Carbon emission digital management system based on cloud platform

Abstract

The invention provides a carbon emission digital management system based on a cloud platform. The carbon emission digital management system comprises a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer and a display layer, according to the system, through multi-source data acquisition and data analysis based on an AI large model, artificial filling errors are avoided, meanwhile, carbon emission factors are dynamically adjusted through introduction of machine learning and automatic calculation, and the carbon emission accounting precision is comprehensively improved; secondly, the system is combined with a digital twinning technology to help enterprises to realize multi-level carbon emission trend analysis of groups, enterprises, processes, equipment and the like, comprehensive strategy optimization is carried out through multi-level data arrangement, and wider application services are provided; besides, the system is in butt joint with international standards such as the international carbon market and CBAM, can help enterprises to monitor carbon price fluctuation and predict in real time, intelligently adjust carbon tax and provide a compliant carbon transaction scheme, and helps the enterprises to reduce cost, thereby realizing comprehensive upgrading of carbon management from static statistics to intelligent prediction and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and more specifically, to a carbon emission digital management system based on a cloud platform. Background Art

[0002] Carbon emissions are greenhouse gas emissions generated by human activities, primarily from energy use, industrial production, transportation, and agricultural activities. Excessive carbon emissions can trigger climate change, damage ecosystems, and threaten human health. Currently, my country is vigorously pursuing net-zero emissions through energy conservation, emission reduction, and compensation efforts, effectively addressing climate change, promoting sustainable development, and enhancing its international competitiveness.

[0003] At present, the status of carbon emission monitoring and management is as follows: 1) For key industries, especially high-emission enterprises, carbon emissions data is fragmented and collection standards are inconsistent due to dispersed plant locations, complex production processes, and diverse equipment types. This leads to a lack of a centralized management platform. Furthermore, data silos weaken internal data sharing and collaboration capabilities, reducing the accuracy and real-time nature of carbon emissions monitoring and hindering inter-industry carbon management collaboration. Therefore, establishing unified data standards and a centralized management platform can enable data interconnection and interoperability, helping to improve real-time monitoring and analysis capabilities and developing more scientific and effective carbon reduction strategies. 2) Most process-based manufacturing industries involve multiple carbon emission processes, such as raw material procurement, production, processing, packaging, and transportation, and detailed records are required for each step. However, carbon emission statistics are often estimated after the fact, lacking real-time monitoring, making accurate tracking and optimization difficult. This prevents companies from identifying problems and adjusting strategies in a timely manner, thus affecting the effectiveness of carbon emission control. Therefore, establishing a real-time monitoring and analysis system can improve the accuracy of carbon management and enable companies to more effectively optimize carbon emission reduction measures. Some platforms for carbon emission monitoring are disclosed in the existing technology. For example, the patent document with publication number "CN116402481A" provides a smart energy carbon emission management platform. However, most existing technologies still rely on enterprises to fill in the data themselves, resulting in insufficient data authenticity. At the same time, manual estimation is delayed and there is a lack of real-time monitoring capabilities, which affects the accuracy of carbon management. Secondly, the carbon emission accounting of existing technologies is mainly based on static carbon emission factor models, which lack dynamic analysis capabilities, making it difficult to accurately predict carbon emission changes and unable to adapt to complex environmental factors (such as changes in production processes, energy structure, etc.). The post-statistical model also makes it difficult for enterprises to adjust their emission strategies in a timely manner and lacks the ability to intelligently predict and optimize. In addition, in terms of carbon trading and international compliance, most existing technologies only support basic quota management and fail to provide intelligent carbon asset optimization solutions. It is difficult for enterprises to participate in carbon market transactions efficiently, and they are not compliant with the EU Carbon Border Adjustment Mechanism (CBAM), IPCC (Intergovernmental Panel on Climate Change), and CCER (China Certified China has been slow to respond to international standards such as China's Certified Emission Reduction (CER), making it difficult to provide carbon accounting solutions that meet international standards in a timely manner, which may cause exporting companies to face additional carbon costs. Summary of the Invention

[0004] In order to overcome the defects of the above-mentioned existing carbon management technologies, such as low carbon management accuracy, lag and lack of dynamic analysis capabilities, the present invention provides a cloud-based carbon emission digital management system. Through automatic data interface and AI analysis, it avoids human reporting errors. At the same time, combined with digital twins and intelligent prediction models, it can realize real-time dynamic analysis of carbon emission trends. In addition, by introducing machine learning to dynamically adjust carbon emission factors, the accuracy of carbon emission accounting is comprehensively improved.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows: A carbon emission digital management system based on a cloud platform, including: The data collection layer is used to collect enterprises' multi-source carbon emission data in real time through manual reporting, the Internet of Things, and sensors; The hardware resource layer is used to provide hardware resource support for computing, storage, and network communication to ensure efficient data transmission and processing; The data management layer is used to build a standardized database to integrate and store the multi-source carbon emission data, and to achieve data traceability and credible evidence storage by combining blockchain technology; The platform algorithm layer is equipped with several different carbon accounting models, including at least one or more of the carbon accounting model of the enterprise's industry, the CCER carbon accounting model, the IPCC carbon accounting model, and the CBAM carbon accounting model. The platform algorithm layer is used to dynamically adjust the carbon emission factors of the carbon accounting model using a machine learning model, and combines digital twin technology to simulate and optimize carbon emissions in real time; The application layer is used to provide enterprises with application services related to carbon emission monitoring based on the cloud platform. The application services include: carbon data management, carbon reporting management, emission source management, carbon verification assistance, carbon emission accounting, carbon emission analysis, carbon asset management, carbon trading management, carbon footprint tracking, carbon quota optimization, CBAM accounting and report generation, CCER accounting and report generation, and IPCC accounting and report generation; The presentation layer is used to present the data output by the application layer in real time through a visual interface and realize user interaction; The data acquisition layer, hardware resource layer, data management layer, platform algorithm layer, application layer and presentation layer are arranged in sequence from the bottom layer to the top layer.

[0006] Preferably, in the data collection layer, manual reporting is performed with the assistance of AI, the manually reported data is parsed through the natural language processing capabilities of the AI ​​large model, and cross-verified with the data collected by the Internet of Things to ensure the authenticity of the data.

[0007] Preferably, in the data collection layer, the manually reported data includes at least: production and operation carbon emission data, transportation carbon emission data, waste treatment carbon emission data, financial data and carbon sink management data; The data collected by the Internet of Things is specifically edge gateway device data collected by energy meters, and the energy meters include at least: a gas meter, a steam meter, a water meter and a temperature meter; The sensor at least includes: an infrared analyzer, a sampling pipeline, an analysis cabinet, an NDIR carbon dioxide sensor and a methane sensor.

[0008] Preferably, the data management layer is further provided with a dynamic rule engine to support automatic conversion of carbon emission data formats among multiple standards such as CCER, IPCC and CBAM; Before storing the multi-source carbon emission data, data cleaning is performed on the multi-source carbon emission data, abnormal data is eliminated based on preset rules, and missing data is supplemented by an interpolation algorithm.

[0009] Preferably, in the platform algorithm layer, a machine learning model is used to update the carbon emission factor of the carbon accounting model in real time based on the enterprise's energy structure, production process parameters and equipment operating status; At the same time, a three-dimensional digital twin model of the company's carbon emissions is built to simulate, predict and optimize carbon emission changes of different production strategies in real time, and output the optimal emission reduction path.

[0010] Preferably, in the platform algorithm layer, the carbon accounting model of the industry in which the enterprise is located is pre-stored in the carbon accounting model library, and the industries include at least: steel, chemical and transportation industries.

[0011] Preferably, in the application layer, the levels of carbon emission accounting include, from high to low: total carbon emission accounting at the group level, carbon emission accounting at the enterprise level, carbon emission accounting at the process level, and carbon emission accounting at the production facility level; The levels of carbon emission analysis include, from high to low: group-level carbon emission analysis, enterprise-level carbon emission analysis, process-level carbon emission analysis, and production facility-level carbon emission analysis; group-level carbon emission analysis includes analysis of the group's total carbon emissions, carbon emission intensity, carbon emission trends, and carbon emission target progress; enterprise-level carbon emission analysis includes analysis of the company's total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, carbon emission target progress, and emission reduction potential; process-level carbon emission analysis includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, and emission reduction potential of each production process; production facility-level carbon emission analysis includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, and carbon emission trends of each production facility; The carbon quota optimization includes: using AI big models, combined with real-time carbon market price data, to recommend optimal carbon trading strategies for enterprises.

[0012] Preferably, the display layer constructs a carbon-aware panoramic map based on the data output by the application layer, and displays the total carbon emissions, carbon emission trend chart, carbon emission hotspot distribution map, equipment energy efficiency comparison chart, carbon asset balance, carbon trading income and international compliance progress in real time.

[0013] Preferably, in the display layer, the visual interface includes at least one or more of a dedicated display large screen, a PC screen, a tablet screen, and a smartphone screen.

[0014] Preferably, the carbon emission digital management system is integrated with the enterprise ERP or MES system through an API interface to achieve automatic synchronization of carbon emission data and production operation data.

[0015] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: The present invention provides a cloud-based digital carbon emissions management system, comprising a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer, and a display layer. This system strengthens data integration with enterprise platforms, avoids human reporting errors through multi-source data acquisition and data analysis based on large AI models, and comprehensively improves the accuracy of carbon emissions accounting by introducing machine learning and automatically calculating and dynamically adjusting carbon emission factors. Secondly, this system combines digital twins with intelligent prediction models to help companies analyze carbon emission trends at multiple levels, including groups, enterprises, processes, and equipment. It also optimizes comprehensive strategies through multi-layered data organization, providing a wider range of application services. In addition, this system has extremely high economic value. On the one hand, this system is connected to international carbon markets and international standards such as CBAM, and complies with the latest international carbon tariff requirements. It can help companies monitor carbon price fluctuations and forecasts in real time, make intelligent carbon tax adjustments, and reduce trade compliance risks; on the other hand, through intelligent carbon quota management and carbon trading decision-making support tools, it helps companies achieve centralized management of carbon assets, optimal cost allocation and value release, avoid idle carbon quotas and loss, improve carbon trading efficiency, create substantial profit space for companies, and help companies reduce costs, thereby realizing a comprehensive upgrade of carbon management from static statistics to intelligent prediction and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a structural diagram of a cloud-based carbon emission digital management system provided in Example 1.

[0017] Figure 2 This is a schematic diagram of the carbon emission analysis results at the group level provided in Example 2.

[0018] Figure 3 This is a schematic diagram of the enterprise-level carbon emission analysis results provided in Example 2.

[0019] Figure 4 This is a schematic diagram of the carbon emission analysis results at the process level provided in Example 2.

[0020] Figure 5 This is a schematic diagram of the carbon emission analysis results at the production facility level provided in Example 2.

[0021] Figure 6 This is the overall carbon management architecture diagram provided in Example 2.

[0022] Figure 7 This is a schematic diagram of carbon asset and carbon trading management provided in Example 2.

[0023] Figure 8This is a schematic diagram of the connection between the carbon emission digital management system provided in Example 2 and the enterprise production and operation data.

[0024] Figure 9 This is an application architecture diagram of the carbon emission digital management system provided in Example 3. DETAILED DESCRIPTION

[0025] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting the present application; In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size; It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0026] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0027] Example 1 like Figure 1 As shown, this embodiment provides a carbon emission digital management system based on a cloud platform, including: The data collection layer is used to collect enterprises' multi-source carbon emission data in real time through manual reporting, the Internet of Things, and sensors; The hardware resource layer is used to provide hardware resource support for computing, storage, and network communication to ensure efficient data transmission and processing; The data management layer is used to build a standardized database to integrate and store the multi-source carbon emission data, and to achieve data traceability and credible evidence storage by combining blockchain technology; The platform algorithm layer is equipped with several different carbon accounting models, including at least one or more of the carbon accounting model of the enterprise's industry, the CCER carbon accounting model, the IPCC carbon accounting model, and the CBAM carbon accounting model. The platform algorithm layer is used to dynamically adjust the carbon emission factors of the carbon accounting model using a machine learning model, and combines digital twin technology to simulate and optimize carbon emissions in real time; The application layer is used to provide enterprises with application services related to carbon emission monitoring based on the cloud platform. The application services include: carbon data management, carbon reporting management, emission source management, carbon verification assistance, carbon emission accounting, carbon emission analysis, carbon asset management, carbon trading management, carbon footprint tracking, carbon quota optimization, CBAM accounting and report generation, CCER accounting and report generation, and IPCC accounting and report generation; The presentation layer is used to present the data output by the application layer in real time through a visual interface and realize user interaction; The data acquisition layer, hardware resource layer, data management layer, platform algorithm layer, application layer and presentation layer are arranged in sequence from the bottom layer to the top layer.

[0028] In the specific implementation process, this system is built on a cloud platform and operates through six layers of collaborative operation to achieve carbon data collection, storage, calculation, accounting, optimization and display. The specific structure is as follows: 1) Data collection layer: Combines manual reporting with automatic data collection from the Internet of Things to collect carbon emission data such as corporate operations, energy consumption, and waste disposal; 2) Hardware resource layer: provides computing, storage, and network support to ensure efficient data processing and transmission; 3) Data management layer: Build a standardized database to integrate collection, business, and statistical data for efficient storage and management; while also integrating blockchain technology to achieve data traceability and trusted evidence storage; 4) Platform algorithm layer: Use industry carbon accounting models (such as steel, IPCC, CBAM, etc.) to calculate, verify and manage carbon emissions compliance; 5) Application layer: Supports various application services such as carbon data management, emission analysis, carbon trading assessment, and CBAM accounting, providing enterprises with carbon asset optimization strategies; 6) Display layer: Visualize carbon emission data through large screens, PCs, and mobile devices to facilitate real-time monitoring and decision-making; This system has the following advantages: 1) Build a credible carbon data system to cope with multi-level compliance supervision: The Carbon Cloud platform in this system provides digital management capabilities covering the entire process of emissions collection, accounting, auditing and reporting. It can help companies establish a compliant, transparent and traceable carbon emissions data system, meet compliance reporting requirements, support carbon compliance reporting under international rules such as the EU CBAM, and comprehensively improve corporate carbon governance. 2) Activate the value of carbon assets and enhance the responsiveness of the carbon market: Through intelligent carbon quota management and carbon trading decision-making tools, the platform helps companies achieve centralized management of carbon assets, optimal cost allocation, and value release, avoiding idle carbon quotas and loss, improving carbon trading efficiency, and creating substantial profit space for companies. 3) Open up multi-base data links to support product-level carbon footprint accounting: The platform centrally accesses carbon emission data from all production bases of an enterprise, enabling automatic data collection, dynamic monitoring, and unified accounting, significantly improving carbon data quality and management efficiency. In conjunction with product-level carbon footprint models, it supports refined carbon emission calculations for complex product chains, serving product carbon labeling, green certification, and international customer needs. 4) Support export-oriented enterprises in addressing CBAM challenges and ensuring cross-border trade compliance: The Carbon Cloud platform can output product carbon footprint reports and quota certificates that meet international standards in accordance with the requirements of the EU Carbon Border Adjustment Mechanism (CBAM), helping export companies build a traceable and verifiable carbon emissions accounting system, reduce trade compliance risks, and enhance global market competitiveness.

[0029] Example 2 This embodiment provides a cloud-based carbon emissions digital management system, including: The data collection layer is used to collect enterprises' multi-source carbon emission data in real time through manual reporting, the Internet of Things, and sensors; The hardware resource layer is used to provide hardware resource support for computing, storage, and network communication to ensure efficient data transmission and processing; The data management layer is used to build a standardized database to integrate and store the multi-source carbon emission data, and to achieve data traceability and credible evidence storage by combining blockchain technology; The platform algorithm layer is equipped with several different carbon accounting models, including at least one or more of the carbon accounting model of the enterprise's industry, the CCER carbon accounting model, the IPCC carbon accounting model, and the CBAM carbon accounting model. The platform algorithm layer is used to dynamically adjust the carbon emission factors of the carbon accounting model using a machine learning model, and combines digital twin technology to simulate and optimize carbon emissions in real time; The application layer is used to provide enterprises with application services related to carbon emission monitoring based on the cloud platform. The application services include: carbon data management, carbon reporting management, emission source management, carbon verification assistance, carbon emission accounting, carbon emission analysis, carbon asset management, carbon trading management, carbon footprint tracking, carbon quota optimization, CBAM accounting and report generation, CCER accounting and report generation, and IPCC accounting and report generation; The presentation layer is used to present the data output by the application layer in real time through a visual interface and realize user interaction; The data acquisition layer, hardware resource layer, data management layer, platform algorithm layer, application layer and presentation layer are arranged in order from the bottom to the top; In the data collection layer, AI is used to assist manual reporting. The natural language processing capabilities of the AI ​​large model are used to analyze the manually reported data and cross-validate it with data collected by the Internet of Things to ensure data authenticity. In the data collection layer, the manually reported data includes at least: production and operation carbon emission data, transportation carbon emission data, waste treatment carbon emission data, financial data and carbon sink management data; The data collected by the Internet of Things is specifically edge gateway device data collected by energy meters, and the energy meters include at least: a gas meter, a steam meter, a water meter and a temperature meter; The sensor comprises at least: an infrared analyzer, a sampling pipeline, an analysis cabinet, an NDIR carbon dioxide sensor and a methane sensor; The data management layer is also equipped with a dynamic rule engine to support automatic conversion of carbon emission data formats between CCER, IPCC and CBAM standards. Before storing the multi-source carbon emission data, the multi-source carbon emission data is cleaned, abnormal data is removed based on preset rules, and missing data is supplemented by an interpolation algorithm; In the platform algorithm layer, the carbon emission factor of the carbon accounting model is updated in real time using a machine learning model based on the enterprise's energy structure, production process parameters, and equipment operating status; At the same time, a three-dimensional digital twin model of the company's carbon emissions is built to simulate, predict and optimize carbon emission changes of different production strategies in real time, and output the optimal emission reduction path; In the platform algorithm layer, the carbon accounting model of the industry in which the enterprise is located is pre-stored in the carbon accounting model library, and the industries include at least: steel, chemical and transportation industries; In the application layer, the levels of carbon emission accounting include, from high to low, total carbon emission accounting at the group level, enterprise-level carbon emission accounting, process-level carbon emission accounting, and production facility-level carbon emission accounting; The levels of carbon emission analysis include, from high to low: group-level carbon emission analysis, enterprise-level carbon emission analysis, process-level carbon emission analysis, and production facility-level carbon emission analysis; group-level carbon emission analysis includes analysis of the group's total carbon emissions, carbon emission intensity, carbon emission trends, and carbon emission target progress; enterprise-level carbon emission analysis includes analysis of the company's total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, carbon emission target progress, and emission reduction potential; process-level carbon emission analysis includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, and emission reduction potential of each production process; production facility-level carbon emission analysis includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, and carbon emission trends of each production facility; The carbon quota optimization includes: using AI big models, combined with real-time carbon market price data, to recommend the best carbon trading strategy for enterprises; The display layer constructs a carbon-aware panoramic map based on the data output by the application layer, and displays in real time the total carbon emissions, carbon emissions trend chart, carbon emissions hotspot distribution chart, equipment energy efficiency comparison chart, carbon asset balance, carbon trading income and international compliance progress; In the display layer, the visual interface includes at least one or more of a dedicated display screen, a PC screen, a tablet screen, and a smartphone screen; The carbon emission digital management system is integrated with the enterprise ERP or MES system through an API interface to achieve automatic synchronization of carbon emission data and production operation data.

[0030] In the specific implementation process, this system focuses on the collection, storage, calculation, accounting and application of carbon emission data. Through multi-source data access, cloud computing processing, security authentication and application display, it covers the entire carbon management process. Specifically, the data collection layer collects multi-source carbon emission data of enterprises in real time through manual reporting, the Internet of Things, and sensors; In this embodiment, AI is used to assist manual reporting, and the natural language processing capabilities of the AI ​​large model are used to analyze the manually reported data and cross-verify it with data collected by the Internet of Things to ensure data authenticity. The manually reported data includes at least: production and operation carbon emission data, transportation carbon emission data, waste treatment carbon emission data, financial data, and carbon sink management data. For data collected by the Internet of Things, specifically edge gateway device data collected by energy meters, such as gas meters, steam meters, water meters, and temperature meters; The sensors in this embodiment include an infrared analyzer, a sampling line, an analysis cabinet, an NDIR carbon dioxide sensor, and a methane sensor, etc., which are used to monitor the carbon emission data of the corresponding equipment in real time and continuously; The hardware resource layer includes computing devices, storage devices, network environments, etc., which are used to provide hardware resource support for computing, storage, and network communications to ensure efficient data transmission and processing; A standardized database is built in the data management layer to integrate and store the multi-source carbon emission data, while blockchain technology is used to achieve data traceability and credible evidence storage; In addition, a dynamic rule engine is also set up in the data management layer to support automatic conversion of carbon emission data formats between multiple standards such as CCER, IPCC and CBAM. Before storing multi-source carbon emission data, it also pre-processes the multi-source carbon emission data, including data cleaning, elimination of abnormal data based on preset rules, and filling in missing data through interpolation algorithms. The platform's algorithm layer includes several different carbon accounting models, including models specific to the industry in which the enterprise operates (such as steel, chemicals, and transportation), the CCER carbon accounting model, the IPCC carbon accounting model, and the CBAM carbon accounting model. Based on the enterprise's energy structure, production process parameters, and equipment operating status, the platform's algorithm layer uses machine learning models to dynamically adjust the carbon emission factors of the carbon accounting model. It also constructs a three-dimensional digital twin model of the enterprise's carbon emissions, simulating, predicting, and optimizing carbon emission changes under different production strategies in real time, and outputting the optimal emission reduction path. The application layer provides enterprises with application services related to carbon emission monitoring based on the cloud platform. The application services include: carbon data management, carbon reporting management, emission source management, carbon verification assistance, carbon emission accounting, carbon emission analysis, carbon asset management, carbon trading management, carbon footprint tracking, carbon quota optimization, CBAM accounting and report generation, CCER accounting and report generation, and IPCC accounting and report generation. Some application services are shown in Table 1: Table 1 Examples of some application services

[0031] At the application layer, the carbon emissions accounting and analysis levels include four levels, from high to low: group, enterprise, process, and production facility; For group-level carbon emissions analysis, including: 1) Total carbon emissions analysis: Calculate the total carbon emissions of all subsidiaries and branches within the group to comprehensively assess the group's carbon emissions; 2) Carbon emission intensity analysis: Calculate and analyze the group's carbon emission intensity (e.g., per unit output value, per unit sales, per unit production volume, etc.); 3) Carbon Emission Trend Analysis: Demonstrates the trend of carbon emissions over time at the group level and predicts future carbon emissions trends; 4) Carbon reduction target progress analysis: Analyze whether the group has achieved its carbon emission reduction targets as planned; 5) Regional carbon emissions analysis: If the group operates in multiple regions, it can break down carbon emissions data by region and compare the carbon emissions of different regions; like Figure 2 The following is a visualization of the carbon emissions analysis results at the group level. Carbon emissions analysis at the group level has the following benefits: 1) Decision-making support: Helping senior management understand the overall carbon emissions situation and guiding the group's carbon emission reduction strategies in different regions or fields; 2) Setting and achieving carbon emission targets: Based on the analysis results, the Group can set reasonable carbon emission reduction targets and monitor their progress; 3) Compliance Assessment: Based on regional policies and regulations, assess whether the group complies with legal requirements related to carbon emissions, such as carbon trading and emission quotas; 4) Enhance brand image: Establish a corporate sense of social responsibility and environmental image through transparent carbon emissions data and target progress; For enterprise-level carbon emissions analysis, including: 1) Carbon emissions and intensity analysis: Analyze the company's overall carbon emissions and the carbon emission intensity per unit product and per unit output value, and identify high-emission links; 2) Carbon emission structure analysis: Analyze the sources of a company's carbon emissions, such as energy consumption (electricity, natural gas, coal, etc.) and production process emissions, to help identify the main sources of carbon emissions; 3) Carbon emission trend analysis: Evaluate the company's past, current, and future carbon emission trends, identify possible emission peaks, and predict future emissions; 4) Emission reduction potential analysis: By analyzing carbon emissions at different stages, we assess the company's potential for energy conservation and emission reduction and propose specific emission reduction measures; 5) Carbon reduction target progress analysis: Track whether the company is meeting the preset targets, evaluate progress and adjust corporate decisions based on the results; like Figure 3 The following is a visualization of the carbon emissions analysis results at the enterprise level. Carbon emissions analysis for an enterprise has the following benefits: 1) Operational Optimization: Helping companies identify key aspects of carbon emissions (such as energy consumption and production processes) and reducing carbon emissions by optimizing production processes and energy use; 2) Compliance and reporting: Supporting enterprises in conducting carbon emissions reporting and auditing in accordance with relevant laws, regulations and policies to ensure that enterprises meet carbon emission requirements; 3) Cost Control: Through carbon emission analysis, companies can identify areas of energy waste and excessive carbon emissions, develop energy conservation and emission reduction plans, and reduce operating costs; 4) Continuous Improvement: Companies can make decisions based on carbon emissions analysis results, improve carbon management, and continuously promote green development; For process-level carbon emission analysis, including: 1) Process carbon emission total amount and intensity analysis: Analyze the total carbon emission amount and carbon emission intensity of each process (production link) and identify high-emission processes; 2) Process emission trend analysis: Analyze the carbon emission trends of each process to help identify possible emission anomalies and adjust the production process in a timely manner; 3) Process emission structure analysis: Analyze the main sources of carbon emissions in each process, such as energy consumption and material consumption, and identify the key links of emissions; 4) Carbon emission reduction potential analysis: Identify processes with high carbon emissions, reduce emissions through process optimization, equipment upgrades, and other means, and propose specific emission reduction measures; like Figure 4 The figure shows the visualization results of carbon emission analysis at the process level; For carbon emissions analysis at the production facility level, including: 1) Analysis of total carbon emissions and intensity of facilities: Collect statistics on carbon emissions of each production facility, calculate the carbon emission intensity of each facility, and evaluate its carbon emission status; 2) Facility emission trend analysis: Analyze the carbon emission trends of each facility over different time periods to identify potential problems in facility operation; 3) Facility emission structure analysis: Analyze the sources of carbon emissions from facilities, such as energy consumption and industrial processes, to identify the main causes of carbon emissions; 4) Equipment optimization and upgrade recommendations: Based on the facility's carbon emission data, propose emission reduction recommendations such as equipment optimization and energy-saving renovations; like Figure 5 Shown is the visualization of carbon emissions analysis at the production facility level; In the application layer, such as Figure 6 The following is a diagram of the carbon management architecture based on this system. The cloud platform provided by this embodiment can realize carbon emission data management, carbon asset management, carbon trading management, and carbon certification / carbon credit management. Among them, the most core ones are "carbon asset management" and "carbon trading management": like Figure 7 As shown in the figure, "Carbon Asset Management" analyzes corporate carbon emission data and generates carbon accounting reports in strict accordance with the provisions and requirements of the corresponding industry greenhouse gas emission accounting and reporting guidelines. It dynamically manages corporate carbon emission quotas, CCER and other asset indicators, formulates forward-looking carbon trading strategies, and helps companies actively respond to the pressure brought by national and international carbon markets. At the same time, by building a unified corporate carbon asset ledger, it comprehensively records the entire process of obtaining, using, adjusting and writing off carbon quotas, thereby realizing multi-cycle and multi-market dynamic management of carbon assets. This system has functions such as carbon asset valuation, compliance forecasting, and risk warning, helping companies achieve optimal allocation and value maximization of carbon resources, and providing data basis and strategic reference for carbon trading and compliance decision-making. For carbon quota optimization, this system uses AI big models, combined with real-time carbon market price data, to recommend optimal carbon trading strategies for companies. Specifically, it can combine production plans and energy consumption data to predict the company's future quarterly carbon emission quota gap or surplus, and dynamically adjust the trading rhythm. It can also establish a carbon asset pool with upstream and downstream companies, reduce transaction fees through centralized transactions, and share emission reduction benefits. "Carbon Trading Management" provides digital support for the entire carbon trading process, covering carbon trading demand forecasting, market analysis, intention matching, and compliance reporting. This system integrates real-time carbon market data to assist companies in analyzing price trends and formulating trading strategies. It also supports internal market adjustments, improving trading efficiency and compliance flexibility. It is a key tool for companies to participate in the national carbon market and carbon transfer within the group. This platform comprehensively integrates corporate carbon emissions, carbon assets, and carbon trading data to achieve accurate carbon emissions accounting, dynamic monitoring, and intelligent optimization, supporting the formulation and implementation of scientific carbon reduction strategies. At the same time, it builds a unified carbon asset management system, promoting the transformation of carbon assets from compliance to value mining and market-oriented operations, helping companies seize opportunities in the global carbon market. The presentation layer is used to present the data output by the application layer in real time through a visual interface and enable user interaction. In this embodiment, the visual interface can be a dedicated large display screen, PC screen, tablet screen, smartphone screen, etc. In addition, if Figure 8 As shown, the carbon emission digital management system provided in this embodiment is also integrated with the enterprise ERP or MES system through an API interface, thereby realizing automatic synchronization of carbon emission data and production operation data.

[0032] Example 3 like Figure 9 As shown, this embodiment provides an application architecture for a carbon emission digital management system and implements specific business association and dynamic conversion.

[0033] In the specific implementation process, based on Figure 9 With the architecture in [1], this system can realize unified collection of data sources in different scenarios and realize individual carbon emission calculation according to their respective standard requirements, thereby meeting their respective business needs and greatly improving the uniformity and flexibility of carbon management. For example, for CCER project application scenarios, the system first collects project monitoring data (such as biogas recovery), then calculates emission reductions according to the filing method, and then generates a CCER monitoring report. For CBAM carbon accounting and report generation, the system first collects production data of products exported to the EU, then converts units according to the CBAM template (such as converting tons of CO2 equivalent to euro carbon cost), and then generates an XML format report with an EU-recognized third-party verification certificate. After generating standardized carbon reports based on real-time carbon monitoring, carbon asset management (such as carbon asset valuation, compliance forecasting, risk warning, quota management, etc.) and carbon trading management (such as carbon asset trading plan drafting, carbon asset purchase approval, carbon asset trading operations, etc.) can be carried out, thereby achieving precise carbon emission control and improving the comprehensive benefits of carbon assets; This system has built an efficient, intelligent, and sustainable carbon management system through six core technological breakthroughs: "data accuracy + intelligent accounting + simulation optimization + transaction management + international compliance + trusted evidence storage." This provides strong support for enterprises' low-carbon transformation and global carbon market competition. At the same time, by building a core architecture of "unified data model + dynamic rule engine," it achieves automated conversion and accurate accounting of carbon data across multiple standards, including CCER (domestic voluntary emission reductions), IPCC (international common accounting), and CBAM (EU carbon tariffs), enabling efficient compliance support for enterprises in a complex international carbon regulatory environment. Specific improvements can be summarized as follows: 1) Multi-source intelligent carbon data collection: Combining Internet of Things (IoT) sensors, AI-assisted data reporting, and blockchain traceability, we build an automated and trusted carbon data collection system. This reduces the traditional carbon management platform's reliance on manual reporting, improves data authenticity and real-time performance, and enables full lifecycle tracking of carbon emissions. 2) AI Dynamic Carbon Emission Calculation Model: This model uses machine learning (ML) and dynamic carbon emission factors to adaptively adjust emission factors based on factors such as energy structure, process optimization, and equipment upgrades, improving carbon accounting accuracy. This model breaks through the existing accounting method based on static carbon factors, enabling precise industry carbon emissions calculations and avoiding the excessive errors of traditional models. This step aims to dynamically learn and predict carbon emission factors (EFs) based on real-time or historical enterprise data (such as energy type, fuel composition, temperature and pressure parameters, and operating efficiency), improving carbon accounting accuracy and replacing static default values ​​(such as those provided by the IPCC). 3) Digital twin-driven carbon emissions simulation and optimization: By building a digital twin model of carbon emissions, real-time simulation, prediction, and optimization of carbon emissions are achieved. Unlike the static accounting of existing technologies, this system can simulate the carbon emission impacts of different production strategies in advance and dynamically optimize emission reduction paths. 4) Intelligent Optimization and Asset Management of Carbon Trading: Regression algorithms are used to predict price fluctuations and trend changes. At the same time, AI carbon quota optimization algorithms are introduced, combined with real-time carbon market data, to intelligently match enterprises with the optimal carbon trading strategy. This breaks through the traditional model of only doing quota accounting, enhances enterprises' trading capabilities in the carbon market, and maximizes the value of carbon assets. This step mainly monitors price changes in real time. The specific implementation method can refer to mature price prediction and strategy optimization models in the financial field, especially analytical tools in futures, spot, and financial derivatives markets. Combining this with AI large-scale model capabilities (such as reinforcement learning, causal reasoning, and time series analysis) can provide enterprises with intelligent trading decision support in the carbon market. 5) Intelligent CBAM (EU Carbon Tariff) Adaptation and International Compliance: By building a carbon accounting model that complies with international standards such as CBAM, IPCC, and CCER, this system automatically matches exporting companies' carbon footprint calculations and compliance assessments. Compared to existing platforms that have lagged behind in CBAM adaptation, this system can provide accurate carbon tariff calculation and optimization, helping companies reduce international carbon costs. 6) Blockchain-based trusted carbon data storage and traceability: Utilizing blockchain technology to ensure the immutability and traceability of carbon emissions data, supporting third-party oversight and credit verification. Compared to traditional database storage methods, this solution enhances data security and transparency, and improves the credibility of corporate carbon compliance. In general, the carbon accounting of this system is more intelligent. It adopts dynamic carbon emission factors + machine learning algorithms, which can adaptively adjust emission calculations, improve accounting accuracy, and break through the limitations of traditional static models. Secondly, this system is more efficient in optimizing emission reduction. Based on digital twin simulation, it predicts the impact of different production strategies on carbon emissions, and can achieve precise optimization and enhance the low-carbon transformation capabilities of enterprises. At the same time, the carbon asset management of this system is more scientific. By intelligently matching carbon trading plans and optimizing carbon quota usage strategies, it can enhance the competitiveness of enterprises in the carbon market and reduce carbon costs. In addition, the international compliance of this system is more complete: built-in international carbon accounting models such as CBAM (EU carbon tariff) can help enterprises accurately calculate export carbon footprints and avoid compliance risks.

[0034] The same or similar reference numerals correspond to the same or similar components; The terms used in the drawings to describe positional relationships are for illustrative purposes only and are not to be construed as limiting the present application. Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A carbon emission digital management system based on a cloud platform, characterized by: include: The data collection layer is used to collect enterprises' multi-source carbon emission data in real time through manual reporting, the Internet of Things, and sensors; The hardware resource layer is used to provide hardware resource support for computing, storage, and network communication to ensure efficient data transmission and processing; The data management layer is used to build a standardized database to integrate and store the multi-source carbon emission data, and to achieve data traceability and credible evidence storage by combining blockchain technology; The platform algorithm layer is equipped with several different carbon accounting models, including at least one or more of the carbon accounting model of the enterprise's industry, the CCER carbon accounting model, the IPCC carbon accounting model, and the CBAM carbon accounting model. The platform algorithm layer is used to dynamically adjust the carbon emission factors of the carbon accounting model using a machine learning model, and combines digital twin technology to simulate and optimize carbon emissions in real time; The application layer is used to provide enterprises with application services related to carbon emission monitoring based on the cloud platform. The application services include: carbon data management, carbon reporting management, emission source management, carbon verification assistance, carbon emission accounting, carbon emission analysis, carbon asset management, carbon trading management, carbon footprint tracking, carbon quota optimization, CBAM accounting and report generation, CCER accounting and report generation, and IPCC accounting and report generation; The presentation layer is used to present the data output by the application layer in real time through a visual interface and realize user interaction; The data acquisition layer, hardware resource layer, data management layer, platform algorithm layer, application layer and presentation layer are arranged in sequence from the bottom layer to the top layer.

2. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the data collection layer, manual reporting is assisted by AI, and the manually reported data is analyzed through the natural language processing capabilities of the AI ​​large model, and cross-verified with the data collected by the Internet of Things to ensure the authenticity of the data.

3. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the data collection layer, the manually reported data includes at least: production and operation carbon emission data, transportation carbon emission data, waste treatment carbon emission data, financial data and carbon sink management data; The data collected by the Internet of Things is specifically edge gateway device data collected by energy meters, and the energy meters include at least: a gas meter, a steam meter, a water meter and a temperature meter; The sensor at least includes: an infrared analyzer, a sampling pipeline, an analysis cabinet, an NDIR carbon dioxide sensor and a methane sensor.

4. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: The data management layer is also equipped with a dynamic rule engine to support automatic conversion of carbon emission data formats between CCER, IPCC and CBAM standards. Before storing the multi-source carbon emission data, data cleaning is performed on the multi-source carbon emission data, abnormal data is eliminated based on preset rules, and missing data is supplemented by an interpolation algorithm.

5. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the platform algorithm layer, the carbon emission factor of the carbon accounting model is updated in real time using a machine learning model based on the enterprise's energy structure, production process parameters, and equipment operating status; At the same time, a three-dimensional digital twin model of the company's carbon emissions is built to simulate, predict and optimize carbon emission changes of different production strategies in real time, and output the optimal emission reduction path.

6. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the platform algorithm layer, the carbon accounting model of the industry in which the enterprise is located is pre-stored in the carbon accounting model library, and the industries include at least: steel, chemical and transportation industries.

7. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the application layer, the levels of carbon emission accounting include, from high to low, total carbon emission accounting at the group level, enterprise-level carbon emission accounting, process-level carbon emission accounting, and production facility-level carbon emission accounting; The levels of carbon emission analysis include, from high to low: group-level carbon emission analysis, enterprise-level carbon emission analysis, process-level carbon emission analysis, and production facility-level carbon emission analysis; group-level carbon emission analysis includes analysis of the group's total carbon emissions, carbon emission intensity, carbon emission trends, and carbon emission target progress; enterprise-level carbon emission analysis includes analysis of the company's total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, carbon emission target progress, and emission reduction potential; process-level carbon emission analysis includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure, carbon emission trends, and emission reduction potential of each production process; Carbon emission analysis at the production facility level includes analysis of the total carbon emissions, carbon emission intensity, carbon emission structure and carbon emission trends of each production facility; The carbon quota optimization includes: using AI big models, combined with real-time carbon market price data, to recommend optimal carbon trading strategies for enterprises.

8. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: The display layer constructs a carbon-aware panoramic map based on the data output by the application layer, and displays the total carbon emissions, carbon emission trend chart, carbon emission hotspot distribution map, equipment energy efficiency comparison chart, carbon asset balance, carbon trading income and international compliance progress in real time.

9. The carbon emission digital management system based on a cloud platform according to claim 1 is characterized in that: In the display layer, the visual interface includes at least one or more of a dedicated display screen, a PC screen, a tablet screen, and a smartphone screen.

10. A cloud-based carbon emission digital management system according to any one of claims 1 to 9, characterized in that: The carbon emission digital management system is integrated with the enterprise ERP or MES system through an API interface to achieve automatic synchronization of carbon emission data and production operation data.

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