Carbon emission monitoring precision optimization method and system based on AI

Abstract

The invention discloses an AI-based carbon emission monitoring precision optimization method and system. The system comprises a data acquisition and preprocessing module, an AI model construction and optimization module, a real-time monitoring and dynamic adjustment module and a system integration and visualization module. According to the invention, through the data acquisition and preprocessing module, the AI model construction and optimization module, the real-time monitoring and dynamic adjustment module and the system integration and visualization module, the purposes of improving the monitoring precision, enhancing the model adaptability, realizing real-time monitoring and dynamic adjustment and improving the decision support capability are achieved.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring technology, and in particular to an AI-based carbon emission monitoring accuracy optimization method and system. Background Art

[0002] As global attention to climate change continues to increase, accurate monitoring of carbon emissions is crucial for formulating effective emission reduction policies and addressing climate change. Traditional carbon emission monitoring methods have many limitations, making it difficult for monitoring accuracy to meet actual needs. On the one hand, the data collection methods relied on by traditional monitoring methods are relatively simple, mainly relying on a limited number of sensor nodes or manual data collection. They are unable to obtain comprehensive and real-time information related to carbon emissions, resulting in poor data integrity and timeliness. On the other hand, in the data processing and analysis stages, traditional methods often use simple statistical models or empirical formulas, which are difficult to effectively handle complex and changeable carbon emission data, and cannot accurately capture the nonlinear and dynamic changes in the carbon emission process, which in turn affects the accuracy of the monitoring results.

[0003] In recent years, although some monitoring systems have introduced some intelligent technologies, there are still deficiencies in the adaptability and accuracy of the algorithms. For example, some monitoring methods based on machine learning have weak model generalization capabilities when faced with different environmental conditions and diverse carbon emission sources, and are prone to overfitting or underfitting, resulting in unstable monitoring accuracy. In addition, the existing monitoring system lacks an effective processing mechanism when dealing with problems such as missing data and noise interference, which further reduces monitoring accuracy. Therefore, there is an urgent need for an innovative AI-based carbon emission monitoring accuracy optimization method and system to overcome the shortcomings of existing technologies and improve the accuracy and reliability of carbon emission monitoring. Summary of the Invention

[0004] The purpose of this invention is to provide an AI-based carbon emission monitoring accuracy optimization method and system, aiming to solve the problems of low monitoring accuracy, insufficient data processing capabilities, poor model adaptability, etc. in existing carbon emission monitoring technologies, and to achieve high-precision, real-time and reliable monitoring of carbon emissions by utilizing advanced artificial intelligence technology.

[0005] The technical solution adopted by the present invention to solve its technical problems is: an AI-based carbon emission monitoring accuracy optimization method and system includes a data acquisition and preprocessing module, which adopts multi-source data acquisition technology to integrate satellite remote sensing data, ground sensor network data (including carbon dioxide concentration sensors, temperature sensors, humidity sensors, wind speed and direction sensors, etc.), industrial production process data (such as energy consumption, product output, etc.) and traffic flow data, etc., to construct a comprehensive carbon emission monitoring data set.

[0006] We designed an adaptive data preprocessing algorithm for the collected data. This algorithm automatically identifies outliers and missing values and uses deep learning-based interpolation methods to repair the data. Furthermore, we standardized the data to eliminate the impact of different data sources and improve the accuracy of subsequent data analysis.

[0007] As the preferred technical solution, the AI model building and optimization module constructs a carbon emissions prediction model based on a deep neural network. This model includes multiple hidden layers and can automatically learn complex features and patterns in carbon emissions data. It uses recurrent neural network structures such as long short-term memory (LSTM) and gated recurrent units (GRU) to effectively process long-term dependencies in time series data and accurately predict changing trends in carbon emissions.

[0008] The constructed model is optimized using a reinforcement learning algorithm. By setting a reasonable reward function, the model automatically adjusts its parameters during continuous interaction with the environment, improving its prediction accuracy and generalization capabilities. Specifically, the reinforcement learning algorithm rewards or penalizes the model based on the error between the model's predictions and actual carbon emissions data, guiding the model to update its parameters towards a more optimal direction.

[0009] By introducing transfer learning technology, the parameters of carbon emission monitoring models trained in other similar regions or fields are used as initialization parameters and applied to the monitoring model in the target area. This can speed up model training, reduce the demand for training data, and improve the model's adaptability and monitoring accuracy in the target area.

[0010] As a preferred technical solution, the real-time monitoring and dynamic adjustment module, based on an optimized AI model, enables real-time monitoring of carbon emissions. The system collects the latest monitoring data in real time, inputs it into the AI model for calculation, and quickly and accurately outputs the current carbon emissions monitoring results.

[0011] A dynamic adjustment mechanism is designed to compare and analyze real-time monitoring results with preset carbon emission thresholds. When monitoring results exceed the threshold, the system automatically triggers an early warning mechanism and dynamically adjusts the monitoring plan according to the preset strategy. For example, this involves increasing the sensor sampling frequency, activating backup monitoring equipment, and optimizing AI model parameters to ensure high monitoring accuracy in complex and changing environments.

[0012] As a preferred technical solution, the system integration and visualization module integrates the data acquisition and preprocessing module, the AI model building and optimization module, and the real-time monitoring and dynamic adjustment module to form a complete AI-based carbon emissions monitoring precision optimization system. Data exchange between these modules is achieved through efficient data interfaces, ensuring stable operation and collaborative work.

[0013] Develop a visualization interface to present real-time monitoring results, historical carbon emission data, model prediction results, and early warning information to users in an intuitive and easy-to-understand graphical format. Users can easily query and analyze monitoring data through the visualization interface to understand carbon emissions. Compared with existing technologies, the advantages of this invention are:

[0014] 1. Improve monitoring accuracy: Through multi-source data collection and advanced AI models, the characteristics and changing patterns of carbon emissions can be captured more comprehensively and accurately, effectively improving monitoring accuracy and reducing errors.

[0015] 2. Enhance model adaptability: Utilize reinforcement learning and transfer learning techniques to enable the model to quickly adapt to changes in different environments and carbon emission sources, thereby improving the model's generalization ability and stability.

[0016] 3. Real-time monitoring and dynamic adjustment: The real-time monitoring function can promptly reflect the latest situation of carbon emissions, and the dynamic adjustment mechanism can automatically optimize the monitoring plan according to the actual situation to ensure that good monitoring results can be maintained in various complex situations.

[0017] 4. Improve decision-making support capabilities: The visual interface provides users with intuitive and convenient data display and analysis tools, helping users better understand carbon emissions data and providing strong support for formulating scientific and reasonable emission reduction policies and decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is an architectural diagram of the AI-based carbon emission monitoring accuracy optimization system of the present invention;

[0019] Figure 2 This is a schematic diagram of the AI model structure;

[0020] Figure 3 It is a data processing flow chart; DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0022] Example

[0023] refer to Figure 1-Figure 3 The data collection and preprocessing shown in the figure below involves multi-source data collection. Within the target monitoring area, a ground sensor network is deployed based on geographic characteristics and emission source distribution. For example, in key carbon emission areas such as industrial clusters, transportation hubs, and urban centers, carbon dioxide concentration sensors, temperature sensors, and humidity sensors are deployed densely to ensure accurate acquisition of local environmental parameters. Simultaneously, through collaboration with meteorological authorities, satellite remote sensing data for the area is obtained, including information such as vegetation cover and land use type, as well as meteorological data such as wind speed, wind direction, and temperature. Furthermore, a data integration mechanism is established with industrial enterprises and transportation management departments to collect real-time industrial production process data and traffic flow data. For example, in one industrial city, 50 carbon dioxide concentration sensors, 30 temperature sensors, and 20 humidity sensors were deployed in its main industrial parks, forming a high-density sensor network. Simultaneously, high-resolution remote sensing imagery data of the city is obtained daily from a satellite remote sensing data platform, real-time meteorological data is obtained from the meteorological authorities, and relevant production and traffic data are extracted from the databases of industrial enterprises and transportation management departments. For data preprocessing, a sliding window algorithm is first used to identify outliers in the collected ground sensor data. For example, a sliding window of length 10 is set. When the standard deviation of the data in the window exceeds the preset threshold, the central data point in the window is judged to be an outlier. For outliers, an interpolation method based on a deep autoencoder is used to repair them. The time series data containing outliers is input into the trained deep autoencoder. The autoencoder outputs the repair results of the outliers by learning the characteristic patterns of normal data. For satellite remote sensing data and meteorological data, format conversion and spatial registration processing are performed to make them consistent with ground sensor data in time and space. Then, all data are standardized using the Z-score standardization method. The formula is: Where X is the original data, μ is the mean of the data, σ is the standard deviation of the data, X normThis is standardized data. Taking carbon dioxide concentration sensor data collected on a particular day as an example, a sliding window algorithm detected five outliers. After repairing them using a deep autoencoder, the data's consistency and accuracy were significantly improved. After standardization, all different types of data were unified into the same dimensional range, providing a sound data foundation for subsequent AI model training. AI model construction and optimization: Model construction constructs an LSTM-based carbon emissions prediction model. The model's input layer receives preprocessed multi-source data, including time series data on carbon dioxide concentration, temperature, humidity, industrial production data, and traffic flow. The model consists of three LSTM hidden layers, each with 128, 64, and 32 neurons, respectively. LSTM units learn long-term dependencies in the time series data. Following the LSTM hidden layer, a fully connected layer with 16 neurons further integrates and transforms the features output by the LSTM. Finally, the output layer outputs the predicted carbon emissions value. Taking a city's carbon emissions monitoring as an example, hourly multi-source monitoring data from the past year is used as training data and fed into the constructed LSTM model for training. During model training, the weights and bias parameters between each layer are continuously adjusted to minimize the mean squared error (MSE) between predicted and actual carbon emissions. Model optimization utilizes a reinforcement learning algorithm to optimize the LSTM model. The reinforcement learning environment is defined as a carbon emissions monitoring system, where the state is the current multi-source monitoring data and the model's prediction results, and the action is an adjustment to the model parameters (such as the step size of weights and biases). The reward function is designed to be correlated with the prediction error: a positive reward is given when the model's prediction error is below a certain threshold, and a negative reward otherwise. The reinforcement learning algorithm uses the Proximal Policy Optimization (PPO) algorithm. Through continuous iterative training, the model gradually finds the optimal parameter adjustment strategy through interaction with the environment, thereby improving prediction accuracy. Transfer learning techniques are also introduced. First, a large amount of carbon emissions monitoring data is collected from other cities with similar climate conditions and industrial structures, and an initial LSTM model is trained. The parameters of this model are then used as the initialization parameters for the monitoring model in the target city. During fine-tuning training on data from a target city, only the parameters of the last few layers of the model are updated, while the parameters of the earlier layers remain relatively fixed. This accelerates the model's convergence in the target city and improves its adaptability. In an experiment predicting carbon emissions for the target city for the next week, the model, optimized through reinforcement learning and transfer learning, achieved a 30% reduction in mean squared error compared to the unoptimized model, significantly improving prediction accuracy. Real-time monitoring and dynamic adjustment: The real-time monitoring system collects the latest multi-source monitoring data in real time, processes it according to the data preprocessing process, and then inputs it into the optimized AI model. Upon receiving the data, the model rapidly performs calculations and outputs the current carbon emissions monitoring results.Monitoring results include real-time carbon dioxide emission concentrations, cumulative emissions, and emission changes compared to the same period in history. Taking the carbon emissions monitoring of a large industrial enterprise as an example, the system collects relevant data from each emission source within the enterprise every 15 minutes. After data processing and model calculation, it can output the enterprise's current carbon emissions monitoring results within 1 minute, providing timely data support for the enterprise's environmental management. Dynamic adjustment is implemented by pre-setting different carbon emission thresholds. When monitoring results exceed the threshold, the system automatically triggers an early warning mechanism. For example, when carbon dioxide emission concentration exceeds the first-level threshold, the system sends an alert via SMS and email to the enterprise's environmental protection manager and relevant regulatory authorities. Simultaneously, based on the preset dynamic adjustment strategy, the system increases the sensor sampling frequency from every 15 minutes to every 5 minutes to obtain more intensive data. In addition, the system activates backup high-precision monitoring equipment for more accurate monitoring of key emission areas. While adjusting the monitoring plan, the system uses newly collected data to fine-tune the AI model online. This online fine-tuning allows the model to adapt more quickly to changes in emissions, further improving monitoring accuracy. For example, during a certain period, due to adjustments to the company's production process, carbon emissions experienced unusual fluctuations. The system, through a dynamic adjustment mechanism, promptly increased monitoring frequency and activated backup equipment. Simultaneously, the AI model was fine-tuned online, enabling the model to accurately track changes in carbon emissions. Monitoring errors were reduced by 20% after these adjustments. System integration and visualization integrate the data acquisition and preprocessing module, the AI model building and optimization module, and the real-time monitoring and dynamic adjustment module. These modules interact through standardized data interfaces, ensuring efficient data transmission and sharing. For example, the data acquisition and preprocessing module sends processed data to the AI model building and optimization module via a RESTful API for model training and real-time monitoring calculations. Simultaneously, the real-time monitoring and dynamic adjustment module, based on monitoring results, sends instructions to other modules through a message queue mechanism, enabling system collaboration and dynamic adjustments. Regarding system deployment, a distributed architecture is employed, with data acquisition nodes distributed across different locations to ensure comprehensive and reliable data collection. AI model computation tasks are distributed to a high-performance server cluster, leveraging the cluster's computing resources to improve the model's computational efficiency and real-time responsiveness. Visualization: Develop a web-based visualization interface that users can access through a browser. This interface displays real-time monitoring results, historical carbon emissions data, model predictions, and early warning information in a variety of formats, including line charts, bar charts, and maps. For example, a line chart can show the changing trend of carbon dioxide emission concentrations over the past month, a bar chart can compare total carbon emissions in different regions, and a map can intuitively display the location of each monitoring point and its real-time emissions status.Users can also perform data query and analysis operations on the visual interface, such as selecting data from a specific time period or specific area for detailed viewing, or performing correlation analysis on different types of data. In addition, the interface also provides a data export function, allowing users to download the required data in Excel or CSV format for further analysis and report writing. Taking the carbon emission monitoring visualization interface of a certain region as an example, environmental protection department staff can use this interface to clearly understand the overall carbon emission status of the region, quickly identify areas and time periods with abnormal emissions, and provide intuitive and convenient data support for the formulation of targeted emission reduction measures. Through the data query and analysis functions, staff can deeply explore the information behind the data and provide a strong basis for scientific decision-making.

[0024] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An AI-based carbon emission monitoring accuracy optimization method and system, characterized by: It includes a data acquisition and preprocessing module, an AI model building and optimization module, a real-time monitoring and dynamic adjustment module, and a system integration and visualization module. The data acquisition and preprocessing module adopts multi-source data acquisition technology to integrate satellite remote sensing data, ground sensor network data (including carbon dioxide concentration sensors, temperature sensors, humidity sensors, wind speed and direction sensors, etc.), industrial production process data (such as energy consumption, product output, etc.) and traffic flow data to build a comprehensive carbon emission monitoring data set. An adaptive data preprocessing algorithm is designed for the collected data. This algorithm automatically identifies outliers and missing values and uses deep learning-based interpolation methods to repair the data. Furthermore, the data is standardized to eliminate the impact of different data sources and improve the accuracy of subsequent data analysis.

2. The AI-based carbon emission monitoring accuracy optimization method and system according to claim 1 is characterized in that The AI model building and optimization module constructs a carbon emissions prediction model based on a deep neural network. This model includes multiple hidden layers and can automatically learn complex features and patterns in carbon emissions data. It uses recurrent neural network structures such as long short-term memory (LSTM) and gated recurrent units (GRU) to effectively process long-term dependencies in time series data and accurately predict carbon emissions trends. The constructed model is optimized using a reinforcement learning algorithm. By setting a reasonable reward function, the model automatically adjusts its parameters during continuous interaction with the environment, improving its prediction accuracy and generalization capabilities. Specifically, the reinforcement learning algorithm rewards or penalizes the model based on the error between the model's predictions and actual carbon emissions data, guiding the model to update its parameters towards a more optimal direction. By introducing transfer learning technology, the parameters of carbon emission monitoring models trained in other similar regions or fields are used as initialization parameters and applied to the monitoring model in the target area. This can speed up model training, reduce the demand for training data, and improve the model's adaptability and monitoring accuracy in the target area.

3. The AI-based carbon emission monitoring accuracy optimization method and system according to claim 1 is characterized in that The optimized AI model enables real-time monitoring of carbon emissions. The system collects the latest monitoring data in real time, inputs it into the AI model for calculation, and quickly and accurately outputs the current carbon emissions monitoring results. A dynamic adjustment mechanism is designed to compare and analyze real-time monitoring results with preset carbon emission thresholds. When monitoring results exceed the threshold, the system automatically triggers an early warning mechanism and dynamically adjusts the monitoring plan according to the preset strategy. For example, this involves increasing the sensor sampling frequency, activating backup monitoring equipment, and optimizing AI model parameters to ensure high monitoring accuracy in complex and changing environments.

4. The AI-based carbon emission monitoring accuracy optimization method and system according to claim 1 is characterized in that The data acquisition and preprocessing module, AI model building and optimization module, and real-time monitoring and dynamic adjustment module are systematically integrated to form a complete AI-based carbon emissions monitoring precision optimization system. The modules exchange data through efficient data interfaces, ensuring stable operation and collaborative work. Develop a visualization interface that presents real-time monitoring results, historical carbon emissions data, model prediction results, and early warning information to users in an intuitive and easy-to-understand graphical format. Users can easily query and analyze monitoring data through the visualization interface, understand carbon emissions trends, and provide strong support for making emission reduction decisions.

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