meters for calamus communities.

[0109] The generation of carbon sequestration capacity change prediction curves employs an ensemble forecasting approach to address uncertainties in meteorological inputs. The system simultaneously runs simulations of multiple climate scenarios, including a baseline scenario, a warming scenario, and a drought scenario, with reasonable parameter fluctuation ranges set for each scenario. Prediction results are expressed as probability distributions, such as the 10%, 50%, and 90th quantile curves of carbon sequestration potential over the next five years. Curve smoothing utilizes a locally weighted regression method, preserving long-term trend characteristics while filtering out short-term fluctuation noise. The prediction timescale supports flexible configuration from seasonal to decadal periods to meet diverse planning needs. In its application to the Poyang Lake wetland, the system successfully captured the interannual variation characteristics of carbon sequestration driven by water level fluctuations, and the prediction results showed a consistent trend with subsequent observational data.

[0110] The synthesis of the long-term carbon flux evolution map is based on the fusion analysis of multi-dimensional simulation results. Topographic water storage capacity parameters are derived from a digital elevation model generated by high-precision lidar measurements, calculating the potential water storage volume for each grid cell. The map integrates three layers of information: spatial pattern of carbon sources and sinks, intensity of evolution trends, and range of uncertainties, and is stored using a raster-vector hybrid data structure. Spatially, it distinguishes three functional zones: core carbon sink areas, stable carbon reservoir areas, and potential carbon source areas. Temporally, it marks key turning points such as carbon sink saturation time nodes. The map update mechanism includes two modes: periodic recalculation and event-triggered updates. Special calculations are initiated when significant environmental changes or model structure updates are detected. The output format supports direct access from mainstream GIS platforms, facilitating integration with land spatial planning systems.

[0111] The carbon sink saturation area identification in the management decision-making module employs a moving window analysis method. The window size is set to a variable range of 100-500 meters based on wetland type, and the time-varying rate of carbon storage within each window is calculated. Saturation is determined based on three criteria: annual carbon accumulation is below the baseline value by 10% for three consecutive years, vegetation productivity reaches the upper limit of light and temperature potential, and soil carbon concentration is close to the theoretical saturation threshold. The regional coordinate set records include the geometric center point, boundary polygons, and topological descriptions, establishing a complete spatial feature archive. In the management of the western area of ​​Dongting Lake wetland, the system identified approximately 12 square kilometers of reed marshland that had reached carbon sink saturation, providing target locations for subsequent restoration projects.

[0112] The database of historical wetland restoration projects is constructed using a standardized metadata framework. Each record contains structured fields such as project location, implementation year, measure type, vegetation configuration, and effect evaluation. Effect evaluation indicators focus on carbon-related parameters, such as soil carbon density increment, vegetation carbon sequestration rate, and greenhouse gas emission intensity. The database supports multi-condition queries, such as filtering successful cases implemented in similar climate zones over the past five years targeting emergent plant communities. The similarity matching algorithm considers a weighted score across three dimensions: environmental background, disturbance history, and restoration objectives, to identify the most valuable precedents. Query results are displayed sorted by matching degree, accompanied by detailed project implementation records and monitoring data.

[0113] The optimization of vegetation configuration schemes employs a multi-objective decision analysis method. Decision variables include three main aspects: species composition, planting density, and community structure, with each variable having a reasonable adjustment range. The objective function simultaneously considers three indicators: carbon storage efficiency, biodiversity maintenance, and engineering cost, and the optimal solution set is found through Pareto front analysis. The scheme output includes detailed planting construction drawings, indicating the spatial arrangement and quantity configuration of preferred species. In the restoration design of the eastern bank of Honghu Wetland, the system-recommended reed-water snowflake mixed planting pattern is expected to increase carbon storage by 15-20% compared to traditional single reed planting, while also enhancing community stability.

[0114] The list of human intervention measures was generated following the principles of operability and quantifiability. Each measure has clearly defined technical specifications, implementation standards, and time requirements, such as water level control accurate to ±5 cm and vegetation replanting density controlled at 4-6 plants / square meter. The list includes priority markers to distinguish between basic and enhancement measures, facilitating phased implementation. The synergistic effects between measures are evaluated using a correlation matrix to avoid conflicts or resource waste. The list format combines structured text with diagrams, ensuring both information completeness and ease of on-site understanding.

[0115] The operation instructions for the ecological restoration equipment are coded using a standardized control language. The instruction set includes four basic fields: equipment type identification code, target location coordinates, action parameters, and execution time. A wireless transmission protocol ensures reliable delivery of instructions in complex wetland environments, and a feedback mechanism monitors equipment status in real time. In the automated management of Shengjin Lake Wetland in Anhui Province, the system successfully coordinated the joint operation of multiple water level regulating gates and intelligent seeders, achieving precise restoration of carbon sequestration hotspots. Equipment operation logs are automatically archived, providing a process record for effect evaluation.

[0116] In terms of technical support, the flux prediction module uses a high-performance computing cluster to process spatiotemporal big data, while the management decision-making module relies on knowledge graph technology to achieve intelligent case recommendations. System maintenance establishes a regular health check mechanism, including standardized processes such as data source verification, model calibration, and hardware inspection. A version control system manages the iterative updates of the core algorithm, retaining model states from different periods for comparative analysis. The user training system includes multiple levels of theoretical courses, practical exercises, and case studies to improve the system application capabilities of management personnel. These measures collectively ensure the effective operation of the wetland carbon flux long-term evolution prediction and remediation decision support system.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wetland carbon flux assessment and management system based on three-dimensional digital twins, characterized in that, The system includes: The 3D modeling module integrates elevation data, vegetation cover density, and soil type stratification information based on wetland geospatial coordinates and ecological parameter sets to construct a multi-resolution 3D wetland topographic mesh model and generate a geotagged 3D wetland digital twin. Based on the real-time environmental parameters of the three-dimensional wetland digital twin, the flux monitoring module collects dynamic data on atmospheric temperature gradient, water level fluctuation frequency and light radiation intensity, extracts carbon flux change characteristics through time series analysis, and outputs dynamic distribution status values ​​of carbon flux. Based on the dynamic distribution state value of carbon flux, the model optimization module uses an adaptive evolutionary algorithm to adjust the matching degree between the vegetation growth rate parameter and the soil respiration coefficient of the three-dimensional wetland digital twin, iteratively updates the model parameter constraints, and generates an optimized three-dimensional twin parameter set. The twin update module identifies abnormal grid cells in the three-dimensional wetland digital twin based on the optimized three-dimensional twin parameter set, corrects the grid cell attributes by comparing with the measured carbon flux data, recalibrates the three-dimensional spatial topology, and outputs the updated wetland carbon flux assessment model. The three-dimensional wetland digital twin includes a set of geospatial grid coordinate parameters and a set of ecological stratification attribute parameters; the dynamic distribution state value of carbon flux includes a set of temperature response coefficients, a set of water level correlation parameters, and a set of light radiation influencing factors; the optimized three-dimensional twin parameter set includes a set of vegetation cover correction parameters and a set of soil carbon release adjustment coefficients; the updated wetland carbon flux assessment model includes a set of grid cell calibration parameters and a set of spatial topological relationship verification parameters; the temperature response coefficient set is established by long-term monitoring of the relationship between soil temperature and carbon dioxide emission rate at different depths, and a nonlinear regression model is used to describe the regulatory effect of temperature on microbial decomposition activities; the water level correlation parameter set quantifies the coupling effect of wetland water level fluctuations and methane emissions, and the data comes from a water level sensor network and static box method methane flux observations; the light radiation influencing factor set is based on the photosynthetically active radiation absorption rate data of the vegetation canopy, combined with a leaf-scale photosynthesis model, to calculate the carbon assimilation efficiency of vegetation under different light conditions; The vegetation cover correction parameter set optimizes the model's simulation accuracy of vegetation carbon absorption by assimilating leaf area index data from remote sensing observations and root distribution data measured on the ground. The soil carbon release adjustment coefficient set corrects the functional relationship between microbial decomposition rate and temperature sensitivity based on soil respiration observation data, enabling the model to more accurately reflect the dynamics of carbon release under different environmental conditions. The flux monitoring module includes: The dynamic acquisition submodule connects to the environmental sensor nodes of the three-dimensional wetland digital twin, captures soil temperature change curves at different depths, water surface carbon dioxide concentration gradients, and vegetation canopy light absorption rates, and generates the original carbon flux time series dataset. The feature extraction submodule analyzes the original carbon flux time series dataset, calculates the sensitivity index of diurnal temperature difference to carbon release rate, statistically analyzes the carbon absorption fluctuation amplitude within the water level change cycle, and outputs a carbon flux change feature vector. The state generation submodule integrates the carbon flux change feature vector with the spatial location information of the three-dimensional wetland digital twin, maps the carbon flux value to the corresponding three-dimensional grid cell, and generates the dynamic distribution state value of carbon flux. The model optimization module includes: The parameter matching submodule analyzes the abnormal fluctuation data in the dynamic distribution state value of carbon flux, compares it with the preset vegetation photosynthetic efficiency threshold of the three-dimensional wetland digital twin, and identifies the parameter matching deviation area. The evolutionary optimization submodule applies a population variation strategy in the parameter matching deviation region to adjust the correlation weight between soil microbial activity coefficient and vegetation root carbon storage capacity, and generates a set of candidate parameter optimization schemes. The constraint iteration submodule verifies the impact of the candidate parameter optimization scheme set on the connectivity of the 3D wetland digital twin mesh, selects parameter combinations that satisfy the carbon flux balance condition, and generates the optimized 3D twin parameter set.

2. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 1, characterized in that: The 3D modeling module includes: The spatial data processing submodule receives wetland boundary coordinates and satellite remote sensing images, segments multispectral band data, extracts vegetation index distribution maps and soil moisture contour lines, and generates a wetland ecological stratification feature dataset. The mesh construction submodule, based on the wetland ecological stratification feature dataset, divides the three-dimensional spatial voxel units, associates the elevation value, vegetation cover and soil type code of each voxel unit, and establishes the initial three-dimensional mesh topology. The attribute fusion submodule integrates the initial 3D mesh topology with real-time sensor network data, labels the temperature conductivity coefficient and water level permeability of each voxel unit, and generates a geotagged 3D wetland digital twin.

3. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 1, characterized in that: The twin update module includes: The anomaly detection submodule traverses the optimized 3D twin parameter set, locates grid cells where the difference rate between the simulated and measured carbon flux values ​​exceeds a threshold, and marks the spatial location coordinates and deviation type. The attribute correction submodule injects the measured gas concentration data from the ground monitoring station based on the marked spatial location coordinates, overwriting the simulated carbon flux attribute value of the corresponding grid cell; The topology calibration submodule recalculates the hydrological connectivity index and carbon diffusion path weights of the corrected grid cells and adjacent cells, and outputs an updated wetland carbon flux assessment model.

4. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 1, characterized in that: The system also includes a carbon component analysis module, which performs the following operations: Receive the grid cell attribute set output by the updated wetland carbon flux assessment model, and separate the data of dissolved organic carbon, methane emissions and plant biomass carbon storage. Multi-source data fusion technology was used to integrate the data on dissolved organic carbon, methane emissions, and plant biomass carbon storage to construct a spatial distribution heat map of carbon components. By linking the spatial distribution heat map of carbon components with hydrological path data from a 3D wetland digital twin, a set of optimized parameters for carbon transport pathways is generated.

5. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 4, characterized in that: The system also includes an exception response module, which performs the following operations: Monitor abrupt event data in the dynamic distribution state value of carbon flux to identify carbon release peaks triggered by a sudden rise in temperature or a sharp drop in water level; The historical state records of the updated wetland carbon flux assessment model are invoked to match carbon flux recovery paths under similar environmental conditions; Generate a three-dimensional mesh cell carbon flux suppression scheme for mutation events and output an abnormal event control instruction set.

6. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 1, characterized in that: The system also includes a flux prediction module, which performs the following operations: By integrating the updated wetland carbon flux assessment model with meteorological forecast data, the impact of future rainfall intensity on soil carbon dissolution rate is simulated. The spatiotemporal convolution algorithm is used to extrapolate the carbon absorption trend during the vegetation growth cycle and generate a prediction curve of carbon sink capacity changes. By integrating the predicted carbon sequestration capacity change curve with the topographic water storage capacity parameters of the three-dimensional wetland digital twin, a long-term carbon flux evolution map is output.

7. The wetland carbon flux assessment and management system based on three-dimensional digital twins according to claim 1, characterized in that: The system also includes a management decision module, which performs the following operations: Receive the coordinate set of carbon sink saturation regions from the long-term carbon flux evolution map; By comparing historical wetland restoration project data, vegetation configuration schemes that improve carbon storage efficiency were selected; Generate a list of human intervention measures for three-dimensional grid cells, and drive the ecological restoration equipment to execute operation instructions.

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