Greenhouse gas collaborative monitoring and analysis platform

Abstract

The invention relates to the technical field of greenhouse gas monitoring, in particular to a greenhouse gas collaborative monitoring and analysis platform. According to the technical scheme, the system comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node and a self-adaptive decision optimization module, and the cross-modal data fusion module is used for integrating satellite remote sensing data, a ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data in real time; a dynamic weight distribution algorithm is adopted, the multi-source data fusion weight is automatically adjusted according to environmental parameters, and a space-time continuous greenhouse gas concentration field is generated; a causal reasoning analysis engine is based on a hybrid architecture. According to the method, data privacy is guaranteed and delay is reduced through multi-source data fusion, causal reasoning analysis, holographic visualization and edge calculation, and comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gas are realized based on decision optimization of dynamic games and block chain smart contracts.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse gas monitoring, and particularly to a greenhouse gas collaborative monitoring and analysis platform. Background Art

[0002] In the context of global climate change, greenhouse gas emission reduction has become the focus of international attention. Accurately grasping the greenhouse gas emission situation and scientifically formulating emission reduction strategies are crucial for mitigating climate change.

[0003] Currently, greenhouse gas monitoring faces many challenges. In terms of data acquisition, there are modal differences in the data obtained by different monitoring means. The spatio-temporal resolution of satellite remote sensing data is low. Although the ground sensor network can provide high-frequency data, its coverage is limited. The mobile monitoring by drones and the data of industrial Internet of Things emission sources also have their limitations, and it is difficult to effectively fuse multi-source data, resulting in the inability to generate an accurate and continuous greenhouse gas concentration field.

[0004] In terms of analysis, traditional methods are difficult to establish an accurate causal relationship between greenhouse gas emission sources and concentration changes, unable to clearly identify cross-regional transmission paths and key contributing sources, and the prediction accuracy of the concentration evolution trend under the emission reduction path is also relatively low. In terms of visualization, existing technologies are difficult to intuitively and dynamically display the complex diffusion process of greenhouse gases and the contribution degree of emission sources.

[0005] In terms of data processing, there are delays in the transmission of the massive amount of raw data generated by monitoring devices, and data privacy protection is insufficient. In the decision-making link, existing emission reduction strategies are difficult to balance economic costs and emission reduction efficiency, lack an effective dynamic optimization mechanism, and the risk warning and regulation of the carbon trading market are not precise enough. Therefore, it is urgent to develop a greenhouse gas collaborative monitoring and analysis platform that can effectively integrate multi-source data, deeply analyze causal relationships, achieve efficient visualization, ensure data security and optimize decisions. Therefore, this application proposes a greenhouse gas collaborative monitoring and analysis platform. Summary of the Invention

[0006] The purpose of the present invention is to propose a greenhouse gas collaborative monitoring and analysis platform for the problems existing in the background art that there are delays in the transmission of the massive amount of raw data generated by existing greenhouse gas monitoring devices and insufficient data privacy protection.

[0007] The technical solution of the present invention: The greenhouse gas collaborative monitoring and analysis platform includes:

[0008] Cross-modal data fusion module: Integrate satellite remote sensing data, ground sensor network, mobile monitoring data of drones and industrial Internet of Things emission source data in real time, and use a dynamic weight allocation algorithm to automatically adjust the fusion weights of multi-source data according to environmental parameters to generate a spatio-temporally continuous greenhouse gas concentration field. The environmental parameters include temperature, wind speed, and terrain;

[0009] Causal Inference Analysis Engine: Based on a hybrid architecture, construct a causal topology map of greenhouse gas emission sources and concentration changes, identify cross-regional transmission paths and key contributing sources, and predict the concentration evolution trend under different emission reduction paths through a dynamic Bayesian network. The hybrid architecture is a graph neural network + causal inference model;

[0010] Holographic Dynamic Visualization System: Utilize the fusion technology of holographic projection and 3D GIS to dynamically display the greenhouse gas concentration field, emission source contribution degree, and diffusion simulation effect in a virtual sandbox, and support gesture interaction to adjust simulation parameters in real time. The parameters include emission reduction intensity and meteorological conditions;

[0011] Distributed Edge Computing Node: Deployed at the monitoring device end, use a lightweight federated learning framework to perform local modeling on the original data, and only upload the model parameters to the central platform to solve the problems of data privacy and transmission delay;

[0012] Adaptive Decision Optimization Module: Integrate digital twin and multi-agent reinforcement learning (MARL) to construct a dynamic game model for regional emission reduction strategies, generate Pareto optimal solutions that take into account both economic costs and emission reduction efficiency in real time, and automatically execute quota allocation and transaction verification based on blockchain smart contracts.

[0013] Optionally, the cross-modal data fusion module includes:

[0014] Heterogeneous Data Spatiotemporal Alignment Unit: Use a spatiotemporal encoder (ST-Encoder) to map the low spatiotemporal resolution of satellite data and the high-frequency data of ground sensors to a unified spatiotemporal grid, fill in the missing areas through an interpolation algorithm, and eliminate the scale differences of multi-source data. The low spatiotemporal resolution is daily / 10km, and the high-frequency data is per minute / 100m;

[0015] Dynamic Weight Allocation Algorithm: Calculate the confidence weights of each data source in real time based on environmental parameters. The environmental parameters include wind speed, humidity, and terrain complexity. The weight calculation formula is:

[0016]

[0017] Wherein, is the data source noise variance, D i is the spatial distance between the monitoring point and the target area, and α, β, γ are dynamic adjustment coefficients, which are optimized in real time through the gradient descent method.

[0018] Optionally, the cross-modal data fusion module further includes:

[0019] Abnormal data self-correction unit: Use a generative adversarial network (GAN) to repair monitoring data disturbed by extreme weather, including sandstorms and heavy rainfall, and generate virtual data consistent with the real environment to fill abnormal areas;

[0020] Fusion optimization unit: Use a variational autoencoder (VAE) to denoise the fused concentration field, and generate a high-fidelity concentration distribution map through KL divergence constraint, with the error rate reduced to less than 3%.

[0021] Optionally, the causal inference analysis engine includes:

[0022] Causal topology graph construction unit: Extract the correlation between emission sources and concentration changes based on a graph neural network (GNN), construct a dynamic causal topology graph in combination with meteorological data, and label the key transmission path nodes;

[0023] Dynamic Bayesian prediction unit: Use the Markov chain Monte Carlo (MCMC) method to simulate the evolution path of the concentration field under different emission reduction policies, and output the confidence interval of the probability distribution.

[0024] Emission source isotope tracing unit: Collect the CH4 isotope ratio through a high-precision mass spectrometer, and the isotope ratio includes δ 13 C, δD, construct an emission source chemical fingerprint library, and match specific industrial sources in combination with real-time monitoring data. The industrial sources include coal mines and paddy fields;

[0025] Cross-border transmission contribution quantification unit: Use the Lagrangian particle diffusion model (LPDM) to reverse-trace the air mass trajectory, and combine with meteorological reanalysis data (ERA5) to calculate the contribution ratio of cross-border transmission to the concentration in the target area, with an accuracy of ±5%.

[0026] Optionally, the holographic dynamic visualization system includes:

[0027] Holographic sand table interaction unit: Capture the user's gesture actions through a millimeter-wave radar, adjust the emission reduction intensity parameters in the virtual sand table in real time, and synchronously update the diffusion path of the CFD simulation;

[0028] Dynamic heat map generation unit: Map the concentration field data to the RGB-alpha channel, generate a penetrable heat map, and overlay it on the 3D terrain model to support multi-view scaling and cross-section analysis;

[0029] Contribution ray analysis unit: Use the ray casting algorithm to reverse-trace the contribution of each emission source from the user-specified target point, and visualize the key transmission channels with dynamic light beams. The brightness of the light beam is positively correlated with the contribution;

[0030] Virtual-reality linkage simulation unit: load historical extreme weather events into the sandbox, simulate the concentration field changes under the same emission conditions, and generate a risk warning report through comparative analysis when the extreme weather event is El Niño.

[0031] Optionally, the distributed edge computing node includes:

[0032] Lightweight federated learning framework: Deploy pruned convolutional neural networks (CNNs) on edge devices, upload only model gradients to the central platform, and add Gaussian noise through differential privacy (DP) technology, reducing the risk of privacy leakage by 90%;

[0033] Edge-cloud collaborative caching: The LRU-K algorithm is used to dynamically cache high-frequency access data, which is the weather forecast result. At the same time, low-frequency data is compressed and uploaded to the cloud, reducing the network load by 65%;

[0034] Device-side self-optimization unit: dynamically adjusts the computing resource allocation of edge nodes based on the Q-learning algorithm, prioritizes high-confidence data, and controls the response delay within 200ms;

[0035] Network interruption and resuming transmission module: When the network is interrupted, the local time series database is used to temporarily store data, and after recovery, the data is retransmitted through the incremental synchronization protocol to ensure data integrity of 99.99%.

[0036] Optionally, the adaptive decision optimization module includes:

[0037] Multi-agent game model: define regional governments, enterprises, and the public as agents, design utility functions covering GDP loss rate, emission reduction costs, and health benefit index, and solve equilibrium strategies through the Nash-Q learning algorithm;

[0038] Blockchain smart contract unit: Encode the emission reduction quota allocation rules into smart contracts, automatically trigger quota freezing, auctioning and transaction verification, and the contract execution records are encrypted through zero-knowledge proof (ZKP) and stored in the alliance chain.

[0039] Optionally, the adaptive decision optimization module further includes:

[0040] Dynamic Pareto front generation unit: uses the NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set in real time, and projects it to the decision maker's field of view through AR glasses, supporting gesture selection of the optimal solution;

[0041] Carbon trading risk warning unit: Based on Monte Carlo simulation, it predicts the fluctuation range of carbon prices. When the price deviates from the threshold, it automatically adjusts the auction reserve price in the smart contract to prevent market manipulation.

[0042] Optionally, in the dynamic Pareto front generation unit, when the NSGA-II multi-objective optimization algorithm updates the Pareto optimal solution set, it adopts the elitist retention strategy, directly inheriting the non-dominated individuals in the previous generation's optimal solution set to the next generation, accelerating the convergence speed. At the same time, the crowding distance comparison operator is used to screen the newly generated solutions to ensure the diversity of the solution set, so that the generated Pareto optimal solution set converges to near the global optimum within 3 iterations, and the coverage index of the solution set is increased to more than 0.9.

[0043] Optionally, in the carbon trading risk warning unit, when Monte Carlo simulation predicts the carbon price fluctuation range, it considers the impact of various macroeconomic factors and industry policy changes on the carbon price. The macroeconomic factors include GDP growth rate, energy price index, and monetary policy adjustment. The industry policy changes include new emission reduction targets and subsidy policy adjustment. By constructing a multiple linear regression model to determine the correlation coefficients between various factors and the carbon price, a more accurate carbon price fluctuation prediction interval is generated, so that the warning accuracy rate reaches more than 95%. When the price deviates from the threshold, the automatic adjustment range of the reserve price in the smart contract is controlled within a reasonable range, which not only avoids excessive market fluctuations but also ensures the liquidity of the carbon trading market.

[0044] Compared with the prior art, the present application includes at least one of the following beneficial technical effects:

[0045] The heterogeneous data spatio-temporal alignment unit solves the problem of multi-source data scale differences. The abnormal data self-correction unit can repair the data disturbed by extreme weather. The post-fusion optimization unit can denoise and generate a high-fidelity concentration distribution map, providing a comprehensive and accurate data basis for subsequent analysis; automatically adjusting the multi-source data fusion weight according to environmental parameters, improving the accuracy and reliability of data fusion, and making the results more in line with the actual environmental changes;

[0046] The present invention can identify the causal relationship between greenhouse gas emission sources and concentration changes, cross-regional transmission paths, and key contributing sources. The emission source isotope tracing unit can accurately locate specific industrial sources. The cross-border transmission contribution quantification unit can quantify the contribution ratio of cross-border transmission to the concentration in the target area. Using a dynamic Bayesian network to predict the concentration evolution trend under different emission reduction paths, providing a basis for formulating scientific and effective emission reduction strategies.

[0047] Through the holographic dynamic visualization system, using the holographic projection and 3D GIS fusion technology, the greenhouse gas-related information is dynamically displayed in the virtual sandbox. The virtual-real linkage simulation unit can simulate the concentration field changes under extreme weather and generate a risk warning report, which is convenient for intuitive understanding and decision-making. Combining differential privacy technology to add Gaussian noise effectively solves the data privacy problem.

[0048] Through edge-cloud collaborative caching, device-side self-optimization units, and offline data transfer modules, etc., the data transmission latency is reduced, ensuring the timeliness and stability of data processing. The dynamic Pareto front generation unit updates the Pareto optimal solution set in real time and supports gesture selection of the optimal solution. The blockchain smart contract is used to automatically execute quota allocation and transaction verification, and the contract execution records are encrypted and stored. The carbon trading risk warning unit can predict carbon price fluctuations and adjust the auction reserve price to ensure the stable operation of the carbon trading market and improve the fairness and transparency of decision execution.

[0049] Through multi-source data fusion, causal reasoning analysis, holographic visualization, edge computing to ensure data privacy and reduce latency, and decision optimization based on dynamic game and blockchain smart contract, the present invention realizes comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gases. Brief Description of the Drawings

[0050] Figure 1 It is the principle block diagram of the greenhouse gas collaborative monitoring and analysis platform. Detailed Embodiments

[0051] The technical solutions of the present invention will be further described below in conjunction with the drawings and specific embodiments.

[0052] Embodiment 1

[0053] As Figure 1 shown, the greenhouse gas collaborative monitoring and analysis platform proposed by the present invention includes a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, distributed edge computing nodes, and an adaptive decision optimization module.

[0054] Platform construction and hardware deployment specifically include:

[0055] Installation of data collection devices: Widely deploy a ground sensor network at different geographical locations, select appropriate installation points to ensure coverage of the target monitoring area. According to factors such as terrain and climate, reasonably set the density of sensors to ensure the comprehensiveness and accuracy of data. Equip drones with high-precision greenhouse gas monitoring devices and formulate reasonable flight routes and monitoring plans to enable mobile monitoring of key areas. Cooperate with industrial enterprises to access industrial Internet of Things (IIoT) emission source data to obtain greenhouse gas emission information of enterprises. Ensure the stable acquisition of satellite remote sensing data, establish a cooperative relationship with relevant satellite data providers to ensure the real-time nature and quality of data.

[0056] Deployment of edge computing nodes: Deploy distributed edge computing nodes at each monitoring device end, and deploy the pruned convolutional neural network (CNN) model to the edge device to achieve lightweight federated learning. Configure sufficient computing resources and storage devices for the edge nodes to meet the needs of data processing and caching.

[0057] Build the central platform, including building the central server and configuring high-performance computing devices and large-capacity storage systems to process and store data and model parameters from various data sources. Install holographic projection devices and 3D GIS systems to build the hardware foundation of the holographic dynamic visualization system.

[0058] The following will provide a detailed description of the cross-modal data fusion module, causal reasoning analysis engine, holographic dynamic visualization system, distributed edge computing nodes, and adaptive decision optimization module.

[0059] The cross-modal data fusion module specifically includes:

[0060] The heterogeneous data spatio-temporal alignment unit processes satellite data and ground sensor data using a spatio-temporal encoder (ST-Encoder). Map the low spatio-temporal resolution (daily / 10km) of satellite data and the high-frequency data (per minute / 100m) of ground sensors to a unified spatio-temporal grid. Use a suitable interpolation algorithm, Kriging interpolation, to fill in the missing data areas and eliminate the scale differences of multi-source data.

[0061] The dynamic weight allocation algorithm obtains environmental parameters in real time. The environmental parameters include wind speed, humidity, and terrain complexity, and calculates the confidence weights of each data source according to the formula. The weight calculation formula is:

[0062]

[0063] Among them, is the data source noise variance, D i is the spatial distance between the monitoring point and the target area, and α, β, γ are dynamically adjustable coefficients, which are optimized in real time by the gradient descent method. Among them, the data source noise variance is obtained through statistical analysis of historical data, and the spatial distance between the monitoring point and the target area can be calculated through a Geographic Information System (GIS). Use the gradient descent method to optimize the dynamically adjustable coefficients k1, k2, k3, k4 in real time to improve the accuracy of data fusion.

[0064] The abnormal data self-correction unit uses a Generative Adversarial Network (GAN) to repair the monitoring data when it is detected that the data is interfered by extreme weather (sandstorms, heavy rainfall). Train the GAN model so that it can generate virtual data consistent with the real environment to fill the abnormal areas.

[0065] In this embodiment, it further includes a post-fusion optimization unit, which uses a variational autoencoder (VAE) to denoise the fused concentration field. By constraining with KL divergence, a high-fidelity concentration distribution map is generated, and the parameters of the VAE model are continuously adjusted to reduce the error rate to below 3%. The cross-modal data fusion module can integrally and timely incorporate satellite remote sensing data, ground sensor network data, UAV mobile monitoring data, and industrial Internet of Things emission source data. The integration of such multi-source data breaks the limitations of a single data source and comprehensively and accurately reflects the emission and distribution status of greenhouse gases. Meanwhile, through the dynamic weight allocation algorithm, the fusion weights are automatically adjusted according to environmental parameters to generate a spatio-temporally continuous greenhouse gas concentration field, making the data more conform to the actual environmental changes and improving the reliability and availability of the data.

[0066] Among them, the abnormal data self-correction unit uses a generative adversarial network (GAN) to repair the monitoring data interfered by extreme weather, and the post-fusion optimization unit uses a variational autoencoder (VAE) for denoising processing to reduce the error rate to below 3%. It effectively solves the problems of data being interfered by external factors and affected by noise, ensures the accuracy and high quality of the data, and provides a solid foundation for subsequent analysis and decision-making.

[0067] In addition, the heterogeneous data spatio-temporal alignment unit uses a spatio-temporal encoder (ST-Encoder) to map satellite data and ground sensor data with different spatio-temporal resolutions to a unified spatio-temporal grid, and fills in the missing areas through an interpolation algorithm, eliminating the scale differences of multi-source data, so that data from different sources can be analyzed and processed under the same standard.

[0068] The causal inference analysis engine specifically includes:

[0069] The causal topology graph construction unit extracts the correlation between emission sources and concentration changes based on a graph neural network (GNN). Taking emission source data, concentration data, and meteorological data as inputs, the GNN model is trained to learn the causal relationships among them. A dynamic causal topology graph is constructed in combination with meteorological data, and the key transmission path nodes are marked. By analyzing the influence of meteorological conditions on the transmission of greenhouse gases, the causal relationships between different emission sources and concentration changes are determined. The causal inference analysis engine constructs a causal topology graph of greenhouse gas emission sources and concentration changes based on a hybrid architecture, which can accurately identify cross-regional transmission paths and key contributing sources. The emission source isotope tracing unit can build a chemical fingerprint library by collecting CH4 isotope ratios to accurately match specific industrial sources; the cross-border transmission contribution quantification unit calculates the contribution ratio of cross-border transmission to the concentration of the target area, with an accuracy of ±5%, providing a scientific basis for formulating targeted emission reduction strategies.

[0070] Dynamic Bayesian Prediction Unit, which uses the Markov Chain Monte Carlo (MCMC) method to simulate the evolution path of the concentration field under different emission reduction policies. Different emission reduction scenarios are set, and the evolution samples of the concentration field are generated through the MCMC algorithm, and the confidence interval of the probability distribution is output. The Dynamic Bayesian Prediction Unit uses the Markov Chain Monte Carlo (MCMC) method to simulate the evolution path of the concentration field under different emission reduction policies, outputs the confidence interval of the probability distribution, helps decision-makers understand in advance the possible effects of different emission reduction measures, and thus make more reasonable decisions.

[0071] Emission Source Isotope Tracing Unit, which collects the CH4 isotope ratios (δ 13 C, δD) through a high-precision mass spectrometer and constructs a chemical fingerprint library of emission sources. Analyze and record the CH4 isotope characteristics of different industrial sources (coal mines, paddy fields). Combine with real-time monitoring data, match the collected CH4 isotope ratios with the chemical fingerprint library to determine specific industrial sources.

[0072] Cross-border Transmission Contribution Quantification Unit, which uses the Lagrangian Particle Diffusion Model (LPDM) to backtrack the air mass trajectory. Input meteorological reanalysis data (ERA5) to simulate the movement trajectory of the air mass. Combine with meteorological reanalysis data to calculate the contribution ratio of cross-border transmission to the concentration in the target area, and continuously optimize the model parameters to achieve an accuracy of ±5%.

[0073] The holographic dynamic visualization system specifically includes:

[0074] Holographic Sand Table Interaction Unit, which installs millimeter-wave radar equipment to capture the user's gesture actions. Calibrate and debug the millimeter-wave radar to ensure that the user's gestures can be accurately recognized.

[0075] When the user makes a gesture action, the emission reduction intensity parameter in the virtual sand table is adjusted in real time, and the diffusion path of the CFD simulation is updated synchronously. The mapping relationship between the gesture action and the parameter adjustment is realized through programming.

[0076] Dynamic Heat Map Generation Unit, which maps the concentration field data to the RGB-alpha channel to generate a penetrable heat map. Through data processing and image generation algorithms, the concentration data is converted into a visual heat map. The heat map is superimposed on the 3D terrain model to support multi-view scaling and cross-section analysis. The fusion and interactive operation of the heat map and the terrain model are realized by using the 3DGIS system.

[0077] Contribution Ray Analysis Unit, which uses the ray casting algorithm to backtrack the contribution of each emission source from the user-specified target point. The ray casting algorithm is realized through programming to calculate the contribution of each emission source. The key transmission channels are visualized with dynamic light beams, and the brightness of the light beams is positively correlated with the contribution. The visualization effect of the light beams is realized through graphics rendering technology.

[0078] The virtual-real linkage simulation unit loads the data of historical extreme weather events (such as El Niño) in the sand table to simulate the changes in the concentration field under the same emission conditions. Through data import and model simulation, the changes in the concentration field under historical extreme weather are reproduced. A risk warning report is generated through comparative analysis. The simulation results are analyzed and evaluated to generate a detailed risk warning report for decision-makers to refer to. The holographic dynamic visualization system uses the fusion technology of holographic projection and 3D GIS to dynamically display the greenhouse gas concentration field, the contribution degree of emission sources, and the diffusion simulation effect in the virtual sand table. Users can adjust the simulation parameters in real time through gesture interaction, such as emission reduction intensity, meteorological conditions, etc., and intuitively observe the changes in greenhouse gases under different conditions, enhancing users' understanding and grasp of the data. The dynamic heat map generation unit, contribution degree ray analysis unit, and virtual-real linkage simulation unit in the system visually display the greenhouse gas data from different perspectives, support multi-perspective zooming and sectional analysis, visualization of key transmission channels, and simulation and comparative analysis of historical extreme weather events, providing users with comprehensive and in-depth data analysis methods.

[0079] The distributed edge computing nodes specifically include:

[0080] The lightweight federated learning framework deploys the pruned convolutional neural network (CNN) at the edge device side to perform local modeling on the original data. Through model pruning technology, the number of parameters of the CNN model is reduced, and the computational complexity is lowered. Only the model gradients are uploaded to the central platform, and Gaussian noise is added through differential privacy technology (DP) to protect data privacy. Appropriate Gaussian noise parameters are set to minimize the impact on model training while ensuring privacy security.

[0081] Edge-cloud collaborative caching uses the LRU-K algorithm to dynamically cache frequently accessed data, such as meteorological prediction results. Through the algorithm, data caching and management are realized to improve data access efficiency. The low-frequency data is compressed and uploaded to the cloud to reduce network load. An appropriate compression algorithm is selected to compress the low-frequency data.

[0082] The device-side self-optimization unit dynamically adjusts the computing resource allocation of edge nodes based on the Q-learning algorithm. By training the Q-learning model, the edge nodes can automatically adjust the computing resource allocation according to the confidence level of the data, give priority to processing high-confidence data, and control the response delay within 200 ms. Through optimizing the algorithm and hardware configuration, the data processing speed is improved.

[0083] The offline data transfer and resume module uses a local time series database to temporarily store data when the network is interrupted. Select a suitable time series database to ensure efficient and reliable data storage and management. After the network is restored, the data is supplemented through an incremental synchronization protocol to ensure that the data integrity reaches 99.99%. Implement the incremental synchronization protocol to ensure accurate data supplementation.

[0084] In this embodiment, the distributed edge computing node adopts a lightweight federated learning framework to perform local modeling on the original data at the edge device end, only uploads the model parameters to the central platform, and adds Gaussian noise through differential privacy technology to reduce the privacy leakage risk by 90%, effectively protecting the privacy of the data.

[0085] The edge-cloud collaborative cache uses the LRU-K algorithm to dynamically cache frequently accessed data, compresses the low-frequency data and uploads it to the cloud, reducing the network load by 65%; the device-side self-optimization unit dynamically adjusts the computing resource allocation based on the Q-learning algorithm, controlling the response delay within 200 ms; the offline data transfer and resume module ensures that the data integrity reaches 99.99%. These measures improve the efficiency and stability of data processing and ensure the smooth operation of the platform.

[0086] The adaptive decision-making optimization module specifically includes:

[0087] A multi-agent game model that defines the regional government, enterprises, and the public as agents, and designs a utility function covering the GDP loss rate, emission reduction cost, and health benefit index. By analyzing the goals and interests of each agent, determine the specific form of the utility function. Solve the equilibrium strategy through the Nash-Q learning algorithm. Train the Nash-Q learning model so that each agent can find the optimal strategy during the game process.

[0088] The blockchain smart contract unit encodes the emission reduction quota allocation rules as smart contracts. Use a smart contract development language, such as Solidity, to implement the automated execution of the emission reduction quota allocation rules. Automatically trigger quota freezing, auctions, and transaction verification, and store the contract execution records encrypted by zero-knowledge proof (ZKP) on the consortium chain. Deploy consortium chain nodes to ensure the secure execution of smart contracts and the encrypted storage of data.

[0089] The dynamic Pareto front generation unit uses the NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set in real time. Appropriate algorithm parameters are set, including the population size and the number of iterations. Through the elitist retention strategy and the crowding distance comparison operator, the convergence speed is accelerated, and the diversity of the solution set is ensured. It is projected onto the decision maker's field of vision through an AR glasses, supporting gesture selection of the optimal solution. An AR application program is developed to realize the visualization and interactive operation of the Pareto optimal solution set. When the NSGA-II multi-objective optimization algorithm updates the Pareto optimal solution set, it adopts the elitist retention strategy, directly inheriting the non-dominated individuals in the previous generation's optimal solution set to the next generation, accelerating the convergence speed. At the same time, the newly generated solutions are screened through the crowding distance comparison operator to ensure the diversity of the solution set, so that the generated Pareto optimal solution set converges to near the global optimum within 3 iterations, and the coverage index of the solution set is increased to more than 0.9.

[0090] The carbon trading risk warning unit predicts the carbon price fluctuation range based on Monte Carlo simulation. Considering the impacts of various macroeconomic factors (GDP growth rate, energy price index, monetary policy adjustment) and industry policy changes (new emission reduction targets, subsidy policy adjustment) on the carbon price, a multiple linear regression model is constructed to determine the correlation coefficients between various factors and the carbon price. When the price deviates from the threshold, the reserve price in the smart contract is automatically adjusted to prevent market manipulation. Reasonable price thresholds and adjustment ranges of the reserve price are set to ensure the stable operation of the carbon trading market. When predicting the carbon price fluctuation range by Monte Carlo simulation, considering the impacts of various macroeconomic factors and industry policy changes on the carbon price, the correlation coefficients between various factors and the carbon price are determined by constructing a multiple linear regression model, and then a more accurate carbon price fluctuation prediction interval is generated. When the price deviates from the threshold, the adjustment range of the reserve price in the smart contract is automatically controlled within a reasonable range.

[0091] It should be noted that through the adaptive decision-making optimization module, digital twin and multi-agent reinforcement learning are integrated to construct a dynamic game model for regional emission reduction strategies, and a Pareto optimal solution that takes into account both economic costs and emission reduction efficiency is generated in real time. Considering the interests and goals of multiple parties such as regional governments, enterprises, and the public, it provides effective decision-making support for achieving sustainable development. The dynamic Pareto front generation unit uses the NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set, making the generated solution set converge to near the global optimum within 3 iterations, and the coverage index is increased to more than 0.9; the carbon trading risk warning unit predicts the carbon price fluctuation range through Monte Carlo simulation, and the warning accuracy rate reaches more than 95%. When the price deviates from the threshold, the reserve price in the smart contract is automatically adjusted to avoid excessive market fluctuations, ensuring the liquidity and stability of the carbon trading market.

[0092] Embodiment 2

[0093] In this embodiment, based on Embodiment 1, it further includes platform testing and optimization, specifically including:

[0094] Data accuracy testing: Conduct accuracy testing on the greenhouse gas concentration field data generated by the cross-modal data fusion module. By comparing and analyzing with actual monitoring data, evaluate the error rate of the data. Verify the prediction results of the causal reasoning analysis engine, and check whether the predicted concentration evolution trend and emission source contribution degree are consistent with the actual situation.

[0095] System performance testing: Test the performance of distributed edge computing nodes, including indicators such as data processing speed, response latency, and network load. By simulating different data traffic and computing tasks, evaluate the performance of edge nodes. Test the interactivity and visualization effects of the holographic dynamic visualization system, and check whether the gesture interaction is sensitive and whether the heat map and beam visualization are clear and accurate.

[0096] Evaluation of decision optimization effect: Evaluate the effect of the emission reduction strategy generated by the adaptive decision optimization module. By simulating different emission reduction scenarios, compare and analyze the economic cost and emission reduction efficiency before and after adopting this strategy. Check the warning accuracy rate of the carbon trading risk warning unit and the rationality of the reserve price adjustment in the auction to ensure the stable operation of the carbon trading market.

[0097] System optimization: According to the test results, optimize each module of the platform. Adjust model parameters, algorithm settings, and hardware configurations to improve the performance and accuracy of the system. Continuously collect and analyze user feedback, and continuously improve the functions and user experience of the platform.

[0098] Embodiment 3

[0099] Based on Embodiment 2, the causal reasoning analysis engine of this embodiment further includes

[0100] 1. Establish a unified data fusion and interaction layer

[0101] Data standardization processing: Define unified data formats and standards for the data of each unit to ensure the smooth circulation of emission source data, concentration data, meteorological data, CH4 isotope ratio data, etc. between different units. Develop unified timestamp formats, geographic coordinate systems, and data encoding rules.

[0102] Data interaction interface design: Develop a set of standardized data interaction interfaces to enable the causal topology graph construction unit, dynamic Bayesian prediction unit, emission source isotope tracing unit, and cross-border transmission contribution quantification unit to conveniently obtain and share each other's data. Implement data requests and responses through RESTful API.

[0103] 2. Strengthen the collaborative analysis mechanism between units

[0104] Causal topology map guiding prediction: The causal relationship information obtained from the causal topology map construction unit is used as prior knowledge and input into the dynamic Bayesian prediction unit. During the MCMC simulation process, according to the correlation strength between the emission sources and concentration changes in the causal topology map, the simulation parameters under different emission reduction scenarios are adjusted to make the simulation results more in line with the actual causal logic. If the causal topology map shows that a certain industrial emission source has a greater impact on the concentration changes in a specific area, when simulating the emission reduction effect in this area, the weight of the emission reduction measures for this emission source is increased.

[0105] Incorporating source tracing and quantification results into causal analysis: The specific industrial source information determined by the emission source isotope tracing unit and the cross-border transmission contribution ratio calculated by the cross-border transmission contribution quantification unit are incorporated into the update of the causal topology map and the input of dynamic Bayesian prediction. When new emission sources are discovered or the cross-border transmission contributions change, the causal relationships and prediction models are adjusted in a timely manner to improve the accuracy and timeliness of causal reasoning.

[0106] 3. Introducing a dynamic update mechanism

[0107] Unit update driven by real-time data: Each unit is dynamically updated according to real-time monitoring data. The causal topology map construction unit adjusts the causal topology map in real time based on new emission source data, concentration data, and meteorological data; the emission source isotope tracing unit continuously updates the chemical fingerprint library to adapt to changes in the characteristics of new industrial sources.

[0108] Adaptive model adjustment: The dynamic Bayesian prediction unit adaptively adjusts the parameters and simulation process of the MCMC algorithm based on real-time causal relationships and source tracing and quantification results. When significant changes occur in the causal relationships, it can quickly adjust the prediction model and output a more accurate concentration field evolution path and probability distribution confidence interval.

[0109] 4. Multi-scale causal analysis and integration

[0110] Microscopic and macroscopic causal integration: In the construction of the causal topology map, not only the causal relationship between emission sources and regional concentration changes at the macroscopic level is considered, but also it delves into the microscopic level to analyze the causal connections between emissions from different production links within an enterprise and local concentration changes in the surrounding area. The microscopic and macroscopic causal relationships are integrated to form a more comprehensive causal topology map.

[0111] Causal analysis at different time and space scales: Causal analysis is carried out for different time scales (day, month, year) and space scales (local area, cross-region). In dynamic Bayesian prediction, according to the causal characteristics at different time and space scales, different simulation strategies and parameter settings are adopted to improve the accuracy and pertinence of prediction.

[0112] 5. Uncertainty quantification and assessment

[0113] Quantification of Causality Uncertainty: During the construction of the causal topology graph, the uncertainty of the causal relationship between emission sources and concentration changes is quantitatively evaluated. Through statistical analysis and machine learning methods, the confidence level and error range of the causal relationship are calculated, and these uncertainty factors are considered in the dynamic Bayesian prediction to make the prediction results more reliable.

[0114] Comprehensive Uncertainty Assessment: Considering the uncertainty of emission source data, the error of meteorological data, and the uncertainty factors of model parameters, a comprehensive uncertainty assessment is carried out on the output results of the entire causal inference analysis engine. It provides a clearer decision-making basis for decision-makers and also provides a direction for further optimizing the model and data.

[0115] In this embodiment, by establishing a unified data fusion and interaction layer and strengthening the collaborative analysis mechanism between units, each unit no longer operates in isolation. The causal relationship constructed by the causal topology graph can effectively guide the dynamic Bayesian prediction, and the tracing and quantification results can also be deeply integrated into the overall causal analysis, enabling the causal inference to comprehensively consider various factors and more comprehensively and accurately reflect the complex causal relationship between greenhouse gas emissions and concentration changes, avoiding analysis biases caused by isolated information.

[0116] In addition, multi-scale causal analysis and fusion are introduced, taking into account the causal relationships at the micro and macro levels and different spatio-temporal scales. At the micro level, the relationship between emissions from internal production links of enterprises and local concentration changes is analyzed, and at the macro level, the correlation between regional emission sources and overall concentration changes is grasped. By analyzing the causal characteristics at different time and space scales, more detailed and comprehensive causal information can be captured, thereby improving the accuracy and pertinence of causal inference.

[0117] It is worth noting that the unit update and adaptive model adjustment driven by real-time data enable the causal inference analysis engine to respond promptly to data changes. With the emergence of new emission source data, concentration data, meteorological data, and isotope characteristics, each unit can quickly update and adjust to ensure that the causal topology graph, prediction model, etc. are always consistent with the actual situation. In the face of complex and changing environments and emission situations, the system can quickly adapt and make accurate analysis and predictions.

[0118] Uncertainty quantification and assessment: Quantifying and assessing the uncertainty of causal relationships and comprehensive results enables decision-makers to clearly understand the reliability range of the analysis results. In practical applications, there is a certain degree of uncertainty in environmental data itself. Considering these factors can avoid decision-making mistakes caused by over-reliance on a single result. At the same time, the uncertainty assessment results also provide directions for further optimizing models and data, contributing to continuously improving the accuracy and stability of the system. The causal topology diagram guides prediction and the introduction of a dynamic update mechanism enables the dynamic Bayesian prediction unit to output a concentration field evolution path and a confidence interval of probability distribution that are more in line with the actual causal logic. Decision-makers can, based on more accurate prediction results, understand in advance the possible effects of different emission reduction measures, thereby formulating more scientific and reasonable emission reduction strategies.

[0119] By deeply integrating the results of the emission source isotope tracing unit and the cross-border transmission contribution quantification unit into the overall analysis, more comprehensive information is provided for decision-makers. Understanding the contribution ratios of specific industrial sources and cross-border transmission to concentrations helps formulate more targeted emission reduction measures, improve emission reduction efficiency, and achieve a balance between economic costs and emission reduction effects.

[0120] Furthermore, a unified data fusion and interaction layer is established, unified data formats and standards are defined, and standardized data interaction interfaces are designed, making the data flow between various units smoother. This not only facilitates the collaborative work of each unit within the current system but also provides convenience for the future expansion of the system and its integration with other relevant systems. For example, new data sources can be easily accessed or data sharing and interaction can be carried out with other monitoring and analysis systems.

[0121] The above specific embodiments are merely several alternative embodiments of the present invention. Based on the technical solution of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. Greenhouse gas collaborative monitoring and analysis platform, characterized in that, Including: Cross-modal data fusion module: Integrate satellite remote sensing data, ground sensor network, UAV mobile monitoring data and industrial Internet of Things emission source data in real time. Adopt the dynamic weight allocation algorithm to automatically adjust the multi-source data fusion weight according to environmental parameters, and generate a spatio-temporally continuous greenhouse gas concentration field; Causal inference analysis engine: Based on the hybrid architecture, construct a causal topology map of greenhouse gas emission sources and concentration changes, identify cross-regional transmission paths and key contributing sources, and predict the concentration evolution trend under different emission reduction paths through a dynamic Bayesian network; Holographic dynamic visualization system: Use the fusion technology of holographic projection and 3D GIS to dynamically display the greenhouse gas concentration field, emission source contribution degree and diffusion simulation effect in the virtual sandbox, and support gesture interaction to adjust simulation parameters in real time; Distributed edge computing node: Deployed at the monitoring device end, use the lightweight federated learning framework to perform local modeling on the original data, and only upload the model parameters to the central platform to solve the problems of data privacy and transmission delay; Adaptive decision optimization module: Integrate digital twin and multi-agent reinforcement learning, construct a dynamic game model for regional emission reduction strategies, generate Pareto solutions that take into account economic costs and emission reduction efficiency in real time, and automatically execute quota allocation and transaction verification based on blockchain smart contracts.

2. The greenhouse gas collaborative monitoring and analysis platform according to claim 1, characterized in that, The cross-modal data fusion module includes: Heterogeneous data spatio-temporal alignment unit: Use spatio-temporal encoders to map the low spatio-temporal resolution of satellite data and the high-frequency data of ground sensors to a unified spatio-temporal grid, fill in the missing areas through interpolation algorithms, and eliminate the scale differences of multi-source data; Dynamic weight allocation algorithm: Calculate the confidence weights of each data source in real time based on environmental parameters, and the weight calculation formula is: Among them, is the data source noise variance, D i is the spatial distance between the monitoring point and the target area, and α, β, γ are dynamic adjustment coefficients, which are optimized in real time by the gradient descent method.

3. The platform according to claim 2, wherein The cross-modal data fusion module also includes: Abnormal data self-correction unit: Use a generative adversarial network to repair the monitoring data affected by extreme weather, and generate virtual data consistent with the real environment to fill the abnormal areas; Post-fusion optimization unit: Use a variational autoencoder to perform noise reduction processing on the fused concentration field, and generate a high-fidelity concentration distribution map through KL divergence constraints.

4. The platform according to claim 1, characterized in that, The causal inference analysis engine includes: Causal topology map construction unit: Extract the correlation between emission sources and concentration changes based on graph neural networks, combine meteorological data to construct a dynamic causal topology map, and label the key transmission path nodes; Dynamic Bayesian prediction unit: Use the Markov chain Monte Carlo method to simulate the evolution path of the concentration field under different emission reduction policies, and output the confidence interval of the probability distribution; Emission source isotope tracing unit: Collect CH4 isotope ratios through a high-precision mass spectrometer, construct an emission source chemical fingerprint library, and match specific industrial sources in combination with real-time monitoring data; Cross-border transmission contribution quantification unit: Use the Lagrangian particle diffusion model to reverse track the air mass trajectory, and combine meteorological reanalysis data to calculate the contribution ratio of cross-border transmission to the concentration of the target area.

5. The platform according to claim 1, characterized in that, The holographic dynamic visualization system includes: Holographic sandbox interaction unit: Capture the user's gesture actions through millimeter-wave radar, adjust the emission reduction intensity parameters in the virtual sandbox in real time, and synchronously update the diffusion path of the CFD simulation; Dynamic heat map generation unit: Maps the concentration field data to the RGB-alpha channels, generates a penetrable heat map, and superimposes it on the 3D terrain model, supporting multi-view scaling and cross-section analysis; Contribution ray analysis unit: Adopts the ray casting algorithm to backtrack the contribution of each emission source from the user-specified target point, visualizes the key transmission channels with dynamic light beams, and the brightness of the light beams is positively correlated with the contribution; Virtual-real linkage simulation unit: Loads historical extreme weather events in the sandbox, simulates the change of the concentration field under the same emission conditions, and generates a risk warning report through comparative analysis.

6. The platform according to claim 1, wherein The distributed edge computing node includes: Lightweight federated learning framework: Deploys the pruned convolutional neural network at the edge device side, only uploads the model gradients to the central platform, and adds Gaussian noise through differential privacy technology; Edge-cloud collaborative caching: Adopts the LRU-K algorithm to dynamically cache the frequently accessed data, where the data is the meteorological prediction results, and at the same time compresses and uploads the low-frequency data to the cloud; Device-side self-optimization unit: Dynamically adjusts the computing resource allocation of the edge node based on the Q-learning algorithm, preferentially processes the high-confidence data, and controls the response delay within 200 ms; Offline data transmission and resumption module: When the network is interrupted, uses the local time series database to temporarily store the data, and resumes the data transmission through the incremental synchronization protocol after the network is restored.

7. The platform according to claim 1, characterized in that The adaptive decision-making optimization module includes: Multi-agent game model: Defines the regional government, enterprises, and the public as agents, designs a utility function covering the GDP loss rate, emission reduction cost, and health benefit index, and solves the equilibrium strategy through the Nash-Q learning algorithm; Blockchain smart contract unit: Encodes the emission reduction quota allocation rules as smart contracts, automatically triggers quota freezing, auctions, and transaction verification, and stores the contract execution records encrypted by zero-knowledge proof to the consortium chain.

8. The platform according to claim 7, characterized in that, The adaptive decision-making optimization module also includes: Dynamic Pareto front generation unit: Utilizes the NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set in real time, and projects it into the decision maker's field of vision through the AR glasses, supporting gesture selection of the optimal solution; Carbon trading risk warning unit: Predicts the carbon price fluctuation range based on the Monte Carlo simulation. When the price deviates from the threshold, automatically adjusts the auction reserve price in the smart contract to prevent market manipulation.

9. The greenhouse gas collaborative monitoring and analysis platform according to claim 8, characterized in that In the dynamic Pareto front generation unit, when the NSGA-II multi-objective optimization algorithm updates the Pareto optimal solution set, it adopts the elitist retention strategy, directly inherits the non-dominated individuals in the previous generation's optimal solution set to the next generation, speeds up the convergence rate, and at the same time screens the newly generated solutions through the crowding distance comparison operator to ensure the diversity of the solution set, so that the generated Pareto optimal solution set converges to near the global optimum within 3 iterations, and the coverage index of the solution set is increased to more than 0.

9.

10. The greenhouse gas collaborative monitoring and analysis platform according to claim 9, characterized in that, In the carbon trading risk warning unit, when Monte Carlo simulation predicts the carbon price fluctuation range, it considers various macroeconomic factors and the impact of industry policy changes on the carbon price. By constructing a multiple linear regression model, it determines the correlation coefficients between various factors and the carbon price, and then generates a more accurate carbon price fluctuation prediction interval. When the price deviates from the threshold, the automatic adjustment range of the reserve price in the smart contract is controlled within a reasonable range.

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