Carbon emission control method and system for super-long-duration data analysis

Abstract

The invention discloses a carbon emission control method and system based on ultra-long duration data analysis. The system comprises an ultra-long duration data acquisition and storage module, a data preprocessing and feature engineering module, an ultra-long duration data analysis model construction module, a carbon emission prediction and control strategy generation module and a system interaction and feedback module. According to the invention, the construction processing is realized through the super-long duration data acquisition and storage module, the data preprocessing and feature engineering module, the super-long duration data analysis model construction module, the carbon emission prediction and control strategy generation module and the system interaction and feedback module, so that the super-long duration data can be efficiently processed, and the carbon emission can be accurately predicted; and a personalized management and control strategy is formulated, and good interactivity and adaptability are provided.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission control technology, and in particular to a carbon emission control method and system based on ultra-long duration data analysis. Background Art

[0002] With global attention paid to climate change, carbon emission control has become a key component of sustainable development. Data analysis plays a crucial role in controlling carbon emissions. However, existing carbon emission analysis and control technologies mostly target short-term or regular timeframes, and have significant limitations when processing and analyzing data over extended periods.

[0003] On the one hand, ultra-long-duration data is characterized by large volumes, long time spans, and complex and changing data characteristics. Traditional data processing methods struggle to efficiently handle such large and complex data. For example, in terms of storage, massive amounts of data take up a significant amount of storage space, increasing storage costs and inefficient data query and retrieval. Furthermore, traditional algorithms suffer from high computational complexity and slow processing speeds when processing long-duration data, failing to meet real-time requirements.

[0004] On the other hand, existing carbon emissions analysis models struggle to capture dynamic patterns and trends in data over extended periods of time. Due to the intertwined seasonality, cyclicality, and long-term trends inherent in the data, simple statistical models or machine learning models trained on short-term data cannot accurately reflect the complex relationship between carbon emissions and various influencing factors. This leads to inaccurate analysis results and a failure to provide a reliable basis for effective carbon emissions control.

[0005] Furthermore, existing carbon emission control systems lack the ability to comprehensively utilize and deeply mine data from very long time periods. This makes it difficult to draw lessons from historical data, predict future trends in carbon emissions, and develop forward-looking and targeted control strategies. Therefore, there is an urgent need for a method and system that can effectively process very long time periods of data and achieve precise control of carbon emissions. Summary of the Invention

[0006] The purpose of the present invention is to provide a carbon emissions control method and system for ultra-long duration data analysis. By efficiently processing and deeply analyzing ultra-long duration data, the changing patterns and influencing factors of carbon emissions can be accurately explored, and accurate prediction and effective control of carbon emissions can be achieved, providing strong support for relevant industries and departments to formulate scientific and reasonable emission reduction policies and measures.

[0007] The technical solution adopted by the present invention to solve its technical problems is: a carbon emissions control method and system for ultra-long duration data analysis: including an ultra-long duration data acquisition and storage module, a data preprocessing and feature engineering module, an ultra-long duration data analysis model construction module, a carbon emissions prediction and control strategy generation module, and a system interaction and feedback module.

[0008] As a further solution, the ultra-long-duration data acquisition and storage module is responsible for collecting ultra-long-duration data related to carbon emissions, including but not limited to energy consumption data (such as the consumption of coal, oil, natural gas, and other energy sources), industrial production data (product output, production process parameters, etc.), transportation operation data (vehicle mileage, transportation volume, etc.), meteorological data (temperature, wind speed, humidity, etc.), and carbon emission monitoring data (emission concentrations and emissions of greenhouse gases such as carbon dioxide and methane). Distributed storage technology is used to store the collected massive data in a distributed file system, such as the Hadoop Distributed File System (HDFS), to improve the reliability and scalability of data storage. At the same time, a data indexing mechanism is established to optimize data query and retrieval efficiency, facilitating subsequent rapid access and analysis of the data.

[0009] As a further solution, the data preprocessing and feature engineering module preprocesses the collected ultra-long duration data, including data cleaning, missing value processing, and data normalization. Data cleaning algorithms are used to remove noise, outliers, and duplicate data from the data; for missing values, time series interpolation methods, machine learning-based prediction models, and other methods are used to fill in the missing values based on the characteristics and distribution of the data; through normalization, data of different dimensions are converted to the same scale to improve data availability. Based on data preprocessing, feature engineering is performed to extract key features related to carbon emissions, such as energy consumption intensity, carbon emissions per unit product, and transportation carbon emission factors. At the same time, Fourier transform, wavelet transform and other methods are used to perform frequency domain analysis on time series data to extract the periodicity and trend characteristics of the data, providing richer feature information for subsequent model training.

[0010] As a further solution, the ultra-long duration data analysis model construction module constructs an ultra-long duration data analysis model based on a deep learning algorithm, selects network structures such as long short-term memory networks (LSTM) or gated recurrent units (GRU) that can effectively process time series data, and combines them with attention mechanisms to enhance the model's ability to capture key data features over long time spans. The preprocessed data is divided into training sets, validation sets, and test sets. The model parameters are optimized using the training set, the model hyperparameters are adjusted using the validation set, and the model's generalization ability is evaluated using the test set. During the model training process, transfer learning technology is introduced, and model parameters trained in other related fields or similar time periods are used as initialization parameters to speed up the model training and improve the model's adaptability to ultra-long duration data. In addition, an ensemble learning method is used to combine multiple models with different structures or parameters, and the prediction results of multiple models are integrated to improve the model's prediction accuracy and stability.

[0011] As a further solution, the carbon emissions prediction and control strategy generation module inputs preprocessed and feature-engineered data into a trained ultra-long-duration data analysis model to predict carbon emissions. Based on the prediction results, combined with industry standards, policy requirements, and the actual situation of the enterprise, a personalized carbon emissions control strategy is formulated. For example, for enterprises with high energy consumption, energy structure optimization strategies are formulated to increase the proportion of clean energy used; for industrial production processes, production process improvement suggestions are proposed to reduce carbon emissions per unit product; for the transportation industry, reasonable transportation operation management measures are formulated to improve transportation efficiency and reduce carbon emissions. At the same time, a carbon emissions early warning mechanism is established. When the predicted carbon emissions exceed the preset threshold, a warning message is issued in a timely manner and corresponding emergency control measures are initiated.

[0012] As a further solution, the system interaction and feedback module provides a user-friendly interface, enabling interaction between users and the system. Users can query historical carbon emissions data, forecast results, and the implementation of control strategies through this interface. Furthermore, users can adjust and optimize system parameters, forecast models, and control strategies based on actual needs. Based on user feedback, the system continuously updates and improves the model to enhance the effectiveness of carbon emissions control. Furthermore, this module is responsible for outputting the system's analysis results and control strategies in the form of reports and charts, allowing users to intuitively understand changes in carbon emissions and the effectiveness of control measures, providing support for decision-making.

[0013] Compared with the prior art, the present invention has the following advantages:

[0014] 1. Efficient processing of ultra-long duration data: Distributed storage technology and optimized data indexing mechanisms enable effective storage and management of massive amounts of ultra-long duration data, improving data query and retrieval efficiency. Advanced data preprocessing and feature engineering methods enable full mining of useful information in the data, providing a high-quality data foundation for subsequent analysis.

[0015] 2. Accurately predict carbon emissions: An ultra-long-duration data analysis model built based on a deep learning algorithm, combined with attention mechanisms, transfer learning, and ensemble learning techniques, can accurately capture the changing patterns and trends of carbon emissions over long time spans, improve the accuracy and stability of predictions, and provide a reliable basis for carbon emissions control.

[0016] 3. Develop personalized management and control strategies: Based on the carbon emission forecast results and combined with actual conditions, develop personalized management and control strategies that are highly targeted and operational, can effectively reduce carbon emissions and achieve the goal of energy conservation and emission reduction.

[0017] 4. Good interactivity and adaptability: The system provides a friendly user interface to facilitate user query, adjustment and optimization operations; at the same time, it can timely update and improve the model based on user feedback, improve the system's adaptability to different application scenarios, and meet the diverse needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is an architectural diagram of the carbon emissions management and control system for ultra-long-duration data analysis of the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of the ultra-long duration data analysis model;

[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 storage system for ultra-long-duration data is shown below. In the energy industry, data collection is performed by connecting with the energy management systems of energy producers and consumers to collect real-time data on the purchase, consumption, and inventory of energy sources such as coal, oil, and natural gas every hour. In the industrial sector, sensors and data acquisition devices installed on production equipment collect product output and production process parameters (such as temperature, pressure, and speed), with complete production data collected daily. In the transportation industry, data is connected to the information systems of transportation management departments and transportation companies to obtain data such as vehicle mileage, transportation volume, and fuel consumption, which is summarized weekly. Simultaneously, real-time meteorological data, including temperature, wind speed, and humidity, is obtained from the meteorological department's monitoring platform, updated every 10 minutes. Furthermore, greenhouse gas monitoring equipment is installed at each carbon emission monitoring site to collect real-time emission concentrations and amounts of gases such as carbon dioxide and methane every minute. Data storage: The collected massive data is stored in the Hadoop Distributed File System (HDFS). Data is stored in different files and directories based on data type and timestamp. For example, energy consumption data is stored in the " / energy_data" directory, further broken down by year and month; carbon emission monitoring data is stored in the " / emission_data" directory. To improve data query efficiency, an indexing mechanism based on timestamps and data types is established, using the HBase database as an index storage and query tool. For example, for energy consumption data, an index table can be created with timestamps as row keys and data types as column families, facilitating quick queries for energy consumption data for specific time periods and types. Data preprocessing and feature engineering: Data cleaning: Outliers in energy consumption data are identified and removed using the 3σ principle. For example, if coal consumption at a given moment exceeds three standard deviations of the mean, it is considered an outlier and deleted. Missing industrial production data is filled using linear interpolation, performing a linear calculation based on production data from adjacent time points to estimate the missing values. Duplicate records in transportation operation data are identified and removed by comparing unique identifiers (such as vehicle numbers and transport order numbers). Data normalization: All numerical data are processed using the Min-Max normalization method to map the data to the [0,1] interval. Taking energy consumption data as an example, assuming that the minimum consumption of a certain type of energy is x min , the maximum value is x max , for the original data x, the normalized data x norm The calculation formula is: Feature Engineering: Energy consumption intensity is calculated, i.e., energy consumption per unit of output value. The formula is: Energy consumption intensity = energy consumption / total industrial output value. For time series carbon emission monitoring data, Fourier transforms are used to convert the time domain data into the frequency domain to extract periodic features, such as daily, weekly, and monthly cycles. Wavelet transforms are used to analyze trend characteristics, such as long-term growth or decline. Furthermore, based on historical data and relevant research, a transportation carbon emission factor is determined to calculate carbon emissions in the transportation sector. Construction of a Very Long-Duration Data Analysis Model, Model Selection and Architecture Design: A long short-term memory (LSTM) network combined with an attention mechanism is selected to construct a very long-duration data analysis model. The model's input layer receives preprocessed and feature-engineered data, with the input dimension being the number of features. Three LSTM hidden layers are set, with 128, 64, and 32 neurons, respectively. LSTM units effectively handle long-term dependencies in time series data. Following the LSTM hidden layers, an attention mechanism is introduced to highlight key time steps and features that have a significant impact on carbon emissions by calculating attention weights for each time step. Finally, a fully connected layer with 2 neurons is connected to output the predicted carbon emissions and the confidence level of the prediction, respectively. Model training: The preprocessed data is divided into training, validation, and test sets in a ratio of 7:1:2. The model is trained on the training set using the Adam optimizer, a learning rate of 0.001, and the mean squared error (MSE) loss function. During training, the model performance is evaluated on the validation set after every 10 training batches. Model hyperparameters, such as the number of neurons in the LSTM hidden layer and the parameters of the attention mechanism, are adjusted based on the validation set loss. After 500 training epochs, the model is evaluated on the test set, achieving a mean squared error of 0.08 and a prediction accuracy of over 85%. Transfer learning and ensemble learning: Parameters of pre-trained LSTM models from other regions or industries with similar energy consumption and carbon emissions are used as initialization parameters for the current model. When fine-tuning the model on data from this region or industry, only the parameters of the last few layers of the model are updated to accelerate training. At the same time, an ensemble learning approach is used to train five LSTM models with different structures and parameters. The prediction results of these five models are weighted averaged to obtain the final carbon emissions forecast, further improving the accuracy and stability of the forecast. Carbon emissions forecasting and control strategy generation: Real-time collected, preprocessed, and feature-engineered data is input into a trained ultra-long-duration data analysis model. The model outputs a carbon emissions forecast for the next week and the confidence level of the forecast within one minute. For example, a forecast of [specific value] tons of carbon emissions for an industrial enterprise in the next week is given with a 90% confidence level. Control strategy generation: Based on the carbon emissions forecast results, a control strategy is formulated in conjunction with industry standards and the company's actual situation.If a company's carbon emissions are predicted to exceed industry standards, an energy structure optimization strategy will be developed, recommending that the company increase its use of clean energy sources such as solar and wind power and reduce coal consumption. For companies with high carbon emissions from their production processes, recommendations for process improvements will be made, such as optimizing the combustion process and improving energy efficiency. Furthermore, a carbon emissions early warning mechanism will be established. When predicted carbon emissions exceed a preset threshold (e.g., 90% of the industry standard), the system will send an alert to company management and relevant regulatory authorities via SMS or email, and initiate emergency control measures, such as limiting the operating hours of high-energy-consuming equipment and adjusting production plans. System Interaction and Feedback, User Interface Design: A web-based user interface will be developed, allowing users to access the system through a browser. A data query module will be included, allowing users to query historical carbon emissions data, forecast results, and the implementation of control strategies. Querying by time range, company name, industry type, and other criteria will be supported. Functional modules for model parameter adjustment and control strategy optimization will be provided. Users can adjust model hyperparameters, such as the learning rate and the number of hidden layer neurons, based on actual experience and needs, and can also modify and improve control strategies. Feedback and Improvement: Users provide feedback and suggestions on the system's analysis results and control strategies through the user interface. Based on this feedback, the system re-evaluates the model's performance and the effectiveness of the control strategy, retrains and optimizes the model, and adjusts and improves the control strategy to continuously improve the effectiveness of carbon emissions control. The system also outputs the analysis results and control strategies in the form of reports and charts, such as carbon emissions trend charts, energy structure comparison charts, and control strategy execution effectiveness analysis reports. This allows users to intuitively understand changes in carbon emissions and the effectiveness of control, providing support for 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. A method and system for controlling carbon emissions through ultra-long-duration data analysis, characterized by: It includes ultra-long duration data collection and storage module, data preprocessing and feature engineering module, ultra-long duration data analysis model construction module, carbon emission prediction and control strategy generation module, and system interaction and feedback module.

2. The carbon emission control method and system for ultra-long duration data analysis according to claim 1 is characterized in that The ultra-long duration data acquisition and storage module is responsible for collecting ultra-long duration data related to carbon emissions, including but not limited to energy consumption data (such as the consumption of coal, oil, natural gas, and other energy sources), industrial production data (product output, production process parameters, etc.), transportation operation data (vehicle mileage, transportation volume, etc.), meteorological data (temperature, wind speed, humidity, etc.), and carbon emission monitoring data (emission concentrations and emissions of greenhouse gases such as carbon dioxide and methane). Distributed storage technology is used to store the collected massive data in a distributed file system, such as the Hadoop Distributed File System (HDFS), to improve the reliability and scalability of data storage. At the same time, a data indexing mechanism is established to optimize data query and retrieval efficiency, facilitating subsequent rapid access and analysis of the data.

3. The carbon emission control method and system for ultra-long duration data analysis according to claim 1 is characterized in that The data preprocessing and feature engineering module preprocesses the collected long-duration data, including data cleaning, missing value processing, and data normalization. Data cleaning algorithms are used to remove noise, outliers, and duplicate data from the data. Missing values are filled using methods such as time series interpolation and machine learning-based predictive models, based on the data's characteristics and distribution. Normalization is used to convert data of varying dimensions to a uniform scale, improving data usability. Based on data preprocessing, feature engineering is performed to extract key features related to carbon emissions, such as energy intensity, carbon emissions per unit of product, and transportation carbon emission factors. Furthermore, frequency domain analysis of time series data is performed using methods such as Fourier transforms and wavelet transforms to extract periodic and trend characteristics, providing richer feature information for subsequent model training.

4. The carbon emission control method and system for ultra-long duration data analysis according to claim 1 is characterized in that The ultra-long duration data analysis model construction module constructs an ultra-long duration data analysis model based on a deep learning algorithm, selects a network structure such as a long short-term memory network (LSTM) or a gated recurrent unit (GRU) that can effectively process time series data, and combines it with an attention mechanism to enhance the model's ability to capture key data features over a long time span. The preprocessed data is divided into a training set, a validation set, and a test set. The model parameters are optimized using the training set, the model hyperparameters are adjusted using the validation set, and the generalization ability of the model is evaluated using the test set. During the model training process, transfer learning technology is introduced, and model parameters trained in other related fields or similar time periods are used as initialization parameters to speed up the model training and improve the model's adaptability to ultra-long duration data. In addition, an integrated learning method is used to combine multiple models with different structures or parameters, and the prediction results of multiple models are integrated to improve the prediction accuracy and stability of the model.

5. The carbon emission control method and system for ultra-long duration data analysis according to claim 1 is characterized in that The carbon emissions prediction and control strategy generation module inputs pre-processed and feature-engineered data into a trained ultra-long-duration data analysis model to predict carbon emissions. Based on the prediction results, combined with industry standards, policy requirements, and the company's actual situation, a personalized carbon emissions control strategy is formulated. For example, for companies with high energy consumption, energy structure optimization strategies are formulated to increase the proportion of clean energy used; for industrial production processes, production process improvement suggestions are proposed to reduce carbon emissions per unit product. For the transportation industry, we will formulate reasonable transportation operation management measures to improve transportation efficiency and reduce carbon emissions. At the same time, we will establish a carbon emissions early warning mechanism. When the predicted carbon emissions exceed the preset threshold, a warning message will be issued in a timely manner and corresponding emergency control measures will be initiated.

6. The carbon emission control method and system for ultra-long duration data analysis according to claim 1 is characterized in that The system interaction and feedback module provides a user-friendly interface, enabling interaction between users and the system. Users can query historical carbon emissions data, forecast results, and the implementation of control strategies through this interface. Furthermore, users can adjust and optimize system parameters, forecast models, and control strategies based on their actual needs. Based on user feedback, the system continuously updates and improves the model to enhance the effectiveness of carbon emissions control. Furthermore, this module outputs the system's analysis results and control strategies in the form of reports and charts, allowing users to intuitively understand changes in carbon emissions and the effectiveness of control measures, providing support for decision-making.

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