Carbon emission continuous monitoring analysis system based on block chain

Abstract

The invention relates to the technical field of carbon emission continuous monitoring and analysis systems, in particular to a block chain-based carbon emission continuous monitoring and analysis system. According to the technical scheme, the carbon emission continuous monitoring and analysis system based on the block chain comprises a carbon emission calculation module; compared with a traditional carbon emission continuous monitoring and analysis system based on a block chain, the system generally depends on manual input and manual calculation, which may cause risks of data errors and tampering, and due to the fact that tedious manual calculation and data processing processes are involved, and meanwhile, the data transparency may be insufficient, the data processing efficiency is greatly improved. The block chain-based carbon emission continuous monitoring and analysis system can accurately calculate the carbon emission in real time through an intelligent contract and an automatic algorithm, so that the calculation efficiency and the automation degree are greatly improved, and the carbon emission continuous monitoring and analysis system is suitable for large-scale popularization and application. The intelligent contract can also automatically trigger the operation of emission reduction measures or purchasing the carbon emission right according to a preset rule, so that the flexibility of carbon emission management is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission continuous monitoring and analysis systems, and in particular to a carbon emission continuous monitoring and analysis system based on blockchain. Background Art

[0002] The carbon emission continuous monitoring and analysis system is a system used to monitor and analyze carbon emissions from carbon emission sources in real time and continuously.

[0003] Existing blockchain-based carbon emissions continuous monitoring and analysis systems generally rely on manual input and manual calculations, which may lead to the risk of data errors and tampering. Due to the cumbersome manual calculations and data processing processes involved, the efficiency is low. At the same time, there may be deficiencies in data transparency, and the data may be difficult to be verified or traced by a third party.

[0004] The existing blockchain-based carbon emissions continuous monitoring and analysis system generally relies on manual input and manual calculations, which may lead to data errors and risks of tampering. This blockchain-based carbon emissions continuous monitoring and analysis system can calculate carbon emissions in real time and accurately through smart contracts and automated algorithms, greatly improving computing efficiency and automation. Smart contracts can also automatically trigger emission reduction measures or purchase carbon emission rights according to preset rules, further enhancing the flexibility of carbon emission management. Summary of the invention

[0005] In order to overcome the existing blockchain-based carbon emission continuous monitoring and analysis system, which generally relies on manual input and manual calculation, this may lead to the risk of data errors and tampering. Due to the cumbersome manual calculation and data processing process, the efficiency is low. At the same time, there may be deficiencies in data transparency, and the data may be difficult to be verified or traced by a third party.

[0006] The technical solution of the present invention is: a carbon emission continuous monitoring and analysis system based on blockchain, comprising:

[0007] Data collection module: used to collect carbon emission related data;

[0008] Data preprocessing module: used to preprocess the carbon emission related data collected by the data acquisition module;

[0009] Carbon emission calculation module: used to calculate carbon emissions based on preprocessed data;

[0010] Regional chain storage and verification module: used to store the calculation results of the carbon emission calculation module;

[0011] Data analysis and visualization module: used to conduct in-depth analysis of the calculation results of the carbon emission calculation module;

[0012] Compliance inspection and reporting module: used to check whether carbon emissions meet the standards according to relevant regulations;

[0013] User interaction and feedback module: used to provide users with operation interaction pages.

[0014] Preferably, the data acquisition module includes multiple groups of low-power IoT sensors and high-precision smart meters. The data acquisition module is responsible for collecting carbon emission data from various carbon emission sources, including factories, vehicles and energy facilities. The carbon emission data includes fuel consumption, production process emissions, energy consumption, etc. The carbon emission data can be collected in real time or periodically, which effectively ensures the accuracy and real-time nature of the data, while reducing the overall energy consumption of the system.

[0015] Preferably, the data preprocessing module is used to clean, format and standardize the collected raw data to eliminate erroneous, missing or inconsistent data. Since the raw data may contain a large amount of redundant information and useless data, which will increase the complexity and computational complexity of subsequent processing, the data preprocessing module can remove this redundant information, simplify the data structure and format, thereby reducing the complexity of subsequent processing and improving the processing efficiency and response speed of the system. At the same time, machine learning algorithms are introduced to automatically identify and correct abnormal data, improve data quality, and reduce human intervention.

[0016] Preferably, the carbon emission calculation module is used to accurately calculate the carbon emissions of each emission source based on the preprocessed data using carbon emission factors and calculation formulas. The carbon emission factors include fuel combustion emission factors, industrial production process emission factors and energy consumption emission factors. The calculation formula can use direct emission calculation formulas or indirect emission calculation formulas. At the same time, the carbon emission factor library is continuously updated to ensure the accuracy and timeliness of the calculation results.

[0017] Preferably, the carbon emission calculation module includes the following steps when operating:

[0018] S11: First, receiving the carbon emission related data preprocessed by the data preprocessing module;

[0019] S12: Select appropriate carbon emission factors according to the characteristics and types of emission sources, and pay attention to the update of carbon emission factors to ensure that the factors used are the latest and accurate;

[0020] S13: Calculate direct carbon emissions using parameters such as fuel consumption and fuel combustion emission factors;

[0021] S14: Calculate the indirect carbon emissions based on parameters such as energy consumption and energy consumption emission factors;

[0022] S15: Add direct emissions and indirect emissions to obtain total carbon emissions;

[0023] S16: Compare the calculated carbon emissions with the preset carbon emissions range to check whether there are abnormal values ​​or errors. If there is a large error between the calculated carbon emissions and the preset range, perform error analysis to find out the cause and make corrections;

[0024] S17: Based on the error analysis results, calibrate the carbon emission data to ensure the accuracy and reliability of the data.

[0025] Preferably, the carbon emissions can be automatically and accurately calculated through preset carbon emission calculation rules and real-time monitored carbon emission data. The use of efficient algorithms and calculation models can process a large amount of carbon emission data in a short time and quickly generate calculation results, so that enterprises can grasp the carbon emission situation in real time and take emission reduction measures in time, thereby optimizing carbon emission management efficiency.

[0026] Preferably, the blockchain storage and verification module is used to store the calculated carbon emission data on the blockchain in a tamper-proof manner, while providing a data verification mechanism to ensure the authenticity and integrity of the data. It also uses distributed ledger technology to improve the security and transparency of data storage, and uses smart contracts to automatically execute data verification and audit processes.

[0027] Preferably, the data analysis and visualization module is used to conduct in-depth analysis of carbon emission data stored on the blockchain, identify emission trends, patterns and anomalies, and intuitively display the analysis results in the form of charts, reports, etc. At the same time, it uses big data analysis and artificial intelligence technology to provide predictive analysis and optimization suggestions to help companies plan emission reduction measures in advance.

[0028] Preferably, the data analysis and visualization module comprises the following steps when operating:

[0029] S21: First, receive the carbon emission related data calculated by the carbon emission calculation module, and convert the data format to meet the requirements of subsequent analysis;

[0030] S22: Classify, label and index the stored data for subsequent rapid retrieval and analysis;

[0031] S23: Calculate the carbon emission data using the smart contract in the blockchain system according to the preset carbon emission calculation rules;

[0032] S24: Use data mining technology to extract useful information and patterns from large amounts of data, identify trends, patterns and anomalies in carbon emissions, and provide support for subsequent decision-making;

[0033] S25: Intuitively display carbon emission data and analysis results through charts, curves, dashboards, etc., including the changing trend of carbon emissions, comparison of carbon emissions in different time periods, etc.;

[0034] S26: Provide a user interaction interface to allow users to adjust the content, form and scope of the display as needed, and continuously optimize the effects and functions of data visualization based on user feedback and needs.

[0035] Preferably, the compliance checking and reporting module is used to automatically check whether the company's carbon emission data meets the compliance requirements according to national and international carbon emission regulations and standards, and generate compliance reports. At the same time, it integrates the regulatory database and updates the compliance requirements in real time to ensure that the company always complies with the latest environmental regulations. The database can be updated in real time according to national carbon emission regulations and standards.

[0036] Preferably, the user interaction and feedback module is used to provide a user-friendly interface that enables enterprises to easily access and manage their carbon emissions data, while collecting user feedback and continuously optimizing system functions and services by: introducing augmented reality and virtual reality technologies to provide immersive data exploration and interactive experience, and enhancing user engagement and satisfaction.

[0037] Beneficial effects of the present invention:

[0038] 1. Compared with the traditional blockchain-based carbon emission continuous monitoring and analysis system, which generally relies on manual input and manual calculation, this may lead to the risk of data errors and tampering. Due to the cumbersome manual calculation and data processing process, the efficiency is low. At the same time, there may be deficiencies in data transparency. The data may be difficult to be verified or traced by a third party. The blockchain-based carbon emission continuous monitoring and analysis system can calculate carbon emissions in real time and accurately through smart contracts and automated algorithms, greatly improving the calculation efficiency and automation. Smart contracts can also automatically trigger emission reduction measures or purchase carbon emission rights according to preset rules, further enhancing the flexibility of carbon emission management;

[0039] 2. Through the preset carbon emission calculation rules and real-time monitored carbon emission data, the carbon emissions can be calculated automatically and accurately. The use of efficient algorithms and calculation models can process a large amount of carbon emission data in a short time and quickly generate calculation results, so that enterprises can grasp the carbon emission situation in real time and take emission reduction measures in time, thereby optimizing the efficiency of carbon emission management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 What is shown is a schematic diagram of the work flow of the carbon emission calculation module of the blockchain-based carbon emission continuous monitoring and analysis system of the present invention;

[0041] Figure 2 What is shown is a schematic diagram of the workflow of the data analysis and visualization module of the blockchain-based carbon emission continuous monitoring and analysis system of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] See also Figure 1-2 The present invention provides an embodiment: a carbon emission continuous monitoring and analysis system based on blockchain, comprising:

[0044] Data collection module: used to collect carbon emission related data;

[0045] Data preprocessing module: used to preprocess the carbon emission related data collected by the data acquisition module;

[0046] Carbon emission calculation module: used to calculate carbon emissions based on preprocessed data;

[0047] Regional chain storage and verification module: used to store the calculation results of the carbon emission calculation module;

[0048] Data analysis and visualization module: used to conduct in-depth analysis of the calculation results of the carbon emission calculation module;

[0049] Compliance inspection and reporting module: used to check whether carbon emissions meet the standards according to relevant regulations;

[0050] User interaction and feedback module: used to provide users with operation interaction pages.

[0051] Preferably, the data acquisition module includes multiple groups of low-power IoT sensors and high-precision smart meters. The data acquisition module is responsible for collecting carbon emission data from various carbon emission sources, including factories, vehicles and energy facilities. The carbon emission data includes fuel consumption, production process emissions, energy consumption, etc. The carbon emission data can be collected in real time or periodically, which effectively ensures the accuracy and real-time nature of the data, while reducing the overall energy consumption of the system.

[0052] Preferably, the data preprocessing module is used to clean, format and standardize the collected raw data to eliminate erroneous, missing or inconsistent data. Since the raw data may contain a large amount of redundant information and useless data, which will increase the complexity and computational complexity of subsequent processing, the data preprocessing module can remove this redundant information, simplify the data structure and format, thereby reducing the complexity of subsequent processing and improving the processing efficiency and response speed of the system. At the same time, machine learning algorithms are introduced to automatically identify and correct abnormal data, improve data quality, and reduce human intervention.

[0053] Preferably, the carbon emission calculation module is used to accurately calculate the carbon emissions of each emission source based on the preprocessed data using carbon emission factors and calculation formulas. The carbon emission factors include fuel combustion emission factors, industrial production process emission factors and energy consumption emission factors. The calculation formula can use direct emission calculation formulas or indirect emission calculation formulas. At the same time, the carbon emission factor library is continuously updated to ensure the accuracy and timeliness of the calculation results.

[0054] Preferably, the carbon emission calculation module includes the following steps when operating:

[0055] S11: First, receiving the carbon emission related data preprocessed by the data preprocessing module;

[0056] S12: Select appropriate carbon emission factors according to the characteristics and types of emission sources, and pay attention to the update of carbon emission factors to ensure that the factors used are the latest and accurate;

[0057] S13: Calculate direct carbon emissions using parameters such as fuel consumption and fuel combustion emission factors;

[0058] S14: Calculate the indirect carbon emissions based on parameters such as energy consumption and energy consumption emission factors;

[0059] S15: Add direct emissions and indirect emissions to obtain total carbon emissions;

[0060] S16: Compare the calculated carbon emissions with the preset carbon emissions range to check whether there are abnormal values ​​or errors. If there is a large error between the calculated carbon emissions and the preset range, perform error analysis to find out the cause and make corrections;

[0061] S17: Based on the error analysis results, calibrate the carbon emission data to ensure the accuracy and reliability of the data.

[0062] Preferably, the carbon emissions can be automatically and accurately calculated through preset carbon emission calculation rules and real-time monitored carbon emission data. The use of efficient algorithms and calculation models can process a large amount of carbon emission data in a short time and quickly generate calculation results, so that enterprises can grasp the carbon emission situation in real time and take emission reduction measures in time, thereby optimizing carbon emission management efficiency.

[0063] Preferably, the blockchain storage and verification module is used to store the calculated carbon emission data on the blockchain in a tamper-proof manner, while providing a data verification mechanism to ensure the authenticity and integrity of the data. It also uses distributed ledger technology to improve the security and transparency of data storage, and uses smart contracts to automatically execute data verification and audit processes.

[0064] Preferably, the data analysis and visualization module is used to conduct in-depth analysis of carbon emission data stored on the blockchain, identify emission trends, patterns and anomalies, and intuitively display the analysis results in the form of charts, reports, etc. At the same time, it uses big data analysis and artificial intelligence technology to provide predictive analysis and optimization suggestions to help companies plan emission reduction measures in advance.

[0065] Preferably, the data analysis and visualization module comprises the following steps when operating:

[0066] S21: First, receive the carbon emission related data calculated by the carbon emission calculation module, and convert the data format to meet the requirements of subsequent analysis;

[0067] S22: Classify, label and index the stored data for subsequent rapid retrieval and analysis;

[0068] S23: Calculate the carbon emission data using the smart contract in the blockchain system according to the preset carbon emission calculation rules;

[0069] S24: Use data mining technology to extract useful information and patterns from large amounts of data, identify trends, patterns and anomalies in carbon emissions, and provide support for subsequent decision-making;

[0070] S25: Intuitively display carbon emission data and analysis results through charts, curves, dashboards, etc., including the changing trend of carbon emissions, comparison of carbon emissions in different time periods, etc.;

[0071] S26: Provide a user interaction interface to allow users to adjust the content, form and scope of the display as needed, and continuously optimize the effects and functions of data visualization based on user feedback and needs.

[0072] Preferably, the compliance checking and reporting module is used to automatically check whether the company's carbon emission data meets the compliance requirements according to national and international carbon emission regulations and standards, and generate compliance reports. At the same time, it integrates the regulatory database and updates the compliance requirements in real time to ensure that the company always complies with the latest environmental regulations. The database can be updated in real time according to national carbon emission regulations and standards.

[0073] Preferably, the user interaction and feedback module is used to provide a user-friendly interface that enables enterprises to easily access and manage their carbon emissions data, while collecting user feedback and continuously optimizing system functions and services by: introducing augmented reality and virtual reality technologies to provide immersive data exploration and interactive experience, and enhancing user engagement and satisfaction.

[0074] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. Carbon emission continuous monitoring and analysis system based on blockchain; its characteristics are: Included are: Data collection module: used to collect carbon emission related data; Data preprocessing module: used to preprocess the carbon emission related data collected by the data acquisition module; Carbon emission calculation module: used to calculate carbon emissions based on preprocessed data; Regional chain storage and verification module: used to store the calculation results of the carbon emission calculation module; Data analysis and visualization module: used to conduct in-depth analysis of the calculation results of the carbon emission calculation module; Compliance inspection and reporting module: used to check whether carbon emissions meet the standards according to relevant regulations; User interaction and feedback module: used to provide users with operation interaction pages.

2. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 1 is characterized in that: The data acquisition module includes multiple groups of low-power IoT sensors and high-precision smart meters. The data acquisition module is responsible for collecting carbon emission data from various carbon emission sources, including factories, vehicles and energy facilities. Carbon emission data includes fuel consumption, production process emissions, energy consumption, etc. Carbon emission data can be collected in real time or periodically, effectively ensuring the accuracy and real-time nature of the data, while reducing the overall energy consumption of the system.

3. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 2 is characterized in that: The data preprocessing module is used to clean, format and standardize the collected raw data to eliminate erroneous, missing or inconsistent data. Since the raw data may contain a large amount of redundant information and useless data, these data will increase the complexity and computational complexity of subsequent processing. Through the processing of the data preprocessing module, these redundant information can be removed, the data structure and format can be simplified, thereby reducing the complexity of subsequent processing and improving the processing efficiency and response speed of the system. At the same time, machine learning algorithms are introduced to automatically identify and correct abnormal data, improve data quality, and reduce human intervention.

4. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 3 is characterized by: The carbon emission calculation module is used to accurately calculate the carbon emissions of each emission source based on the preprocessed data, using carbon emission factors and calculation formulas. The carbon emission factors include fuel combustion emission factors, industrial production process emission factors and energy consumption emission factors. The calculation formula can use direct emission calculation formulas or indirect emission calculation formulas. At the same time, the carbon emission factor library is continuously updated to ensure the accuracy and timeliness of the calculation results.

5. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 4 is characterized in that: The carbon emission calculation module includes the following steps when working: S11: First, receiving the carbon emission related data preprocessed by the data preprocessing module; S12: Select appropriate carbon emission factors according to the characteristics and types of emission sources, and pay attention to the update of carbon emission factors to ensure that the factors used are the latest and accurate; S13: Calculate direct carbon emissions using parameters such as fuel consumption and fuel combustion emission factors; S14: Calculate the indirect carbon emissions based on parameters such as energy consumption and energy consumption emission factors; S15: Add direct emissions and indirect emissions to obtain total carbon emissions; S16: Compare the calculated carbon emissions with the preset carbon emissions range to check whether there are abnormal values ​​or errors. If there is a large error between the calculated carbon emissions and the preset range, perform error analysis to find out the cause and make corrections; S17: Based on the error analysis results, calibrate the carbon emission data to ensure the accuracy and reliability of the data.

6. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 5 is characterized in that: The blockchain storage and verification module is used to store the calculated carbon emission data on the blockchain in a tamper-proof manner, while providing a data verification mechanism to ensure the authenticity and integrity of the data. It also uses distributed ledger technology to improve the security and transparency of data storage, and uses smart contracts to automatically execute data verification and audit processes.

7. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 6 is characterized in that: The data analysis and visualization module is used to conduct in-depth analysis of carbon emission data stored on the blockchain, identify emission trends, patterns and anomalies, and intuitively display the analysis results in the form of charts, reports, etc. At the same time, it uses big data analysis and artificial intelligence technology to provide predictive analysis and optimization suggestions to help companies plan emission reduction measures in advance.

8. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 7 is characterized in that: The data analysis and visualization module includes the following steps when working: S21: First, receive the carbon emission related data calculated by the carbon emission calculation module, and convert the data format to meet the requirements of subsequent analysis; S22: Classify, label and index the stored data for subsequent rapid retrieval and analysis; S23: Calculate the carbon emission data using the smart contract in the blockchain system according to the preset carbon emission calculation rules; S24: Use data mining technology to extract useful information and patterns from large amounts of data, identify trends, patterns and anomalies in carbon emissions, and provide support for subsequent decision-making; S25: Intuitively display carbon emission data and analysis results through charts, curves, dashboards, etc., including the changing trend of carbon emissions, comparison of carbon emissions in different time periods, etc.; S26: Provide a user interaction interface to allow users to adjust the content, form and scope of the display as needed, and continuously optimize the effects and functions of data visualization based on user feedback and needs.

9. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 8 is characterized in that: The compliance checking and reporting module is used to automatically check whether the company's carbon emission data meets the compliance requirements according to national and international carbon emission regulations and standards, and generate compliance reports. At the same time, it integrates the regulatory database and updates the compliance requirements in real time to ensure that the company always complies with the latest environmental regulations. The database can be updated in real time according to national carbon emission regulations and standards.

10. The blockchain-based carbon emission continuous monitoring and analysis system according to claim 9 is characterized in that: The user interaction and feedback module is used to provide a user-friendly interface that enables enterprises to easily access and manage their carbon emission data, while collecting user feedback and continuously optimizing system functions and services by: introducing augmented reality and virtual reality technologies to provide immersive data exploration and interactive experience, and enhancing user engagement and satisfaction.

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