A collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks based on deep learning

Abstract

The present invention relates to the field of biomass and carbon sink synergistic technology, and in particular to a tropical rainforest biomass and carbon sink synergistic optimization decision-making system based on deep learning. Its technical solution includes a multimodal data acquisition system, a spatiotemporal coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic assessment system, a multi-objective decision optimization engine, and an intelligent interactive execution platform. The present invention uses a multimodal data acquisition system to comprehensively and safely acquire data, optimizes data quality through a spatiotemporal coupling preprocessing module, uses an ecological coupling deep learning model and a carbon sink dynamic assessment system to accurately analyze biomass and carbon sinks, formulates scientific decisions and ensures execution through a multi-objective decision optimization engine, and relies on an intelligent interactive execution platform to achieve convenient interaction and efficient execution, thereby significantly improving the accuracy, scientific nature, and execution efficiency of tropical rainforest biomass and carbon sink synergistic optimization decisions, and effectively promoting tropical rainforest ecological protection and sustainable development.

Description

Technical Field

[0001] The present invention relates to the field of biomass and carbon sink synergy technology, and in particular to a tropical rainforest biomass and carbon sink synergy optimization decision-making system based on deep learning. Background Art

[0002] In the field of tropical rainforest ecosystem research and management, achieving coordinated optimization of biomass and carbon sinks is crucial, but currently faces a complex set of technical challenges. Existing technologies suffer from a series of issues in data acquisition, processing and analysis, model building, decision-making, and execution, making it difficult to comprehensively and accurately grasp the dynamic changes in tropical rainforest biomass and carbon sinks, and thus to develop scientifically effective coordinated optimization decision-making plans.

[0003] From a data perspective, the complex environment of tropical rainforests makes it difficult to comprehensively capture three-dimensional ecological data across the canopy, surface, and subsurface, using traditional data collection methods. Different data sources (such as satellite imagery, drone data, and ground-based sensor data) suffer from format differences and mismatches in spatiotemporal resolution, making data fusion difficult. This makes it difficult to form a unified, high-quality dataset, which in turn provides a solid foundation for subsequent analysis.

[0004] In terms of data processing and analysis, the spatial registration and temporal alignment of multi-source data are inaccurate, making it difficult to accurately reflect the spatiotemporal variations in ecosystems. Furthermore, in the face of special circumstances such as extreme climates, a lack of training data leads to poor model generalization and an inability to accurately predict changes in biomass and carbon sequestration.

[0005] In terms of model construction, the existing models are not accurate enough in extracting the heterogeneous characteristics of tropical rainforest vegetation, making it difficult to quantify the ecological coupling effects of vegetation communities and unable to fully consider the mechanisms of ecological processes. This leads to large errors in biomass and carbon sink prediction results and cannot meet actual decision-making needs.

[0006] Decision-making and execution also suffer from flaws. The lack of a scientifically sound multi-objective decision-making optimization model makes it difficult to balance ecological protection with economic development. Pre-tests of the ecological impacts of decision-making options are ineffective, and the verification and implementation of carbon sequestration plans lack effective oversight and assurance mechanisms. Furthermore, the lack of an efficient intelligent interactive execution platform prevents real-time decision-making from mobile terminals in the field and makes it difficult to track key influencing factors, hindering the timely adjustment of decision-making strategies.

[0007] In summary, this application proposes a deep learning-based collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks. Summary of the Invention

[0008] The purpose of the present invention is to address the problem in the background technology that it is difficult to comprehensively and accurately grasp the dynamic changes of tropical rainforest biomass and carbon sinks, and to propose a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning.

[0009] The technical solution of the present invention is a deep learning-based collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks, comprising:

[0010] Multimodal data acquisition system: A cross-platform heterogeneous data integration unit is configured to integrate sub-meter hyperspectral satellite imagery, drone Light Detection and Ranging (LiDAR) point clouds, and ground-based IoT sensor networks. This system securely acquires canopy-surface-subsurface three-dimensional ecological data through quantum encryption channels.

[0011] Spatiotemporal coupling preprocessing module: This module uses an improved Cycle Generative Adversarial Network (CycleGAN) network to achieve spatial registration of multi-source data, combines a dynamic time warping long short-term memory network-attention mechanism (LSTM-Attention) model to solve the temporal alignment problem, integrates a generative adversarial network to expand extreme climate training data, and uses a hierarchical feature alignment method for multi-source data fusion.

[0012] Ecological coupling deep learning model: Build a 3D graph convolutional attention network, extract vegetation heterogeneity features through deformable convolution, combine the graph attention mechanism to quantify the ecological coupling effect of vegetation communities, and design a dual-stream gated fusion mechanism to integrate spatiotemporal feature streams;

[0013] Carbon sink dynamic assessment system: Based on the Bayesian neural network framework, biomass probability prediction is achieved, δ13C isotope tracer data is integrated to analyze carbon source contribution ratios, and physical constraint layers are embedded to couple ecological process mechanism equations.

[0014] Multi-objective decision-making optimization engine: This engine uses the third-generation non-dominated sorting genetic algorithm (NSGA-III) framework to construct an ecological and economic Pareto frontier model, integrates a digital twin simulator to preview the ecological impact of decisions, and uses blockchain smart contracts to automatically verify and execute carbon sequestration plans.

[0015] Intelligent interactive execution platform: Deploy a mixed reality 3D visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to enable real-time decision-making for mobile terminals in the field, and combine feature inversion networks to trace the spatial distribution of key influencing factors.

[0016] Optionally, the ground IoT sensor network includes:

[0017] Canopy vertical profile monitoring array: composed of liftable nodes, each of which integrates a chlorophyll fluorescence sensor, a multispectral reflectometer, and a microenvironment recorder, deployed at 0.5m intervals on a 30m high tower;

[0018] Underground biomass monitoring unit: uses a carbon fiber reinforced probe, implanted at a depth of 2 meters, equipped with an impedance-type fine root detector and a micro-CT scanning module;

[0019] Dynamic self-organizing network protocol: Using the Time Synchronous Channel Hopping (TSCH) protocol, it completes the 200-node network topology reconstruction within 1.5 seconds and achieves 10km-level ultra-long-distance transmission based on the Long-Range Wide Area Network (LoraWAN) architecture.

[0020] Optionally, the multi-source data fusion adopts a hierarchical feature alignment method:

[0021] First-level alignment: Using the third-generation ORB-based real-time visual SLAM system (ORB-SLAM3) algorithm, the UAV LiDAR point cloud and satellite imagery are registered at the sub-pixel level, and a feature matching model based on the scale-invariant feature transform-generalized matching strategy (SIFT-GMS) is established.

[0022] Second level alignment: developing adaptive kernel transformers:

[0023]

[0024] Among them, K(x,y): kernel function value, used to measure the similarity between data points x and y;

[0025] ||xy||: Euclidean distance between data points x and y;

[0026] σ: bandwidth parameter of the kernel function, dynamically adjusted through Bayesian optimization;

[0027] The heterogeneous data collected by the ground sensor network are mapped into a unified feature space, and the kernel function parameters are dynamically adjusted through Bayesian optimization;

[0028] Third-level alignment: Construct a three-dimensional space-time cube, use the trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to achieve cross-modal feature visualization.

[0029] Optionally, the improved cycle generative adversarial network (CycleGAN) includes:

[0030] Adaptive attention mechanism: Embed a coordinate attention module in the generator U-Net architecture to capture spatial position-sensitive features through 2D global pooling;

[0031] Multi-scale discriminator: A cascade structure is constructed using three Patch Generative Adversarial Network (PatchGAN) discriminators, and spectral normalization constraints are introduced;

[0032] Dynamic cycle consistency loss: Design a weight-adaptive cycle loss function, the formula is:

[0033] L cyc =λ1·||G AB (GBA (x))-x||1+λ2·SSIM(G AB (G BA (x)),x)

[0034] Among them, G AB and G BA : represents the generator from domain A to domain B and from domain B to domain A respectively; x: input data sample; ||·||1: L1 norm; SSIM(·): structural similarity index, λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss.

[0035] Optionally, the three-dimensional graph convolutional attention network (3D-CGAT) specifically includes:

[0036] Vegetation patch influence domain division: Based on the Voronoi diagram algorithm, a dynamic influence domain is generated with the dominant tree species as the center, with a radius of R = 5 × tree height + 3 × crown width;

[0037] Heterogeneous graph convolution layer: defines three types of nodes and five types of edges, using the second-generation graph attention network (GATv2) convolution kernel to achieve feature propagation. The three types of nodes include tree layer, shrub layer, and surface layer. The five types of edges include light competition, root interaction, nutrient transfer, seed dispersal, and microbial association.

[0038] Dynamic graph structure update: The graph topology is updated every 6 months through transfer learning. The Gromov-Wasserstein distance is used to measure the similarity of the graph structure. The update threshold is set to 0.35. The Gromov-Wasserstein distance is:

[0039]

[0040] Among them, G1 and G2 are two graph structures, representing the influence domain of vegetation patches at different time points respectively;

[0041] γ: joint probability distribution, and denote the distance metric between pairs of nodes in graphs G1 and G2 respectively; Γ(u1,u2): the set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively.

[0042] Optionally, the physical constraint layer is implemented as follows:

[0043] Embed the CENTURY model mechanism equation: The soil organic carbon decomposition rate equation is used as a hard constraint, formula:

[0044]

[0045] in, The rate of change of soil organic carbon content over time; k: decomposition rate constant; C: current soil organic carbon content; f(T): temperature correction function, in the form of Arrhenius equation: Among them E a is the activation energy, R is the gas constant, and T is the absolute temperature; f(θ): moisture correction function, usually expressed as a nonlinear function of soil moisture content; f(pH): pH correction function, reflecting the effect of pH on microbial activity;

[0046] Construct a hybrid loss function:

[0047] L total =α·L data +β·L physics +γ·L reg

[0048] Among them, L data : Data-driven loss, using mean square error, is used to measure the difference between the model prediction value and the true value; L physics : physical constraint loss; L reg : Regularization loss, used to prevent model overfitting, using L2 regularization or weight decay; α, β, γ: weight coefficients, respectively controlling the relative importance of data-driven loss, physical constraint loss, and regularization loss; α=0.6, β=0.3, γ=0.1, L physics Contains 21 ecological process conservation equations;

[0049] Develop a differentiable solver: Implement automatic differentiation of mechanism equations based on the PyTorch framework, supporting GPU accelerated computing.

[0050] Optionally, the digital twin simulator includes:

[0051] Multi-resolution modeling framework: at 1km 2 The process-based biome-biogeochemical cycle model (Biome-BGC) was used at the 1ha scale, the individual tree model was used at the 1m scale, and the 2 The scale adopts a microenvironment fluid model based on the lattice Boltzmann method (LBM);

[0052] Cross-scale coupling interface: Develop a dynamic downscaling converter and implement parameter transfer through a wavelet generative adversarial network (Wavelet-GAN) network to ensure conservation of energy and material flux;

[0053] Reinforcement learning training environment: A Markov decision process with a 12-dimensional state space and a 7-dimensional action space is designed, and the soft actor-critic algorithm (SAC) is used to train the policy network.

[0054] Optionally, the blockchain smart contract implementation method includes:

[0055] Carbon Tokenization Protocol: Divide each hectare of carbon sink into 1,000 Ethereum Fungible and Non-Fungible Token Standard-Coded 1155 (ERC-1155) tokens, each corresponding to 1kg of CO2 equivalent, and store satellite imagery verification data for the corresponding area through IPFS;

[0056] Dynamic Oracle Mechanism: Deploy an off-chain oracle based on a trusted execution environment to obtain real-time weather data from the National Oceanic and Atmospheric Administration (NOAA) and Landsat-9 remote sensing data as trigger conditions for smart contracts;

[0057] Zero-knowledge verification layer: uses zero-knowledge succinct non-interactive argument of knowledge (zk-SNARKs) technology to generate carbon sink calculation proof, with verification time <2 seconds, proof size <1KB, and supports verification by a million-node network.

[0058] Optionally, the edge-cloud collaborative architecture includes:

[0059] Dynamic model slicing technology: Divides the 3D-CGAT network into 12 independently run sub-modules, automatically selecting cloud inference or edge computing based on network bandwidth;

[0060] Incremental federated learning framework: Each edge node maintains a local model copy and updates the global model every 24 hours through a secure aggregation protocol, using differential privacy technology to protect data privacy.

[0061] Disaster recovery mechanism: When a network interruption of more than 5 minutes is detected, it automatically switches to emergency communication mode based on Beidou short messages, and the transmission efficiency is optimized to 140 bytes per time.

[0062] Optionally, the feature inversion network adopts:

[0063] Deep Taylor decomposition algorithm: calculates the contribution of each input pixel to the carbon sink prediction results and generates a heat map with a resolution of 0.5m;

[0064] Spatiotemporal Attention Tracing Module: Identifies key time nodes and spatial regions through a multi-head attention mechanism;

[0065] Uncertainty visualization engine: uses the Marching Cubes algorithm to render 3D probability distribution cloud maps, supports dynamic adjustment of transparency and color mapping of 95% confidence intervals, and probability distribution rendering in the Marching Cubes algorithm.

[0066]

[0067] Where: P(x,y,z): probability density value at a point (x,y,z) in three-dimensional space; E(x,y,z): energy function, which represents the deviation of the ecological parameters (such as carbon storage and biomass) at that point from the predicted value; T: temperature parameter, which controls the smoothness of the probability distribution; Z: normalization constant, which ensures that the integral of the probability density function is 1.

[0068] Compared with the prior art, this application has at least one of the following beneficial technical effects:

[0069] The multimodal data acquisition system comprehensively collects three-dimensional ecological data and ensures safe transmission. The spatiotemporal coupling preprocessing module effectively integrates multi-source data, solves spatiotemporal alignment problems, and expands training data, laying a solid foundation for subsequent analysis.

[0070] The ecological coupling deep learning model accurately extracts vegetation characteristics and quantifies ecological coupling effects. The carbon sink dynamic assessment system combines physical constraints to improve prediction accuracy and interpretability, and can more accurately assess biomass and carbon sinks.

[0071] The multi-objective decision-making optimization engine balances ecological and economic goals, the digital twin simulator previews the impact of decisions, and the blockchain smart contract guarantees the implementation of the carbon sequestration plan, ensuring that the decision is scientific and feasible.

[0072] The intelligent interactive execution platform's visual interface is easy to operate, the edge-cloud collaborative architecture enables real-time decision-making, and the feature inversion network traces key factors, improving the timeliness and accuracy of decision-making.

[0073] The present invention uses a multimodal data acquisition system to comprehensively and safely acquire data, optimizes data quality through a spatiotemporal coupling preprocessing module, accurately analyzes biomass and carbon sinks using an ecological coupling deep learning model and a carbon sink dynamic assessment system, formulates scientific decisions and ensures execution through a multi-objective decision-making optimization engine, and relies on an intelligent interactive execution platform to achieve convenient interaction and efficient execution, thereby significantly improving the accuracy, scientific nature and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decisions, and effectively promoting the ecological protection and sustainable development of tropical rainforests. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 Schematic diagram of the structure of a deep learning-based collaborative optimization decision-making system for tropical rainforest biomass and carbon sequestration. DETAILED DESCRIPTION

[0075] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0076] Example 1

[0077] like Figure 1As shown, the present invention proposes a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning, including a multimodal data acquisition system, a spatiotemporal coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic assessment system, a multi-objective decision optimization engine and an intelligent interactive execution platform. Each part is described in detail below.

[0078] In this embodiment, a multimodal data collection system forms the foundation of the entire tropical rainforest biomass and carbon sink coordinated optimization decision-making system. It integrates a variety of advanced equipment and technologies to achieve comprehensive, multi-level data collection for the tropical rainforest. It employs a cross-platform, heterogeneous data integration unit, integrating sub-meter hyperspectral satellite imagery, drone LiDAR point clouds, and a ground-based IoT sensor network. Through quantum encryption, it securely captures three-dimensional ecological data from the canopy, surface, and subsurface. By integrating sub-meter hyperspectral satellite imagery, drone LiDAR point clouds, and ground-based IoT sensor network data, comprehensive information on the tropical rainforest is acquired from the macro to the micro, from the air to the ground. Satellite imagery covers large areas, providing comprehensive information on vegetation distribution and ecological patterns; drone LiDAR point clouds accurately capture three-dimensional vegetation structure, such as tree height and canopy width; and the ground-based IoT sensor network monitors microenvironmental parameters in real time. This data encompasses multiple levels of the tropical rainforest ecosystem, providing a rich and comprehensive foundation for subsequent analysis.

[0079] Data from different sources are complementary. Spectral information from hyperspectral satellite imagery can help identify vegetation types and health. Three-dimensional structural data from LiDAR point clouds can be combined with spectral information to more accurately assess biomass. Real-time environmental data from ground-based sensor networks can be used to explain changes in vegetation growth and carbon sequestration, enhancing the value of comprehensive data analysis.

[0080] Among them, the ground IoT sensor network includes:

[0081] Canopy vertical profile monitoring array: Consists of liftable nodes, each integrating a chlorophyll fluorescence sensor (measuring wavelength 400-750nm), a multispectral reflectometer (8 bands, including the red-edge band), and a microenvironment recorder. These nodes are deployed at 0.5m intervals on a 30m-high tower.

[0082] Underground biomass monitoring unit: uses a carbon fiber reinforced probe, implanted at a depth of 2 meters, equipped with an impedance-type fine root detector (resolution 0.1 mm) and a micro-CT scanning module (scanning frequency 3 times per day);

[0083] Dynamic Ad Hoc Networking Protocol: Utilizing the Time Slotted Channel Hopping (TSCH) protocol, the system reconfigures the 200-node network topology within 1.5 seconds and achieves ultra-long-distance transmission of 10 km based on the LoraWAN architecture. The canopy vertical profile monitoring array deploys nodes at 0.5-meter intervals, enabling precise monitoring of physiological and environmental parameters at varying canopy heights, such as chlorophyll fluorescence and multispectral reflectance, contributing to in-depth research on vegetation photosynthesis and growth. The underground biomass monitoring unit's carbon fiber-reinforced probes, implanted at a depth of 2 meters and equipped with advanced detection equipment, accurately measure underground root structure and biomass distribution, filling a gap in underground ecological monitoring. The dynamic ad hoc network using the Time Slotted Channel Hopping (TSCH) protocol rapidly reconfigures the network topology, ensuring smooth communication between sensor nodes. The 10-km ultra-long-distance transmission enabled by the Long Range Wide Area Network (LoraWAN) architecture adapts to the complex geographical environment of tropical rainforests and ensures timely data transmission to data centers. The quantum encryption channel ensures the security of data transmission and prevents data from being stolen or tampered with during transmission.

[0084] The spatiotemporal coupling preprocessing module preprocesses the collected multi-source data to improve data quality and usability. It uses an improved cyclic generative adversarial network (CycleGAN) network to achieve multi-source data spatial registration, combines the dynamic time warping long short-term memory network-attention mechanism (LSTM-Attention) model to solve the temporal alignment problem, and integrates the adversarial generative network to expand extreme climate training data. Multi-source data fusion adopts a hierarchical feature alignment method:

[0085] First-level alignment: The UAV LiDAR point cloud (density ≥ 200 points / m) is aligned using the real-time visual SLAM system (third generation) based on ORB features, the full name of which is Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3, referred to as ORB-SLAM3 algorithm. 2 ) and satellite images (resolution 0.3m) for sub-pixel registration, and establish a feature matching model based on scale-invariant feature transform-generalized matching strategy SIFT-GMS (Grid-based Motion Statistics);

[0086] Second level alignment: developing adaptive kernel transformers:

[0087]

[0088] Among them, K(x,y): kernel function value, used to measure the similarity between data points x and y;

[0089] ||xy||: Euclidean distance between data points x and y;

[0090] σ: bandwidth parameter of the kernel function, dynamically adjusted through Bayesian optimization;

[0091] The heterogeneous data collected by the ground sensor network (including leaf area index time series data and soil CO2 flux pulse data) are mapped into a unified feature space, and the kernel function parameters are dynamically adjusted through Bayesian optimization.

[0092] Third-level alignment: Construct a three-dimensional space-time cube (resolution 1m×1m×0.1m), use the trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to achieve cross-modal feature visualization. Through three levels of alignment, data from different sources and scales are unified into the same feature space and space-time framework. Sub-pixel registration and feature matching improve the spatial consistency of the data, the adaptive kernel function transformer solves the problem of fusion of heterogeneous data, and the three-dimensional space-time cube and interpolation algorithm enhance the continuity and integrity of the data, enabling multi-source data to complement and verify each other, providing high-quality input for subsequent deep learning models. The differentiable rendering layer achieves cross-modal feature visualization, which helps researchers intuitively observe and analyze the relationship between different modal data, explore potential features hidden in the data, improve the feature expression ability of the data, and provide richer information for the training of ecologically coupled deep learning models.

[0093] It is worth noting that the improved CycleGAN network includes:

[0094] Adaptive attention mechanism: A coordinate attention module is embedded in the generator U-Net architecture to capture spatial position-sensitive features through 2D global pooling.

[0095] Multi-scale discriminator: A cascade structure is constructed using three PatchGAN discriminators (with receptive fields of 16×16, 32×32, and 64×64 pixels, respectively), and a spectral normalization constraint is introduced;

[0096] Dynamic cycle consistency loss: Design a weight-adaptive cycle loss function, the formula is:

[0097] L cyc =λ1·||G AB (G BA (x))-x||1+λ2·SSIM(G AB (G BA (x)),x)

[0098] Among them, G AB and G BA : represent the generators from domain A to domain B and from domain B to domain A respectively;

[0099] x: input data sample;

[0100] ||·||1: L1 norm, used to measure the absolute difference between the generated data and the original data;

[0101] SSIM(·): Structural Similarity Index, used to assess the similarity between generated data and original data in terms of structure, brightness, and contrast. The improved CycleGAN network combined with an adaptive attention mechanism enables more accurate spatial registration of multi-source data, resolving spatial inconsistencies between different data sources. The Dynamic Time Warping LSTM-Attention model effectively addresses the mismatch of time series data, ensuring accurate data correspondence in time and space, and improving the model's efficient use of spatiotemporal information.

[0102] Through accurate registration and alignment, the model can better learn the true patterns and regularities in the data, reduce errors caused by data inconsistencies, and thus improve the model's generalization ability, enabling it to perform well in different data sets and scenarios. The integrated adversarial generative network expands extreme climate training data and increases the diversity of training data. In practical applications, the impact of extreme climate events on tropical rainforest biomass and carbon sequestration is crucial, but such data is often scarce. By generating extreme climate data through generative adversarial networks, the model can better learn ecological response patterns under extreme conditions, improving the model's prediction accuracy and robustness when faced with various complex climate conditions.

[0103] Ecologically coupled deep learning model: A three-dimensional graph convolutional attention network (3D-CGAT) is constructed to extract vegetation heterogeneity features through deformable convolution. Graph attention mechanisms are combined to quantify the ecological coupling effects of vegetation communities. A dual-stream gated fusion mechanism is designed to integrate spatiotemporal feature streams. The 3D-CGAT network specifically includes:

[0104] Vegetation patch influence domain division: Based on the Voronoi diagram algorithm, a dynamic influence domain is generated with the dominant tree species as the center, with a radius of R = 5 × tree height + 3 × crown width;

[0105] Heterogeneous graph convolution layer: defines three types of nodes (tree layer, shrub layer, and ground layer) and five types of edges (light competition, root interaction, nutrient transfer, seed dispersal, and microbial association), and uses the second-generation graph attention network (GATv2) convolution kernel to achieve feature propagation;

[0106] Dynamic graph structure update: The graph topology is updated every 6 months through transfer learning. The Gromov-Wasserstein distance is used to measure the similarity of the graph structure. The update threshold is set to 0.35. The Gromov-Wasserstein distance is:

[0107]

[0108] Among them, G1 and G2 are two graph structures, representing the influence domain of vegetation patches at different time points respectively;

[0109] γ: joint probability distribution, used to align nodes of two graph structures;

[0110] and denote the distance measure between pairs of nodes in graphs G1 and G2 respectively;

[0111] Γ(u1,u2): The set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively, and λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss. 3D-CGAT network:

[0112] Accurate ecological modeling: The Voronoi diagram algorithm is used to delineate the influence domains of vegetation patches, dynamically adjusting the impact range based on actual vegetation growth and more accurately simulating interactions between plants. The heterogeneous graph convolutional layer defines multiple nodes and edges, taking into account the relationships between different levels and ecological processes within the vegetation community, such as light competition and root interactions, enabling the model to more comprehensively capture the complexity of the ecosystem.

[0113] Adapting to dynamic ecosystem changes: By updating the graph topology every six months through transfer learning, the model can promptly reflect the dynamic changes in the tropical rainforest ecosystem. When the ecological environment changes, the model can quickly adjust its structure to maintain accurate predictions of biomass and carbon sinks, improving its adaptability and timeliness. Deformable convolution can adaptively adjust the sampling position of the convolution kernel to better capture the complex shape and texture information of vegetation and extract more representative heterogeneous features. These features are important for accurately assessing tropical rainforest biomass and carbon sinks, as the heterogeneity of different vegetation types and growth states significantly affects their ecological functions.

[0114] The graph attention mechanism assigns different attention weights based on the relationships between nodes, highlighting important ecological coupling relationships and enabling the model to more accurately quantify the ecological coupling effects of vegetation communities. The dual-stream gating fusion mechanism integrates spatiotemporal feature streams, fully leveraging information from both temporal and spatial dimensions, improving the model's ability to understand and predict ecosystem dynamics.

[0115] Carbon sink dynamic assessment system: Based on the Bayesian neural network framework, biomass probability prediction is achieved, δ13C isotope tracer data is integrated to analyze carbon source contribution ratios, and a physical constraint layer is embedded to couple the ecological process mechanism equation. The physical constraint layer is implemented as follows:

[0116] Embed the CENTURY model mechanism equation: The soil organic carbon decomposition rate equation is used as a hard constraint, formula:

[0117]

[0118] in, the rate of change of soil organic carbon content over time;

[0119] k: decomposition rate constant, which is related to soil type and microbial activity;

[0120] C: current soil organic carbon content;

[0121] f(T): Temperature correction function, in the form of Arrhenius equation: Among them E a is the activation energy, R is the gas constant, and T is the absolute temperature;

[0122] f(θ): moisture correction function, usually expressed as a nonlinear function of soil moisture content;

[0123] f(pH): pH correction function, reflecting the effect of pH on microbial activity;

[0124] Construct a hybrid loss function:

[0125] L total =α·L data +β·L physics +γ·L reg

[0126] Among them, L data : Data-driven loss, using mean square error, is used to measure the difference between the model's predicted value and the true value;

[0127] L physics : Physical constraint loss;

[0128] L reg : Regularization loss, used to prevent model overfitting, using L2 regularization or weight decay;

[0129] α, β, γ: weight coefficients, which control the relative importance of data-driven loss, physical constraint loss, and regularization loss respectively;

[0130] α=0.6, β=0.3, γ=0.1, L physics Contains 21 ecological process conservation equations;

[0131] Develop a differentiable solver: Implement automatic differentiation of mechanism equations based on the PyTorch framework, supporting GPU accelerated computing.

[0132] In this example, a probabilistic biomass prediction is implemented based on a Bayesian neural network framework, taking into account model uncertainty. In real ecosystems, there are many factors that are difficult to accurately measure and predict, such as the uncertainty of climate change and the randomness of individual organisms. By outputting the probabilistic distribution of biomass, decision makers can gain a more comprehensive understanding of the reliability of the prediction results and make more reasonable decisions. Integrating δ13C isotope tracer data to analyze carbon source contribution ratios enables a more accurate analysis of the contribution of different carbon sources to the carbon sink. This helps to gain a deeper understanding of the mechanisms of the tropical rainforest carbon cycle and provides a scientific basis for formulating targeted carbon sink management strategies, such as determining which carbon sources are the main contributors and which carbon sources require key control or protection. The CENTURY model mechanism equation is embedded, and the soil organic carbon decomposition rate equation is used as a hard constraint to construct a hybrid loss function. This approach incorporates the physical mechanisms of ecological processes into the model, improving its interpretability. At the same time, the constraints ensure that the model's prediction results conform to the basic laws of the ecosystem, improving the accuracy and reliability of the predictions. The differentiable solver developed based on the PyTorch framework supports GPU-accelerated computing and can efficiently solve the mechanism equations. When processing large-scale ecological data and complex models, GPU acceleration can significantly shorten computing time, improve model training and inference efficiency, and enable the system to provide carbon sequestration assessment results more promptly.

[0133] Multi-objective decision-making optimization engine: This uses the NSGA-III framework to construct an ecological and economic Pareto frontier model, integrates a digital twin simulator to preview the ecological impact of decisions, and uses blockchain smart contracts to automatically verify and execute carbon sequestration plans. The digital twin simulator includes:

[0134] Multi-resolution modeling framework: at 1km 2 The process-based biome-biogeochemical cycle model (Biome-BGC) was used at the 1ha scale, the individual tree model (including 26 morphological parameters) was used at the 1m scale, and the 2 The scale adopts the microenvironment fluid model based on (lattice Boltzmann method) LBM;

[0135] Cross-scale coupling interface: Develop a dynamic downscaling converter and implement parameter transfer through the Wavelet-GAN network to ensure conservation of energy and material flux;

[0136] Reinforcement learning training environment: A Markov decision process with a 12-dimensional state space (carbon storage, species diversity) and a 7-dimensional action space (thinning intensity, replanting density, etc.) was designed, and the SAC algorithm was used to train the policy network.

[0137] In addition, the blockchain smart contract implementation method includes:

[0138] Carbon sink tokenization protocol: divide each hectare of carbon sink into 1,000 ERC-1155 tokens, each corresponding to 1 kg of CO2 equivalent, and store satellite image verification data for the corresponding area through the InterPlanetary File System (IPFS);

[0139] Dynamic Oracle Mechanism: Deploy an off-chain oracle based on TEE (Trusted Execution Environment) to obtain NOAA weather data and Landsat-9 remote sensing data in real time as trigger conditions for smart contracts;

[0140] Zero-knowledge verification layer: zk-SNARKs technology is used to generate carbon sink calculation proof, with verification time <2 seconds, proof size <1KB, and support for verification by a million-node network.

[0141] In this example, the Pareto Frontier model uses the third-generation non-dominated sorting genetic algorithm (NSGA-III) framework to construct an eco-economic Pareto Frontier model, which can find the optimal balance between ecological protection and economic development. The series of non-dominated solutions generated by this model provides decision makers with multiple options, allowing them to balance ecological and economic benefits based on different policy objectives and resource constraints, and develop decision plans that better meet practical needs.

[0142] Among them, the multi-resolution modeling framework uses models of different scales, from the macro regional scale to the micro individual scale, which can comprehensively and accurately describe the ecosystem of the tropical rainforest. The dynamic downscaling converters between models of different scales ensure the cross-scale coupling of the conservation of energy and material fluxes, improve the accuracy and consistency of the simulation, and provide a more reliable basis for decision-making. The reinforcement learning training environment trains the policy network through the Markov decision process and the (soft actor-critic algorithm) SAC algorithm, which can simulate the dynamic changes of the tropical rainforest and continuously optimize the decision-making strategy. In practical applications, the system can automatically adjust management strategies according to real-time ecological data and decision-making goals, thereby improving the scientific nature and effectiveness of decision-making.

[0143] Furthermore, the carbon sink tokenization protocol transforms carbon sinks into tradable assets, promoting the development of the carbon sink market. Using Ethereum's standard for fungible and non-fungible tokens (ERC-1155), this protocol makes carbon sink trading more convenient and transparent, attracting more investors to the carbon sink market and providing financial support for rainforest conservation and management. A dynamic oracle mechanism ensures that smart contracts are automatically executed based on real-time environmental data, improving the timeliness and accuracy of decision-making. The zero-knowledge verification layer utilizes zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) technology to verify carbon sink calculations without compromising data privacy, ensuring the security and credibility of carbon sink transactions and reducing transaction risks and disputes.

[0144] Intelligent interactive execution platform: Deploy a mixed reality (MR) 3D visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to enable real-time decision-making by mobile terminals in the field, and combine feature inversion networks to trace the spatial distribution of key influencing factors. The edge-cloud collaborative architecture includes:

[0145] Dynamic model slicing technology: divides the 3D-CGAT network into 12 independently run sub-modules, automatically selecting cloud inference or edge computing based on network bandwidth (threshold set at 10Mbps);

[0146] Incremental federated learning framework: Each edge node maintains a local model copy and updates the global model every 24 hours through a secure aggregation protocol (Secure Aggregation), using differential privacy technology (ε=0.5, δ=1e -5 ) Protect data privacy;

[0147] Disaster recovery mechanism: When a network interruption of more than 5 minutes is detected, it automatically switches to emergency communication mode based on Beidou short messages, and the transmission efficiency is optimized to 140 bytes per time.

[0148] Among them, the feature inversion network adopts:

[0149] Deep Taylor Decomposition algorithm: Calculates the contribution of each input pixel to the carbon sink prediction results and generates a heat map with a resolution of 0.5m;

[0150] Spatiotemporal attention tracing module: This module uses a multi-head attention mechanism to identify key time nodes (30 days before the drought event) and spatial regions (such as sensitive areas with slopes greater than 25°).

[0151] Uncertainty visualization engine: uses the Marching Cubes algorithm to render 3D probability distribution cloud maps, supports dynamic adjustment of transparency and color mapping of 95% confidence intervals, and probability distribution rendering in the Marching Cubes algorithm

[0152]

[0153] Where: P(x,y,z): probability density value at a point (x,y,z) in three-dimensional space; E(x,y,z): energy function, which represents the deviation of the ecological parameters (such as carbon storage and biomass) at that point from the predicted value; T: temperature parameter, which controls the smoothness of the probability distribution; Z: normalization constant, which ensures that the integral of the probability density function is 1.

[0154] Intelligent interactive execution platform, mixed reality 3D visualization interface: This platform provides a holographic terrain interactive mixed reality 3D visualization interface, allowing users to intuitively observe the 3D topography and ecological information of the rainforest. This immersive interactive experience helps users better understand the structure and function of the rainforest ecosystem, conduct virtual field investigations and decision-making simulations, and improve the intuitiveness and accuracy of decision-making.

[0155] Edge-Cloud Collaborative Architecture: Dynamic model slicing technology automatically selects cloud-based inference or edge computing based on network bandwidth, enabling flexible adjustment of computing resource allocation. When network bandwidth is sufficient, the cloud's powerful computing capabilities are leveraged for large-scale model inference. When network bandwidth is limited, some computing tasks are allocated to edge nodes, improving system efficiency and reducing data transmission costs. The incremental federated learning framework maintains a local model copy at each edge node, updates the global model through a secure aggregation protocol, and employs differential privacy technology to protect data privacy. This approach ensures data security while fully utilizing edge node data for model training, improving model accuracy and generalization capabilities. Disaster recovery mechanisms ensure that in the event of a network interruption, the system automatically switches to emergency communication mode, ensuring data transmission and decision execution, and improving system reliability and stability.

[0156] Heat maps generated by the deep Taylor decomposition algorithm can intuitively demonstrate the contribution of different regions to carbon sequestration, helping decision makers quickly identify key areas and influencing factors. The spatiotemporal attention tracing module uses a multi-head attention mechanism to identify key time nodes and spatial regions, providing a deeper understanding of the dynamic changes in tropical rainforest ecosystems and providing a basis for developing targeted management strategies. The uncertainty visualization engine presents the uncertainty of carbon sequestration forecasts as a three-dimensional probability distribution cloud map, supporting dynamic adjustment of the transparency and color mapping of confidence intervals. This allows decision makers to more intuitively understand the reliability of forecast results, fully consider uncertainty factors in the decision-making process, and develop more robust decision-making plans.

[0157] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant inspirations of the above embodiments, those skilled in the art may make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A deep learning-based collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks, characterized by: include: Multimodal data acquisition system: A cross-platform heterogeneous data integration unit is configured to integrate sub-meter hyperspectral satellite imagery, drone light detection and ranging point clouds, and ground-based IoT sensor networks. This system uses quantum encryption channels to securely acquire three-dimensional ecological data from the canopy, surface, and underground. Spatiotemporal coupling preprocessing module: This module uses an improved recurrent generative adversarial network to achieve spatial registration of multi-source data, combines a dynamic time warping long short-term memory network-attention mechanism model to solve the temporal alignment problem, integrates a generative adversarial network to expand extreme climate training data, and uses a hierarchical feature alignment method for multi-source data fusion; Ecological coupling deep learning model: Build a 3D graph convolutional attention network, extract vegetation heterogeneity features through deformable convolution, combine the graph attention mechanism to quantify the ecological coupling effect of vegetation communities, and design a dual-stream gated fusion mechanism to integrate spatiotemporal feature streams; Carbon sink dynamic assessment system: Based on the Bayesian neural network framework, biomass probability prediction is achieved, δ13C isotope tracer data is integrated to analyze carbon source contribution ratios, and physical constraint layers are embedded to couple ecological process mechanism equations. Multi-objective decision-making optimization engine: This engine uses a third-generation non-dominated sorting genetic algorithm framework to construct an ecological and economic Pareto frontier model, integrates a digital twin simulator to preview the ecological impact of decisions, and uses blockchain smart contracts to automatically verify and execute carbon sequestration plans. Intelligent interactive execution platform: Deploy a mixed reality 3D visualization interface to support holographic terrain interaction, establish an edge-cloud collaborative architecture to enable real-time decision-making for mobile terminals in the field, and combine feature inversion networks to trace the spatial distribution of key influencing factors; The improved cyclic generative adversarial network comprises: Adaptive attention mechanism: Embed a coordinate attention module in the generator U-Net architecture to capture spatial position-sensitive features through 2D global pooling; Multi-scale discriminator: A cascade structure is formed by three PatchGAN discriminators, and spectral normalization constraints are introduced; Dynamic cycle consistency loss: Design a weight-adaptive cycle loss function, the formula is: L cyc =λ1·||G AB (G BA (x))-x||1+λ2·SSIM(G AB (G BA (x)),x) Among them, G AB and G BA : represents the generator from domain A to domain B and from domain B to domain A respectively; x: input data sample; ||·||1: L1 norm; SSIM(·): structural similarity index, λ1 and λ2 are learnable weight parameters used to balance the importance of L1 loss and SSIM loss; The physical constraint layer is implemented as follows: Embed the CENTURY model mechanism equation: The soil organic carbon decomposition rate equation is used as a hard constraint, formula: in, The rate of change of soil organic carbon content over time; k: decomposition rate constant; C: current soil organic carbon content; f(T): temperature correction function, in the form of Arrhenius equation: Among them E a is the activation energy, R is the gas constant, and T is the absolute temperature; f(θ): moisture correction function, usually expressed as a nonlinear function of soil moisture content; f(pH): pH correction function, reflecting the effect of pH on microbial activity; Construct a hybrid loss function: L total =α·L data +β·L physics +γ·L reg Among them, L data : Data-driven loss, using mean square error, is used to measure the difference between the model prediction value and the true value; L physics : physical constraint loss; L reg : Regularization loss, used to prevent model overfitting, using L2 regularization or weight decay; α, β, γ: weight coefficients, respectively controlling the relative importance of data-driven loss, physical constraint loss, and regularization loss; α=0.6, β=0.3, γ=0.1, L physics Contains 21 ecological process conservation equations; Develop a differentiable solver: Implement automatic differentiation of mechanism equations based on the PyTorch framework, supporting GPU accelerated computing.

2. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 1, characterized in that: The ground IoT sensor network includes: Canopy vertical profile monitoring array: composed of liftable nodes, each of which integrates a chlorophyll fluorescence sensor, a multispectral reflectometer, and a microenvironment recorder, deployed at 0.5m intervals on a 30m high tower; Underground biomass monitoring unit: uses a carbon fiber reinforced probe, implanted at a depth of 2 meters, equipped with an impedance-type fine root detector and a micro-CT scanning module; Dynamic self-organizing network protocol: Using time-synchronized channel hopping protocol, it can complete the 200-node network topology reconstruction within 1.5 seconds, and achieve 10km-level ultra-long-distance transmission based on the long-distance wide area network architecture.

3. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 2, characterized in that: The multi-source data fusion adopts a hierarchical feature alignment method: First-level alignment: Using the third-generation ORB feature-based real-time visual SLAM system algorithm, the UAV LiDAR point cloud and satellite imagery are registered at the sub-pixel level, and a feature matching model based on scale-invariant feature transformation and generalized matching strategy is established. Second level alignment: developing adaptive kernel transformers: Among them, K(x,y): kernel function value, used to measure the similarity between data points x and y; ||xy||: Euclidean distance between data points x and y; σ: bandwidth parameter of the kernel function, dynamically adjusted through Bayesian optimization; The heterogeneous data collected by the ground sensor network are mapped into a unified feature space, and the kernel function parameters are dynamically adjusted through Bayesian optimization; Third-level alignment: Construct a three-dimensional space-time cube, use the trilinear interpolation algorithm to fuse multi-scale data, and embed a differentiable rendering layer to achieve cross-modal feature visualization.

4. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 1, characterized in that: The three-dimensional graph convolutional attention network specifically includes: Vegetation patch influence domain division: Based on the Voronoi diagram algorithm, a dynamic influence domain is generated with the dominant tree species as the center, with a radius of R = 5 × tree height + 3 × crown width; Heterogeneous graph convolution layer: defines three types of nodes and five types of edges, using the second-generation graph attention network convolution kernel to achieve feature propagation. The three types of nodes include tree layer, shrub layer, and surface layer. The five types of edges include light competition, root interaction, nutrient transfer, seed dispersal, and microbial association. Dynamic graph structure update: The graph topology is updated every 6 months through transfer learning. The Gromov-Wasserstein distance is used to measure the similarity of the graph structure. The update threshold is set to 0.

35. The Gromov-Wasserstein distance is: Among them, G1 and G2 are two graph structures, representing the influence domain of vegetation patches at different time points respectively; γ: joint probability distribution, and denote the distance metric between pairs of nodes in graphs G1 and G2 respectively; Γ(u1,u2): the set of all possible joint probability distributions, where u1 and u2 are the node distributions of graphs G1 and G2 respectively.

5. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 1, characterized in that: The digital twin simulator comprises: Multi-resolution modeling framework: at 1km 2 The process-based biome-biogeochemical cycle model was used at the 1ha scale, the individual tree model was used at the 1m scale, and the 2 The scale adopts a microenvironment fluid model based on the lattice Boltzmann method; Cross-scale coupling interface: Develop a dynamic downscaling converter and implement parameter transfer through wavelet generative adversarial networks to ensure conservation of energy and material flux; Reinforcement learning training environment: Design a Markov decision process with a 12-dimensional state space and a 7-dimensional action space, and use the soft actor-critic algorithm to train the policy network.

6. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 1, characterized in that: The blockchain smart contract implementation method includes: Carbon sink tokenization protocol: divide each hectare of carbon sink into 1,000 Ethereum fungible and non-fungible tokens (ERC-1155) tokens, each corresponding to 1kg of CO2 equivalent, and store satellite imagery verification data for the corresponding area through IPFS; Dynamic Oracle Mechanism: Deploy an off-chain oracle based on a trusted execution environment to obtain real-time NOAA weather data and Landsat-9 remote sensing data as trigger conditions for smart contracts; Zero-knowledge verification layer: zk-SNARKs technology is used to generate carbon sink calculation proof, with verification time <2 seconds, proof size <1KB, and support for verification by a million-node network.

7. The deep learning-based tropical rainforest biomass and carbon sink collaborative optimization decision-making system according to claim 1, characterized in that: The edge-cloud collaborative architecture includes: Model dynamic slicing technology: divides the 3D graph convolutional attention network into 12 independently run sub-modules, automatically selecting cloud inference or edge computing based on network bandwidth; Incremental Federated Learning Framework: Each edge node maintains a local model copy and updates the global model every 24 hours through a secure aggregation protocol, using differential privacy technology to protect data privacy; Disaster recovery mechanism: When a network interruption of more than 5 minutes is detected, it automatically switches to emergency communication mode based on Beidou short messages, and the transmission efficiency is optimized to 140 bytes per time.

8. The deep learning-based collaborative optimization decision-making system for tropical rainforest biomass and carbon sinks according to claim 1 is characterized in that: The feature inversion network adopts: Deep Taylor decomposition algorithm: calculates the contribution of each input pixel to the carbon sink prediction results and generates a heat map with a resolution of 0.5m; Spatiotemporal Attention Tracing Module: Identifies key time nodes and spatial regions through a multi-head attention mechanism; Uncertainty visualization engine: uses the marching cubes algorithm to render three-dimensional probability distribution cloud maps, supports dynamic adjustment of transparency and color mapping of 95% confidence intervals, and probability distribution rendering in the Marching Cubes algorithm Where: P(x,y,z): probability density value at the point (x,y,z) in three-dimensional space; E(x,y,z): Energy function, which represents the deviation between the ecological parameter at that point and the predicted value; T: temperature parameter, which controls the smoothness of the probability distribution; Z: Normalization constant, ensuring that the integral of the probability density function is 1.

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