Greenhouse gas monitoring and analysis platform

Abstract

The present application relates to the technical field of greenhouse gas monitoring, and more particularly to a greenhouse gas monitoring and analysis platform. The technical scheme comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node, and an adaptive decision optimization module. The cross-modal data fusion module integrates satellite remote sensing data, ground sensor networks, unmanned aerial vehicle mobile monitoring data, and industrial Internet of Things emission source data in real time, uses a dynamic weight distribution algorithm to automatically adjust the multi-source data fusion weight according to environmental parameters, and generates a time-space continuous greenhouse gas concentration field. The causal reasoning analysis engine is based on a hybrid architecture. The present application realizes comprehensive and accurate monitoring, scientific prediction and analysis, efficient decision execution, and risk prevention and control of greenhouse gases 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 contracts.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of greenhouse gas monitoring, and in particular to a greenhouse gas collaborative monitoring and analysis platform. BACKGROUND

[0002] Under the background of global climate change, greenhouse gas emission reduction has become the focus of international attention. Accurate understanding of greenhouse gas emission and scientific formulation of 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 data obtained by different monitoring methods. Satellite remote sensing data has low temporal and spatial resolution. Although ground sensor networks can provide high-frequency data, they have limited coverage. Unmanned aerial vehicle mobile monitoring and industrial Internet of Things emission source data also have their own limitations. Moreover, multi-source data is difficult to effectively fuse, resulting in the inability to generate accurate and continuous greenhouse gas concentration fields.

[0004] In terms of analysis, traditional methods cannot accurately establish the causal relationship between greenhouse gas emission sources and concentration changes, cannot clearly identify cross-regional transmission paths and key contributing sources, and have low prediction accuracy of concentration evolution trend under emission reduction paths. In terms of visualization, existing technologies cannot intuitively and dynamically display the complex diffusion process of greenhouse gases and the contribution of emission sources.

[0005] In terms of data processing, there is a delay in the transmission of massive raw data generated by monitoring equipment, and data privacy protection is insufficient. In terms of decision-making, existing emission reduction strategies cannot balance economic cost and emission reduction efficiency, lack effective dynamic optimization mechanisms, and the risk warning and regulation of carbon trading markets are not accurate 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, the present application proposes a greenhouse gas collaborative monitoring and analysis platform. SUMMARY

[0006] The purpose of the present application is to address the problem of delay in transmission of massive raw data generated by existing greenhouse gas monitoring equipment and insufficient data privacy protection in the background art, and to propose a greenhouse gas collaborative monitoring and analysis platform.

[0007] The technical solution of the present application is a greenhouse gas collaborative monitoring and analysis platform, which comprises:

[0008] The cross-modal data fusion module integrates satellite remote sensing data, ground sensor network data, unmanned aerial vehicle mobile monitoring data, and industrial Internet of Things emission source data in real time, uses a dynamic weight distribution algorithm, automatically adjusts the multi-source data fusion weight according to environmental parameters including temperature, wind speed, and terrain, and generates a spatiotemporally continuous greenhouse gas concentration field.

[0009] Causal inference analysis engine: Based on a hybrid architecture, a causal topology graph of greenhouse gas emission sources and concentration changes is constructed to identify cross-regional transmission paths and key contributing sources, and a dynamic Bayesian network is used to predict concentration evolution trends under different emission reduction paths. The hybrid architecture is a combination of a graph neural network and a causal inference model.

[0010] Holographic dynamic visualization system: Using holographic projection and 3D GIS fusion technology, the greenhouse gas concentration field, emission source contribution, and diffusion simulation results are dynamically displayed on a virtual sand table. Gesture interaction is supported for real-time adjustment of simulation parameters, including emission reduction intensity and meteorological conditions.

[0011] Distributed edge computing nodes: Deployed at monitoring devices, these nodes use a lightweight federated learning framework to model raw data locally, uploading only model parameters to the central platform to address data privacy and transmission delay issues.

[0012] Adaptive decision optimization module: Integrating digital twins and multi-agent reinforcement learning (MARL), this module constructs a dynamic game model for regional emission reduction strategies, generates Pareto optimal solutions that balance economic cost and emission reduction efficiency in real time, and automatically executes quota allocation and transaction verification based on smart contracts on the blockchain.

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

[0014] Heterogeneous data spatio-temporal alignment unit: Using a spatio-temporal encoder (ST-Encoder), low spatio-temporal resolution satellite data and high-frequency ground sensor data are mapped to a unified spatio-temporal grid. Interpolation algorithms are used to fill in missing areas, eliminating scale differences between multiple data sources. Low spatio-temporal resolution is daily / 10km, and high-frequency data is every minute / 100m.

[0015] Dynamic weight distribution algorithm: Based on environmental parameters such as wind speed, humidity, and terrain complexity, the confidence weights of each data source are calculated in real time. The weight calculation formula is:

[0016]

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

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

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

[0020] Post-fusion optimization unit: uses a variational autoencoder (VAE) to denoise the fused concentration field, generates a high-fidelity concentration distribution map by KL divergence constraint, and reduces the error rate to below 3%.

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

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

[0023] 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, and outputs the probability distribution confidence interval.

[0024] Emission source isotope tracing unit: collects CH4 isotope ratios, including δ 13 C, δD, using a high-precision mass spectrometer, constructs an emission source chemical fingerprint library, and matches specific industrial sources, including coal mines and rice paddies, based on real-time monitoring data;

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

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

[0027] Holographic sand table interaction unit: captures user hand gestures through millimeter wave radar, adjusts emission reduction intensity parameters in the virtual sand table in real time, and synchronously updates the diffusion path of the CFD simulation;

[0028] Dynamic heat map generation unit: maps concentration field data to RGB-alpha channels to generate a penetrable heat map, which is superimposed on a 3D terrain model to support multi-view scaling and profile analysis;

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

[0030] Virtual-real linkage simulation unit: load historical extreme weather events in the sand table, simulate the concentration field change under the same emission conditions, the extreme weather event is El Nino, and generate a risk warning report through comparative analysis.

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

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

[0033] Edge-cloud collaborative caching: use LRU-K algorithm to dynamically cache high-frequency access data, which is meteorological prediction results, and compress low-frequency data and upload to the cloud, reducing network load by 65%;

[0034] Device-side self-optimization unit: dynamically adjust the allocation of computing resources of edge nodes based on Q-learning algorithm, prioritize processing high-confidence data, and control response delay within 200ms;

[0035] Offline transmission module: when the network is interrupted, use the local time series database to store data temporarily, and after recovery, use the incremental synchronization protocol to supplement the data, ensuring data integrity of 99.99%.

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

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

[0038] Blockchain smart contract unit: encode emission reduction quota allocation rules as a smart contract, automatically trigger quota freezing, auction, and transaction verification, and store contract execution records encrypted by zero-knowledge proof (ZKP) to the consortium chain.

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

[0040] Dynamic Pareto frontier generation unit: use NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set in real time, and project 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 to predict the carbon price fluctuation range, when the price deviates from the threshold, automatically adjust the auction reserve price in the smart contract to prevent market manipulation.

[0042] Optionally, in the dynamic Pareto front generation unit, the NSGA-II multi-objective optimization algorithm adopts an elite reservation strategy when updating the Pareto optimal solution set, directly inherits the individuals in the optimal solution set of the last generation to the next generation to speed up the convergence speed, and selects the newly generated solutions through a crowding degree comparison operator to ensure the diversity of the solution set, so that the generated Pareto optimal solution set converges to the global optimum within 3 iterations, and the coverage index of the solution set is improved to 0.9 or more.

[0043] Optionally, in the carbon trading risk early warning unit, when predicting the carbon price fluctuation range, the Monte Carlo simulation considers the influence of various macroeconomic factors and industry policy changes on the carbon price, the macroeconomic factors include GDP growth rate, energy price index, monetary policy adjustment, the industry policy changes include new emission reduction target, subsidy policy adjustment, the correlation coefficient of each factor and the carbon price is determined through a multiple linear regression model, and then a more accurate carbon price fluctuation prediction interval is generated, so that the early warning accuracy is 95% or more, when the price deviates from the threshold, the auction reserve price in the smart contract is automatically adjusted within a reasonable range, which avoids excessive market fluctuations and ensures the liquidity of the carbon trading market.

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

[0045] The heterogeneous data space-time alignment unit solves the scale difference problem of multi-source data, the abnormal data self-correction unit can repair data disturbed by extreme weather, and the fusion and optimization unit can generate a high-fidelity concentration distribution map by denoising, providing a comprehensive and accurate data basis for subsequent analysis;According to the environmental parameters, the multi-source data fusion weight is automatically adjusted to improve the accuracy and reliability of data fusion, and the result is more in line with the actual environmental changes;

[0046] The present application can identify the causal relationship of greenhouse gas emission sources and concentration changes, cross-regional transmission path and key contribution source. Through the emission source isotope tracing unit, the specific industrial source can be accurately located, and the cross-border transmission contribution quantification unit can quantify the contribution proportion of cross-border transmission to the concentration of the target region, and the dynamic Bayesian network is used to predict the concentration evolution trend under different emission reduction paths, which provides a basis for formulating scientific and effective emission reduction strategies.

[0047] Through the holographic dynamic visualization system, the holographic projection and 3D GIS fusion technology are used to dynamically display the information related to greenhouse gases in the virtual sand table, and the virtual-real linkage simulation unit can simulate the concentration field change under extreme weather and generate a risk warning report, which is convenient for intuitive understanding and decision-making, and the differential privacy technology is combined to add Gaussian noise, effectively solving the data privacy problem.

[0048] By edge-cloud collaborative caching, device-side self-optimization unit and offline transmission module, data transmission delay is reduced, timeliness and stability of data processing are ensured, dynamic Pareto frontier generation unit updates Pareto optimal solution set in real time and supports gesture selection of optimal solution. Carbon trading risk early warning unit can predict carbon price fluctuation and adjust auction reserve price, so as to ensure stable operation of carbon trading market and improve fairness and transparency of decision execution.

[0049] The present application realizes comprehensive and accurate monitoring, scientific prediction and analysis, efficient decision execution and risk prevention and control of greenhouse gases through multi-source data fusion, causal reasoning analysis, holographic visualization, edge computing to ensure data privacy and reduce delay, and decision optimization based on dynamic game and smart contract of blockchain. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The figure is a principle block diagram of the greenhouse gas collaborative monitoring and analysis platform. DETAILED DESCRIPTION

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

[0052] Embodiment 1

[0053] As shown in Figure 1 The greenhouse gas collaborative monitoring and analysis platform proposed by the present application includes 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.

[0054] Platform building and hardware deployment, specifically including:

[0055] Data acquisition equipment installation, ground sensor network is widely deployed in different geographical locations, appropriate installation points are selected to ensure that the target monitoring area can be covered. According to the terrain, climate and other factors, the density of the sensor is reasonably set to ensure the comprehensiveness and accuracy of the data. High-precision greenhouse gas monitoring equipment is provided for the unmanned aerial vehicle, and reasonable flight route and monitoring plan are made so that it can conduct mobile monitoring on 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 stable acquisition of satellite remote sensing data, establish cooperation relationship with relevant satellite data providers to ensure real-time and quality of data.

[0056] Edge computing node deployment, distributed edge computing nodes are deployed at each monitoring equipment end, pruned convolutional neural network (CNN) model is deployed on edge devices to realize lightweight federated learning. The edge node is configured with sufficient computing resources and storage devices to meet the needs of data processing and caching.

[0057] The central platform is built, the central server is built, the high-performance computing device and the large-capacity storage system are configured to process and store data and model parameters from various data sources. Install holographic projection equipment and 3D GIS system, build the hardware foundation of the holographic dynamic visualization system.

[0058] The cross-modal data fusion module, the causal reasoning analysis engine, the holographic dynamic visualization system, the distributed edge computing node, and the adaptive decision optimization module are described in detail below.

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

[0060] The heterogeneous data space-time alignment unit processes satellite data and ground sensor data using a space-time encoder (ST-Encoder). The low space-time resolution of satellite data (daily / 10km) and the high-frequency data of ground sensors (every minute / 100m) are mapped to a unified space-time grid. A suitable interpolation algorithm, Kriging interpolation, is used to fill in the missing data area and eliminate the scale difference of multi-source data.

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

[0062]

[0063] wherein, is the noise variance of the data source, D i is the spatial distance between the monitoring point and the target area, and α, β, γ are dynamic adjustment coefficients that are optimized in real time through gradient descent method. Among them, the noise variance of the data source is obtained through historical data statistical analysis, and the spatial distance between the monitoring point and the target area can be calculated through the geographic information system (GIS). The dynamic adjustment coefficients k1, k2, k3, k4 are optimized in real time using the gradient descent method 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 disturbed by extreme weather (sandstorm, heavy rain) when it is monitored. Train the GAN model to generate virtual data consistent with the real environment and fill in the abnormal area.

[0065] In this embodiment, a post-fusion optimization unit is also included, which uses a variational autoencoder (VAE) to perform noise reduction processing on the fused concentration field. By constraining the 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 integrate satellite remote sensing data, ground sensor network data, unmanned aerial vehicle mobile monitoring data, and industrial Internet of Things emission source data in real time. This integration of multiple data sources breaks the limitations of a single data source and comprehensively and accurately reflects the emission and distribution of greenhouse gases. At the same time, through a dynamic weight distribution algorithm, the fusion weight is automatically adjusted according to environmental parameters to generate a spatiotemporally continuous greenhouse gas concentration field, making the data more consistent with actual environmental changes and improving the reliability and usability of the data.

[0066] Among them, the abnormal data self-correction unit uses a generative adversarial network (GAN) to repair monitoring data disturbed by extreme weather, and the post-fusion optimization unit uses a variational autoencoder (VAE) for noise reduction processing, reducing the error rate to below 3%. This effectively solves the problem of data interference and noise caused by external factors, ensuring the accuracy and high quality of the data, and providing a solid foundation for subsequent analysis and decision-making.

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

[0068] The causal reasoning 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). The emission source data, concentration data, and meteorological data are used as inputs to train the GNN model and learn the causal relationships between them. A dynamic causal topology graph is constructed in combination with meteorological data to label key transmission path nodes. By analyzing the impact of meteorological conditions on greenhouse gas transmission, the causal relationships between different emission sources and concentration changes are determined. The causal reasoning 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 construct 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 of cross-border transmission to the concentration of the target region, with an accuracy of ±5%, providing a scientific basis for developing targeted emission reduction strategies.

[0070] The dynamic Bayesian prediction unit simulates the evolution path of the concentration field under different emission reduction policies by using the Markov Chain Monte Carlo (MCMC) method. Different emission reduction scenarios are set, and the evolution samples of the concentration field are generated by the MCMC algorithm, and the probability distribution confidence interval is output. The dynamic Bayesian prediction unit simulates the evolution path of the concentration field under different emission reduction policies by using the Markov Chain Monte Carlo (MCMC) method, outputs the probability distribution confidence interval, and helps decision makers understand the possible effects of different emission reduction measures in advance, so as to make more reasonable decisions.

[0071] The emission source isotope tracing unit collects CH4 isotope ratio (δ 13 C, δD) by high-precision mass spectrometer to construct the emission source chemical fingerprint library. The CH4 isotope characteristics of different industrial sources (coal mines, rice fields) are analyzed and recorded. Combined with real-time monitoring data, the collected CH4 isotope ratio is matched with the chemical fingerprint library to determine the specific industrial source.

[0072] The cross-border transmission contribution quantification unit adopts the Lagrangian particle diffusion model (LPDM) to track the trajectory of the air mass in reverse. The meteorological reanalysis data (ERA5) is input to simulate the motion trajectory of the air mass. Combined with the meteorological reanalysis data, the contribution proportion of cross-border transmission to the concentration of the target region is calculated, and the precision is reached ± 5% by continuously optimizing the model parameters.

[0073] The holographic dynamic visualization system specifically comprises:

[0074] The holographic sand table interaction unit is installed with a millimeter wave radar device to capture the user's gesture action. The millimeter wave radar is calibrated and debugged to ensure accurate recognition of the user's gestures.

[0075] When the user makes a gesture action, the emission 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 gesture action and parameter adjustment is realized by programming.

[0076] The dynamic heat map generation unit 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-angle zooming and profile analysis. The heat map and the terrain model are fused and interacted by using the 3DGIS system.

[0077] The contribution degree ray analysis unit adopts the light ray projection algorithm to reversely track the contribution degree of each emission source from the user-specified target point. The light ray projection algorithm is realized by programming to calculate the contribution degree of each emission source. The dynamic light beam visualizes the key transmission channel, and the brightness of the light beam is positively correlated with the contribution degree. The visualization effect of the light beam is realized by graphic rendering technology.

[0078] The virtual-real interaction simulation unit loads data of historical extreme weather events (such as El Nino) in the sand table to simulate 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. Through comparative analysis, a risk warning report is generated. The simulation results are analyzed and evaluated to generate a detailed risk warning report for decision-makers. The holographic dynamic visualization system uses holographic projection and 3D GIS fusion technology to dynamically display the greenhouse gas concentration field, emission source contribution, and diffusion simulation effect in the virtual sand table. Users can adjust simulation parameters such as emission reduction intensity and meteorological conditions in real time through gesture interaction, intuitively observe the changes in greenhouse gases under different conditions, and enhance the user's understanding and grasp of the data. The dynamic heat map generation unit, contribution ray analysis unit, and virtual-real interaction simulation unit in the system visually display greenhouse gas data from different angles, support multi-angle zooming and profile analysis, key transmission channel visualization, and historical extreme weather event simulation comparative analysis, providing users with comprehensive and in-depth data analysis tools.

[0079] The distributed edge computing node specifically includes:

[0080] The lightweight federated learning framework deploys a pruned convolutional neural network (CNN) on the edge device end to model the original data locally. Through model pruning technology, the number of CNN model parameters is reduced, and the computational complexity is reduced. Only the model gradient is uploaded to the center platform, and Gaussian noise is added through differential privacy technology (DP) to protect data privacy. Set appropriate Gaussian noise parameters to minimize the impact on model training while ensuring privacy security.

[0081] Edge-cloud collaborative caching uses the LRU-K algorithm to dynamically cache high-frequency access data such as weather prediction results. Through the algorithm, data caching and management are achieved, improving data access efficiency. Low-frequency data is compressed and uploaded to the cloud to reduce network load. Select an appropriate compression algorithm to compress low-frequency data.

[0082] The device-end self-optimization unit dynamically adjusts the allocation of computing resources of the edge node based on the Q-learning algorithm. Through training of the Q-learning model, the edge node can automatically adjust the allocation of computing resources according to the confidence of the data, giving priority to processing high-confidence data and controlling the response delay within 200 ms. Through optimization algorithms and hardware configuration, data processing speed is improved.

[0083] The off-network continuation transmission module temporarily stores data in the local timing database when the network is interrupted. A suitable timing database is selected to ensure efficient and reliable storage and management of data. After the network is restored, the data is transmitted through an incremental synchronization protocol to ensure data integrity of 99.99%. The incremental synchronization protocol is implemented to ensure accurate data transmission.

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

[0085] The edge-cloud collaborative caching adopts the LRU-K algorithm to dynamically cache high-frequency access data, and uploads the compressed low-frequency data to the cloud, reducing the network load by 65%. The device-side self-optimization unit dynamically adjusts the allocation of computing resources based on the Q-learning algorithm, and controls the response delay within 200ms. The off-network continuation transmission module ensures data integrity of 99.99%. These measures improve the efficiency and stability of data processing, ensuring smooth operation of the platform.

[0086] The adaptive decision optimization module specifically includes:

[0087] The multi-agent game model defines the regional government, enterprises, and the public as agents, and designs an utility function that covers GDP loss rate, emission reduction cost, and health benefit index. By analyzing the goals and interests of each agent, the specific form of the utility function is determined. The Nash-Q learning algorithm is used to solve the equilibrium strategy. The Nash-Q learning model is trained to enable each agent to find the optimal strategy during the game process.

[0088] The blockchain smart contract unit encodes the emission reduction quota allocation rules as a smart contract. Solidity, a smart contract development language, is used to implement the automatic execution of the emission reduction quota allocation rules. The contract automatically triggers quota freezing, auction, and transaction verification, and the contract execution record is stored in the consortium chain after being encrypted by zero-knowledge proof (ZKP). The consortium chain node is deployed to ensure the safe execution of the smart contract and the encrypted storage of data.

[0089] The dynamic Pareto frontier generation unit updates the Pareto optimal solution set in real time using the NSGA-II multi-objective optimization algorithm. By setting appropriate algorithm parameters, including population size, iteration number, and using elite preservation strategy and crowding comparison operator, the convergence speed is accelerated and the diversity of the solution set is ensured. The AR glasses project the optimal solution set to the decision maker's field of view, supporting gesture selection of the optimal solution. The AR application is developed to realize the visualization and interactive operation of the Pareto optimal solution set. The NSGA-II multi-objective optimization algorithm uses the elite preservation strategy to directly inherit the undominated individuals in the previous generation optimal solution set to the next generation, accelerating the convergence speed, and at the same time, the crowding comparison operator is used to screen the newly generated solutions, ensuring the diversity of the solution set, so that the generated Pareto optimal solution set converges to the global optimum within 3 iterations, and the coverage index of the solution set is improved to more than 0.9.

[0090] The carbon trading risk early warning unit predicts the carbon price fluctuation range based on Monte Carlo simulation. Considering the influence of various macroeconomic factors (GDP growth rate, energy price index, monetary policy adjustment) and industry policy changes (new emission reduction target, subsidy policy adjustment) on carbon price, a multiple linear regression model is constructed to determine the correlation coefficient of each factor and carbon price. When the price deviates from the threshold value, the auction reserve price in the smart contract is automatically adjusted to prevent market manipulation. By setting reasonable price threshold and auction reserve price adjustment range, the stable operation of the carbon trading market is ensured. Monte Carlo simulation considers the influence of various macroeconomic factors and industry policy changes on carbon price when predicting the carbon price fluctuation range. By constructing a multiple linear regression model to determine the correlation coefficient of each factor and carbon price, a more accurate carbon price fluctuation prediction interval is generated. When the price deviates from the threshold value, the auction reserve price in the smart contract is automatically adjusted within a reasonable range.

[0091] It is worth noting that by integrating digital twins and multi-agent reinforcement learning through the adaptive decision optimization module, a dynamic game model of regional emission reduction strategies is constructed, and Pareto optimal solutions that balance economic cost and emission reduction efficiency are generated in real time. Considering the interests and goals of regional governments, enterprises, and the public, it provides effective decision support for sustainable development. Through the dynamic Pareto frontier generation unit, the NSGA-II multi-objective optimization algorithm is used to update the Pareto optimal solution set, so that the generated solution set converges to the global optimum within 3 iterations, and the coverage index is improved to more than 0.9. The carbon trading risk early warning unit predicts the carbon price fluctuation range through Monte Carlo simulation, with an early warning accuracy of more than 95%. When the price deviates from the threshold value, the auction 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] Example 2

[0093] In this embodiment, on the basis of Embodiment 1, platform testing and optimization are also included, specifically including:

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

[0095] System performance testing, testing the performance of the distributed edge computing nodes, including data processing speed, response delay, network load and other indicators. By simulating different data traffic and computing tasks, the performance of the edge nodes is evaluated. The interactivity and visualization effect of the holographic dynamic visualization system are tested to check whether the gesture interaction is sensitive and the heat map and light beam visualization are clear and accurate.

[0096] Decision optimization effect evaluation, evaluating the effect of the emission reduction strategies generated by the adaptive decision optimization module. By simulating different emission reduction scenarios, the economic cost and emission reduction efficiency before and after adopting the strategy are compared and analyzed. The warning accuracy of the carbon trading risk early warning unit and the rationality of the auction reserve price adjustment are checked to ensure the stable operation of the carbon trading market.

[0097] System optimization, according to the test results, the modules of the platform are optimized. Adjust the model parameters, algorithm settings and hardware configuration to improve the performance and accuracy of the system. Continuously collect and analyze user feedback to continuously improve the functions and user experience of the platform.

[0098] Embodiment 3

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

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

[0101] Data standardization processing: define a unified data format and standard for the data of each unit to ensure that the emission source data, concentration data, meteorological data, CH4 isotope ratio data, etc. can flow smoothly between different units. Develop a unified timestamp format, geographic coordinate system and data coding rules.

[0102] Data interaction interface design: develop a standardized data interaction interface 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 easily obtain and share each other's data. Realize data request and response through RESTful API.

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

[0104] Causal topology map guides prediction: The causal relationship information obtained by the causal topology map construction unit is input as prior knowledge into the dynamic Bayesian prediction unit. During the MCMC simulation process, according to the correlation strength between the emission sources and the concentration changes in the causal topology map, the simulation parameters under different emission reduction scenarios are adjusted to make the simulation results more consistent 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, the weight of the emission reduction measures of that source is increased when simulating the emission reduction effect in that area.

[0105] Integrating traceability and quantitative 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 the dynamic Bayesian prediction. When new emission sources or changes in cross-border transmission contributions are found, the causal relationship and prediction model are adjusted in a timely manner to improve the accuracy and timeliness of causal reasoning.

[0106] 3. Introducing dynamic updating mechanism

[0107] Real-time data-driven unit update: Each unit is dynamically updated based on 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 new industrial source characteristics.

[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 traceability and quantitative results. When there are significant changes in causal relationships, the prediction model can be quickly adjusted to output more accurate concentration field evolution paths and probability distribution confidence intervals.

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

[0110] Micro and macro causal integration: In the construction of the causal topology map, not only the causal relationship between the macro emission sources and the regional concentration changes is considered, but also the causal connection between the emissions of different production links within an enterprise and the local concentration changes is analyzed. The micro and macro causal relationships are integrated to form a more comprehensive causal topology map.

[0111] Causal analysis at different temporal and spatial scales: Causal analysis is performed for different time scales (day, month, year) and spatial scales (local area, cross-regional). In the dynamic Bayesian prediction, different simulation strategies and parameter settings are used according to the causal characteristics of different temporal and spatial scales to improve the accuracy and relevance of the prediction.

[0112] 5. Uncertainty quantification and evaluation

[0113] Causal relationship uncertainty quantification: During the construction of the causal topology graph, the uncertainty of the causal relationship between the emission source and the concentration change is quantitatively evaluated. Through statistical analysis and machine learning methods, the confidence and error range of the causal relationship are calculated, and these uncertainty factors are considered in the dynamic Bayesian prediction, making the prediction result more reliable.

[0114] Comprehensive uncertainty evaluation: Considering the uncertainty of emission source data, the error of meteorological data, and the uncertainty factors of model parameters, the output results of the entire causal reasoning analysis engine are comprehensively evaluated. This provides clearer decision-making basis for decision-makers and also provides direction for further optimization of models 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 of the causal topology graph can effectively guide the dynamic Bayesian prediction, and the traceability and quantification results can also be deeply integrated into the overall causal analysis, so that the causal reasoning can comprehensively consider multiple factors to more comprehensively and accurately reflect the complex causal relationship between greenhouse gas emissions and concentration changes, avoiding analysis bias caused by information isolation.

[0116] In addition, multi-scale causal analysis and fusion are introduced, taking into account the causal relationship of micro and macro, different time and space scales. Micro-level analysis of the relationship between enterprise internal production link emissions and local concentration changes, macro-level understanding of the relationship between regional emission sources and overall concentration changes, combined with causal characteristics of different time and space scales, can capture more detailed and comprehensive causal information, thereby improving the accuracy and pertinence of causal reasoning.

[0117] It is worth noting that real-time data-driven unit updating and adaptive model adjustment enable the causal reasoning analysis engine to respond to changes in data in a timely manner. 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. always conform to the actual situation. In the face of complex and variable environment and emission conditions, the system can quickly adapt and make accurate analysis and prediction.

[0118] Uncertainty quantification and evaluation: Quantifying and evaluating the uncertainty of causal relationships and comprehensive results can help decision-makers 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 errors caused by excessive reliance on a single result. At the same time, the uncertainty evaluation results also provide a direction for further optimization of the model and data, which helps to continuously improve the accuracy and stability of the system. The causal topology diagram guides the prediction, and the introduction of the dynamic updating mechanism enables the dynamic Bayesian prediction unit to output concentration field evolution paths and probability distribution confidence intervals that are more consistent with actual causal logic. Decision-makers can base on more accurate prediction results to understand the possible effects of different emission reduction measures in advance, and thus develop more scientific and reasonable emission reduction strategies.

[0119] The results of the emission source isotope tracing unit and the cross-border transmission contribution quantification unit are deeply integrated into the overall analysis, providing decision-makers with more comprehensive information. Understanding the contribution of specific industrial sources and cross-border transmission to the concentration can help develop more targeted emission reduction measures, improve emission reduction efficiency, and achieve a balance between economic cost and emission reduction effect.

[0120] Further, a unified data fusion and interaction layer is established, a unified data format and standard are defined, and a standardized data interaction interface is designed, making the data flow between units more smooth. This not only facilitates the collaborative work of the current system's internal units, but also provides convenience for the future expansion of the system and integration with other related systems. For example, it can easily access new data sources or share and interact with other monitoring and analysis systems.

[0121] The above specific embodiments are only a few optional embodiments of the present application. Based on the technical solutions of the present application and the related inspiration of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A platform for synergistic monitoring and analysis of greenhouse gases, characterized in that, Comprise: Cross-modal data fusion module: Real-time integration of satellite remote sensing data, ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data, using dynamic weight distribution algorithm, automatically adjusting the weight of multi-source data fusion according to environmental parameters, generating a continuous greenhouse gas concentration field in space and time; The cross-modal data fusion module comprises: Heterogeneous data space-time alignment unit: Using a space-time encoder to map the low space-time resolution of satellite data and the high-frequency data of ground sensors to a unified space-time grid, filling in the missing areas through interpolation algorithm, and eliminating the scale difference of multi-source data; Dynamic weight distribution algorithm: Based on environmental parameters, the confidence weight of each data source is calculated in real time, and the weight calculation formula is: wherein, is the data source noise variance, is the spatial distance between the monitoring point and the target region, , , is the dynamic adjustment coefficient, which is optimized in real time by the gradient descent method; Causal reasoning analysis engine: Based on a hybrid architecture, a causal topology graph of greenhouse gas emission sources and concentration changes is constructed to identify cross-regional transmission paths and key contributing sources, and to predict the concentration evolution trend under different emission reduction paths through a dynamic Bayesian network; The causal reasoning analysis engine comprises: Causal topology graph construction unit: Based on a graph neural network, the correlation between emission sources and concentration changes is extracted, and a dynamic causal topology graph is constructed combined with meteorological data to label key transmission path nodes; Dynamic Bayesian prediction unit: Using the Markov chain Monte Carlo method to simulate the evolution path of the concentration field under different emission reduction policies, outputting the probability distribution confidence interval; Emission source isotope tracing unit: Collect CH4 isotope ratio through high-precision mass spectrometer, build emission source chemical fingerprint library, combined with real-time monitoring data to match specific industrial sources; Cross-border transmission contribution quantization unit: Using the Lagrangian particle diffusion model to track the trajectory of air masses in reverse, combined with meteorological reanalysis data, calculate the contribution proportion of cross-border transmission to the concentration of the target area; Holographic dynamic visualization system: Using holographic projection and 3D GIS fusion technology, dynamically display the greenhouse gas concentration field, emission source contribution and diffusion simulation effect in the virtual sand table, support gesture interaction to adjust simulation parameters in real time; Distributed edge computing node: Deployed at the monitoring device end, using a lightweight federated learning framework to model the original data locally, only upload model parameters to the center platform, solve the problem of data privacy and transmission delay; Adaptive decision optimization module: Integrating digital twin and multi-agent reinforcement learning, constructing a dynamic game model of regional emission reduction strategies, generating a Pareto solution that takes into account economic cost and emission reduction efficiency in real time, and automatically executing quota allocation and transaction verification based on a blockchain smart contract.

2. The platform of claim 1, wherein, The cross-modal data fusion module further comprises: Abnormal data self-correction unit: Using a generative adversarial network to repair monitoring data disturbed by extreme weather, generating virtual data consistent with the real environment to fill in the abnormal area; Optimization unit after fusion: Using a variational autoencoder to denoise the fused concentration field, generating a high-fidelity concentration distribution map by constraining the KL divergence.

3. The platform of claim 1, wherein, The holographic dynamic visualization system comprises: Holographic sand table interaction unit: Capture user gesture actions through millimeter wave radar, adjust emission reduction intensity parameters in virtual sand table in real time, and update diffusion path of CFD simulation synchronously; A dynamic thermal map generation unit: maps the concentration field data to RGB-alpha channels to generate a penetrable thermal map, which is superimposed on a 3D terrain model to support multi-view zooming and profile analysis; A contribution degree ray analysis unit: uses a ray casting algorithm to trace the contribution degree of each emission source from a user-specified target point to dynamically visualize the key transmission channels in the form of light beams, with the brightness of the light beams being positively correlated with the contribution degree; A virtual-real interactive simulation unit: loads historical extreme weather events in a sand table to simulate changes in the concentration field under the same emission conditions and generates a risk warning report through comparative analysis.

4. The platform of claim 1, wherein, The distributed edge computing node includes: A lightweight federated learning framework: deploys a pruned convolutional neural network on the edge device, uploads only the model gradient to the central platform, and adds Gaussian noise through differential privacy technology; Edge-cloud collaborative caching: uses the LRU-K algorithm to dynamically cache high-frequency access data, which is the result of weather prediction, and compresses low-frequency data for uploading to the cloud; A device-side self-optimization unit: dynamically adjusts the allocation of computing resources of the edge node based on the Q-learning algorithm, prioritizes high-confidence data, and controls the response delay to within 200 ms; A network interruption continuation module: when the network is interrupted, temporarily stores data in a local time series database, and after recovery, uses an incremental synchronization protocol to supplement the data.

5. The platform of claim 1, wherein, The adaptive decision optimization module includes: A multi-agent game model: defines regional governments, enterprises, and the public as agents, designs an utility function that covers GDP loss rate, emission reduction cost, and health benefit index, and solves the equilibrium strategy through the Nash-Q learning algorithm; A blockchain smart contract unit: encodes the emission reduction quota allocation rules as a smart contract, automatically triggers quota freezing, auction, and transaction verification, and stores the contract execution records in the consortium chain after encryption through zero-knowledge proof.

6. The platform of claim 5, wherein, The adaptive decision optimization module further includes: A dynamic Pareto frontier generation unit: uses the NSGA-II multi-objective optimization algorithm to update the Pareto optimal solution set in real time, projects it to the decision maker's field of view through AR glasses, and supports gesture selection of the optimal solution; A carbon trading risk warning unit: based on Monte Carlo simulation to predict the carbon price fluctuation range, when the price deviates from the threshold, automatically adjusts the auction reserve price in the smart contract to prevent market manipulation.

7. The greenhouse gas co-monitoring and analysis platform of claim 6, wherein, In the dynamic Pareto frontier generation unit, the NSGA-II multi-objective optimization algorithm uses an elitist strategy to directly inherit the undominated individuals in the previous generation to the next generation when updating the Pareto optimal solution set, accelerating the convergence speed, and through the crowding degree comparison operator, the newly generated solutions are screened to ensure the diversity of the solution set, so that the generated Pareto optimal solution set converges to the global optimum within 3 iterations, and the coverage index of the solution set improves to more than 0.

9.

8. The greenhouse gas co-monitoring and analysis platform of claim 7, wherein, In the carbon trading risk early warning unit, Monte Carlo simulation considers the influence of various macroeconomic factors and industry policy changes on carbon price when predicting the carbon price fluctuation range, determines the correlation coefficient of each factor and carbon price by building a multiple linear regression model, and then generates a more accurate carbon price fluctuation prediction interval. When the price deviates from the threshold, the automatic adjustment range of the auction reserve price in the smart contract is controlled within a reasonable range.

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