Application method of AI auxiliary decision-making in carbon transaction and carbon integrated management

Abstract

The invention relates to the technical field of carbon transaction, and discloses an application method of AI aid decision in carbon transaction and carbon integrated management, comprising the following steps: S1, data collection: collecting carbon emission data of an enterprise in real time through an intelligent sensor, an Internet of Things platform and a third-party data source; s2, intelligent analysis: analyzing the collected data by using a machine learning algorithm, identifying the carbon emission trend, predicting the carbon emission in the future, and evaluating the dynamic state of the carbon transaction market; and S3, transaction decision making: making a carbon transaction strategy through an AI algorithm based on an analysis result. According to the invention, a machine learning algorithm is used to analyze the collected data, identify the carbon emission trend, predict the future carbon emission, evaluate the dynamic state of the carbon trading market, draw a situation map according to the data, sense the future trend, provide suggestions, and provide potential trading risks and solution suggestions. Automatic data processing and transaction decision-making reduce manual operation and improve work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon trading, and specifically to a method for the application of AI-assisted decision-making in carbon trading and carbon comprehensive management. Background Art

[0002] With the increasing global attention to climate change and the growing demand for carbon emission control, the carbon trading market has emerged. Carbon trading is a market-based emission reduction mechanism that allows enterprises or countries to buy and sell carbon emission quotas; this helps to find the lowest-cost emission reduction methods and promote the reduction of the total global carbon emissions. The carbon trading mechanism is a trading market that takes greenhouse gas emission reduction as the basis and treats emission rights as commodities in circulation, which helps to more effectively allocate resources and control greenhouse gas emissions by using market mechanisms.

[0003] Currently, in the fields of carbon trading and carbon comprehensive management, enterprises are facing many challenges. Traditional carbon emission management and trading methods rely on a large amount of manual operations and cumbersome data processing, resulting in inaccurate data, low trading efficiency, and difficulty in real-time tracking of carbon emissions. With the increasing global attention to climate change, how to efficiently and accurately manage carbon emissions and conduct carbon trading has become an urgent problem to be solved. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides a method for the application of AI-assisted decision-making in carbon trading and carbon comprehensive management, which improves the efficiency and accuracy of carbon emission management through intelligent means, and at the same time optimizes the carbon trading process. The system uses AI algorithms to deeply analyze carbon emission data to achieve automated carbon emission monitoring, prediction, and trading decision-making, thereby helping enterprises achieve the carbon neutrality goal.

[0006] (II) Technical Solutions

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

[0008] A method for the application of AI-assisted decision-making in carbon trading and carbon comprehensive management, comprising the following steps:

[0009] S1: Data collection, real-time collection of the enterprise's carbon emission data through intelligent sensors, Internet of Things platforms, and third-party data sources;

[0010] S2: Intelligent analysis, using machine learning algorithms to analyze the collected data, identify carbon emission trends, predict future carbon emissions, and evaluate the dynamics of the carbon trading market;

[0011] S3: Trading decision-making, based on the analysis results, formulating carbon trading strategies through AI algorithms;

[0012] S4: Compliance check. The system monitors the enterprise's carbon emissions in real time to ensure compliance with carbon emission regulations.

[0013] S5: Continuous optimization. According to the actual operation results and feedback information, the AI model parameters are continuously updated to improve the system's prediction accuracy and decision-making ability.

[0014] As a further solution of the present invention, the carbon emission data in S1 includes energy consumption data and production process emission data.

[0015] Furthermore, S1 includes a data collection module. The data collection module collects data through multiple link channels, and the multiple link channels include: manual entry, upstream and downstream synchronization, supply chain, manufacturers, channel parties, and multi-party platform docking.

[0016] On the basis of the foregoing solution, S2 includes an analysis module. The analysis module uses the Qwen2.5 model combined with parameter tuning to deeply analyze the collected data, draws a situation map based on the data, perceives future trends, provides opinions, and proposes potential trading risks and solutions.

[0017] Furthermore, the carbon trading strategy in S3 includes trading timing, trading volume, and trading time to achieve the optimal allocation of carbon assets.

[0018] As a further solution of the present invention, S3 includes an AI module. For the identified abnormal problems in the carbon trading technology field, the AI module will automatically trigger a decision response mechanism. At the same time, the AI system will also feedback the processing results to the operation and maintenance and management personnel in real time.

[0019] On the basis of the foregoing solution, S5 includes an optimization module. The optimization module conducts continuous learning and optimization according to the processing results and feedback information, records each customer's answer in the database, and feeds it back to the local model as a parameter for continuous training and iteration.

[0020] (III) Beneficial effects

[0021] Compared with the prior art, the present invention provides a method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management, having the following beneficial effects:

[0022] 1. In the present invention, machine learning algorithms are used to analyze the collected data, identify carbon emission trends, predict future carbon emissions, and evaluate the dynamics of the carbon trading market. A situation map is drawn based on the data, future trends are perceived, opinions are provided, and potential trading risks and solutions are proposed. Automated data processing and trading decisions reduce manual operations and improve work efficiency.

[0023] 2. In the present invention, when the AI module identifies an abnormal problem in the carbon trading technology field, it will automatically trigger the decision-making response mechanism. At the same time, the AI system will also feedback the processing results to the operation and maintenance and management personnel in real time and provide detailed information. The AI algorithm can accurately predict the carbon emission trend and the dynamics of the trading market and formulate a scientific trading strategy.

[0024] 3. In the present invention, real-time monitoring and compliance inspection ensure that enterprises meet the requirements of carbon emission regulations, reduce compliance risks, reduce the carbon trading costs of enterprises by optimizing carbon asset allocation and trading strategies, and continuously learn and optimize according to the processing results and feedback information. Each answer of the customer is recorded in the database and used as a parameter to feedback to the local model for continuous training and iteration.

[0025] 4. In the present invention, the log module saves the information of reconnection for subsequent analysis and troubleshooting. When an important event occurs, the notification module sends an alarm to the designated contact. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of the steps of a method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Embodiment 1

[0029] Refer to Figure 1 , a method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management, including the following steps:

[0030] S1: Data collection, real-time collection of the carbon emission data of the enterprise through intelligent sensors, the Internet of Things platform and third-party data sources. The carbon emission data in S1 includes energy consumption data and production process emission data;

[0031] S2: Intelligent analysis. Use machine learning algorithms to analyze the collected data, identify carbon emission trends, predict future carbon emissions, and evaluate the dynamics of the carbon trading market. S2 includes an analysis module that uses the Qwen2.5 model combined with parameter tuning to deeply analyze the collected data, draw a situation map (daily situation map, monthly situation map, annual situation map) based on the data, perceive future trends, provide opinions, and propose potential trading risks and solutions. Automated data processing and trading decisions reduce manual operations and improve work efficiency;

[0032] S3: Trading decision-making. Based on the analysis results, formulate a carbon trading strategy through AI algorithms. The carbon trading strategy in S3 includes the trading opportunity, trading volume, and trading time to achieve the optimal allocation of carbon assets. S3 includes an AI module. For the identified abnormal problems in the carbon trading technology field, the AI module will automatically trigger a decision response mechanism. At the same time, the AI system will also feedback the processing results to the operation and maintenance and management personnel in real time and provide detailed information. The AI algorithm can accurately predict carbon emission trends and trading market dynamics and formulate a scientific trading strategy;

[0033] S4: Compliance check. The system monitors the carbon emissions of enterprises in real time to ensure compliance with carbon emission regulations. Real-time monitoring and compliance checks ensure that enterprises comply with carbon emission regulations, reduce compliance risks, and reduce the carbon trading costs of enterprises by optimizing carbon asset allocation and trading strategies;

[0034] S5: Continuous optimization. According to the actual operation results and feedback information, continuously update the AI model parameters to improve the prediction accuracy and decision-making ability of the system.

[0035] Example 2

[0036] Refer to Figure 1 , A method for the application of AI-assisted decision-making in carbon trading and carbon comprehensive management, including the following steps:

[0037] S1: Data collection. Real-time collect the carbon emission data of enterprises through intelligent sensors, the Internet of Things platform, and third-party data sources. The carbon emission data in S1 includes energy consumption data and production process emission data. S1 includes a data collection module that collects data through multiple link channels. The multiple link channels include: manual entry, upstream and downstream synchronization, supply chain, manufacturers, channel parties, and multi-party platform docking;

[0038] S2: Intelligent analysis. Use machine learning algorithms to analyze the collected data, identify carbon emission trends, predict future carbon emissions, and evaluate the dynamics of the carbon trading market. The S2 includes an analysis module. The analysis module uses the Qwen2.5 model combined with parameter tuning to deeply analyze the collected data, draw a situation map (daily situation map, monthly situation map, annual situation map) based on the data, perceive future trends, provide opinions, and put forward potential trading risks and solutions. Automated data processing and trading decisions reduce manual operations and improve work efficiency;

[0039] S3: Trading decision-making. Based on the analysis results, formulate carbon trading strategies through AI algorithms. The carbon trading strategies in S3 include trading timing, trading volume, and trading time to achieve the optimal allocation of carbon assets. The S3 includes an AI module. The AI module will automatically trigger a decision response mechanism for the identified abnormal problems in the carbon trading technology field. At the same time, the AI system will also feedback the processing results to the operation and maintenance and management personnel in real time and provide detailed information. The AI algorithm can accurately predict carbon emission trends and trading market dynamics and formulate scientific trading strategies;

[0040] S4: Compliance check. The system monitors the carbon emissions of enterprises in real time to ensure compliance with carbon emission regulations. Real-time monitoring and compliance checks ensure that enterprises comply with carbon emission regulations and reduce compliance risks. By optimizing carbon asset allocation and trading strategies, the carbon trading costs of enterprises are reduced;

[0041] S5: Continuous optimization. According to the actual operation results and feedback information, continuously update the AI model parameters to improve the prediction accuracy and decision-making ability of the system. The S5 includes an optimization module. The optimization module conducts continuous learning and optimization based on the processing results and feedback information, records each customer's answer in the database, and feeds it back as a parameter to the local model for continuous training and iteration.

[0042] In the description of this article, it should be noted that relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0043] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various 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 equivalents.

Claims

1. A method for the application of AI-assisted decision-making in carbon trading and carbon comprehensive management, characterized in that, It includes the following steps: S1: Data collection, real-time collection of the enterprise's carbon emission data through intelligent sensors, Internet of Things platforms, and third-party data sources; S2: Intelligent analysis, using machine learning algorithms to analyze the collected data, identify carbon emission trends, predict future carbon emissions, and evaluate the dynamics of the carbon trading market; S3: Trading decision-making, based on the analysis results, formulating carbon trading strategies through AI algorithms; S4: Compliance inspection, the system monitors the enterprise's carbon emissions in real time to ensure compliance with carbon emission regulations; S5: Continuous optimization, according to the actual operation results and feedback information, continuously update the AI model parameters to improve the system's prediction accuracy and decision-making ability.

2. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 1, wherein The carbon emission data in S1 includes energy consumption data and production process emission data.

3. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 2, characterized in that, S1 includes a data collection module, and the data collection module collects data through multi-link channels. The multi-link channels include: manual entry, upstream and downstream synchronization, supply chain, manufacturers, channel parties, and multi-party platform docking.

4. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 1, wherein S2 includes an analysis module, and the analysis module uses the Qwen2.5 model combined with parameter tuning to deeply analyze the collected data, draw a situation map based on the data, perceive future trends, provide opinions, and propose potential trading risks and solutions.

5. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 1, characterized in that, The carbon trading strategy in S3 includes trading timing, trading volume, and trading time to achieve the optimal allocation of carbon assets.

6. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 5, characterized in that, S3 includes an AI module. For the identified abnormal problems in the carbon trading technology field, the AI module will automatically trigger a decision response mechanism. At the same time, the AI system will also feedback the processing results to the operation and maintenance and management personnel in real time.

7. The method for applying AI-assisted decision-making in carbon trading and carbon comprehensive management according to claim 1, wherein S5 includes an optimization module. The optimization module conducts continuous learning and optimization according to the processing results and feedback information, records each customer's answer in the database, and feeds it back as a parameter to the local model for continuous training and iteration.

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