Carbon data assessment method based on machine learning

Abstract

The present invention relates to the field of carbon data processing and evaluation, and proposes a carbon data evaluation algorithm system based on machine learning and blockchain technology. The system improves the evaluation accuracy, standardization degree and transaction value of carbon emission data through four core modules: data collection, data processing, carbon data evaluation and data assetization. Specifically, the data collection module is responsible for collecting carbon emission related data, the data processing module cleans, removes duplicates and standardizes the original data, and the carbon data evaluation module uses a machine learning algorithm to analyze the data, generate key indicators such as carbon footprint and carbon intensity, and pass the evaluation results to the data assetization module, which is converted into a digital asset with market transaction value through blockchain technology. The present invention can effectively improve the accuracy of carbon data management, promote the market-oriented transaction of carbon assets, and help enterprises and institutions gain a competitive advantage in carbon management and carbon market transactions.

Description

Technical Field

[0001] The present invention relates to the field of carbon data processing and evaluation, and more specifically, to an algorithm system for evaluating carbon emission data, generating carbon assets, and improving the accuracy and value of carbon data management. The system can be used for monitoring, analyzing, and trading carbon emissions, helping enterprises and organizations gain competitive advantages in carbon management and carbon market transactions. Background Art

[0002] With the increasing global attention to climate change, carbon emission reduction and carbon asset management have become important issues that companies and governments need to solve urgently. In order to achieve the goal of carbon neutrality, companies and institutions need to accurately record and evaluate their carbon emission data in order to implement emission reduction measures more effectively. However, the current carbon data management field has the following problems: 1. Irregular data collection: The carbon data collection methods and formats of different companies are different, which makes the data difficult to compare and difficult to standardize. 2. Insufficient accuracy of evaluation results: Many traditional carbon data evaluation methods rely only on simple rules or empirical formulas, and fail to make full use of modern machine learning technology, so it is difficult to provide accurate evaluation results. 3. High difficulty in data assetization: The evaluation results of carbon data cannot usually be directly converted into tradable carbon assets, which hinders the circulation and appreciation of carbon data in the market. Based on the above problems, there is an urgent need for a new type of carbon data evaluation algorithm that can improve the accuracy, standardization and transaction value of carbon emission data evaluation, and provide support for carbon data management and trading. Summary of the invention

[0003] The present invention proposes a carbon data evaluation algorithm system based on machine learning and blockchain technology. The system can automatically collect, clean and standardize carbon emission related data, provide high-precision carbon emission evaluation results, and convert these data into digital assets with market trading value. Specifically, the present invention includes the following functional modules: 1. Data acquisition module: responsible for collecting carbon emission related data, including energy consumption data, production activity data, supply chain data and logistics data. 2. Data processing module: clean, remove duplicates and standardize the collected raw data to ensure the quality and consistency of the data for subsequent evaluation. 3. Carbon data evaluation module: analyze carbon emission data based on machine learning algorithms, generate key indicators such as carbon footprint, carbon intensity and carbon emissions, and ensure high accuracy and reliability of evaluation results. 4. Data assetization module: convert the evaluated carbon data into digital assets through blockchain technology to achieve the uniqueness, traceability and tradability of carbon data. Through the present invention, the accuracy, automation and transaction value of carbon data evaluation can be greatly improved, helping enterprises and institutions achieve sustainable development goals and market competitive advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] The accompanying drawings show the system framework of the present invention and the workflow of each module, so as to more clearly describe the principle and implementation method of the present invention. Carbon data assessment algorithm system framework Figure 1 : The overall structure of the carbon data assessment algorithm system is shown. The system consists of four main modules: data acquisition module, data processing module, carbon data assessment module and data assetization module. The modules are connected by arrows, indicating the direction of data flow and the dependencies between the modules. Data acquisition module: responsible for collecting raw data related to carbon emissions, including energy consumption data, production activity data, supply chain data, etc. Data processing module: cleans, de-duplicates and standardizes the collected raw data to ensure data quality and consistency. Carbon data assessment module: uses machine learning algorithms to analyze the processed data and generate key assessment indicators such as carbon footprint and carbon intensity. Data assetization module: converts the assessed carbon data into digital assets, and uses blockchain technology to ensure the uniqueness and traceability of the data, and ultimately realizes the marketization of assets.

[0005] Data collection and processing flow Figure 2 :It shows the complete process from data collection to processing, including the steps of data collection, cleaning, deduplication and standardization. Data collection: This step includes collecting raw data related to carbon emissions, such as energy consumption data and production activity data. The data is connected to the system through a standardized interface. Data cleaning: Remove outliers and noise data to ensure data accuracy. Data deduplication: Remove duplicate records during processing to prevent errors caused by data duplication. Data standardization: Convert data from different sources and formats into a unified standard to ensure data consistency and facilitate subsequent evaluation. This process ensures the high quality of carbon data and provides a reliable data foundation for subsequent carbon data evaluation modules.

[0006] Carbon Data Assessment Process Figure 3 :The internal structure of the carbon data assessment module is shown, and the analysis process of the data by the machine learning algorithm is described. The process includes four main steps:Data input: The processed carbon emission data is input into the assessment system. The data can include energy consumption data, production activity data, etc. Feature extraction: Useful features are extracted from the input data to build appropriate model inputs. This step uses historical data and industry standards to extract key features. Model prediction: The data is analyzed based on the machine learning algorithm to generate evaluation indicators such as carbon footprint and carbon intensity. The model improves the accuracy of the prediction through training and tuning. Result output: Output the evaluation results, such as carbon emissions, carbon intensity, etc., which are used for further decision-making and report generation. The design of this module ensures the accurate assessment of carbon emission data and provides actionable indicators for subsequent carbon management.

[0007] Data assetization process Figure 4:Describes the assetization process of the assessment results, including the processes of data on-chain, asset generation, and transaction record generation. Data on-chain: In this step, the assessment results are converted into digital assets and encrypted and stored through blockchain technology to ensure the immutability and uniqueness of the data. Data on-chain is a key step to ensure data security and transparency. Asset generation: The assessment results are converted into digital assets with market value, a unique digital identifier is generated, and related metadata is recorded to ensure the traceability of assets. Transaction record generation: Transaction records related to assets are generated and the transaction history is recorded on the blockchain to ensure the transparency and immutability of transactions. The generation of transaction records supports the circulation and appreciation of assets. This process provides feasibility for the marketization of carbon data and ensures the security and traceability of data assets. DETAILED DESCRIPTION

[0008] The specific implementation of the present invention is described in detail below to achieve accurate assessment and assetization of carbon data.

[0009] Implementation of data collection and processing: 1. Data collection The data collection module is responsible for collecting carbon emission-related data, including the company's energy consumption data (such as electricity, natural gas, etc.), production activity data (emission factors of the production process), upstream and downstream supply chain data, logistics and transportation data, etc. The collected data is connected to the system through a standardized interface to ensure the diversity and integrity of the data. 2. Data processing The data processing module cleans, deduplicates and standardizes the collected raw data. The processing process includes: Cleaning: Remove outliers and noise data to ensure data accuracy. Deduplication: Avoid errors caused by duplicate records. Standardization: Convert data in different formats to a unified standard for subsequent evaluation.

[0010] Implementation of carbon data assessment: 1. Machine learning model training. The carbon data assessment module uses a machine learning model to assess carbon emissions. First, the system extracts features from historical carbon data, industry benchmarks, and carbon emission standards, and uses these data to train the machine learning model to improve the accuracy of the model. 2. Assessment process. The processed carbon data is input into the machine learning model. The model generates indicators such as carbon emissions, carbon footprint, and carbon intensity based on the input data. These indicators are calibrated using standardized formulas to ensure that the results meet international carbon emission standards. 3. Assessment result output. The carbon data assessment module passes the assessment results to the data assetization module to generate tradable carbon data assets.

[0011] Implementation of carbon data assetization: 1. Asset generation: The data assetization module uses blockchain technology to convert the assessed carbon data into digital assets with unique identifiers. Each carbon data asset is recorded in a distributed ledger to ensure the traceability and security of the data. 2. Asset trading: Digital assets can be traded through carbon trading markets or data trading platforms. The system generates transaction records for each transaction and allows pricing based on market prices to achieve market circulation of carbon data. 3. Asset management and tracking: The data assetization module provides asset management and tracking functions, allowing users to query and track the transaction history of carbon data assets to ensure the transparency and credibility of transactions.

Claims

1. A carbon data collection and processing method, comprising the following steps: (a) Collecting carbon emission related data, including but not limited to energy consumption data, production activity data, supply chain data, logistics data, etc.; (b) cleaning, deduplication and standardization of the collected data to improve data quality and consistency; (c) The processed data is input into the carbon data assessment model to generate key indicators such as carbon emissions, carbon footprint, and carbon intensity.

2. The method according to claim 1, wherein the carbon data assessment model is trained using a machine learning algorithm to generate carbon emission assessment results that meet a predetermined accuracy based on historical carbon data, industry benchmarks, and international carbon standards.

3. A carbon data assessment system, comprising: Data collection module, used to collect carbon emission related data, including energy consumption, production activity data, supply chain data and logistics data; A data processing module is used to clean, remove duplicates and standardize the collected data to improve the quality and consistency of the data; The carbon data assessment module is used to input the processed data into the carbon data assessment model to generate key indicators such as carbon emissions, carbon footprint and carbon intensity; Data output module, used to generate carbon emission reports and transaction records of carbon data assets.

4. The system according to claim 3, wherein the carbon data assessment module includes a rule system and a machine learning model, and different assessment rules are set according to different industry categories, production processes and logistics methods to enhance the accuracy of the assessment.

5. A method for converting carbon data into digital assets, comprising the following steps: (a) generating key indicators such as carbon emissions, carbon footprint, and carbon intensity according to the carbon data assessment method of claim 1; (b) digitally storing the assessment data and recording it on a distributed database or blockchain to ensure the uniqueness and traceability of the data; (c) Generate digital assets corresponding to the carbon data assessment results, which can be used and circulated in the carbon trading market or data exchange.

6. The method according to claim 5, wherein the digital assets ensure the security of data through encryption means, and can be priced and traded based on real market prices in data exchanges.

7. A system for carbon emission assessment and data assetization, comprising the data acquisition module, data processing module, carbon data assessment module and data output module as claimed in claim 3, wherein: The data output module further includes an asset generation module for converting carbon data assessment results into tradable digital assets; The asset generation module is connected to a blockchain or a distributed database to ensure the immutability and traceability of carbon data assets.

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