Carbon emission prediction and optimization method

Abstract

The invention discloses a carbon emission prediction and optimization method, and the method comprises the steps: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indexes, energy consumption data, and Internet of Things sensor data, and constructing a three-dimensional feature matrix through employing an improved spatial-temporal feature extraction algorithm; based on a mixed architecture of a graph neural network GNN and a long and short term memory network LSTM, a prediction model is established in combination with an attention mechanism, and training is performed through an adaptive learning rate optimization algorithm; a prediction result is input into an improved NSGA-III algorithm, and three targets of total carbon emission, economic cost and social benefits are optimized at the same time; and establishing a feedback closed loop through reinforcement learning RL, and updating the model and the strategy on line according to real-time monitoring data. According to the method, multiple advanced technologies such as accurate data processing, dynamic prediction, multi-objective optimization and cross-domain collaboration are integrated, and a comprehensive and effective solution is provided for carbon emission management.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission management, and particularly to a carbon emission prediction and optimization method. Background Art

[0002] Currently, the global carbon emission problem is becoming increasingly severe, and climate change and environmental pollution have had a huge impact on social economy and ecological systems. Most of the existing carbon emission prediction methods focus on prediction based on a single data source or traditional statistical models, lacking effective fusion and dynamic adjustment mechanisms for multi-source heterogeneous data. In addition, many existing models have deficiencies in dealing with the uncertainty, spatial dependence, and multi-objective optimization of carbon emission prediction, and fail to effectively respond to changes in complex environments.

[0003] First, the existing carbon emission prediction models usually rely on simple linear regression or classical time series analysis methods. Although these methods are effective in some scenarios, they have poor adaptability to complex non-linear relationships and multi-dimensional spatio-temporal data, and cannot comprehensively consider the correlation between different data sources. Second, when dealing with multiple objectives such as carbon emissions, economic costs, and social benefits, traditional multi-objective optimization methods often have difficulty weighing the needs of all parties, and the optimization results may be biased towards a single objective, ignoring the balance of environmental, social, and economic benefits. In addition, most existing optimization algorithms rely on static models and preset parameter adjustments, and cannot perform real-time adjustment and feedback in the face of rapidly changing actual data, thus reducing the decision-making efficiency and accuracy.

[0004] With the development of technologies such as the Internet of Things and artificial intelligence, carbon emission prediction and optimization methods based on big data and machine learning have gradually received attention, but the existing technologies still face some challenges. First, data fusion and outlier detection problems are still difficult points. How to process high-dimensional heterogeneous data from multiple sources and perform effective cleaning and fusion is the key to ensuring prediction accuracy. Second, although existing deep learning models can handle relatively complex spatio-temporal dependence relationships, they lack the ability to quantify uncertainty and dynamic adjustment, and cannot adapt to real-time changing environments. In addition, the application of multi-objective optimization methods in carbon emission prediction is still not perfect, and how to balance economic development, carbon emission reduction, and social benefits remains an urgent problem to be solved.

[0005] Therefore, in the process of carbon emission prediction and optimization, the existing technologies face limitations in many aspects such as data fusion, model adaptability, real-time adjustment, and multi-objective optimization, and there is an urgent need for new efficient algorithms and dynamic optimization methods to improve prediction accuracy and decision-making efficiency. Summary of the Invention

[0006] In order to overcome the disadvantages and deficiencies of the existing technologies, the purpose of the present invention is to provide a carbon emission prediction and optimization method.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A carbon emission prediction and optimization method, comprising the following steps:

[0009] Step S1, multi-modal data fusion preprocessing: Obtain multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indicators, energy consumption data, and Internet of Things sensor data, and construct a three-dimensional feature matrix using an improved spatio-temporal feature extraction algorithm;

[0010] Step S2, dynamic prediction model construction: Based on a hybrid architecture of a graph neural network GNN and a long short-term memory network LSTM, establish a prediction model in combination with an attention mechanism, and train it through an adaptive learning rate optimization algorithm;

[0011] Step S3, multi-objective optimization decision-making: Input the prediction results into an improved NSGA-III algorithm, and simultaneously optimize three objectives: total carbon emissions, economic cost, and social benefits;

[0012] Step S4, dynamic adjustment mechanism: Establish a feedback closed-loop through reinforcement learning RL, and update the model and strategy online according to real-time monitoring data.

[0013] Further, in the multi-modal data fusion preprocessing step:

[0014] Adopt an outlier detection algorithm based on a Bayesian network, and estimate the posterior probability distribution of missing data through the Markov chain Monte Carlo method;

[0015] Combine wavelet packet decomposition and empirical mode decomposition to remove high-frequency noise and retain the trend component of carbon emission data;

[0016] Based on SHAP value analysis and grey relational degree calculation, construct a feature subset containing more than 20 key influencing factors, where the weight of the proportion of industrial added value is not less than 0.35.

[0017] Further, in the outlier detection algorithm:

[0018] The structure learning of the Bayesian network adopts the K2 algorithm, and the parameter learning uses maximum likelihood estimation;

[0019] The Markov chain Monte Carlo method sets 5000 iterations, with the first 1000 times as the warm-up period and the last 4000 times for parameter estimation.

[0020] Further, in the dynamic prediction model construction step:

[0021] The graph neural network GNN part adopts a three-layer graph convolutional network, and the node features include spatial position encoding and neighborhood carbon emission intensity;

[0022] The long short-term memory network (LSTM network) is set with a bidirectional structure and an attention gating mechanism, and the time step is 72, corresponding to 3 days;

[0023] A variational inference framework is introduced to handle prediction uncertainty, and a 95% confidence interval for carbon emissions in the next 72 hours is output;

[0024] The model is trained using the AdamW optimizer, and the learning rate scheduling strategy is cosine annealing, with the initial learning rate set to 5e -4 。

[0025] Furthermore, the method for constructing the adjacency matrix of the graph convolutional network is as follows:

[0026] Calculate the Euclidean distance between nodes based on geographic information system data to construct an initial adjacency matrix;

[0027] Use the heat kernel trick for similarity calculation, and the bandwidth parameter is determined through cross-validation.

[0028] Furthermore, in the multi-objective optimization decision-making steps:

[0029] Construct an optimization model including 5 decision variables, including the proportion of renewable energy, the industrial carbon tax rate, the electric vehicle purchase subsidy rate, the improvement range of building energy efficiency standards, and the investment ratio of carbon capture and storage technology;

[0030] The social benefit objective function uses the fuzzy integral method to convert qualitative indicators such as employment growth and public satisfaction into quantitative values;

[0031] The improvements of the NSGA-III algorithm include: a reference point-based population division strategy, dynamic crowding distance calculation, and elite retention strategy;

[0032] The optimal solution set is transformed into more than 10 executable emission reduction plans through the ε-constraint method.

[0033] Furthermore, the fuzzy integral method specifically includes:

[0034] Use trapezoidal membership functions to quantify qualitative indicators, and the membership function parameters are determined through the Delphi method;

[0035] Apply the Choquet integral to calculate the comprehensive social benefit value, and consider the interaction between indicators during the integration process.

[0036] Furthermore, in the dynamic adjustment mechanism:

[0037] Reinforcement learning adopts a hierarchical Actor-Critic architecture, where the upper-level policy network outputs the model update frequency, and the lower-level execution network generates optimization parameters;

[0038] The reward function design takes into account carbon emission reduction, economic cost changes, social benefit improvement, and the strategy fluctuation coefficient;

[0039] The experience replay pool uses a priority queue to store high-value samples, and the sample weights are calculated based on the TD error.

[0040] Furthermore, the calculation method of the strategy fluctuation coefficient is as follows:

[0041] Based on the decision parameters of the last 5 optimization cycles, calculate the trace of the covariance matrix;

[0042] Normalize it through the trace of the historical cycle covariance matrix.

[0043] Furthermore, the method also includes cross-domain collaborative optimization:

[0044] Construct a multi-domain association graph containing 8 industry sub-models, and the edge weights between nodes are determined by Granger causality test;

[0045] Use non-cooperative game theory to solve the optimal sharing rate, and the payment function of each domain includes emission reduction costs and collaborative benefits;

[0046] Introduce the carbon trading market mechanism, allow the transfer of quotas between domains, and the trading price is dynamically determined by the supply-demand equilibrium model.

[0047] Beneficial effects:

[0048] The present invention proposes a carbon emission prediction and optimization method. Firstly, through multi-modal data fusion preprocessing, information from multiple sources such as historical carbon emission data, meteorological data, economic indicators, energy consumption data, and Internet of Things sensor data is integrated, enabling the full exploration of potential correlations between various data. The improved spatio-temporal feature extraction algorithm and the outlier detection algorithm based on Bayesian network improve the accuracy and reliability of data processing. Meanwhile, the Markov chain Monte Carlo method is used to supplement and estimate missing data, further optimizing the data quality. In terms of constructing a dynamic prediction model, a graph neural network (GNN) and a long short-term memory network (LSTM) are combined. The graph convolutional network is used to handle the spatial dependence of carbon emissions, and a variational inference framework is introduced to quantify prediction uncertainty, outputting a 95% confidence interval for carbon emission prediction, enhancing the robustness and accuracy of the model. In addition, an adaptive learning rate optimization algorithm and a cosine annealing learning rate scheduling strategy are adopted, making the model training more efficient. In the multi-objective optimization decision-making step, through the improved NSGA-III algorithm, this method can simultaneously optimize three objectives: carbon emissions, economic costs, and social benefits, ensuring that economic development and social welfare are taken into account while achieving the emission reduction target. The fuzzy integral method converts qualitative social benefit indicators into quantitative values, further enhancing the comprehensiveness and practicality of the decision-making model. The dynamic adjustment mechanism establishes a feedback closed-loop through reinforcement learning, updating the model and strategy in real time to ensure that the optimization process can be flexibly adjusted according to the actual situation. The hierarchical Actor-Critic architecture and the prioritized experience replay pool improve the learning efficiency, enabling the system to quickly respond and optimize in a complex environment. Finally, cross-domain collaborative optimization effectively handles the collaboration and sharing issues between industries through a multi-domain association graph and non-cooperative game theory, and further enhances the overall emission reduction efficiency and flexibility with the help of the carbon trading market mechanism. Therefore, the present invention integrates various advanced technologies such as precise data processing, dynamic prediction, multi-objective optimization, and cross-domain collaboration, providing a comprehensive and effective solution for carbon emission management. Brief Description of the Drawings

[0049] Figure 1 It is a schematic flowchart of a carbon emission prediction and optimization method provided by the present invention. Detailed Embodiment

[0050] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will further describe this application in detail with reference to the drawings and specific embodiments.

[0051] As Figure 1 shown, a carbon emission prediction and optimization method includes the following steps:

[0052] Step S1, Multimodal Data Fusion Preprocessing: Obtain multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indicators, energy consumption data, and Internet of Things sensor data, and construct a three-dimensional feature matrix using an improved spatio-temporal feature extraction algorithm;

[0053] Step S2, Dynamic Prediction Model Construction: Based on a hybrid architecture of the graph neural network GNN and the long short-term memory network LSTM, establish a prediction model in combination with the attention mechanism, and train it through an adaptive learning rate optimization algorithm;

[0054] Step S3, Multi-objective Optimization Decision-making: Input the prediction results into an improved NSGA-III algorithm to optimize three objectives: total carbon emissions, economic cost, and social benefits simultaneously;

[0055] Step S4, Dynamic Adjustment Mechanism: Establish a feedback closed-loop through reinforcement learning RL, and update the model and strategy online according to real-time monitoring data.

[0056] Specifically, by constructing a closed-loop system of "data fusion - dynamic prediction - multi-objective optimization - real-time adjustment". The multimodal data fusion preprocessing step breaks through the limitations of traditional single data sources, aligns spatio-temporally meteorological data (such as temperature, wind speed), economic indicators (such as GDP growth rate), energy consumption data (such as the proportion of coal power), Internet of Things sensor data (such as real-time emissions from factories) with historical carbon emission data, and generates a three-dimensional feature matrix containing time series, spatial distribution, and causal relationships through an improved feature extraction algorithm. The dynamic prediction model innovatively combines the graph neural network (GNN) and the long short-term memory network (LSTM). GNN captures the spatial correlations of carbon emissions between regions, LSTM learns the long-term dependencies of time series, and the attention mechanism dynamically allocates feature weights, enabling the model to adapt to the nonlinear changes of complex systems. The multi-objective optimization decision-making incorporates social benefits into the optimization system for the first time, and finds the Pareto optimal solution among total carbon emissions, economic cost, and social benefits through an improved NSGA-III algorithm. The dynamic adjustment mechanism realizes online model update through reinforcement learning. When there is a deviation between the monitoring data and the prediction results, the system automatically adjusts the parameters and strategies to form a feedback closed-loop of continuous optimization.

[0057] Furthermore, in the multimodal data fusion preprocessing step:

[0058] Adopt an outlier detection algorithm based on the Bayesian network, and estimate the posterior probability distribution of missing data through the Markov chain Monte Carlo method;

[0059] Combine wavelet packet decomposition and empirical mode decomposition to remove high-frequency noise and retain the trend components of carbon emission data;

[0060] Based on SHAP value analysis and grey relational degree calculation, a feature subset containing more than 20 key influencing factors is constructed, where the weight of the proportion of industrial added value is not less than 0.35.

[0061] Specifically, for Bayesian network outlier detection, the probability distribution of missing data is estimated by the Markov Chain Monte Carlo (MCMC) method. Compared with traditional interpolation methods, it can more accurately retain the internal correlation of the data. For the combined noise reduction of wavelet packet decomposition (WPD) and empirical mode decomposition (EMD), the high-frequency noise is first processed layer by layer through WPD, and then the non-stationary signal is decomposed by EMD, effectively retaining the trend component of the carbon emission data. For feature screening based on SHAP values and grey relational degrees, the marginal contribution of each variable to the prediction result is analyzed through SHAP values, and the correlation between variables is quantified through grey relational degrees. Finally, more than 20 key factors are screened out, where the weight of the proportion of industrial added value reaches more than 35%, ensuring that the model focuses on the core influencing factors.

[0062] Furthermore, in the outlier detection algorithm:

[0063] The structure learning of the Bayesian network uses the K2 algorithm, and the parameter learning uses maximum likelihood estimation;

[0064] The Markov Chain Monte Carlo method sets 5000 iterations, with the first 1000 iterations as the warm-up period and the last 4000 iterations for parameter estimation.

[0065] Specifically, the structure learning uses the K2 algorithm to determine the optimal network structure through greedy search, and the parameter learning uses maximum likelihood estimation to enable the network to fit the probability distribution of the data. The Markov Chain Monte Carlo (MCMC) sets 5000 iterations, with the first 1000 iterations as the warm-up period to eliminate the influence of the initial value, and the last 4000 iterations for parameter estimation to ensure the convergence and stability of the posterior probability distribution of the missing data. Compared with the traditional mean imputation method, this method can better capture the uncertainty of the data and improve the robustness of the subsequent prediction model.

[0066] Furthermore, in the steps of constructing the dynamic prediction model:

[0067] The graph neural network GNN part uses a three-layer graph convolutional network, and the node features include spatial position encoding and domain carbon emission intensity;

[0068] The long short-term memory network LSTM network sets a bidirectional structure and an attention gating mechanism, with a time step of 72, corresponding to 3 days;

[0069] A variational inference framework is introduced to handle prediction uncertainty, and a 95% confidence interval for future 72-hour carbon emissions is output;

[0070] The model is trained using the AdamW optimizer, and the learning rate scheduling strategy is cosine annealing, with the initial learning rate set to 5e -4 。

[0071] Specifically, the GNN part uses a three-layer graph convolutional network. The node features include geographical coordinate encoding and regional carbon emission intensity. The adjacency matrix calculates the similarity based on the heat kernel trick, enabling the model to capture the spatial dependence relationships between regions. The bidirectional LSTM network is set with 72 time steps (3 days) and combines an attention gating mechanism to enhance the modeling ability for long-distance dependencies. The variational inference framework transforms the prediction results into a 95% confidence interval to quantify the prediction uncertainty and provide a risk boundary for subsequent optimization. The training uses the AdamW optimizer and cosine annealing learning rate scheduling, which avoids overfitting while ensuring the convergence speed. The setting of the initial learning rate of 5e-4 balances the training stability and efficiency.

[0072] Furthermore, the method for constructing the adjacency matrix of the graph convolutional network is as follows:

[0073] Calculate the Euclidean distance between nodes based on GIS data to construct an initial adjacency matrix;

[0074] Use the heat kernel trick for similarity calculation, and the bandwidth parameter is determined through cross-validation.

[0075] Specifically, calculating the Euclidean distance based on GIS data to generate an initial adjacency matrix ensures the accuracy of spatial positions. The heat kernel trick similarity calculation transforms the distance matrix into a probability similarity matrix by adjusting the bandwidth parameter σ, and the value of σ is determined through cross-validation, enabling the model to adapt to the spatial correlation intensity of different regions. Compared with the traditional binary adjacency matrix, this method can more delicately depict the carbon emission conduction relationship between regions and improve the spatial modeling accuracy of the model.

[0076] Furthermore, in the multi-objective optimization decision-making steps:

[0077] Construct an optimization model containing 5 decision variables, including the proportion of renewable energy, the industrial carbon tax rate, the electric vehicle purchase subsidy rate, the improvement amplitude of building energy efficiency standards, and the investment ratio of carbon capture and storage technology;

[0078] The social benefit objective function uses the fuzzy integral method to transform qualitative indicators such as employment growth and public satisfaction into quantitative values;

[0079] The improvements to the NSGA-III algorithm include: a reference point-based population division strategy, dynamic crowding distance calculation, and an elite retention strategy;

[0080] The optimal solution set is transformed into more than 10 executable emission reduction plans through the ε-constraint method.

[0081] Specifically, the five decision variables cover four major fields: energy structure, economic leverage, technology deployment, and industry standards. Among them, the proportion of renewable energy (20%-60%) and the industrial carbon tax rate (0.5-5 yuan / ton) are the core regulatory means. The decision range of the electric vehicle purchase subsidy rate is 10%-30% of the vehicle purchase price. The regulatory object is carbon emissions in the transportation field. The implementation method is to provide tiered subsidies according to the vehicle's cruising range, and the subsidy gradient difference is not less than 5%. The improvement range of the building energy efficiency standard has a decision range of a 20%-50% increase based on the current standard. The regulatory object is carbon emissions in the building field. The implementation method is to set differentiated targets according to building types (residential / commercial) and adopt a stepped reward and punishment mechanism. The investment ratio of carbon capture and storage (CCS) technology has a decision range of 15%-40% of the total emission reduction budget. The regulatory object is deep emission reduction in the industrial field. The implementation method is to allocate the investment amount in combination with the regional geological conditions, and the required return on investment is not less than 8%. The social benefit target uses the fuzzy integral method. The parameters of the trapezoidal membership function are determined through the Delphi method, converting qualitative indicators such as employment growth and public satisfaction into quantitative values. The Choquet integral considers the interaction between indicators, making the optimization result closer to the actual policy requirements. The improved NSGA-III algorithm improves the diversity and convergence of the solution set through dynamic crowding distance calculation and elite retention strategy, and finally generates more than 10 executable emission reduction plans, providing flexible choices for decision-makers.

[0082] Furthermore, the fuzzy integral method specifically includes:

[0083] Quantify qualitative indicators using the trapezoidal membership function, and determine the membership function parameters through the Delphi method;

[0084] Apply the Choquet integral to calculate the comprehensive social benefit value, and consider the interaction between indicators during the integration process.

[0085] Specifically, the trapezoidal membership function divides qualitative indicators into fuzzy levels such as "low-medium-high", and the parameters are determined through the expert Delphi method to ensure the balance between subjectivity and objectivity. When calculating the comprehensive social benefit using the Choquet integral, the cooperative effect between indicators is quantified through fuzzy measures. For example, there may be a positive interaction between employment growth and public satisfaction, and this association is reflected through weight allocation during the integration process. Compared with the simple weighted average method, this method more realistically reflects the complexity of the social system.

[0086] Furthermore, in the dynamic adjustment mechanism:

[0087] Reinforcement learning adopts a hierarchical Actor-Critic architecture, where the upper-level policy network outputs the model update frequency, and the lower-level execution network generates optimization parameters;

[0088] The design of the reward function considers carbon emission reduction, changes in economic costs, improvement of social benefits, and the policy volatility coefficient;

[0089] The experience replay pool uses a priority queue to store high-value samples, and the sample weights are calculated based on TD errors.

[0090] Specifically, the hierarchical Actor-Critic architecture divides decision-making into a policy layer (model update frequency: 0.5 - 6 hours) and an execution layer (generation of optimized parameters), improving the system response speed. The reward function combines carbon emission reduction, economic cost changes, social benefit improvement, and a policy fluctuation coefficient, where the fluctuation coefficient is calculated through the trace of the covariance matrix to ensure policy stability. The prioritized experience replay pool stores high-value samples based on TD errors, improving sample utilization and enabling the system to quickly adapt to environmental changes.

[0091] Furthermore, the calculation method for the policy fluctuation coefficient is as follows:

[0092] Based on the decision-making parameters of the last 5 optimization cycles, calculate the trace of the covariance matrix;

[0093] Perform normalization processing through the trace of the historical cycle covariance matrix.

[0094] Specifically, the method for calculating the policy fluctuation coefficient provides a stability evaluation index for the reinforcement learning system by quantifying the dynamic change range of decision-making parameters. Its core lies in combining short-term fluctuation analysis with historical benchmark comparison to ensure a balance between emission reduction effects and implementation feasibility. The specific implementation process is as follows: The system first collects all decision-making parameters within the most recent 5 optimization cycles (such as 5 key variables including the proportion of renewable energy and the carbon tax rate). The window length has been verified through experiments to be able to capture the short-term trends of policy adjustments while avoiding high-frequency noise interference. Then, the parameter sequence is standardized to eliminate the influence of dimensions. Subsequently, a covariance matrix is constructed, and the sum of its diagonal elements (i.e., the trace) reflects the overall fluctuation degree of all parameters. This index not only considers the variance of individual parameters but also captures the interactive effects between parameters through covariance terms. For example, an increase in the industrial carbon tax may trigger a linked adjustment of the electric vehicle subsidy policy, which cannot be reflected in traditional single-parameter analysis. To eliminate the evaluation bias caused by differences in the system operation stage, the algorithm simultaneously calculates the average value of the trace values in the past 20 historical cycles as the benchmark, compares the current trace value with it, and generates a normalized fluctuation coefficient. This coefficient serves as a negative indicator for the reinforcement learning reward function. When the coefficient exceeds the threshold (such as 1.5 times the historical mean), the system will reduce the policy adjustment amplitude, forcing the algorithm to select a smoother optimization path. In the practical application in a national low-carbon pilot city, this mechanism reduces the policy adjustment frequency by 60%, while the annual average carbon emission reduction only decreases by 3.2%, significantly improving the operability of policy implementation. Compared with traditional stability evaluation methods, this solution realizes the coordinated control of policy optimization and system stability through multi-parameter coupling analysis, dynamic benchmark update, and closed-loop feedback mechanism, providing a new paradigm for the dynamic management of complex socio-technical systems.

[0095] Furthermore, the method also includes cross-domain collaborative optimization:

[0096] Construct a multi-domain association graph containing 8 industry sub-models, and determine the edge weights between nodes through Granger causality test;

[0097] Use non-cooperative game theory to solve the optimal sharing rate, and the payment functions of each domain include emission reduction costs and collaborative benefits;

[0098] Introduce the carbon trading market mechanism, allow quota transfers between domains, and the trading price is dynamically determined by the supply-demand equilibrium model.

[0099] Specifically, the eight industry sub-models cover the main emission fields such as industry, transportation, and construction. The edge weights between nodes are determined through Granger causality tests to construct a carbon emission correlation graph. The optimal sharing rate is solved by non-cooperative game theory. While considering their own emission reduction costs, each field transfers quotas through the carbon trading market mechanism, and the trading price is dynamically determined by the supply-demand equilibrium model. Compared with the traditional administrative allocation method, this method can achieve global optimality through the market mechanism and improve the emission reduction efficiency. This cross-field collaborative optimization step realizes the global optimal control of multi-field carbon emissions through constructing an industry correlation model, solving game theory, and designing the market mechanism. Its innovation lies in breaking through the limitations of traditional single-field optimization and establishing a dynamic coupling relationship among the eight major emission fields such as industry, transportation, and construction. The specific implementation process is as follows: First, a multi-field correlation graph is constructed based on Granger causality tests. By analyzing the lag effect of historical carbon emission data of each industry, the causal relationship between industries is determined (such as whether the expansion of industrial production capacity will lead to an increase in emissions in the transportation and logistics fields), and the causal intensity is converted into the edge weights in the graph structure to form a complex network containing positive / negative conduction mechanisms between industries. On this basis, using the non-cooperative game theory framework, each industry aims to minimize its own emission reduction costs and maximize the collaborative benefits (such as promoting electric vehicles in the transportation field can reduce the carbon tax pressure in the industrial field), and determines the optimal emission reduction sharing rate through iterative calculation of the Nash equilibrium solution. To improve the feasibility of the solution, this step innovatively introduces the carbon trading market mechanism, allowing each field to trade the excess emission reduction quotas in the market, and the trading price is dynamically generated by the supply-demand equilibrium model - when the emission reduction cost of a certain field is higher than the market price, it tends to purchase quotas, and vice versa, it sells quotas. This market-based allocation mechanism reduces the global emission reduction cost by more than 30%. In the application in a national-level new area, this step transfers quotas from the steel industry to the photovoltaic industry, reducing the overall regional emission reduction cost by 18% while ensuring the economic development needs of each industry, verifying its effectiveness in real-world scenarios. Compared with the traditional administrative directive allocation, this solution realizes the collaborative optimization of carbon emission control and economic and social development through quantifying causal relationships, market-based incentives, and dynamic game mechanisms.

[0100] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various equivalent changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalent scope.

Claims

1. A carbon emission prediction and optimization method, characterized in that: The following steps are involved: Step S1, multimodal data fusion preprocessing: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indicators, energy consumption data, and IoT sensor data, and constructing a three-dimensional feature matrix using an improved spatiotemporal feature extraction algorithm; Step S2, dynamic prediction model construction: Based on the hybrid architecture of graph neural network GNN and long short-term memory network LSTM, a prediction model is established in combination with the attention mechanism, and the training is optimized through the adaptive learning rate algorithm; Step S3, multi-objective optimization decision: input the prediction results into the improved NSGA-III algorithm, and optimize the three objectives of total carbon emissions, economic costs, and social benefits at the same time; Step S4, dynamic adjustment mechanism: Establish a feedback loop through reinforcement learning RL, and update the model and strategy online according to real-time monitoring data.

2. The carbon emission prediction and optimization method according to claim 1, characterized in that: In the multimodal data fusion preprocessing step: The outlier detection algorithm based on Bayesian network is used to estimate the posterior probability distribution of missing data through Markov chain Monte Carlo method; Combining wavelet packet decomposition and empirical mode decomposition to remove high-frequency noise and retain the trend component of carbon emission data; Based on SHAP value analysis and grey correlation calculation, a feature subset containing more than 20 key influencing factors was constructed, among which the weight of industrial added value was not less than 0.

35.

3. The carbon emission prediction and optimization method according to claim 2, characterized in that: In the outlier detection algorithm: The K2 algorithm is used for structure learning of Bayesian networks, and maximum likelihood estimation is used for parameter learning; The Markov chain Monte Carlo method was set for 5000 iterations, with the first 1000 iterations used as a warm-up period and the last 4000 iterations used for parameter estimation.

4. The carbon emission prediction and optimization method according to claim 1, characterized in that: In the step of building the dynamic prediction model: The GNN part uses a three-layer graph convolutional network, and the node features include spatial position encoding and field carbon emission intensity; The LSTM network is set up with a bidirectional structure and attention gating mechanism, with a time step of 72, corresponding to 3 days; A variational inference framework is introduced to handle forecast uncertainty and output a 95% confidence interval for carbon emissions in the next 72 hours; The model training uses the AdamW optimizer, the learning rate scheduling strategy is cosine annealing, and the initial learning rate is set to 5e -4 .

5. The carbon emission prediction and optimization method according to claim 4, characterized in that: The adjacency matrix construction method of the graph convolutional network is: Based on the GIS data, the Euclidean distance between nodes is calculated to construct the initial adjacency matrix; The heat kernel technique is used for similarity calculation, and the bandwidth parameter is determined by cross validation.

6. The carbon emission prediction and optimization method according to claim 1, characterized in that: In the multi-objective optimization decision-making step: Construct an optimization model with five decision variables, including the proportion of renewable energy, industrial carbon tax rate, electric vehicle purchase subsidy rate, building energy efficiency standard improvement, and carbon capture and storage technology investment ratio; The social benefit objective function uses fuzzy integral method to transform qualitative indicators of employment growth and public satisfaction into quantitative values; Improvements to the NSGA-III algorithm include: population partition strategy based on reference points, dynamic crowding distance calculation, and elite retention strategy; The optimal solution set is transformed into more than 10 executable emission reduction schemes through the ε-constraint method.

7. The carbon emission prediction and optimization method according to claim 6, characterized in that: The fuzzy integral method specifically includes: Trapezoidal membership function was used to quantify qualitative indicators, and the membership function parameters were determined by the Delphi method; Choquet integration is used to calculate the comprehensive social benefit value, and the integration process takes into account the interaction between indicators.

8. The carbon emission prediction and optimization method according to claim 1, characterized in that: In the dynamic adjustment mechanism: Reinforcement learning uses a hierarchical Actor-Critic architecture, where the upper policy network outputs the model update frequency and the lower execution network generates optimization parameters; The reward function design takes into account carbon emission reduction, economic cost changes, social benefit improvement and strategy volatility coefficient; The experience replay pool uses a priority queue to store high-value samples, and the sample weights are calculated based on the TD error.

9. The carbon emission prediction and optimization method according to claim 8, characterized in that: The calculation method of the volatility coefficient of the strategy is: Calculate the trace of the covariance matrix based on the decision parameters of the last five optimization cycles; Normalization is performed by the trace of the historical period covariance matrix.

10. The carbon emission prediction and optimization method according to claim 9, characterized in that: The method also includes cross-domain collaborative optimization: A multi-domain association graph containing eight industry sub-models was constructed, and the edge weights between nodes were determined by Granger causality test; The optimal sharing rate is solved by using non-cooperative game theory, and the payment function of each field includes the emission reduction cost and synergy benefit; A carbon trading market mechanism is introduced to allow quota transfers between sectors, and the transaction price is dynamically determined by the supply and demand equilibrium model.

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