Green asset management system based on multi-layer system structure

Abstract

The invention provides a green asset management system based on a multi-layer system structure, relates to the technical field of green asset management, and aims at obtaining green asset data; data analysis and behavior simulation verification are carried out based on the carbon emission activity nodes, carbon emission factors are obtained, carbon footprint calculation is carried out, and a first carbon circulation path is obtained; performing economic division on the quota carbon asset data by taking nodes in the first carbon flow path as constraints to obtain a plurality of node quotas; dynamically correcting each node quota in combination with carbon transaction flow and hedging influence to obtain a plurality of node quota threshold values, performing node footprint optimization by taking the node quota threshold values as a target, generating a second carbon flow path, performing association clustering on nodes in the second carbon flow path, obtaining a plurality of emission reduction management stages, and performing emission reduction management and quota optimization updating by taking the node quota threshold values as a strategy target. The problems of poor flexibility and dynamic nature of carbon asset management and untimely market response are solved, and the effects of improving the efficiency and adaptability of carbon asset management and enhancing economic benefits are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of green asset management, and in particular to a green asset management system based on a multi-layer architecture. Background Art

[0002] In the context of environmental protection and sustainable development, green asset management has become a global focus. Green assets refer to assets that can generate environmental benefits and support sustainable development goals. These assets usually involve energy efficiency, renewable energy, low-carbon technologies and projects, as well as other investments and activities that help reduce environmental impacts and promote ecological balance. Among them, carbon assets are particularly prominent and have been circulated in the market in an economic benefit model. Carbon assets mainly include quota carbon assets and emission reduction carbon assets, which refer to assets generated by the trading of carbon emission rights. At present, most carbon asset management adopts a static data processing and management model, which is usually inflexible and cannot effectively respond to rapid changes in the market, especially with the increasingly active carbon trading market.

[0003] In summary, existing technologies have technical problems such as poor flexibility and dynamics in carbon asset management, untimely market response, and inability to adjust and optimize carbon asset management strategies in real time, leading to inefficiency and potential economic losses. Summary of the invention

[0004] This application provides a green asset management system based on a multi-layer architecture, which is used to solve the technical problems existing in the prior art, such as poor flexibility and dynamics of carbon asset management, untimely market response, and inability to adjust and optimize carbon asset management strategies in real time, resulting in inefficiency and potential economic losses.

[0005] The present application provides a green asset management system based on a multi-layer architecture, the system comprising:

[0006] The data acquisition module is used to obtain green asset structure information and perform data collection to obtain data collection results, wherein the data collection results include quota carbon asset data and emission reduction carbon asset data; the first carbon flow path acquisition module is used to perform data analysis and behavior simulation verification on the emission reduction carbon asset data based on the carbon emission activity node, obtain the carbon emission factor, and calculate the carbon footprint in combination with the carbon emission factor and the carbon emission activity node to obtain the first carbon flow path, wherein the first carbon flow path includes multiple carbon emission activity nodes; the carbon asset division module is used to perform data analysis and behavior simulation verification on the emission reduction carbon asset data based on the carbon emission activity node, obtain the carbon emission factor, and calculate the carbon footprint in combination with the carbon emission factor and the carbon emission activity node to obtain the first carbon flow path, wherein the first carbon flow path includes multiple carbon emission activity nodes; the carbon asset division module is used to divide the quota carbon asset data into The module is used to dynamically modify each of the node quotas in combination with the carbon trading flow and the hedging effect to obtain multiple node quota thresholds; the module is used to obtain a second carbon circulation path, and is used to optimize multiple node footprints with multiple node quota thresholds as the target, generate a second carbon circulation path, and perform association clustering on the nodes in the second carbon circulation path to obtain multiple emission reduction management stages; the module is used to update the quota with the multiple emission reduction management stages as the strategy formulation target, and perform emission reduction management and quota optimization update.

[0007] The green asset management system based on the multi-layer architecture proposed in this application is intended to obtain data collection results by acquiring green asset structure information and performing data collection, and the data collection results include quota carbon asset data and emission reduction carbon asset data; based on the carbon emission activity node, the emission reduction carbon asset data is subjected to data analysis and behavior simulation verification to obtain the carbon emission factor, and the carbon footprint is calculated in combination with the carbon emission factor and the carbon emission activity node to obtain the first carbon flow path, wherein the first carbon flow path includes multiple carbon emission activity nodes; with the nodes in the first carbon flow path as constraints, the quota carbon asset data is economically divided to obtain multiple node quotas, and the economic division refers to the division based on the historical consumption contribution of the node. The total quota assets are divided; the quota of each node is dynamically corrected in combination with the carbon trading flow and hedging impact to obtain multiple node quota thresholds; multiple node footprints are optimized with multiple node quota thresholds as targets to generate a second carbon flow path, and the nodes in the second carbon flow path are associated and clustered to obtain multiple emission reduction management stages; the multiple emission reduction management stages are used as strategy formulation targets to carry out emission reduction management and quota optimization and update, which solves the technical problems of poor flexibility and dynamics in carbon asset management, untimely market response, inability to adjust and optimize carbon asset management strategies in real time, resulting in inefficiency and potential economic losses, and achieves the technical effect of improving the efficiency and adaptability of carbon asset management, thereby enhancing economic benefits while protecting the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the structure of a green asset management system based on a multi-layer architecture provided in an embodiment of the present application.

[0009] Explanation of the reference numerals: data acquisition module 11 , first carbon circulation path acquisition module 12 , carbon asset division module 13 , node quota correction module 14 , second carbon circulation path acquisition module 15 , quota updating module 16 . DETAILED DESCRIPTION

[0010] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0011] The present application embodiment provides a green asset management system based on a multi-layer architecture, such as Figure 1 As shown, the system comprises:

[0012] The data acquisition module 11 is used to acquire green asset structure information and perform data collection to obtain data collection results, which include quota carbon asset data and emission reduction carbon asset data.

[0013] Specifically, the data acquisition module is used to crawl the green asset structure information comprehensively to ensure the effective management of carbon assets. This module is responsible for acquiring quota carbon asset data and emission reduction carbon asset data, which are the basis of carbon trading and management. Among them, quota carbon assets refer to the carbon emission quotas allocated to enterprises by relevant institutions according to the emission cap in the carbon emission trading system, while emission reduction carbon assets refer to the carbon credits generated by enterprises through overfulfillment of carbon emission reduction tasks or certified carbon emission reduction projects. The acquisition of data collection results is to crawl data through the carbon asset management interface. By acquiring quota carbon assets and emission reduction carbon asset data, it can provide enterprises with accurate carbon management decision support, so that they can more efficiently buy and sell carbon quotas and trade carbon credits in the carbon trading market, and ultimately achieve the dual optimization of economic benefits and environmental protection. In addition, the data acquisition module also has a data verification function to ensure the accuracy and completeness of the collected data, providing a solid data foundation for subsequent carbon footprint calculation and quota management.

[0014] The first carbon flow path acquisition module 12 is used to perform data analysis and behavior simulation verification on the emission reduction carbon asset data based on the carbon emission activity node, obtain the carbon emission factor, and calculate the carbon footprint in combination with the carbon emission factor and the carbon emission activity node to obtain the first carbon flow path, wherein the first carbon flow path includes multiple carbon emission activity nodes.

[0015] Optionally, the emission reduction carbon asset data is analyzed in depth based on the carbon emission activity node. In this process, the carbon emission activity node refers to a specific space, operating unit or equipment with carbon emission behavior, such as a furnace in a factory or a generator in a power company. The data collected from these nodes include but are not limited to energy consumption, carbon emissions and other related environmental factors, which are processed by analytical tools and algorithms to identify carbon emission patterns and trends. Next, behavioral simulation verification is performed to test the effectiveness of emission reduction measures. Simulation verification is to create a virtual model of the carbon emission activity node and apply actual data in a control environment to verify the impact of different emission reduction strategies on carbon emissions. In this way, the specific impact of various operational changes on carbon emissions can be predicted and verified, thereby obtaining a theoretical carbon emission factor. The carbon emission factor is a parameter that measures the amount of carbon emissions generated by a specific activity, process unit product or service. Once these factors are obtained, they are combined with the corresponding carbon emission activity nodes to calculate the carbon footprint of each node according to the formula "carbon footprint = activity × carbon emission factor". Finally, these individual carbon footprint data are combined to form the first carbon circulation path, which illustrates the carbon flow in a series of activities from raw material procurement to product production to waste treatment, providing companies with a panoramic view of carbon emissions, so that companies can manage their carbon footprint more systematically and comprehensively and optimize carbon assets. Through continuous monitoring and optimization of this path, companies can not only effectively reduce environmental impact, but also improve market competitiveness.

[0016] The carbon asset division module 13 is used to perform economic division on the quota carbon asset data based on the nodes in the first carbon circulation path as constraints to obtain multiple node quotas. The economic division refers to the division of total quota assets based on the historical consumption contribution of the nodes.

[0017] Furthermore, the economic division is a process of allocating quota carbon assets in proportion to the historical contribution ratio of carbon emission activity nodes to total carbon emissions. The purpose of this step is to ensure that the allocation of carbon quotas is both fair and effective, reflecting the actual role and responsibility of each node in carbon emissions. First, all node data of the first carbon flow path are extracted, including but not limited to the historical energy consumption and historical carbon emissions of each node. Subsequently, the system uses these historical data to calculate the quota carbon assets of each node. The quota calculation formula is "node quota = total quota assets × node historical consumption contribution ratio". In this formula, total quota assets refer to the total carbon emission rights quota available to the enterprise in a specific time period, and the node historical consumption contribution ratio is a ratio calculated based on past data. After completing these calculations, the system will allocate specific carbon emission quotas to each node. This allocation method not only ensures the scientificity and accuracy of quota allocation, but also can be adjusted dynamically to cope with future changes or adjustments to the production process. Ultimately, this form of economic division based on the first carbon circulation path node provides enterprises with a refined carbon asset management strategy, enabling them to more effectively respond to the needs of the carbon trading market while optimizing their environmental impact and economic benefits. It not only improves the transparency and traceability of carbon asset management, but also enhances the competitiveness of enterprises in the carbon emission rights market.

[0018] The node quota correction module 14 is used to dynamically correct each of the node quotas in combination with the carbon trading flow and the hedging effect to obtain multiple node quota thresholds.

[0019] Exemplarily, the carbon trading flow is the trading activity of quota carbon assets in the carbon market, and the hedging effect refers to the strategy of countering market price fluctuations through financial instruments such as futures and options. These two factors are crucial to the management of carbon quotas because they directly affect carbon costs and the economic performance of enterprises. Specifically, the system first monitors the price fluctuations and trading volume of the carbon market in real time, and analyzes these data to obtain market trend forecast results. For example, if market data shows that carbon prices are expected to rise, the system may recommend increasing the carbon quotas of certain nodes to use lower-cost quotas for future sales or use. Subsequently, the system uses the obtained market trend forecast results to evaluate the specific impact on the quotas of each node. This evaluation includes calculating the quota ratio impact factor, which is a quantitative indicator that indicates the specific impact of market changes on the carbon quota of each node. The calculation of the quota ratio impact factor may take into account historical data, market forecasts, and the effects of hedging strategies. Based on these impact factors, the carbon quota of each node is dynamically revised to ensure that the allocation of quotas meets both market conditions and the carbon reduction goals of the enterprise. Dynamic corrections may involve increasing or decreasing quotas, or reallocating quotas to nodes that need them more. The result of the correction is multiple node quota thresholds, which are set to ensure the operational flexibility and cost efficiency of each node under market changes. Through this dynamic correction mechanism, enterprises can maintain their competitiveness in the carbon market while optimizing their carbon emission management strategies, reducing risks, and maximizing economic returns. This not only improves the market adaptability of enterprises, but also strengthens the fulfillment of their environmental responsibilities.

[0020] The second carbon flow path acquisition module 15 is used to optimize multiple node footprints with multiple node quota thresholds as targets, generate a second carbon flow path, associate and cluster the nodes in the second carbon flow path, and obtain multiple emission reduction management stages.

[0021] Node footprint optimization refers to adjusting the carbon emission activities of each node according to its quota threshold to minimize the overall carbon footprint without exceeding the allocated carbon quota. This optimization is done by monitoring the carbon emissions of each node in real time and comparing it with its quota threshold. For example, if the actual carbon emissions of a node exceed its quota threshold, the system will automatically make adjustment suggestions, such as changing the production process, increasing energy efficiency measures, or using cleaner energy, to ensure that carbon emissions do not exceed the standard. Next, the second carbon flow path is generated through this optimization, which is a re-adjusted carbon emission path diagram showing the new carbon flow after the implementation of node footprint optimization. This not only reflects the carbon emission status of each node after the adjustment measures, but also provides a more environmentally friendly and economical operation mode. Then, the nodes in the second carbon flow path are associated clustered. This step involves using statistical analysis techniques to classify nodes according to the similarity of carbon emission characteristics and emission reduction measures. Through cluster analysis, it is possible to identify which nodes have common characteristics in carbon management, thereby forming different emission reduction management stages, each of which includes a group of nodes with similar carbon emission reduction potential or requiring similar emission reduction strategies. Finally, each emission reduction management stage will carry out personalized carbon emission management and optimization strategy formulation according to its specific needs and characteristics. For example, for node groups with low energy efficiency but great improvement potential, energy efficiency improvement projects can be implemented as a priority. Such classified management not only makes carbon emission reduction measures more targeted and efficient, but also improves the environmental performance and economic benefits of the enterprise as a whole. Through this systematic approach, carbon asset management is effectively optimized, ensuring that enterprises can achieve maximum economic benefits while meeting environmental protection requirements, providing enterprises with a comprehensive carbon management solution.

[0022] The quota updating module 16 is used to formulate strategies based on the multiple emission reduction management stages to perform emission reduction management and quota optimization and updating.

[0023] Specifically, the emission reduction management stage is the result of grouping carbon emission activity nodes according to carbon emission characteristics and emission reduction potential. Each stage contains a group of nodes with similar carbon emission behaviors and emission reduction needs. This grouping makes it possible to formulate targeted emission reduction strategies, thereby achieving more effective carbon emission control and resource allocation. In actual operation, the strategy formulation of each emission reduction management stage first relies on a detailed assessment of the current carbon quota usage of each node in the stage. The assessment results will guide the optimization and update of the quota to ensure that the carbon quota of each node is consistent with its actual emission reduction capacity and meets broader environmental goals. For example, for a group of nodes with low energy efficiency but great improvement potential, more carbon quotas may be allocated to encourage investment in emission reduction technologies and facility improvements. Strategy formulation includes regular updates and adjustments to carbon quota allocations, a process called quota optimization updates. This step takes into account factors such as market changes, technological advances, and policy adjustments. By dynamically adjusting quotas, it ensures that each node can use its quota most effectively to achieve emission reduction goals. Dynamic adjustments may include increasing quotas for high-efficiency nodes or reducing quotas for low-efficiency nodes to encourage improvements and innovations. In addition, the implementation strategy also involves monitoring and evaluating the emission reduction effectiveness of each management stage, which is achieved by comparing actual carbon emissions with target carbon emissions. Through this continuous evaluation, it can be ensured that the strategies of each emission reduction management stage are not only effective in theory, but also can achieve the expected environmental and economic benefits in practice. Ultimately, these steps form a closed-loop management system that not only optimizes the allocation of carbon quotas, but also improves the carbon management efficiency of the entire enterprise or organization. In this way, a solution that emphasizes both economic benefits and environmental protection is provided. This systematic emission reduction management and quota optimization and update mechanism provides strong support for enterprises to achieve sustainable development goals.

[0024] Furthermore, the first carbon transfer path obtaining module 12 is also used for:

[0025] Collect historical data related to carbon emission activity nodes, including historical energy consumption, historical carbon emissions and historical environmental impact factors of carbon emission activity nodes, wherein the carbon emission activity node refers to an operation node that generates carbon emissions.

[0026] The collected historical data are analyzed using a linear matrix analysis algorithm to obtain a plurality of theoretical carbon emission factors, wherein the theoretical carbon emission factor refers to a correlation coefficient between the energy consumption of a node activity and the carbon emission per unit time.

[0027] In the simulation environment, the corresponding node historical behavior pattern and theoretical carbon emission factor are used to verify the carbon emissions of each carbon emission activity node to obtain the verification results.

[0028] If the verification result meets the preset accuracy, the current theoretical carbon emission factor is determined to be the carbon emission factor of the corresponding node. If not, the current theoretical carbon emission factor is discarded and recalculated until the preset threshold is reached.

[0029] Optionally, historical data of each carbon emission activity node is collected, including but not limited to historical energy consumption, historical carbon emissions, and other environmental factors that may affect carbon emissions. Carbon emission activity nodes refer to specific locations or equipment that generate carbon emissions during production or operation, such as a production line in a factory or a combustion device in a power station. Next, a linear matrix analysis algorithm is used to conduct a comprehensive analysis of these collected historical data. The linear matrix can identify theoretical carbon emission factors by establishing a linear relationship between energy consumption and carbon emissions. These factors represent the correlation between the energy consumption of each node activity and its carbon emissions per unit time. In addition, in order to verify the accuracy of these theoretical carbon emission factors, these factors are applied to a simulation environment to reproduce the historical behavior patterns of the nodes. Through this simulation, it is possible to observe and verify whether the actual performance of the carbon emission factor under control conditions meets expectations. If the deviation between the carbon emissions of the simulation results and the actual observed values ​​is within an acceptable accuracy range, then the theoretical carbon emission factor will be confirmed as a valid factor for the node. If the verification result obtained does not meet the preset accuracy requirements, the current theoretical carbon emission factor will be discarded and needs to be recalculated and verified until the new carbon emission factor meets the preset threshold. The iteration of this process ensures that the final carbon emission factor is highly accurate and reliable in practical applications. Through the above steps, not only can solid data support be provided for carbon asset management, but also the scientificity and effectiveness of the entire system in carbon emission calculation and management can be ensured. The application of this method helps companies to more accurately evaluate and manage their carbon footprint, thereby optimizing their operating strategies and environmental performance.

[0030] Furthermore, the first carbon transfer path obtaining module 12 is also used for:

[0031] The corresponding carbon emission factors are calibrated for all carbon emission activity nodes, and the carbon footprint of each activity node is calculated, where the carbon footprint calculation formula of the activity node is:

[0032] Carbon footprint = activity amount × carbon emission factor.

[0033] Among them, the activity volume is the energy consumption of the node activity per unit time, the carbon emission factor is the carbon emission factor of the node per unit time, and the carbon footprint is the carbon emission of the node per unit time.

[0034] Based on the calculated carbon footprints of each node and the operation flow, a first carbon flow path is constructed.

[0035] Furthermore, each carbon emission activity node, that is, the specific location or equipment that generates carbon emissions during the production or operation process, is assigned a corresponding carbon emission factor. This factor is based on the previous analysis and verification steps, obtained through the linear matrix analysis algorithm and verified by the simulated environment. The factor reflects the relationship between the energy consumption of the node activity and the carbon emissions per unit time. Next, the carbon footprint of each node is calculated using the formula "carbon footprint = activity volume × carbon emission factor". In this formula, the activity volume refers to the energy consumption of the node per unit time, which can be fuel consumption, electricity usage, or any other form of energy usage; the carbon emission factor is a previously determined factor specific to each node. Through this calculation, the total carbon emissions of each node in a given time can be quantified. With the carbon footprint data of each node, these data are combined with the node's operation process to construct the first carbon flow path. This path illustrates the carbon flow in a series of activities from raw material procurement to product production to waste treatment, providing enterprises with a clear panoramic view of carbon emissions. Such a path not only helps enterprises to understand their carbon emission patterns more comprehensively, but also identifies the nodes with the greatest emission reduction potential, thereby implementing more effective carbon emission reduction strategies. Through the above methods, enterprises can achieve precise management and optimization of carbon emissions, further enhance their competitiveness in the market, and ensure the scientificity, accuracy and practicality of carbon management strategies.

[0036] Furthermore, the carbon asset classification module 13 is also used for:

[0037] Analyze the historical carbon emission data of each carbon emission activity node, evaluate the contribution ratio of each node to the total carbon emissions, and allocate node quota carbon assets in proportion based on the historical contribution ratio of each node. The node quota carbon asset allocation formula is:

[0038]

[0039] Among them, the node historical consumption is the historical carbon emissions of the node, and the total historical consumption is the sum of the historical carbon emissions of all nodes.

[0040] Optionally, each carbon emission activity node, that is, a unit that specifically generates carbon emissions during production or operation, such as a factory's production equipment or energy use facilities, needs to collect its historical carbon emission data, including but not limited to the total carbon emissions of each node in the past specific time, which are usually derived from monitoring equipment or environmental reports. Then, the historical carbon emissions of each node are analyzed in detail to evaluate its contribution to the total carbon emissions. This evaluation process involves calculating the percentage of carbon emissions of each node to the total carbon emissions. For example, if node A produces 1,000 tons of carbon dioxide in a certain year, and the total emissions of the enterprise are 10,000 tons, the contribution ratio of node A is 10%. Based on the historical contribution ratio of each node obtained, the node quota carbon assets will be allocated proportionally. This allocation process follows the formula "node quota = total quota assets × node historical consumption / total historical consumption". In this formula, total quota assets refer to the total carbon emission quota available to the enterprise in a specific time period, while node historical consumption refers to the historical carbon emissions of the node, and total historical consumption is the sum of the historical carbon emissions of all nodes. This approach ensures that the allocation of carbon quotas reflects both the actual carbon emission responsibility of each node and the need for overall carbon management. For example, more carbon quotas will be allocated to nodes with historically high carbon emissions to cope with their higher emission needs; and fewer quotas may be allocated to nodes with higher carbon emission efficiency to encourage continued emission reduction measures. In addition, this approach not only improves the transparency and fairness of quota management, but also helps companies plan their carbon reduction strategies more effectively, optimize resource use, and achieve dual optimization of environmental protection and economic benefits.

[0041] Furthermore, the node quota correction module 14 is also used for:

[0042] Analyze the price fluctuations and trading volume of the carbon trading market in real time to obtain market trend forecast results.

[0043] Based on the market trend forecast results, the impact of carbon trading financial hedging on quota trading is evaluated to obtain the quota ratio influencing factor.

[0044] The node quota is dynamically adjusted based on the quota ratio influencing factor to obtain multiple node quota thresholds.

[0045] Specifically, the price fluctuations and trading volume of the carbon trading market are monitored in real time, which is achieved by connecting to the databases and trading platforms of major carbon trading markets. Real-time data includes information such as the buying and selling prices of carbon credits, the number and frequency of transactions, etc., which are the basis for understanding market dynamics and making predictions. Subsequently, data analysis and prediction models are used to process this real-time information to generate market trend prediction results, which not only involve price trends, but also include a comprehensive analysis of factors such as market supply and demand status and policy change expectations. For example, if the model predicts that carbon prices will rise, this may affect the company's carbon quota purchase and use strategy. Based on the obtained market trend prediction results, the specific impact of financial hedging tools in carbon trading on quota trading is evaluated next. Financial hedging, such as futures contracts and options, is a strategy used by market participants to protect themselves from future price fluctuations. By analyzing how these hedging tools affect the value of carbon quotas and the carbon asset management of enterprises, the quota allocation impact factor is obtained, which reflects the specific impact of market forecasts and hedging strategies on the quota demand of each node. Finally, the carbon quota of each node is dynamically adjusted according to the quota ratio influencing factors. This adjustment takes into account the historical carbon emission data of the node, current market conditions and future market forecasts, ensuring that the quota of each node can meet its operational needs and conform to the overall market trends and risk management strategies. The result of the dynamic adjustment is multiple node quota thresholds, which provide enterprises with operational flexibility, enabling them to maintain competitiveness and compliance in a changing market environment. Through the above steps, not only the speed and accuracy of enterprises' response to carbon market dynamics are improved, but also the decision-making ability and risk management efficiency of enterprises in the carbon trading market are enhanced.

[0046] Furthermore, the second carbon flow path obtaining module 15 is also used for:

[0047] The carbon footprint of the current node is monitored in real time, and the difference between the current carbon footprint of each node and the quota threshold is calculated to obtain the deviation quota.

[0048] When the deviation quota is a negative value, the corresponding node is identified and the quota of the node is dynamically adjusted to obtain an updated quota node.

[0049] Taking the updated quota node as a reference, nodes in the first carbon flow path are updated and replaced based on node identifiers to obtain an adjusted second carbon flow path.

[0050] Exemplarily, a real-time monitoring mechanism is implemented to continuously track the carbon footprint of each carbon emission activity node. Then, the system calculates the deviation quota, which is the difference between the current carbon footprint and the preset quota threshold. The quota threshold refers to the carbon emission quota range set to adapt to the market and achieve carbon emission reduction targets. If the deviation quota is negative, that is, the actual carbon emissions exceed the quota threshold, this situation requires immediate response. The system will automatically identify the nodes that exceed the standard and make dynamic adjustments to the quota. The dynamic adjustment may include reducing the operating intensity of the node, optimizing the operating process, or adopting more efficient technical solutions. After the quota adjustment is completed, the system will update the quota settings of these nodes and synchronize these changes to the first carbon flow path, thereby generating an adjusted second carbon flow path. The second carbon flow path not only reflects the latest carbon emission status of each node, but also shows the new carbon emission trend of the entire system after taking adjustment measures. Such path updates help companies better understand the changes in their carbon emission patterns, evaluate the effectiveness of emission reduction strategies, and make further adjustments when necessary. In addition, through the application of this method, enterprises can ensure their position in the carbon emissions trading market. This real-time monitoring and dynamic adjustment mechanism improves the flexibility and responsiveness of corporate carbon management and is an indispensable part of the transition to a low-carbon economy.

[0051] Furthermore, the second carbon flow path obtaining module 15 is also used for:

[0052] Pattern recognition and cluster analysis are performed on the nodes in the second carbon flow path, and the nodes are clustered and grouped according to the similarity of the carbon emission characteristics and conditions of the nodes to obtain multiple management cluster groups, wherein each management cluster group corresponds to an emission reduction management stage.

[0053] Furthermore, by analyzing the data of each node in the second carbon flow path, a pattern recognition program is started. The purpose of this program is to automatically identify the patterns and laws of each node in carbon emission behavior through an algorithm, such as periodic fluctuations, sudden increases or continuous declines. These patterns usually reflect the operating characteristics, energy efficiency levels and environmental influencing factors of the nodes. Subsequently, the nodes are clustered and grouped using cluster analysis technology. Cluster analysis is a statistical method used to group objects into multiple clusters based on the similarity of their attributes. Here, cluster analysis is based on the carbon emission characteristics of the nodes, such as carbon emissions, carbon efficiency and emission reduction potential. The system will apply algorithms such as K-means, hierarchical clustering or density-based clustering to perform this task. For example, if two nodes show similar energy consumption and carbon emissions, they may be classified into the same cluster group. This grouping can make emission reduction strategies more targeted, because nodes belonging to the same cluster group often require similar management measures and optimization strategies. Each identified cluster group will correspond to an emission reduction management stage, which means that the nodes of each cluster group will perform emission reduction operations according to the corresponding management strategies, which may include technology upgrades, process optimization or energy substitution. This classification management not only makes carbon emission reduction measures more accurate and effective, but also facilitates monitoring and evaluation of emission reduction results, thereby achieving more systematic environmental management and continuous improvement. Through this method, companies can achieve data-driven carbon management decisions, handle carbon emissions issues at each link in a refined manner, and ultimately achieve the goal of reducing the overall carbon footprint. This highly organized carbon emission management strategy not only improves operational efficiency, but also enhances the company's sense of responsibility and brand value in environmental protection.

[0054] Furthermore, the quota updating module 16 is further used for:

[0055] Adjust and optimize quota allocation and usage strategies based on the specific needs of each of the emission reduction management stages.

[0056] Regularly evaluate the carbon management efficiency of the entire process and update strategies and quotas based on the evaluation results.

[0057] Specifically, for each emission reduction management stage, the system will formulate and implement targeted quota allocation and use strategies based on the specific needs and characteristics of the node groups included in that stage. For example, for node groups with relatively low energy efficiency but great potential for improvement, the strategy may tend to allocate more carbon quotas and may include investing in energy efficiency improvement technologies or process optimization measures. This strategy adjustment is not only based on current performance, but also takes into account the potential and strategic goals of future emission reductions at each node. Subsequently, the system will perform regular assessments, which include a comprehensive review of the carbon management efficiency of the entire process. The assessment may cover changes in carbon emissions, the effectiveness of quota use, and the economic and environmental impacts of emission reduction measures. The methods used in the assessment process may include data analysis, performance comparison, and standard review to ensure the accuracy and reliability of the assessment results. Based on the assessment results, the enterprise will update the strategy and quota. If the assessment finds that some strategies have failed to achieve the expected results, or the effectiveness of the strategy has been affected by changes in the external environment, the system will make adjustment suggestions, which may include increasing or decreasing the carbon quota of a specific node or node group, adjusting the emission reduction target, or modifying the implementation measures. All these updates are aimed at optimizing carbon emission management, improving overall carbon management efficiency, and ensuring that the company's carbon emission reduction activities can adapt to changing external conditions and internal goals. Through this continuous monitoring, evaluation and update mechanism, companies can maintain the foresight and adaptability of their carbon management strategies, thereby maximizing their economic and environmental benefits while meeting regulatory requirements. This systematic approach not only enhances the company's environmental responsibility and market competitiveness, but also helps promote its transformation to sustainable development.

[0058] Through the technical solutions of the above-mentioned embodiments, the green asset management system based on the multi-layer architecture provided by the present application solves the technical problems of poor flexibility and dynamics in carbon asset management, untimely market response, and inability to adjust and optimize carbon asset management strategies in real time, resulting in inefficiency and potential economic losses, and achieves the technical effect of improving the efficiency and adaptability of carbon asset management, thereby enhancing economic benefits while protecting the environment.

[0059] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A green asset management system based on a multi-layer architecture, characterized in that: The system comprises: A data acquisition module is used to acquire green asset structure information and perform data collection to obtain data collection results, wherein the data collection results include quota carbon asset data and emission reduction carbon asset data; A first carbon flow path acquisition module is used to perform data analysis and behavior simulation verification on the emission reduction carbon asset data based on the carbon emission activity node to obtain a carbon emission factor, and to calculate the carbon footprint in combination with the carbon emission factor and the carbon emission activity node to obtain a first carbon flow path, wherein the first carbon flow path includes a plurality of carbon emission activity nodes; A carbon asset division module, used to perform economic division on the quota carbon asset data based on the nodes in the first carbon flow path as constraints to obtain multiple node quotas, wherein the economic division refers to the division of total quota assets based on the historical consumption contribution of the nodes; A node quota correction module, used to dynamically correct each node quota in combination with the carbon trading flow and the hedging effect, and obtain multiple node quota thresholds; A second carbon flow path acquisition module, used to optimize multiple node footprints with multiple node quota thresholds as targets, generate a second carbon flow path, associate and cluster the nodes in the second carbon flow path, and obtain multiple emission reduction management stages; The quota updating module is used to formulate strategies based on the multiple emission reduction management stages to perform emission reduction management and quota optimization and updating.

2. The green asset management system based on multi-layer architecture as claimed in claim 1, characterized in that: The first carbon circulation path obtaining module is also used for: Collect historical data related to carbon emission activity nodes, including historical energy consumption, historical carbon emissions, and historical environmental impact factors of carbon emission activity nodes, wherein the carbon emission activity nodes refer to operation nodes that generate carbon emissions; The collected historical data are analyzed using a linear matrix analysis algorithm to obtain a plurality of theoretical carbon emission factors, wherein the theoretical carbon emission factor refers to a correlation coefficient between the energy consumption of a node activity and the carbon emission per unit time; In the simulation environment, the corresponding node historical behavior pattern and theoretical carbon emission factor are used to verify the carbon emissions of each carbon emission activity node to obtain the verification results; If the verification result meets the preset accuracy, the current theoretical carbon emission factor is determined to be the carbon emission factor of the corresponding node. If not, the current theoretical carbon emission factor is discarded and recalculated until the preset threshold is reached.

3. The green asset management system based on multi-layer architecture as claimed in claim 2, characterized in that: The first carbon circulation path obtaining module is also used for: The corresponding carbon emission factors are calibrated for all carbon emission activity nodes, and the carbon footprint of each activity node is calculated. The carbon footprint calculation formula of the activity node is: Carbon footprint = activity amount × carbon emission factor; Among them, the activity volume is the energy consumption of the node activity per unit time, the carbon emission factor is the carbon emission factor of the node per unit time, and the carbon footprint is the carbon emission of the node per unit time; Based on the calculated carbon footprints of each node and the operation flow, a first carbon flow path is constructed.

4. The green asset management system based on multi-layer architecture as claimed in claim 1, characterized in that: The carbon asset division module is also used for: Analyze the historical carbon emission data of each carbon emission activity node, evaluate the contribution ratio of each node to the total carbon emissions, and allocate node quota carbon assets in proportion based on the historical contribution ratio of each node. The node quota carbon asset allocation formula is: Among them, the node historical consumption is the historical carbon emissions of the node, and the total historical consumption is the sum of the historical carbon emissions of all nodes.

5. The green asset management system based on multi-layer architecture as claimed in claim 4, characterized in that: The node quota correction module is also used for: Analyze the price fluctuations and trading volume of the carbon trading market in real time to obtain market trend forecast results; Based on the market trend forecast results, the impact of carbon trading financial hedging on quota trading is evaluated to obtain the quota ratio impact factor; The node quota is dynamically adjusted based on the quota ratio influencing factor to obtain multiple node quota thresholds.

6. The green asset management system based on multi-layer architecture as claimed in claim 3, characterized in that: The second carbon transfer path obtaining module is also used for: Monitor the carbon footprint of the current node in real time, and calculate the difference between the current carbon footprint of each node and the quota threshold to obtain the deviation quota; When the deviation quota is a negative value, the corresponding node is identified and the quota of the node is dynamically adjusted to obtain an updated quota node; Taking the updated quota node as a reference, nodes in the first carbon flow path are updated and replaced based on node identifiers to obtain an adjusted second carbon flow path.

7. The green asset management system based on multi-layer architecture as claimed in claim 6, characterized in that: The second carbon transfer path obtaining module is also used for: Pattern recognition and cluster analysis are performed on the nodes in the second carbon flow path, and the nodes are clustered and grouped according to the similarity of the carbon emission characteristics and conditions of the nodes to obtain multiple management cluster groups, wherein each management cluster group corresponds to an emission reduction management stage.

8. The green asset management system based on multi-layer architecture as claimed in claim 7, characterized in that: The quota update module is also used for: Adjust and optimize quota allocation and use strategies based on the specific needs of each of the emission reduction management stages; Regularly evaluate the carbon management efficiency of the entire process and update strategies and quotas based on the evaluation results.

Images