Enterprise group carbon asset full-chain management and cross-border compliance authentication system and method based on digital twinning

Abstract

The invention provides an enterprise group carbon asset full-chain management and cross-border compliance authentication system and method based on digital twinning. The system comprises a global data perception and acquisition module, a core carbon digital twinning engine module, an intelligent optimization and transaction strategy module and a compliance audit report generation module. According to the invention, real-time acquisition and automatic accounting of global carbon data are realized by constructing an energy-economy-carbon digital twinborn body of an enterprise group; optimizing an emission reduction path and a carbon transaction strategy by using an intelligent algorithm to minimize compliance cost; and traceable digital auditing reports conforming to European Union CBAM and other international standards can be generated through one key, enterprises are assisted to efficiently deal with global carbon policies, and carbon management is converted from a cost center to a value creation center.

Description

A digital twin-based enterprise group carbon asset end-to-end management and cross-border compliance certification system and method Technical Field

[0001] This invention relates to the field of energy and carbon management system technology, and in particular to a system and method for full-chain management and cross-border compliance certification of corporate carbon assets based on digital twins. Background Technology

[0002] Large enterprise groups face multiple pain points in carbon management, specifically: 1. Data Management Challenges: Enterprise groups operate globally, resulting in dispersed carbon data sources. This includes data from energy assets, production facilities, and green electricity / certificate procurement, among other dimensions. Data formats are inconsistent and lack real-time updates, relying heavily on manual collection and processing, which is extremely labor-intensive and prone to errors due to human intervention, affecting the accuracy of carbon accounting. 2. Insufficient Compliance and Traceability: International regulations such as the EU's CBAM have extremely high requirements for the traceability of carbon emission data, requiring precise tracking of the source, accounting basis, and supporting documentation for each carbon emission. Existing carbon management methods are mostly post-event accounting, with incomplete data chains, failing to achieve end-to-end traceability "from raw data to accounting results," making it difficult to meet compliance requirements and potentially leading to risks such as increased tariffs and restricted market access. 3. Disconnection Between Carbon Costs and the Market: Internal carbon cost accounting lacks linkage with external carbon markets (such as carbon prices and green electricity prices), making it impossible to adjust carbon management strategies according to market dynamics. For example, when external carbon prices rise, companies struggle to optimize their green electricity procurement ratios or carbon credit purchase plans in a timely manner, leading to persistently high compliance costs. 4. Lack of Optimization Capabilities: Existing systems primarily focus on the statistics and reporting of carbon emissions, lacking the ability to proactively optimize carbon assets. Companies are unable to formulate scientific carbon reduction paths and internal carbon pricing strategies based on their own carbon footprint and market trends. Carbon management has long remained a "cost center," failing to translate into strategic value. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a digital twin-based system and method for the full-chain management and cross-border compliance certification of corporate carbon assets. The technical solution of this invention is implemented as follows: The first aspect of this invention discloses a digital twin-based system for the full-chain management and cross-border compliance certification of corporate carbon assets. The system includes: a full-domain data sensing and acquisition module, which collects multi-source data through IoT gateways and standardized API interfaces deployed in various regions of the corporate group; a core carbon digital twin engine module, which constructs and runs a digital twin model of the energy-economy-carbon relationship of the corporate group based on the data collected by the full-domain data sensing and acquisition module; and automatically and continuously calculates the carbon emissions of each region of the corporate group according to a pre-set emission factor library and accounting rules; an intelligent optimization and trading strategy module, which performs multi-objective dynamic optimization based on the predicted data output by the digital twin model to generate the most cost-effective carbon asset management strategy; and a compliance audit report generation module, used to convert the verified carbon data in the digital twin engine into auditable compliance documents that conform to international standards.

[0004] Furthermore, the data collected by the comprehensive data perception and acquisition module includes a wide range of data, specifically: direct emission data, from fossil fuel consumption monitoring equipment; indirect emission data, from smart meters of purchased electricity and heat, and energy management systems; asset and process data, from production execution systems and enterprise resource planning systems; external market data, such as external carbon market trading prices, green electricity / green certificate prices, carbon credit prices, etc., obtained through data interfaces; compliance rule base; and domestic and international carbon accounting and reporting standards and rules.

[0005] Furthermore, the core carbon digital twin engine module calculates carbon emissions as follows: First, based on the Greenhouse Gas Protocol system, carbon emissions are divided into direct emissions, indirect emissions, and value chain emissions; where, direct emissions E1 = ΣE1_i; E1_i is the emission amount of the i-th emission source; E1_i = AD_i × EF_i; AD_i is the activity data of the i-th emission source, and EF_i is the emission factor corresponding to the activity data of the i-th emission source; indirect emissions E2 E2_location + E2_market; E2_location = Electricity_Purchased × EF_Grid; E2_market = Electricity_Purchased × (EF_Grid - EFGreen); Electricity_Purchased is the total electricity purchased by the enterprise, EF_Grid is the emission factor based on the regional grid average, and EF_Green is the green electricity emission factor; value chain emissions E3 = ΣE3_j; E3_j is the emission amount of raw material j; E3_j = Quantity_j × CEF_j; CEF_j is the industry average carbon footprint factor of raw material j, and Quantity_j is the quantity of raw material j purchased.

[0006] Furthermore, the intelligent optimization and trading strategy module first performs trend prediction. Through integrated machine learning algorithms, it analyzes historical and real-time market data to predict the fluctuation trends of carbon prices, green electricity, and green certificate prices in the external carbon market within a specific future time period. Based on the trend prediction, it performs path optimization and generates trading strategies.

[0007] Furthermore, the intelligent optimization and trading strategy module includes a multi-objective dynamic optimization algorithm; the objective function of the multi-objective dynamic optimization algorithm, Minimize Total_Cost, is as follows: Minimize Total_Cost = Capital_Cost + Operating_Cost + Carbon_Cost; Carbon_Cost = (E_predicted - E_allowance) × P_carbon + Cost_credit_purchase; where E_predicted is the total emissions predicted by the carbon twin engine; E_allowance is the free allowance allocated by the government; P_carbon is the predicted carbon market price; and Cost_credit_purchase is the cost of purchasing external carbon credits / green certificates; the above function includes compliance constraints, technical constraints, and financial constraints.

[0008] Furthermore, when a user triggers a report generation command, the compliance audit report generation module automatically retrieves the corresponding, verified carbon footprint data with a complete traceability chain from the core carbon digital twin engine, based on the data structure requirements of the template selected by the user.

[0009] The second aspect of this invention discloses a method for full-chain management and cross-border compliance certification of corporate carbon assets based on digital twins. This method is implemented using the system disclosed in the first aspect of this invention. The method includes the following steps: Constructing a digital twin model of the corporate group: Based on a pre-set compliance rule base and accounting standards, the system initializes and constructs a digital twin model of the energy-economy-carbon relationship of the corporate group; Multi-source carbon data acquisition and standardized transmission: A full-domain data sensing and acquisition module collects multi-source heterogeneous data in real-time or near real-time through IoT gateways and standardized API interfaces deployed in various regions of the corporate group. All collected data, after standardized cleaning and formatting, is continuously transmitted to the core carbon digital twin engine; Real-time carbon asset accounting and intelligent optimization: After receiving the data, the core carbon digital twin engine automatically performs real-time calculation, source tracing, and dynamic simulation of carbon emissions based on a pre-set algorithm model; Based on the output of the digital twin model, intelligent... The optimization and trading strategy module utilizes machine learning algorithms to predict market trends and employs multi-objective dynamic optimization algorithms to generate cost-optimized emission reduction paths and trading strategies while meeting compliance, technical, and financial constraints. The automatic cross-border compliance check and certification report module automatically retrieves verified and traceable data from the carbon digital twin engine based on user-selected international standards and populates standardized report templates. The system performs automatic compliance checks and generates a digitally auditable certification report that meets cross-border trade requirements with a single click. Digital twin visualization and intelligent decision support: Key data, calculation results, optimization schemes, and compliance status generated during system operation are centrally and dynamically displayed through a visual dashboard. Decision-makers can comprehensively monitor the group's carbon footprint, carbon asset costs, and compliance progress, and make interactive decisions based on the simulation analysis and strategy recommendations provided by the system, achieving strategic management of carbon assets.

[0010] The advantages of this invention are as follows: 1. Full-chain automation and precision: It realizes full-process automation from data collection, accounting, optimization to report generation, which greatly improves efficiency and the accuracy and timeliness of data.

[0011] 2. Deeply meets international compliance requirements: For the precise traceability needs of complex regulations such as the EU CBAM, it provides an end-to-end, verifiable data chain and one-click report generation capabilities, which greatly reduces the compliance risks and potential tax burdens of enterprises in cross-border trade.

[0012] 3. From cost center to value center: Through intelligent optimization and trading modules, carbon management is transformed from a passive compliance behavior into a proactive and value-creating strategic activity, which can effectively reduce the group's total compliance costs and even discover new profit growth points.

[0013] 4. Building a strong business barrier: The accurate and reliable carbon data assets and certification capabilities formed by this invention can themselves become a core competitive advantage and green barrier for enterprises, enhancing brand image and international market competitiveness.

[0014] 5. High degree of productization and scalability: The system adopts a layered and modular design, which can serve the group internally and can also be easily transformed into a SaaS (Software as a Service) product for vertical industries, with broad market application prospects. Detailed Implementation

[0015] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0016] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used in the detailed description is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification and claims of this invention are intended to cover non-exclusive inclusion.

[0017] In the description of specific embodiments of the present invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, "multiple" means two or more, unless otherwise explicitly defined.

[0018] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0019] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this invention, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0020] To address the multiple pain points faced by large enterprise groups in carbon management, this invention discloses a digital twin-based system and method for the full-chain management and cross-border compliance certification of enterprise group carbon assets. By constructing a digital twin of the enterprise group's "energy-economy-carbon" structure, it achieves real-time collection and automatic calculation of global carbon data; it utilizes intelligent algorithms to optimize emission reduction paths and carbon trading strategies to minimize compliance costs; and it can generate traceable digital audit reports that comply with international standards such as the EU's CBAM with a single click, helping enterprises efficiently respond to global carbon policies and transforming carbon management from a cost center to a value creation center.

[0021] The embodiments of the present invention will be described in more detail below through examples. It should be noted that the embodiments of the present invention are not limited to these examples.

[0022] Example: A digital twin-based enterprise group carbon asset full-chain management and cross-border compliance certification system, mainly including the following modules: (1) Full-domain data perception and collection layer (full-domain data perception and collection module) This module serves as the basic data entry point of the system. Through the Internet of Things gateways and standardized API interfaces deployed in the group's subsidiaries and factories around the world, it realizes real-time or near-real-time collection of multi-source data.

[0023] The data it collects covers a wide range, including: direct emissions data: data from fossil fuel consumption monitoring equipment.

[0024] Indirect emissions data: Data from smart meters and energy management systems for purchased electricity and heat.

[0025] Asset and process data: Data such as output and raw material consumption from Manufacturing Execution System (MES) and Enterprise Resource Planning System (ERP).

[0026] External market data: External carbon market trading prices, green electricity / green certificate prices, carbon credit (such as CCER) prices, etc., obtained through data interfaces.

[0027] Compliance rule library: Pre-set domestic and international carbon accounting and reporting standards and rules such as EU CBAM, IFRS / ISSB, etc.

[0028] (2) Core Carbon Digital Twin Engine (Core Carbon Digital Twin Engine Module) This module connects to the full-domain data perception and acquisition layer. Its core function is to construct and run a digital twin model that dynamically maps the "energy-economy-carbon" relationship of the enterprise group. Based on real-time data collected from the lower layer, it establishes a hierarchical carbon flow digital twin from the group as a whole to branches and even key equipment, thereby accurately reflecting the carbon emission status of the physical world. Another key function of the engine is to perform real-time calculation and traceability, that is, to automatically and continuously calculate the carbon emissions of range one, range two and range three according to the pre-set emission factor library and accounting rules, and ensure that every ton of carbon emissions can be traced back to its corresponding original data voucher, forming a complete and reliable data chain.

[0029] The calculation method is illustrated below: following the Greenhouse Gas Protocol (GHG Protocol) framework, carbon emissions are divided into Scope 1, Scope 2 and Scope 3.

[0030] ① Scope 1 (Direct Emission) Calculation Model.

[0031] Based on the product of activity level data and emission factors, the emission amount E1_i for the i-th emission source is calculated as follows: E1_i = AD_i × EF_i; where AD_i is the activity data (such as fuel consumption, raw material input), and EF_i is the corresponding emission factor (such as IPCC recommended value or measured value). For complex processes, a mass balance model is used for accurate calculation.

[0032] ② Scope 2 (Indirect Emissions) Calculation Model.

[0033] A location- and market-based approach is adopted. The emission E2 is calculated as follows: E2 = E2_location + E2_market; where, E2_location = Electricity_Purchased × EF_Grid (based on the regional grid average emission factor); E2_market = Electricity_Purchased × (EF_Grid - EF_Green) (reflecting the emission reduction from purchasing green electricity, where EF_Green is the green electricity emission factor, typically 0).

[0034] ③ Scope 3 (Value Chain Emissions) Calculation Model.

[0035] A combined input-output (EIO) approach and life cycle assessment (LCA) method is employed. For purchased raw material j, its emissions E3_j are estimated as: E3_j = Quantity_j × CEF_j; where CEF_j is the industry average carbon footprint factor for this type of material, sourced from a database (such as Ecoinvent). For critical materials, suppliers are required to provide verified LCA data, which is recorded via blockchain to ensure traceability.

[0036] (3) Intelligent Optimization and Trading Strategy Module The intelligent optimization and trading strategy module establishes a data connection with the core carbon digital twin engine. Its core function is to perform multi-objective dynamic optimization based on the predicted data output by the twin model, thereby generating the most cost-effective carbon asset management strategy. This module first performs trend prediction. It analyzes historical and real-time market data through integrated machine learning algorithms (such as Long Short-Term Memory Network LSTM or Gradient Boosting Machine XGBoost) to predict the fluctuation trend of carbon prices, green electricity, and green certificate prices in the external carbon market within a specific future time period. Based on the trend prediction, path optimization and trading strategy generation are performed.

[0037] The core of this module lies in establishing a multi-objective dynamic optimization algorithm aimed at minimizing the total compliance cost. Its methodology is as follows: the objective function aims to minimize the total cost over the planning period (e.g., one year), and can be expressed as follows.

[0038] Minimize Total_Cost = Capital_Cost + Operating_Cost + Carbon_Cost; where: Carbon_Cost = (E_predicted - E_allowance) × P_carbon + Cost_credit_purchase; E_predicted is the total emissions predicted by the carbon twin engine; E_allowance is the free allowance allocated by the government; P_carbon is the predicted carbon market price; Cost_credit_purchase is the cost of purchasing external carbon credits / green certificates.

[0039] The solution process of this optimization algorithm needs to satisfy multiple constraints.

[0040] (a) Compliance constraints, namely, ensuring that the sum of optimized carbon emissions and carbon credit offsets must meet the statutory emission cap requirements, the mathematical expression is: E_predicted + Credit_Offset <= E_allowance +Cap.

[0041] (ii) Technological constraints, namely the nonlinear relationship between investment in emission reduction technologies and the amount of emission reduction, expressed as f(Investment) = Emission_Reduction.

[0042] (iii) Financial constraints: Total capital expenditure shall not exceed the budget.

[0043] (4) Compliance Audit Report Generation Module The compliance audit report generation module establishes a data link with the core carbon digital twin engine, aiming to transform the verified carbon data in the twin engine into auditable compliance documents that meet international standards.

[0044] This module has a pre-built library of report templates covering mainstream international standards, such as the EU Carbon Border Adjustment Mechanism (CBAM) report, the Environmental, Social and Governance (ESG) reporting framework, and the International Financial Reporting Standards (IFRS) sustainability disclosure documents. When a user triggers a report generation command, the module automatically retrieves the corresponding verified carbon footprint data with a complete traceability chain from the core carbon digital twin engine, based on the data structure requirements of the selected template. The technical framework of this embodiment is shown in the table below:

[0045] The operational flow of this embodiment includes the following steps in sequence: S1: Constructing a digital twin model of the enterprise group. Based on a pre-set compliance rule library and accounting standards, the system initializes and constructs an "energy-economy-carbon" digital twin of the enterprise group. This model establishes a virtual mapping for the group's major emission facilities, energy assets, and supply chains worldwide, and sets corresponding emission factor libraries, calculation rules, and dynamic update mechanisms to provide a computational foundation for full-chain carbon management. S2: Multi-source carbon data collection and standardized transmission. Through IoT gateways and API interfaces deployed in the group's subsidiaries and factories worldwide, multi-source heterogeneous data, including direct emission data, indirect emission data, asset and process data, and external market data, are collected in real time or near real time. All collected data is continuously transmitted to the core carbon digital twin engine after standardization, cleaning, and formatting. S3: Real-time carbon asset accounting and intelligent optimization. After receiving the data, the core carbon digital twin engine automatically performs real-time carbon emission calculation, source tracing, and dynamic simulation according to pre-set models (such as the calculation formulas for ranges 1, 2, and 3). Based on the output of the carbon twin model, the intelligent optimization and trading strategy module uses machine learning algorithms to predict market trends and employs multi-objective dynamic optimization algorithms to generate cost-optimized emission reduction paths and trading strategies (such as internal carbon pricing and green electricity / carbon credit procurement programs) while meeting compliance, technical, and financial constraints. S4: Automatic Cross-Border Compliance Check and Certification Report. The compliance audit report generation module automatically retrieves verified and traceable data from the carbon digital twin engine based on user-selected international standards and populates standardized report templates. The system performs automatic compliance verification and generates a digitally auditable certification report that meets cross-border trade requirements with a single click. S5: Digital Twin Visualization and Intelligent Decision Support. The system centrally and dynamically displays the key data, calculation results, optimization schemes, and compliance status generated in steps S1-S5 through a visual dashboard. Decision-makers can globally monitor the group's carbon footprint, carbon asset costs, and compliance progress, and make interactive decisions based on the system's simulation analysis and strategy recommendations, achieving strategic management of carbon assets.

[0046] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital twin-based enterprise group carbon asset end-to-end management and cross-border compliance certification system, characterized in that, include: The full-domain data sensing and acquisition module collects multi-source data through IoT gateways and standardized API interfaces deployed in various regions of the enterprise group; the core carbon digital twin engine module, based on the data collected by the full-domain data sensing and acquisition module, constructs and runs a digital twin model of the energy-economy-carbon relationship of the enterprise group; and automatically and continuously calculates the carbon emissions of each region of the enterprise group according to the pre-set emission factor library and accounting rules; the intelligent optimization and trading strategy module, based on the predicted data output by the digital twin model, performs multi-objective dynamic optimization to generate the most cost-effective carbon asset management strategy; The compliance audit report generation module is used to transform verified carbon data from the digital twin engine into auditable compliance documents that meet international standards.

2. The enterprise group carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in claim 1, characterized in that, The data collected by the comprehensive data perception and acquisition module includes: a wide range of data, specifically including: direct emission data from fossil fuel consumption monitoring equipment; indirect emission data from smart meters of purchased electricity and heat, and data from energy management systems; asset and process data from production execution systems and enterprise resource planning systems; external market data, such as external carbon market trading prices, green electricity / green certificate prices, and carbon credit prices obtained through data interfaces; compliance rule bases; and domestic and international carbon accounting and reporting standards and rules.

3. The enterprise group carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in claim 1, characterized in that, The core carbon digital twin engine module calculates carbon emissions as follows: First, based on the Greenhouse Gas Protocol framework, carbon emissions are divided into direct emissions, indirect emissions, and value chain emissions. Direct emissions are defined as follows: E1 = ΣE1_i; E1_i is the emission amount from the i-th emission source; E1_i = AD_i × EF_i; AD_i is the activity data of the i-th emission source, and EF_i is the emission factor corresponding to the activity data of the i-th emission source. Indirect emissions are defined as follows: E2 = E2_location + E2_market; E2_location = Electricity_Purchased × EF_Grid; E2_market = Electricity_Purchased × (EF_Grid - EFGreen); Electricity_Purchased is the total electricity purchased by the enterprise, EF_Grid is the emission factor based on the regional grid average, and EF_Green is the green electricity emission factor. Value chain emissions are defined as follows: E3 = ΣE3_j; E3_j is the emission amount of raw material j; E3_j = Quantity_j × CEF_j; CEF_j is the industry average carbon footprint factor of raw material j, and Quantity_j is the quantity of raw material j purchased.

4. The enterprise group carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in claim 1, characterized in that, The intelligent optimization and trading strategy module first performs trend prediction. Through integrated machine learning algorithms, it analyzes historical and real-time market data to predict the fluctuation trends of carbon prices, green electricity, and green certificate prices in the external carbon market within a specific future time period. Based on the trend prediction, it performs path optimization and generates trading strategies.

5. The enterprise group carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in claim 4, characterized in that, The intelligent optimization and trading strategy module includes a multi-objective dynamic optimization algorithm. The objective function of the multi-objective dynamic optimization algorithm, Minimize Total_Cost, is as follows: Minimize Total_Cost = Capital_Cost + Operating_Cost + Carbon_Cost; Carbon_Cost = (E_predicted - E_allowance) × P_carbon + Cost_credit_purchase; where E_predicted is the total emissions predicted by the carbon twin engine; E_allowance is the free allowance allocated by the government; P_carbon is the predicted carbon market price; and Cost_credit_purchase is the cost of purchasing external carbon credits / green certificates. The above function includes compliance constraints, technical constraints, and financial constraints.

6. The enterprise group carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in claim 1, characterized in that, When a user triggers a report generation command, the compliance audit report generation module automatically retrieves the corresponding, verified carbon footprint data with a complete traceability chain from the core carbon digital twin engine, based on the data structure requirements of the template selected by the user.

7. A method for full-chain management and cross-border compliance certification of corporate carbon assets based on digital twins, employing the corporate carbon asset full-chain management and cross-border compliance certification system based on digital twins as described in any one of claims 1-6, characterized in that, The method includes the following steps: Constructing a digital twin model of the enterprise group: Based on a pre-set compliance rule base and accounting standards, the system initializes and constructs a digital twin model of the energy-economy-carbon relationship of the enterprise group; Multi-source carbon data acquisition and standardized transmission: The full-domain data sensing and acquisition module collects multi-source heterogeneous data in real time or near real time through IoT gateways and standardized API interfaces deployed in various regions of the enterprise group. All collected data, after standardized cleaning and formatting, is continuously transmitted to the core carbon digital twin engine; Real-time carbon asset accounting and intelligent optimization: After receiving the data, the core carbon digital twin engine automatically performs real-time calculation, source tracing, and dynamic simulation of carbon emissions based on a pre-set algorithm model. Based on the output of the digital twin model, the intelligent optimization and trading strategy module uses machine learning algorithms to predict market trends and employs multi-objective dynamic optimization algorithms to generate the most cost-effective emission reduction paths and trading strategies while meeting compliance, technical, and financial constraints. The cross-border compliance automatic inspection and certification report module automatically retrieves verified and traceable data from the carbon digital twin engine based on the user-selected international standards and populates it into a standardized report template. The system performs automatic compliance verification and generates a digitally auditable certification report that meets cross-border trade requirements with a single click. Digital twin visualization and intelligent decision support: Key data, accounting results, optimization solutions and compliance status generated during system operation are centrally and dynamically displayed through a visual dashboard; decision-makers can monitor the group's carbon footprint, carbon asset costs and compliance progress globally, and make interactive decisions based on the simulation analysis and strategy recommendations provided by the system to achieve strategic management of carbon assets.

Images