Greenhouse gas emission checking data processing and predicting method based on deep learning
By combining deep learning and satellite monitoring data, a neural network model was constructed, which solved the problem of accuracy in greenhouse gas emission prediction and achieved high-precision greenhouse gas emission prediction and emission reduction management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-04-07
AI Technical Summary
Existing technologies are insufficient to accurately predict greenhouse gas emissions, and the lack of effective data processing and forecasting methods hinders the implementation of emission reduction initiatives.
By employing deep learning methods, a point-to-point neural network model is constructed by acquiring and processing multi-dimensional data. Combined with satellite monitoring data, data preprocessing and model training are performed, and an uncertainty quantification mechanism is introduced to establish an early warning model to improve prediction accuracy.
It has achieved accurate prediction of greenhouse gas emissions, and improved prediction accuracy and the effectiveness of emission reduction management through multi-dimensional data monitoring and model feedback adjustment.
Smart Images
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Abstract
Description
Technical Field
[0001] This invention relates to the field of greenhouse gas emission monitoring technology, specifically a method for processing and predicting greenhouse gas emission verification data based on deep learning. Background Technology
[0002] Greenhouse gases are gases in the atmosphere that absorb long-wave radiation reflected from the Earth's surface and then re-emit it, such as water vapor, carbon dioxide, and most refrigerants. Their effect is to warm the Earth's surface, similar to how a greenhouse traps solar radiation and heats the air inside. This warming effect of greenhouse gases is known as the "greenhouse effect."
[0003] With the increasing number of climate disasters caused by global warming, reducing greenhouse gas emissions has become a common challenge facing the world. Accurately predicting greenhouse gas emissions is a crucial step before taking emission reduction actions. Therefore, a technical method for processing greenhouse gas emission prediction verification data is needed. Deep learning can learn data characteristics through multi-layered neural networks and perform tasks such as classification and prediction. In greenhouse gas emission prediction, deep learning can predict future greenhouse gas emissions by analyzing and learning from historical data, helping people to better manage and reduce greenhouse gas emissions. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a deep learning-based method for processing and predicting greenhouse gas emission verification data, which solves the problems mentioned in the background section.
[0005] To achieve the above objectives, the present invention provides a method for processing and predicting greenhouse gas emission verification data based on deep learning, comprising the following steps: S1. Obtain and input data The area to be monitored is determined using latitude and longitude coordinates, and residential areas, administrative areas, and industrial areas are identified by combining the industry attribute information of the area. Then, economic activity index data, energy consumption data, and meteorological environment data are collected in sequence. S2, Data Preprocessing The raw data obtained in step 1 cannot be used as a reference. It is also necessary to process the missing values of the raw data, perform associative linear interpolation on the missing data, correct abnormal data, and combine the reconstruction error of the AI artificial intelligence Autoencoder to correct or remove data confirmed to be entered incorrectly. Through long-term training of AI artificial intelligence, automated operation can be achieved, and the spatiotemporal data is marked and registered. The training set, validation set and test set are strictly divided according to the time order, and then the data is standardized. S3, AI model training A point-to-point neural network model is constructed. This neural network model includes resource integration, which connects and integrates the data processed in step 2 according to time, space and feature data, and outputs carbon emission prediction values for multiple time steps. Then, the historical data obtained in step 2 is used to train the neural network model. The mean squared error (MSE) and mean absolute error (MAE) are defined for the training set, validation set and test set divided in step 2. The neural network model is continuously trained and cross-validated to prevent overfitting. S4, Greenhouse Gas Emissions Forecast The input feature data of the period to be predicted is input into the trained deep learning model, and the corresponding greenhouse gas emission prediction results are output. S5. Result Output The carbon emission predictions generated by the trained deep learning model are output in a structured, interactive, and integrable manner.
[0006] Furthermore, S1 also incorporates satellite observations, where satellites are used to monitor NO2, CO2, SO2, and PM2.5 emission data, as well as meteorological data and space observations. Satellite observations are used to obtain multi-source, multi-dimensional, and multi-granular historical and real-time data as model inputs.
[0007] Furthermore, the mean square error (MSE) of the Autoencoder reconstruction error in S2 is: ; Mean Absolute Error (MAE): ; This error is the objective function that the Autoencoder aims to minimize during training. The training goal of the Autoencoder is to minimize the reconstruction error so as to learn an effective compressed representation of the data. In anomaly detection tasks, normal samples have small reconstruction errors, while anomalous samples have large reconstruction errors.
[0008] Furthermore, an uncertainty quantification mechanism is introduced in S3 to evaluate the prediction confidence interval.
[0009] Furthermore, in step S4, the greenhouse gas emission prediction results are corrected and adjusted and fed back into the deep learning model, and the model is continuously adjusted to ensure accuracy.
[0010] Furthermore, the output results in S5 vary depending on the usage scenario, including charts and data files. The predicted data is fed back to the deep learning model, enabling it to automatically predict and learn online. An early warning model is also established in the deep learning model. When the predicted data deviates too much from the actual data in multiple consecutive instances, the staff is reminded to manually intervene and adjust the model in real time. The prediction is applied to feedback adjustment to improve the model's prediction accuracy.
[0011] This invention provides a deep learning-based method for processing and predicting greenhouse gas emission verification data, which has the following beneficial effects: This deep learning-based method for processing and predicting greenhouse gas emission verification data improves performance by establishing a point-to-point model combined with monitoring multi-dimensional data. Simultaneously, it defines mean squared error (MSE) and mean absolute error (MAE) in well-defined training, validation, and test sets to continuously train the neural network model and perform cross-validation to prevent overfitting. Furthermore, it establishes an early warning model within the deep learning model. When the predicted data deviates significantly from the actual data in multiple consecutive iterations, it alerts staff to manually intervene and adjust the model in real time. This feedback adjustment method applies to the prediction process, thereby improving the model's prediction accuracy. Detailed Implementation
[0012] A deep learning-based method for processing and predicting greenhouse gas emission verification data includes the following steps: S1. Obtain and input data The area to be monitored is determined using latitude and longitude coordinates, and residential areas, administrative areas, and industrial areas are identified by combining the industry attribute information of the area. Then, economic activity index data, energy consumption data, and meteorological environment data are collected in sequence. S2, Data Preprocessing The raw data obtained in step 1 cannot be used as a reference. It is also necessary to process the missing values of the raw data, perform associative linear interpolation on the missing data, correct abnormal data, and combine the reconstruction error of the AI artificial intelligence Autoencoder to correct or remove data confirmed to be entered incorrectly. Through long-term training of AI artificial intelligence, automated operation can be achieved, and the spatiotemporal data is marked and registered. The training set, validation set and test set are strictly divided according to the time order, and then the data is standardized. S3, AI model training A point-to-point neural network model is constructed. This neural network model includes resource integration, which connects and integrates the data processed in step 2 according to time, space and feature data, and outputs carbon emission prediction values for multiple time steps. Then, the historical data obtained in step 2 is used to train the neural network model. The mean squared error (MSE) and mean absolute error (MAE) are defined for the training set, validation set and test set divided in step 2. The neural network model is continuously trained and cross-validated to prevent overfitting. S4, Greenhouse Gas Emissions Forecast The input feature data of the period to be predicted is input into the trained deep learning model, and the corresponding greenhouse gas emission prediction results are output. S5. Result Output The carbon emission predictions generated by the trained deep learning model are output in a structured, interactive, and integrable manner.
[0013] Furthermore, S1 also incorporates satellite observations, where satellites are used to monitor NO2, CO2, SO2, and PM2.5 emission data, as well as meteorological data and space observations. Satellite observations are used to obtain multi-source, multi-dimensional, and multi-granular historical and real-time data as model inputs.
[0014] Furthermore, the mean square error (MSE) of the Autoencoder reconstruction error in S2 is: ; Mean Absolute Error (MAE): ; This error is the objective function that the Autoencoder aims to minimize during training. The training goal of the Autoencoder is to minimize the reconstruction error so as to learn an effective compressed representation of the data. In anomaly detection tasks, normal samples have small reconstruction errors, while anomalous samples have large reconstruction errors.
[0015] Furthermore, an uncertainty quantification mechanism is introduced in S3 to evaluate the prediction confidence interval.
[0016] Furthermore, in step S4, the greenhouse gas emission prediction results are corrected and adjusted and fed back into the deep learning model, and the model is continuously adjusted to ensure accuracy.
[0017] Furthermore, the output results in S5 vary depending on the usage scenario, including charts and data files. The predicted data is fed back to the deep learning model, enabling it to automatically predict and learn online. An early warning model is also established in the deep learning model. When the predicted data deviates too much from the actual data in multiple consecutive instances, the staff is reminded to manually intervene and adjust the model in real time. The prediction is applied to feedback adjustment to improve the model's prediction accuracy.
[0018] In summary, the deep learning-based method for processing and predicting greenhouse gas emissions verification data includes the following specific steps: S1. Data Acquisition and Input: The area to be detected is determined using latitude and longitude coordinates, and residential areas, administrative areas, and industrial areas are determined by combining the industry attribute information of the area. Then, economic activity index data, energy consumption data, and meteorological environment data are collected in sequence, which uses satellite observation. The satellite is used to monitor NO2, CO2, SO2, and PM2.5 emission data, as well as meteorological data and space observation. Multi-source, multi-dimensional, and multi-granular historical and real-time data are obtained using satellite observation as model input. S2, Data Preprocessing The raw data obtained in step 1 cannot be used as a reference. Missing values in the raw data need to be processed. Associative linear interpolation is performed on the missing data, and abnormal data is corrected. Combined with the reconstruction error of the AI-powered Autoencoder, data confirmed to have input errors is corrected or removed. Through long-term training of the AI, automated operations can be achieved. The data is spatially and temporally labeled and registered, strictly divided into training, validation, and test sets according to time sequence. Subsequently, data standardization is performed, including the mean squared error (MSE) of the Autoencoder reconstruction error. ; Mean Absolute Error (MAE): ; This error is the objective function that the Autoencoder aims to minimize during training. The training objective of the Autoencoder is to minimize the reconstruction error so as to learn an effective compressed representation of the data. In anomaly detection tasks, normal samples have small reconstruction errors, while anomalous samples have large reconstruction errors. S3. AI Model Training: Construct a point-to-point neural network model. This neural network model includes resource integration, which connects and integrates the data processed in step 2 according to time, space and feature data, and outputs carbon emission prediction values for multiple time steps. Then, the historical data obtained in step 2 is used to train the neural network model. The mean squared error (MSE) and mean absolute error (MAE) are defined for the training set, validation set and test set divided in step 2. The neural network model is continuously trained and cross-validated to prevent overfitting. An uncertainty quantification mechanism is introduced to evaluate the prediction confidence interval. S4. Greenhouse Gas Emission Prediction: Input feature data for the period to be predicted is fed into a trained deep learning model, which outputs the corresponding greenhouse gas emission prediction results. The greenhouse gas emission prediction results are corrected and adjusted, and then fed back into the deep learning model, which is continuously adjusted to ensure accuracy. S5. Result Output The carbon emission prediction results generated by the trained deep learning model are output in a structured, interactive, and integrable manner. The output results vary depending on the use scenario and may include charts, data files, etc. The predicted data is fed back into the deep learning model, enabling it to predict automatically and learn online. An early warning model is also built into the deep learning model. When the predicted data deviates too much from the actual data in multiple consecutive instances, it alerts staff to manually intervene and adjust the model in real time. This feedback adjustment is applied to the prediction to improve the model's prediction accuracy.
Claims
1. A method for processing and predicting greenhouse gas emission verification data based on deep learning, characterized in that, Includes the following steps: S1. Obtain and input data The area to be monitored is determined using latitude and longitude coordinates, and residential areas, administrative areas, and industrial areas are identified by combining the industry attribute information of the area. Then, economic activity index data, energy consumption data, and meteorological environment data are collected in sequence. S2, Data Preprocessing The raw data obtained in step 1 cannot be used as a reference. It is also necessary to process the missing values of the raw data, perform associative linear interpolation on the missing data, correct abnormal data, and combine the reconstruction error of the AI artificial intelligence Autoencoder to correct or remove data confirmed to be entered incorrectly. Through long-term training of AI artificial intelligence, automated operation can be achieved, and the spatiotemporal data is marked and registered. The training set, validation set and test set are strictly divided according to the time order, and then the data is standardized. S3, AI model training A point-to-point neural network model is constructed. This neural network model includes resource integration, which connects and integrates the data processed in step 2 according to time, space and feature data, and outputs carbon emission prediction values for multiple time steps. Then, the historical data obtained in step 2 is used to train the neural network model. The mean squared error (MSE) and mean absolute error (MAE) are defined for the training set, validation set and test set divided in step 2. The neural network model is continuously trained and cross-validated to prevent overfitting. S4, Greenhouse Gas Emissions Forecast The input feature data of the period to be predicted is input into the trained deep learning model, and the corresponding greenhouse gas emission prediction results are output. S5. Result Output The carbon emission predictions generated by the trained deep learning model are output in a structured, interactive, and integrable manner.
2. The method for processing and predicting greenhouse gas emission verification data based on deep learning according to claim 1, characterized in that, S1 also incorporates satellite observations, where satellites are used to monitor NO2, CO2, SO2, and PM2.5 emission data, as well as meteorological data and space observations. Satellite observations are used to obtain multi-source, multi-dimensional, and multi-granular historical and real-time data as model inputs.
3. The method for processing and predicting greenhouse gas emission verification data based on deep learning according to claim 1, characterized in that: The mean square error (MSE) of the Autoencoder reconstruction error in S2 is as follows: ; Mean Absolute Error (MAE): ； This error is the objective function that the Autoencoder aims to minimize during training. The training goal of the Autoencoder is to minimize the reconstruction error so as to learn an effective compressed representation of the data. In anomaly detection tasks, normal samples have small reconstruction errors, while anomalous samples have large reconstruction errors.
4. The method for processing and predicting greenhouse gas emission verification data based on deep learning according to claim 1, characterized in that: The uncertainty quantification mechanism is introduced in S3 to evaluate the prediction confidence interval.
5. The method for processing and predicting greenhouse gas emission verification data based on deep learning according to claim 1, characterized in that: In step S4, the predicted greenhouse gas emissions are corrected and adjusted and fed back into the deep learning model, and the model is continuously adjusted to ensure accuracy.
6. The method for processing and predicting greenhouse gas emission verification data based on deep learning according to claim 1, characterized in that: The output of S5 varies depending on the usage scenario, including charts and data files. The predicted data is fed back to the deep learning model, enabling it to predict automatically and learn online. An early warning model is also established in the deep learning model. When the predicted data deviates too much from the actual data in multiple consecutive instances, the model is alerted to prompt manual intervention to adjust the model in real time. The prediction is applied to feedback adjustment to improve the model's prediction accuracy.