Enterprise carbon emission double-control data management platform based on block chain

Abstract

The invention discloses an enterprise carbon emission double-control data management platform based on a block chain, and relates to the technical field of carbon emission management and control, and the platform comprises the steps: obtaining the total carbon emission and total production value data in real time through a data collection module, carrying out the preprocessing, firstly judging whether the total amount exceeds the standard or not through a total amount exceeding analysis module, and directly triggering the early warning if the total amount exceeds the standard; if the carbon emission intensity does not exceed the standard, entering a carbon emission intensity prediction module which calculates the carbon emission intensity and further analyzes the severity of the change trend of the carbon emission intensity, and if the trend is serious, triggering early warning, otherwise, returning the system to the data acquisition module to form a closed loop. According to the invention, collaborative analysis and prospective early warning of the total carbon emission amount and intensity are realized; a rigid-flexible double-layer threshold management system is constructed, compliance is ensured, and lean emission reduction is guided; intelligent threshold setting based on historical data self-adaption is adopted, so that early warning is more accurate; and a full-link credible closed loop is formed based on a block chain technology, so that a complete solution is provided for efficient and credible carbon management.

Description

Technical Field

[0001] This invention relates to the field of carbon emission control technology, specifically to a blockchain-based enterprise carbon emission dual control data management platform. Background Technology

[0002] The blockchain-based enterprise carbon emission dual-control data management platform is a digital carbon management system for the industrial and energy sectors. It focuses on assisting high-energy-consuming industries and enterprises included in the national carbon regulatory system to achieve dual control targets for total carbon emissions and carbon emission intensity. The platform builds an immutable and fully traceable carbon emission data management system based on blockchain technology. By collecting enterprise production and energy consumption data in real time, it automatically calculates total carbon emissions and carbon emission intensity per unit of output value, and stores key data on the blockchain to ensure data transparency and credibility. The system also provides functions such as dual-control target early warning, emission reduction path analysis, and compliance report generation, helping enterprises to optimize carbon asset management and emission reduction decisions while meeting government regulatory requirements, and promoting green and low-carbon transformation.

[0003] For example, Chinese Patent Publication No. CN115601175A discloses an internet-based fiber optic intelligent online monitoring system for coal mine roadways. First, a distributed carbon emission rights trading platform is established. This platform stores the carbon emission rights surplus, demand, and initial quotes of online manufacturers, and the contracts are stored in a blockchain system. Then, a buyer or seller initiates a transaction application, deploying a smart contract to control the transaction. The buyer and seller negotiate carbon emission rights and carbon prices to determine the transaction price. Next, the buyer pays the amount to the smart contract, and the seller signs and submits a carbon emission rights transfer agreement to the smart contract. A verification agency updates the information synchronously, locking the carbon emission rights accounts of both parties. Finally, the smart contract transfers the buyer's payment to the seller's wallet and sends the carbon emission rights transfer information to the buyer, completing the transaction. This invention offers transparent transactions, high data security and privacy, and intelligent assistance to traders in pricing, thereby increasing the profits for both parties.

[0004] However, existing technical solutions monitor total carbon emissions and intensity as independent indicators, severing the dynamic relationship between the two and failing to build a real-time quantitative model that drives changes in total carbon emissions through changes in carbon emission intensity. This deficiency creates a key management blind spot: even if total carbon emissions have not yet exceeded the limit, the continuous rise in carbon emission intensity has already foreshadowed the risk of exceeding the limit in the future. Due to the lack of a forward-looking collaborative analysis mechanism, enterprises can only passively trigger alarms after total carbon emissions exceed the limit, missing the best intervention window during the risk accumulation stage. Summary of the Invention

[0005] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a blockchain-based enterprise carbon emission dual-control data management platform. This platform solves the problem that existing technologies, by severing the dynamic correlation between total carbon emissions and intensity, cannot provide early warnings of the risk of exceeding total emissions limits based on the trend of increasing intensity, thus forcing enterprises to only passively respond to the aftermath and miss the critical opportunity for proactive intervention.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a blockchain-based enterprise carbon emission dual-control data management platform, comprising the following specific modules: a data acquisition module: real-time acquisition of total carbon emission data and GDP data, and preprocessing them; a total emission exceedance analysis module: analysis of whether the total carbon emission exceeds the standard based on the preprocessed total carbon emission data; if it exceeds the standard, an exceedance warning module is executed; otherwise, a carbon emission intensity prediction module is executed; a carbon emission intensity prediction module: the ratio of the total carbon emission data to the GDP data is calculated to obtain carbon emission intensity data; an intensity severity coefficient is calculated from the carbon emission intensity data; the severity coefficient is used to analyze whether the carbon emission intensity is severe; if severe, an exceedance warning module is executed; otherwise, the process returns to the data acquisition module; an exceedance warning module: used to issue a warning when the total carbon emission exceeds the standard or the carbon emission intensity is severe.

[0007] Furthermore, the specific method by which the total emission exceedance analysis module analyzes whether the total carbon emissions exceed the standard based on the preprocessed total carbon emission data is as follows: a preset total carbon emission exceedance threshold is set, and the value of the total carbon emission data is compared with the total carbon emission exceedance threshold. If the value of the total carbon emission data is greater than the total carbon emission exceedance threshold, it indicates that the total carbon emissions exceed the standard. If the value of the total carbon emission data is less than or equal to the total carbon emission exceedance threshold, it indicates that the total carbon emissions do not exceed the standard.

[0008] Furthermore, the specific preset method for the total carbon emission exceeding threshold is as follows: First, the annual carbon emission allowance obtained by the enterprise, i.e. the statutory upper limit of the total compliance amount, is used as the rigid exceeding threshold to ensure that the total carbon emission at the time of warning is directly aligned with the emission control compliance requirements. Then, the average value of the enterprise's historical total carbon emission is used as the soft exceeding threshold. That is, the value of the total carbon emission must never exceed the rigid exceeding threshold, but the soft exceeding threshold can be dynamically adjusted according to the actual situation. Therefore, the total carbon emission exceeding threshold includes a static rigid exceeding threshold and a dynamic soft exceeding threshold, and the soft exceeding threshold is less than or equal to the rigid exceeding threshold.

[0009] Furthermore, the specific method for obtaining the intensity severity coefficient is as follows: in the time series, the carbon emission intensity data at different times are sequentially differentially calculated to obtain several intensity differences. The increasing coefficient is analyzed based on the intensity differences. The proportion of the increasing coefficient is analyzed to obtain the proportion weight. The proportion weight and the increasing coefficient are comprehensively calculated and standardized to obtain the intensity severity coefficient.

[0010] Furthermore, the specific method for obtaining the intensity difference is as follows: calculate the difference between the carbon emission intensity data at the next moment and the carbon emission intensity data at the previous moment to obtain the intensity difference.

[0011] Furthermore, the specific method for obtaining the increment coefficient is as follows: compare the strength difference with zero. If the strength difference is greater than zero, then record this strength difference as the increment coefficient; if the strength difference is less than or equal to zero, then continue to compare the strength difference with zero.

[0012] Furthermore, the specific method for obtaining the percentage weight is as follows: count the number of increasing coefficients to obtain the number of increasing coefficients, count the number of intensity differences, and calculate the ratio between the number of increasing coefficients and the number of intensity differences to obtain the percentage weight.

[0013] Furthermore, the specific method for obtaining the intensity severity coefficient is as follows: ;in, Indicates the severity coefficient. Indicates the percentage weight. Indicates the number of increments. This represents the increment coefficient.

[0014] Furthermore, the specific method for analyzing whether carbon emission intensity is severe based on the intensity severity coefficient is as follows: a carbon emission intensity threshold is preset, and the intensity severity coefficient is compared with the carbon emission intensity threshold. If the intensity severity coefficient is greater than the carbon emission intensity threshold, it indicates that the carbon emission intensity is severe; if the intensity severity coefficient is less than or equal to the carbon emission intensity threshold, it indicates that the carbon emission intensity is normal.

[0015] Furthermore, the specific preset method for the carbon emission intensity threshold is as follows: statistical analysis is performed on the carbon emission intensity of a period of time before the total carbon emissions exceed the standard in history, and the average value is calculated. Then, the average value is multiplied by half to obtain the carbon emission intensity threshold.

[0016] Beneficial effects Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By using the intensity severity coefficient, the platform not only monitors the total carbon emissions in real time, but also quantifies the trend, persistence, and magnitude of changes in carbon emission intensity. This enables the platform to accurately identify the risk of future total emissions exceeding the limit due to the continuous deterioration of carbon emission efficiency before the total emissions have exceeded the limit, thus securing a critical intervention window for enterprises.

[0017] 2. A dual-layer threshold indicator system combining rigid and dynamic soft thresholds is established. This not only ensures that any warnings are directly aligned with national compliance requirements and strictly adhere to legal red lines, but also enables lean internal management through soft thresholds based on the company's own historical data. This system allows companies to make dynamic adjustments within the rigid upper limit, satisfying both compliance rigidity and management flexibility, and guiding companies to proactively pursue higher energy efficiency targets below the statutory limits.

[0018] 3. By analyzing the average intensity of carbon emission intensity for a period of time before a company exceeds its total emission limit in history, and multiplying it by half, the threshold for judging the severity of carbon emission intensity is automatically set. This method makes the risk warning line adaptable to individual companies and based on historical experience, rather than a uniform fixed value, so that the system becomes smarter and the warnings become more accurate with use.

[0019] 4. By leveraging blockchain technology to build a data storage layer, the immutability and traceability of data throughout the entire process are ensured. At the same time, the early warning module is integrated with log ledgers, workflow systems, and government regulatory platforms to achieve an automated closed-loop operation from risk identification, tiered alerts, task assignment to external reporting. This not only greatly improves management efficiency but also builds a carbon management system that is auditable, with clear responsibilities and efficient response, providing a solid and reliable data foundation for compliance and green decision-making.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] Figure 1 This invention provides a flowchart of a blockchain-based enterprise carbon emission dual-control data management method.

[0022] Figure 2 This invention is a structural diagram of a blockchain-based enterprise carbon emission dual-control data management platform. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0025] Example 1: like Figures 1-2 As shown, this embodiment of the invention provides a blockchain-based enterprise carbon emission dual-control data management platform, which includes the following specific modules: Data Acquisition Module: This module collects total carbon emissions data and GDP data in real time, cleans the data to remove redundancy, and improves the quality of the total carbon emissions data and GDP data. The platform connects to smart metering instruments such as electricity meters and gas meters, as well as the CEMS continuous emission monitoring system, to build a real-time monitoring network covering energy consumption and direct emissions, thereby obtaining total carbon emissions data. These devices automatically collect key parameters such as electricity consumption, fuel consumption, and carbon dioxide concentration and flow rate in flue gas at a frequency of no less than 15 minutes per time. At the same time, this raw collected data and its metadata, such as timestamps and device IDs, are generated into data hashes in real time and uploaded to the blockchain network for storage, realizing an immutable record from the source of the data. This provides a high-precision and traceable real-time data foundation for carbon emissions accounting. Meanwhile, the platform is deeply integrated with the ERP production management system, automatically acquiring data reflecting gross production value such as production orders, product output, and material consumption. This data serves as the denominator for calculating carbon emission intensity and is linked and synchronized with total carbon emission data in real time within the platform. This linking process and key data snapshots are recorded on the blockchain through smart contracts, ensuring the transparency of the data linking logic and the auditability of the process. This ensures that the data for the two core dimensions of total carbon emission and carbon emission intensity are from the same source and consistent in time and space. In addition, the platform uses the emission factor library built into the IPCC international methodology and the national GB / T 32150 "Greenhouse Gas Emissions Accounting and Reporting Requirements" series of standards to provide a unified and authoritative standard for the quantitative accounting of carbon emissions. It automatically and accurately converts various physical activity data monitored by enterprises on a daily basis into standard carbon dioxide equivalent emission values. The emission factor versions, calculation logic and input / output data hashes used in the accounting process are all anchored to the blockchain to form a verifiable accounting evidence chain, ensuring the scientificity, compliance and comparability of the accounting results. It is the computing cornerstone for enterprises to generate legally valid performance reports, conduct internal and external benchmarking analysis and achieve precise carbon management. Among them, the IPCC international methodology has constructed a scientific, systematic and globally recognized greenhouse gas accounting framework. This methodology clarifies key specifications such as emission source classification, accounting boundaries, activity data collection and emission factor selection, ensuring that the carbon emission calculation process conforms to international scientific consensus and best practices. It not only enhances the international comparability and credibility of accounting results, but also provides a solid technical foundation for enterprises to participate in the international carbon market, cope with carbon constraints in transnational supply chains and conduct international benchmarking. The emission factor library built into the national standard GB / T 32150 "Requirements for Greenhouse Gas Emission Accounting and Reporting" series is the authoritative technical basis for carbon emission accounting and reporting in my country. It clarifies the accounting methods, data quality management and reporting content requirements for greenhouse gas emissions at the enterprise level. Adherence to this standard ensures that the platform's output results have legal effect and regulatory recognition in China. It can be directly used in official procedures such as emission control enterprise compliance declaration, carbon emission trading, energy conservation supervision and green factory evaluation. It is an important guarantee for achieving compliance, standardization and institutionalization of carbon management in China.

[0026] Total Emission Exceedance Analysis Module: Analyzes whether the total carbon emissions exceed the standard based on the cleaned carbon emission data. If the total emissions exceed the standard, the excess warning module is executed; otherwise, the carbon emission intensity prediction module is executed. Carbon emission intensity prediction module: This module calculates the carbon emission intensity data by comparing the total carbon emission data with the gross domestic product (GDP) data. It then uses this carbon emission intensity data to calculate the severity coefficient. The severity coefficient is used to analyze whether the carbon emission intensity is severe. If it is severe, the over-limit warning module is executed; otherwise, the data acquisition module is returned. The exceedance warning module is used to issue warnings when total carbon emissions exceed the limit or carbon emission intensity is severe. When a warning is triggered, the platform first dynamically highlights relevant indicators on a visual monitoring dashboard using different colors (e.g., red for total carbon emissions exceeding the limit, orange for severe carbon emission intensity) based on the type of exceedance (total carbon emissions or carbon emission intensity and severity). A warning window automatically pops up containing specific exceedance values, location (e.g., specific workshop or emission source), and preliminary causes. Simultaneously, according to preset communication rules, a structured warning notification is sent to designated management personnel via WeChat, SMS, or email through an integrated interface. The early warning judgment result, trigger time, and on-chain data fingerprint on which it is based will be automatically written into the blockchain to ensure the non-repudiation and traceability of the early warning behavior. Subsequently, the early warning event will be automatically recorded in a dedicated log ledger, generating a traceable record containing time, data snapshot, and processing status. It may also automatically generate to-do tasks through API interface and push them to the workflow system of relevant responsible persons. If government integration is configured, for carbon emission exceeding the standard, an early warning report will be automatically generated and sent to the superior regulatory platform according to the set rules, thus forming a complete and automated operation closed loop from internal perception and hierarchical notification to task generation and external reporting.

[0027] Example 2 differs from Example 1 in that: The specific method for analyzing whether total carbon emissions exceed the standard based on the cleaned carbon emission data is as follows: A preset threshold for total carbon emissions is established to set a clear quantitative benchmark for the legal upper limit and internal management red line of a company's total carbon emissions. The value of total carbon emissions data is compared with the threshold. If the value of total carbon emissions data is greater than the threshold, it means that the total carbon emissions have exceeded the standard. If the value of total carbon emissions data is less than or equal to the threshold, it means that the total carbon emissions have not exceeded the standard.

[0028] The specific method for presetting the threshold for total carbon emissions exceeding the standard is as follows: First, the annual carbon emission allowance obtained by the enterprise, i.e. the legally mandated upper limit of compliance, is used as the rigid exceedance threshold to ensure that the total carbon emissions at the time of warning are directly aligned with the emission control compliance requirements. Then, the average value of the enterprise's historical total carbon emissions is used as the soft exceedance threshold to establish a management and early warning baseline for the enterprise based on its own operating rules and with realistic reference significance. The historical average value reflects the enterprise's emission level under normal conditions. Using this as the soft threshold, whether it deviates from its normal track is used as an early risk judgment criterion. When the value of the real-time total carbon emission data exceeds the average value, even if there is still room from the legal upper limit, i.e. the rigid exceedance threshold, it means that the enterprise's current emission efficiency or production mode is deviating from its controllable historical normal. That is, the value of the total carbon emissions must never exceed the rigid exceedance threshold, but the soft exceedance threshold can be dynamically adjusted according to the actual situation. Therefore, the threshold for exceeding the total carbon emission standard includes a static rigid threshold and a dynamic soft threshold. The soft threshold is less than or equal to the rigid threshold. That is, the static rigid threshold directly corresponds to the legally mandated annual carbon emission quota limit of an enterprise. It is a legal red line that cannot be crossed. Once it is crossed, it means the risk of violation. Its role is to set the ultimate defense line. The dynamic soft threshold is an internal management line set based on the enterprise's historical average emission level or internal emission reduction target. It is lower than the rigid threshold and can be flexibly adjusted according to the production plan or emission reduction progress.

[0029] The specific method for obtaining the severity index is as follows: In the time series analysis, the carbon emission intensity data at different times are interpolated sequentially to obtain the change in carbon emission intensity within each time interval, resulting in several intensity differences. The increasing coefficient is analyzed based on the intensity differences to filter out pure carbon emission intensity deterioration signals. The proportion of the increasing coefficient is analyzed to obtain the proportion weight, which assesses the persistence or prevalence of the deterioration trend. That is, if the deterioration occurs for most of the time, it indicates that the deterioration is not an accidental fluctuation but a continuous result, and the certainty of severe carbon emission intensity is greater. The proportion weight and the increasing coefficient are comprehensively calculated and standardized to eliminate the difference in the dimensions of the proportion weight and the increasing coefficient, and to transform the values ​​of the proportion weight and the increasing coefficient of different orders of magnitude into a unified numerical range, thus obtaining the intensity severity coefficient.

[0030] The specific method for obtaining the strength difference is as follows: The difference between the carbon emission intensity data at the next moment and the carbon emission intensity data at the previous moment is calculated to quantify the rate of change of the enterprise's carbon emission intensity index between two adjacent monitoring time points, i.e., whether the index is rising, falling, or remaining the same, thus obtaining the intensity difference value.

[0031] The specific method for obtaining the increment coefficient is as follows: The intensity difference is compared with zero. If the intensity difference is greater than zero, it is recorded as an increment coefficient. In the early warning scenario, the goal is to accurately identify the risk of deterioration in carbon emission intensity from the mixed trend. Only an intensity difference greater than zero is a direct result of deterioration. By marking only intensity differences greater than zero as increment coefficients, we avoid being diluted and disturbed by decreasing or stable intensity differences, thus achieving precise focus. If the intensity difference is less than or equal to zero, the intensity difference is compared with zero again, and the monitoring state continues.

[0032] The specific methods for obtaining the percentage weight are as follows: The number of increasing coefficients is counted, and the number of intensity differences is also counted. The ratio of the number of increasing coefficients to the number of intensity differences is calculated to obtain the proportion weight, which reflects the probability of a severe trend in carbon emission intensity. That is, the larger the proportion weight, the greater the probability of a severe trend in carbon emission intensity.

[0033] The specific method for obtaining the severity index is as follows: ; in, The intensity severity coefficient reflects the severity of carbon emission intensity trends. The multiplicative model couples the probability and severity of deteriorating carbon emission intensity—two key risk dimensions—to generate a comprehensive severity index that fully reflects the trend risk. Indicates the percentage weight. Indicates the number of increments. The increment coefficient represents the carbon emission intensity. If the carbon emission intensity reflected by the increment coefficient is severe, the subsequent judgment cannot be made solely based on the sum of the increment coefficients. Instead, the probability must be increased by combining the proportion weight. That is, the subsequent judgment can only be accurate when the probability of carbon emission intensity deteriorating increases and the deterioration is severe.

[0034] The specific method for analyzing the severity of carbon emission intensity based on the severity coefficient is as follows: A preset carbon emission intensity threshold is set, and the severity coefficient is compared with the carbon emission intensity threshold. If the severity coefficient is greater than the carbon emission intensity threshold, it indicates that the carbon emission intensity is severe. If the severity coefficient is less than or equal to the carbon emission intensity threshold, it indicates that the carbon emission intensity is normal.

[0035] The specific method for setting the carbon emission intensity threshold is as follows: The carbon emission intensity is calculated by statistically analyzing the carbon emission intensity of a period of time before the total carbon emissions exceed the limit in history, and then multiplying the average value by half to obtain the carbon emission intensity threshold. That is, the average value of the calculated carbon emission intensity is used as the critical value of the current carbon emission intensity, and half is used as the critical value of the proportion weight. That is, the proportion weight exceeds half, indicating that the probability of carbon emission intensity deteriorating exceeds 50%. Therefore, the carbon emission intensity threshold needs to be combined with half and the average value of carbon emission intensity.

[0036] Technology stack: The platform's backend utilizes the Java / SpringBoot framework, providing stable, efficient, and easily scalable backend services to ensure the high concurrency and reliability requirements of enterprise-level applications. The frontend employs Vue.js + ECharts for visualization, achieving a responsive, high-performance user interface and rich data visualization charts, enhancing the monitoring and analysis experience of carbon emissions and energy consumption data. The database uses MySQL to store business data and InfluxDB to store time-series data. MySQL, as a relational database, ensures reliable storage and management of structured business data from enterprises, equipment, and users, while InfluxDB, as a time-series database, efficiently stores and queries massive amounts of energy consumption and emission data generated chronologically from sensors and instruments. The platform supports industrial protocols such as Modbus, OPC UA, and MQTT for interfacing with devices, ensuring broad compatibility and direct access to various intelligent instruments, sensors, and equipment systems in the factory, enabling automatic data collection at the source. It also provides RESTful API interfaces for external systems, establishing a standard and open external data and service channel, facilitating standardized integration and data exchange with government regulatory platforms or other internal enterprise systems.

[0037] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A blockchain-based enterprise carbon emission dual-control data management platform, characterized in that: Includes the following specific modules: Data acquisition module: Collects total carbon emissions data and gross domestic product data in real time and performs preprocessing; Total Emission Exceedance Analysis Module: Analyzes whether the total carbon emissions exceed the standard based on the preprocessed total carbon emission data. If the total emissions exceed the standard, the excess warning module is executed; otherwise, the carbon emission intensity prediction module is executed. Carbon emission intensity prediction module: This module calculates the carbon emission intensity data by comparing the total carbon emission data with the gross domestic product (GDP) data. It then uses this carbon emission intensity data to calculate the severity coefficient. The severity coefficient is used to analyze whether the carbon emission intensity is severe. If it is severe, the over-limit warning module is executed; otherwise, the data acquisition module is returned. Exceeding Standard Early Warning Module: Used to issue early warnings when total carbon emissions exceed the standard or when carbon emission intensity is severe.

2. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 1, characterized in that: The specific method by which the total emission exceedance analysis module analyzes whether the total carbon emissions exceed the standard based on the preprocessed total carbon emission data is as follows: A preset threshold for total carbon emissions is set. The value of total carbon emissions data is compared with the threshold. If the value of total carbon emissions data is greater than the threshold, it means that the total carbon emissions have exceeded the standard. If the value of total carbon emissions data is less than or equal to the threshold, it means that the total carbon emissions have not exceeded the standard.

3. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 2, characterized in that: The specific preset method for the total carbon emission exceeding the threshold is as follows: First, the annual carbon emission allowance obtained by enterprises, i.e. the statutory upper limit of total compliance, is used as the rigid exceedance threshold to ensure that the total carbon emissions at the time of warning are directly aligned with the emission control compliance requirements. Then, the average value of the enterprise's historical total carbon emissions is used as the soft exceedance threshold. That is, the value of the total carbon emissions must never exceed the rigid exceedance threshold, but the soft exceedance threshold can be dynamically adjusted according to the actual situation. Therefore, the total carbon emission exceedance threshold includes a static rigid exceedance threshold and a dynamic soft exceedance threshold, and the soft exceedance threshold is less than or equal to the rigid exceedance threshold.

4. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 1, characterized in that: The specific method for obtaining the severity coefficient is as follows: In the time series, the carbon emission intensity data at different times are successively interpolated to obtain several intensity differences. The increasing coefficient is analyzed based on the intensity differences. The proportion of the increasing coefficient is analyzed to obtain the proportion weight. The proportion weight and the increasing coefficient are comprehensively calculated and standardized to obtain the intensity severity coefficient.

5. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 4, characterized in that: The specific method for obtaining the strength difference is as follows: The intensity difference is calculated by taking the difference between the carbon emission intensity data at the next moment and the carbon emission intensity data at the previous moment.

6. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 5, characterized in that: The specific method for obtaining the increment coefficient is as follows: The strength difference is compared with zero. If the strength difference is greater than zero, it is recorded as an increment coefficient. If the strength difference is less than or equal to zero, the strength difference is compared with zero again.

7. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 6, characterized in that: The specific method for obtaining the percentage weight is as follows: The number of increasing coefficients is counted, and the number of intensity differences is also counted. The ratio of the number of increasing coefficients to the number of intensity differences is calculated to obtain the proportion weight.

8. The blockchain-based enterprise carbon emission dual-control data management platform according to claim 7, characterized in that: The specific method for obtaining the severity coefficient is as follows: ； in, Indicates the severity coefficient. Indicates the percentage weight. Indicates the number of increments. This represents the increment coefficient.

9. A blockchain-based enterprise carbon emission dual-control data management platform according to claim 1, characterized in that: The specific method for analyzing the severity of carbon emission intensity based on the severity coefficient is as follows: A preset carbon emission intensity threshold is set, and the severity coefficient is compared with the carbon emission intensity threshold. If the severity coefficient is greater than the carbon emission intensity threshold, it indicates that the carbon emission intensity is severe. If the severity coefficient is less than or equal to the carbon emission intensity threshold, it indicates that the carbon emission intensity is normal.

10. A blockchain-based enterprise carbon emission dual-control data management platform according to claim 9, characterized in that: The specific preset method for the carbon emission intensity threshold is as follows: The carbon emission intensity threshold is calculated by statistically analyzing the carbon emission intensity of the period before the total carbon emissions exceed the limit in history, calculating the average value, and then multiplying it by half.

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