An esg-based carbon emission optimization method and system

Abstract

The application discloses a carbon emission optimization method and system based on ESG, relates to the technical field of carbon emission optimization, and comprises the following steps: collecting data based on an ESG framework, calculating characteristic indexes, analyzing carbon emission flow value composition cooperation matrices of different emission points, calculating optimization values by using a geometric optimization function according to difference values and average adaptability values of analyzed areas of emission intensity, and combining the cooperation matrices to construct geometric constraint matrices as dynamic constraints. The method disclosed by the application further strengthens the analysis ability of differentiated cooperation between different emission points by calculating the difference values of emission intensity in the areas and the average adaptability parameters, and finally relates all emission points in the areas to the geometric constraint matrices by standardizing the maximum optimization values and the cooperation value constraints, so that the optimization direction of the emission point cooperation behavior is effectively ensured by taking the optimization values as weights to guide the behavior regulation and control between the points.

Description

Technical Field

[0001] This invention relates to the field of carbon emission optimization technology, and in particular to an ESG-based carbon emission optimization method and system. Background Technology

[0002] The ESG framework is gradually being widely applied in the management and evaluation systems of enterprises and organizations. The ESG framework not only focuses on the specific total carbon emissions, but also emphasizes achieving green and low-carbon development throughout the entire life cycle through methods such as optimizing management methods and improving resource utilization efficiency. At present, research on carbon emission optimization is mainly focused on the establishment of carbon emission statistics and assessment models, the development of carbon emission measurement methods, and technical support for carbon trading markets.

[0003] However, in complex regions and scenarios with diverse logistics flows, existing technologies have significant shortcomings in combining dynamic carbon emission behavior with spatial distribution information. First, the modeling of collaborative relationships between regional emission points often relies on empirical assumptions, making it difficult to dynamically reflect emission flow behavior between emission points and collaborative effects between regions. This results in a lack of accuracy and adaptability, and makes it inconvenient to conduct precise dynamic analysis and control of complex emission behaviors involving the time dimension. During the optimization process, there is a lack of a strong feedback correction mechanism, making it difficult to achieve closed-loop management from real-time deviation monitoring to dynamic adjustment. Consequently, the carbon emission optimization process struggles to balance the synergistic efficiency of achieving steady-state goals and adjusting dynamic behavior. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an ESG-based carbon emission optimization method and system to address the problem that modeling the collaborative relationships between regional emission points often relies on empirical assumptions, making it difficult to dynamically reflect the emission flow behavior between emission points and the collaborative effects between regions. This results in a lack of accuracy and adaptability, and makes it inconvenient to conduct precise dynamic analysis and control of complex emission behaviors involving the time dimension. During the optimization process, the lack of a strong feedback correction mechanism makes it difficult to achieve closed-loop management from real-time deviation monitoring to dynamic adjustment, leading to the problem that the carbon emission optimization process cannot simultaneously achieve the synergistic efficiency of steady-state target achievement and dynamic behavior adjustment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an ESG-based carbon emission optimization method, comprising:

[0008] Data is collected based on the ESG framework, characteristic indicators are calculated, and the carbon emission flow values ​​at different emission points are analyzed to form a cooperation matrix. Based on the difference and average fitness values ​​of emission intensity in the analysis area, the optimization value is calculated using a geometric optimization function, and the geometric constraint matrix is ​​constructed by combining the cooperation matrix as a dynamic constraint.

[0009] Data on the rate of change of emissions at emission points over time are collected. The geometric constraint matrix is ​​extended to the time dimension to construct a dynamic behavior matrix. Based on the information on the number of logistics flows, the transfer probability between emission points is defined in combination with the dynamic behavior matrix. An initial steady-state emission distribution vector is defined. The total emission constraint is embedded into the optimization objective function through the Lagrange method for iterative optimization to obtain the optimized transfer sequence.

[0010] Calculate the principal eigenvalues ​​to quantitatively measure the stability of emission flow behavior, perform steady-state analysis and adjust for excessive clustering distribution, and rebalance and optimize the transfer matrix;

[0011] A monitoring matrix is ​​constructed based on real-time logistics flow information to compare deviations, analyze the deviation magnitude and dynamic feedback time weight factor, update the final feedback matrix, and decompose it into emission components for different emission points based on the actual capacity of the emission points, thereby adjusting the emission plans of the emission points.

[0012] As a preferred embodiment of the ESG-based carbon emission optimization method of the present invention, the step of calculating the optimized value using a geometric optimization function based on the difference value and average fitness value of the emission intensity analysis area, and constructing a geometric constraint matrix by combining the cooperation matrix as a dynamic constraint, includes:

[0013] Data collection was conducted based on the ESG framework to address carbon emission intensity, energy efficiency, and emission control information.

[0014] Based on the emissions at each emission point, characteristic indicators are calculated according to energy consumption, total energy per unit of output, emission limits, and emission reduction from capture and storage equipment. These indicators include emission intensity, carbon emission efficiency, and emission reduction capacity, and a characteristic matrix is ​​constructed.

[0015] Collect carbon emission flow values ​​between different emission points, including mass flow and energy transfer records between each emission point, define the cooperation value between two emission points as the ratio of shared mass flow to maximum flow, and form a cooperation matrix.

[0016] The difference in emission intensity within the region is calculated based on the emission intensity index of different emission points. At the same time, the average adaptability between the carbon emission efficiency index and the cooperation value of the emission points is analyzed based on the cooperation value of the emission points.

[0017] The emission point indicators in the feature matrix are processed using a geometric optimization function, and the cooperation matrix is ​​used as a dynamic constraint for optimization. By calculating the emission intensity difference value and the average fitness parameter, the efficiency superposition of different emission points and the self-efficacy combination of the same emission point are analyzed. A dynamic geometric constant optimization function is defined, and the corresponding optimization value is calculated.

[0018] The maximum optimal value of all emission points in the region is taken as the optimization result. It is then standardized by combining emission intensity and carbon emission efficiency, and the result is constrained according to the cooperation value to construct a geometric constraint matrix.

[0019] As a preferred embodiment of the ESG-based carbon emission optimization method of the present invention, the following steps are included: constructing a dynamic behavior matrix, defining the transition probability between emission points based on logistics flow frequency information and the dynamic behavior matrix, defining an initial steady-state emission distribution vector, and iteratively optimizing the total emission constraint by embedding it into the objective function using the Lagrange method.

[0020] Collect emission change rate data at emission points over time and construct a dynamic growth rate matrix;

[0021] The geometric constraint matrix is ​​extended to the time dimension based on the dynamic growth rate matrix, and time-step calculations are performed to construct a dynamic behavior matrix.

[0022] Collect information on the number of logistics flows between emission points within the region, construct a regional logistics flow relationship matrix, and define the transfer probability between emission points in conjunction with a dynamic behavior matrix;

[0023] The transition probability of each emission point is normalized and constructed into a Markov chain transition probability matrix. The transition matrices at all time steps are stored in time series data format.

[0024] An initial steady-state emission distribution vector is defined based on historical data of emission points, and the average historical emissions of emission points are used as the initial share of each emission point in the steady-state emission distribution.

[0025] Based on the steady-state emission distribution vector, the constraints are defined, including the optimized transition matrix. The cumulative result of each emission point should reach the predetermined steady-state emission ratio, while also meeting the global emission constraints. That is, the optimization process must ensure that the total emissions do not exceed the specified limit. The optimization objective function is defined in this way.

[0026] The total emissions constraint is embedded into the optimization objective function using the Lagrange method to construct the Lagrange function;

[0027] The transition probability matrix is ​​solved iteratively by using gradient descent. When the iterative change of the objective function is lower than a preset value, the iteration stops and the optimized transition matrix is ​​obtained. The optimization results at different time steps are stored as an optimized transition sequence.

[0028] As a preferred embodiment of the ESG-based carbon emission optimization method of the present invention, the steps of calculating principal eigenvalues ​​to quantitatively measure the stability of emission flow behavior, performing steady-state analysis and adjusting for excessive clustering distribution, and rebalancing the optimized transition matrix include:

[0029] The stability of emission flow behavior is quantitatively measured by calculating the principal eigenvalues ​​based on the optimized transfer matrix.

[0030] Based on the actual steady-state flow and the degree of deviation from the target, the characteristic value threshold is defined as the weighted average of the steady-state proportions of all emission points;

[0031] If the principal eigenvalue is less than or equal to the eigenvalue threshold, it indicates a stable state. If the principal eigenvalue is greater than the eigenvalue threshold, an anomaly is marked, and an over-aggregated distribution is adjusted to rebalance and optimize the transition matrix.

[0032] As a preferred embodiment of the ESG-based carbon emission optimization method of the present invention, the step of constructing a monitoring matrix based on real-time logistics flow information, comparing deviations, analyzing the deviation magnitude and dynamic feedback time weighting factor, and updating to obtain the final feedback matrix includes:

[0033] Real-time logistics flow data between emission points is collected, a monitoring matrix is ​​constructed, and it is compared with the rebalanced optimized transfer matrix.

[0034] Calculate the deviation matrix between the real-time monitoring value and the current optimization matrix, and analyze the deviation magnitude and the dynamic feedback time weight factor of the time change rate.

[0035] The final feedback matrix is ​​obtained by updating the deviation matrix using dynamic feedback time weighting factors.

[0036] As a preferred embodiment of the ESG-based carbon emission optimization method described in this invention, the step of decomposing the emission capacity of the emission point into emission components for different emission points and adjusting the emission plan for the emission points includes:

[0037] Based on the feedback matrix, the optimal steady-state target ratio among different emission points is determined, and combined with the actual capacity of the emission points, it is decomposed into emission components for different emission points.

[0038] Adjust the emission plans for different emission points based on the emission components at those emission points.

[0039] As a preferred embodiment of the ESG-based carbon emission optimization method of the present invention, the data collection based on the ESG framework includes:

[0040] Historical carbon emissions within the framework area, as well as data on major equipment and carbon emission factors, are collected to determine timestamps and construct a matrix. Each row represents the annual emissions of a single emission point, and the average annual emission intensity is calculated based on the overall regional data.

[0041] The energy benefits within the data collection framework area include total energy consumption and total industrial output as contribution benefits, and the total energy consumption per unit of output is calculated.

[0042] The collection of emission control information within the framework area includes obtaining emission limits through publicly available policies and governance frameworks, and determining adjustment factors for emission reductions by obtaining the annual processing capacity of carbon capture and storage facilities.

[0043] Secondly, the present invention provides an ESG-based carbon emission optimization system, comprising,

[0044] Data acquisition module: Collects regional carbon emission-related data based on the ESG framework, including emission point flow values, intensity difference values, and emission change rate data over time;

[0045] Collaboration matrix construction module: By analyzing the composition of emission flow values ​​and geometric optimization functions, and combining emission regional differences and average fitness values, the optimization index is calculated and a collaborative matrix with dynamic constraints is formed.

[0046] Geometric constraint matrix extension module: Extends the cooperation matrix through the time dimension and combines logistics flow information to construct a dynamic behavior matrix, which is used to define the transition probability between emission points and the initial steady-state emission distribution;

[0047] Iterative optimization module: The total emissions constraint is embedded into the optimization function using the Lagrange method, and the dynamic behavior matrix is ​​iteratively optimized to generate an optimization transition sequence;

[0048] Stability Analysis Module: Calculates principal eigenvalues, quantitatively measures the stability of flow behavior, and performs adjustments for abnormal over-aggregation to rebalance and optimize the transfer matrix;

[0049] Deviation Feedback Module: Constructs a monitoring matrix based on real-time logistics flow data, compares and analyzes the deviation magnitude, and updates the final feedback matrix through dynamic feedback time weighting factor;

[0050] Emissions decomposition module: Based on the actual capacity of emission points, the results of the feedback matrix are decomposed into specific emission components, and the emission plans of each emission point are dynamically adjusted to achieve the global carbon emission target.

[0051] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein the computer program, when executed by the processor, implements any step of the ESG-based carbon emission optimization method as described in the first aspect of the present invention.

[0052] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the ESG-based carbon emission optimization method as described in the first aspect of the present invention.

[0053] The beneficial effects of this invention are as follows: By calculating the emission intensity difference value and average adaptability parameter within the region, the analytical capability of differentiated cooperation among different emission points is further enhanced. Through the standardization of the maximum optimization value and the constraint of the cooperation value, all emission points within the region are ultimately associated with the geometric constraint matrix. The optimization value is used as the weight to guide the behavior regulation between points, effectively ensuring the optimization direction of the cooperative behavior of emission points. By extending the geometric constraint matrix to the time dimension based on the dynamic growth rate matrix, the dynamic behavior matrix is ​​calculated and constructed step by step, making the geometric constraint matrix a dynamic model that can express the changes in the cooperation of emission points. By comparing the principal eigenvalue with the eigenvalue threshold in real time, abnormal points of flow behavior can be quickly located, thereby simplifying the need for extensive monitoring and prioritizing the focus on abnormal areas. By analyzing the deviation amplitude and the rate of change over time, the introduction of the dynamic feedback time weight factor effectively combines the short-term fluctuations and long-term trends of logistics flow. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the ESG-based carbon emission optimization method in Example 1.

[0056] Figure 2 This is a schematic diagram of the ESG-based carbon emission optimization system in Example 1. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0060] Example 1, referring to Figures 1 to 2 This is the first embodiment of the present invention, which provides an ESG-based carbon emission optimization method, including the following steps:

[0061] S1. Data is collected based on the ESG framework, characteristic indicators are calculated, and the carbon emission flow values ​​at different emission points are analyzed to form a cooperation matrix. Based on the difference value and average fitness value of the emission intensity analysis area, the geometric optimization function is used to calculate the optimization value, and the geometric constraint matrix is ​​constructed by combining the cooperation matrix as a dynamic constraint.

[0062] Preferably, data collection is based on an ESG framework, including:

[0063] Historical carbon emissions within the framework area, as well as data on major equipment and carbon emission factors, are collected to determine timestamps and construct a matrix. Each row represents the annual emissions of a single emission point, and the average annual emission intensity is calculated based on the overall regional data.

[0064] The energy benefits within the data collection framework area include total energy consumption and total industrial output as contribution benefits, and the total energy consumption per unit of output is calculated.

[0065] The collection of emission control information within the framework area includes obtaining emission limits through publicly available policies and governance frameworks, and determining adjustment factors for emission reductions by obtaining the annual processing capacity of carbon capture and storage facilities.

[0066] Furthermore, based on the differences and average fitness values ​​of emission intensity across the analysis area, the optimized value is calculated using a geometric optimization function, and a geometric constraint matrix is ​​constructed using the cooperation matrix as a dynamic constraint, including:

[0067] Data collection was conducted based on the ESG framework to address carbon emission intensity, energy efficiency, and emission control information.

[0068] Based on the emissions at each emission point, characteristic indicators are calculated according to energy consumption, total energy per unit of output, emission limits, and emission reduction from capture and storage (CPS) equipment. These indicators include emission intensity, carbon emission efficiency, and emission reduction capacity. A characteristic matrix is ​​then constructed, where each row represents the emission intensity indicator, carbon emission efficiency indicator, and emission reduction capacity indicator for each emission point, respectively.

[0069]

[0070]

[0071]

[0072] in, This represents the emission intensity index at the emission point, where i represents the i-th emission point. This indicates the amount of emissions at the emission point. This indicates the energy consumption at the emission point. Indicators representing the carbon emission efficiency of emission points. This represents the total energy output per unit of output at the emission point. This indicates the emission reduction capacity of the emission point. Indicates the emission limit value at the emission point. This indicates the emission reduction from the capture and storage equipment at the emission point;

[0073] Collect carbon emission flow values ​​between different emission points, including mass flow and energy transfer records between each emission point, define the cooperation value between two emission points as the ratio of shared mass flow to maximum flow, and form a cooperation matrix.

[0074] The difference in emission intensity within a region is calculated based on the emission intensity indices of different emission points. Simultaneously, the average adaptability between the carbon emission efficiency index and the cooperation value of the emission points is analyzed, expressed as follows:

[0075]

[0076]

[0077] in, Indicates the difference in emission intensity. This represents the average fitness value. This represents the variance of the emission intensity index across all emission points. This represents the average emission intensity index across all emission points, where n represents the total number of emission points. The overall efficiency represents the carbon emission efficiency index. This represents the cooperation value between the i-th emission point and the j-th emission point;

[0078] The emission point indices in the feature matrix are processed using a geometric optimization function, and the cooperation matrix is ​​used as a dynamic constraint for optimization. By calculating the emission intensity difference and the average fitness parameter, the efficiency superposition of different emission points and the self-efficacy combination of the same emission points are analyzed. A dynamic geometric constant optimization function is defined, and the corresponding optimized value is calculated, expressed as:

[0079]

[0080] in, This represents the corresponding optimized values ​​for the i-th emission point and the j-th emission point;

[0081] The maximum optimal value of all emission points within the region is taken as the optimization result. This result is then standardized by combining emission intensity and carbon emission efficiency, and the result is constrained according to the cooperation value. A geometric constraint matrix is ​​constructed, represented as follows:

[0082]

[0083]

[0084] in, This represents the result constraint value for the i-th emission point and the j-th emission point. Indicates the maximum optimization value. This represents the geometric constraint matrix.

[0085] By collecting data based on the ESG framework, specific information on carbon emissions, energy efficiency, and emission reduction capabilities at emission points was clarified, ensuring that emission efficiency and emission reduction technology capabilities can be directly correlated with the processing of the optimization model, thus avoiding the deviation in optimization results caused by traditional single-dimensional data analysis.

[0086] By calculating emission intensity, carbon emission efficiency, and emission reduction capacity indicators, energy consumption, total energy per unit of output, and emission limits are integrated into a unified matrix, enabling overall control of the complex relationships between different emission points. Through the construction of a cooperation matrix, the material flow and energy transmission records between emission points are fully utilized, and the maximum emission transfer value is used as a benchmark to measure the cooperation capacity between points. This integration not only accurately quantifies the strength of the relationship between emission points but also realizes the dynamic linkage between carbon emission capture technology and flow behavior, so that the optimization process is no longer limited to a single emission point but extends to the regional cooperation level, thereby enhancing the systematic nature of the optimization scheme.

[0087] By calculating the regional emission intensity difference value and average adaptability parameter, the analytical capability of differentiated cooperation among different emission points is further enhanced. This process reveals the interdependence that some emission points may reduce efficiency due to higher intensity. Furthermore, by analyzing the matching level between cooperation value and efficiency index, and by introducing a geometric optimization function, a deep integration of emission intensity difference value and average adaptability parameter is achieved. The nonlinear optimization method based on the efficiency superposition between emission points and the combination of their own efficiencies fully demonstrates the interactive optimization effect of the data layer and the technology layer.

[0088] By standardizing the maximum optimization value and constraining the collaborative value, all emission points in the region are ultimately associated with the geometric constraint matrix. The optimization value is used as the weight to guide the behavior regulation between points, which effectively ensures the optimization direction of the collaborative behavior of emission points. At the same time, it enables the maximization of energy efficiency and the improvement of emission reduction capacity in the region to be dynamically balanced. When the scheme is actually implemented, the compliance of regulation and the optimization benefits can be quickly verified, avoiding the problems of broken data relationships or insufficient dynamic association in traditional single-dimensional optimization.

[0089] S2: Collect emission change rate data of emission points over time, extend the geometric constraint matrix to the time dimension, construct a dynamic behavior matrix, define the transfer probability between emission points based on the logistics flow frequency information and the dynamic behavior matrix, define the initial steady-state emission distribution vector, and embed the total emission constraint into the optimization objective function through the Lagrange method for iterative optimization to obtain the optimized transfer sequence.

[0090] Preferably, a dynamic behavior matrix is ​​constructed. Based on the logistics flow frequency information, the transition probability between emission points is defined using the dynamic behavior matrix. An initial steady-state emission distribution vector is defined. The total emission constraint is embedded into the optimization objective function using the Lagrange multiplier method for iterative optimization, including...

[0091] Data on the rate of change of emissions at emission points over time are collected, and a dynamic growth rate matrix is ​​constructed, represented as follows:

[0092]

[0093]

[0094] in, This represents the dynamic change percentage of the i-th emission point. This represents the change in emissions at emission point i per unit time. Represents the dynamic growth rate matrix. This represents the dynamic change percentage of the nth emission point;

[0095] Based on the dynamic growth rate matrix, the geometric constraint matrix is ​​extended to the time dimension and calculated step-by-step to construct the dynamic behavior matrix, which is represented as follows:

[0096]

[0097]

[0098]

[0099] Where t represents the time step, T represents the upper limit of the number of time steps. The t-th power represents the dynamic growth rate matrix. This represents the dynamic behavior matrix at time step t, where Elements in the dynamic growth rate matrix The value at time step t serves as the growth factor for the emission point at time t. This represents the dynamic adjustment value of the cooperative behavior between emission points at time t and serves as... , This represents the complete dynamic behavior matrix, including data from all time steps. A sequence of dynamic behavior matrices representing different time steps;

[0100] Information on the frequency of material flows between emission points within the data collection area is used to construct a regional material flow relationship matrix. This matrix, combined with a dynamic behavior matrix, defines the transfer probability between emission points, as follows:

[0101]

[0102] in, This represents the transition probability between emission points i and j. This represents the dynamic adjustment value for time step t. Values ​​representing the flow relationships in regional logistics statistics;

[0103] The transition probabilities at each emission point are normalized to construct a Markov chain transition probability matrix. The transition matrices at all time steps are then stored in time series data format, as follows:

[0104]

[0105]

[0106] in, This represents the transition probability matrix at time step t. Represents a sequence of transition probability matrices at different time steps;

[0107] An initial steady-state emission distribution vector is defined based on historical data from emission points. The average historical emissions from each emission point are used as the initial share of each emission point in the steady-state emission distribution, expressed as:

[0108]

[0109] in, This represents the steady-state emission distribution value at the i-th emission point, and forms the steady-state target distribution vector. , This represents the emissions at historical emission point i;

[0110] Based on the steady-state emission distribution vector, constraints are defined including the optimized transition matrix. The cumulative result for each emission point should reach the predetermined steady-state emission ratio, while also complying with global emission constraints, meaning the optimization process must ensure that the total emissions do not exceed the specified limit. The optimization objective function is defined accordingly, expressed as:

[0111] ;

[0112] in, Let denote the objective function, and denote the optimized transition matrix. The goal is to minimize the deviation from the steady-state emission distribution. This represents the transition probability of emission point i transitioning from emission point i to emission point j at time t in the optimized transition matrix;

[0113] By embedding the total emissions constraint into the optimization objective function using the Lagrange method, the Lagrange function is constructed as follows:

[0114] ;

[0115] in, This represents the Lagrangian optimization function, which includes the objective function and constraint terms. This represents the Lagrange multiplier, used to force the system to meet total emissions constraints. This indicates the upper limit of total emissions. This represents the amount of emissions at emission point i at time step t;

[0116] The transition probability matrix is ​​iteratively updated using the gradient descent method. The iteration stops when the change in the objective function falls below a preset value, and the optimized transition matrix is ​​obtained. Furthermore, the optimization results at different time steps are stored as an optimization transition sequence, represented as:

[0117] ;

[0118]

[0119] in, express The results of iterative optimization This represents the transition matrix for iterative optimization. This represents the learning rate.

[0120] By collecting emission change rate data of emission points over time and constructing a dynamic growth rate matrix, the dynamic changes of emission points at different times can be accurately captured, ensuring that the scheme can quantify the time dynamic behavior of each point, effectively avoiding the problem that traditional static modeling cannot reflect fluctuations at the time level, and laying the foundation for analyzing emission change characteristics at different times.

[0121] By extending the geometric constraint matrix to the time dimension based on the dynamic growth rate matrix, and calculating and constructing the dynamic behavior matrix step by step, the geometric constraint matrix becomes a dynamic model capable of expressing the collaborative changes of emission points. By collecting and constructing a regional logistics flow relationship matrix, and combining it with the dynamic behavior matrix to define the transfer probability between emission points, a linkage model is formed between regional logistics flow data and time-series emission behavior, successfully designing a collaborative modeling process that integrates time behavior and spatial logistics. This combination enhances the adaptability of the scheme to the dual characteristics of logistics and emissions, and can further improve the accuracy of public resource allocation and energy flow control in modeling.

[0122] By normalizing the transition probabilities and constructing a Markov chain transition probability matrix, the time-series collaborative behavior between emission points is transformed into an analyzable steady-state transition model, ensuring that the long-term behavior of the emission system gradually tends to be controllable and stable. Secondly, the transition matrices of all time steps are stored in time-series form, so that the dynamic data of the scheme can support long-term stability and trend analysis, avoiding the limitations of single-point-of-time optimization schemes.

[0123] By defining a steady-state emission distribution vector and using historical averages as the initial rule for iteration, regional imbalances and long-term historical data can participate in the optimization process. This ensures that the scheme can comprehensively consider historical legacy issues and long-term goals, making the optimization results more acceptable. At the same time, by combining steady-state targets as the optimization benchmark, the scheme data initialization bias is eliminated.

[0124] By embedding comprehensive constraints into the Lagrange optimization function, the steady-state distribution of emission points and the regional global emission target are unified into an optimization framework, enabling the scheme to meet the hard constraint of emission limits while improving local efficiency.

[0125] By combining the above technologies, the dynamic data of the solution across multiple dimensions of time, space, and behavior is fully linked. The mutual reinforcement of technology and data enables the solution to achieve the goal of accurate emission flow control based on spatiotemporal prediction and collaborative optimization of dynamic behavior matrix. At the same time, the combination of global optimization and local optimization at different time steps allows the solution to quickly adapt to the actual needs of emission management in the region and provides flexibility for long-term optimization.

[0126] S3, calculate the principal eigenvalues ​​to quantitatively measure the stability of emission flow behavior, perform steady-state analysis and adjust for excessive aggregation distribution, and rebalance and optimize the transfer matrix;

[0127] Preferably, the process involves calculating principal eigenvalues ​​to quantitatively measure the stability of emission flow behavior, performing steady-state analysis and adjusting for excessive clustering distributions, and rebalancing and optimizing the transfer matrix, including:

[0128] The stability of emission flow behavior is quantitatively measured by calculating the principal eigenvalues ​​based on the optimized transition matrix, and is expressed as follows:

[0129]

[0130] in, Represents the principal eigenvalue. This represents the function for calculating the eigenvalues ​​of a matrix. This represents optimizing the transition matrix. This represents the eigenvalue with the largest modulo value;

[0131] Based on the actual steady-state flow and the degree of deviation from the target, the characteristic value threshold is defined as the weighted average of the steady-state proportions of all emission points, expressed as:

[0132]

[0133]

[0134] in, Indicates the eigenvalue threshold. This represents the principal characteristic value of emission point i. Indicates deviation from control factor;

[0135] If the principal eigenvalue is less than or equal to the eigenvalue threshold, it indicates a stable state. If the principal eigenvalue is greater than the eigenvalue threshold, an anomaly is identified, and an over-aggregated distribution adjustment is performed. The transition matrix is ​​then rebalanced and optimized, as shown below:

[0136]

[0137] in, This represents the optimal transition matrix for rebalancing. This represents the identity matrix, which has the same size as the optimization transition matrix. .

[0138] By calculating the principal eigenvalues ​​based on the optimized transfer matrix, the stability of emission flow behavior can be quantitatively measured. As a core indicator, the principal eigenvalues ​​can keenly capture the overall steady-state changes of flow behavior. By quantitatively defining the eigenvalue thresholds, the deviation between the steady-state flow ratio and the target is introduced into the weighted calculation. Combined with the dynamic effect of the adjustment coefficient, an adaptive characterization of the allowable range of stability can be achieved, avoiding misjudgment of boundary conditions and effectively improving the reliability and adaptability of stability analysis.

[0139] By comparing the principal eigenvalue with the eigenvalue threshold in real time, abnormal points in flow behavior can be quickly located, thereby simplifying the need for extensive monitoring and prioritizing the focus on abnormal areas, which helps in the targeted allocation of resources and efficient problem localization. By adjusting and rebalancing the transfer matrix through over-aggregated distribution, it is possible to avoid the deterioration of overall stability caused by the concentrated flow of abnormal points. At the same time, the introduction of the identity matrix provides a benchmark for flow behavior, realizes the balanced adjustment of matrix optimization, and further enhances the stability of the overall flow structure.

[0140] S4. Based on real-time logistics flow information, a monitoring matrix is ​​constructed to compare deviations, analyze the deviation magnitude and time change rate, dynamically feed back the time weight factor, update the final feedback matrix, and combine it with the actual capacity of the emission point to decompose it into emission components for different emission points, and adjust the emission plan of the emission point.

[0141] Preferably, a monitoring matrix is ​​constructed based on real-time logistics flow information for deviation comparison. The deviation magnitude and time change rate are analyzed, and the dynamic feedback time weight factor is updated to obtain the final feedback matrix, including:

[0142] Real-time logistics flow data between emission points is collected, a monitoring matrix is ​​constructed, and it is compared with the rebalanced optimized transfer matrix.

[0143] Calculate the deviation matrix between the real-time monitoring value and the current optimization matrix, and analyze the deviation magnitude and dynamic feedback time weighting factor, expressed as:

[0144]

[0145]

[0146]

[0147] in, Represents the deviation matrix. Represents the monitoring matrix. A measure representing the magnitude of the deviation. This represents the deviation between emission points i and j in the deviation matrix. This represents the dynamic feedback time weighting factor. The mean of historical deviations;

[0148] The final feedback matrix is ​​obtained by updating the deviation matrix using a dynamic feedback time weighting factor, and is expressed as follows:

[0149]

[0150] in, This represents the feedback matrix.

[0151] By collecting real-time logistics flow data between emission points and constructing a monitoring matrix, actual logistics deviations can be captured in the real-time dynamic environment where flow behavior occurs. By optimizing the deviation comparison between the transfer matrix and the real-time monitoring matrix, abnormal behavior in logistics flow can be accurately identified. The use of this deviation matrix provides a fine-grained behavior analysis method, enabling anomaly calculation not only to target changes in overall characteristic values ​​but also to capture details of local flow pattern mismatches, thereby enhancing the ability to trace the source of problems.

[0152] By analyzing the deviation magnitude and the rate of change over time, the introduction of the dynamic feedback time weight factor effectively combines the short-term fluctuations and long-term trends of logistics flow. When the deviation of logistics flow fluctuates drastically, the weight factor can be adjusted quickly to highlight the importance of the current anomaly. When the stability of logistics deviation increases, the weight factor, combined with the historical average, can smooth the impact of fluctuations on system adjustment and avoid excessive intervention. This not only improves the accuracy of feedback but also effectively suppresses the instability of intervention.

[0153] By utilizing the deviation matrix and the dynamic feedback time weight factor, a feedback matrix is ​​finally obtained, forming a closed-loop control system based on real-time monitoring and dynamic adjustment. The generation of the feedback matrix combines historical averages and time weights, enabling the system to regulate emission flow behavior from both global and local perspectives. This avoids interference from short-term conflicts on long-term optimization goals, not only increasing the system's adaptability to complex environmental changes but also gradually optimizing the flow strategy between emission points, resulting in more stable and efficient logistics flow behavior.

[0154] Furthermore, based on the actual capacity of the emission points, the emission plans are broken down into emission components for different emission points, and the emission plans for each emission point are adjusted accordingly, including...

[0155] Based on the feedback matrix, the optimal steady-state target ratio among different emission points is determined. Combined with the actual capacity of each emission point, this is decomposed into emission components for different emission points, as follows:

[0156]

[0157] in, This represents the decomposed emission component at time step t of emission point i. Indicates the regional total carbon emissions target. This represents the steady-state target proportion for the optimized emission point i;

[0158] Adjust the emission plans for different emission points based on the emission components at those emission points.

[0159] By determining the optimal steady-state target ratio among different emission points based on the feedback matrix, the actual flow behavior of emission points and the overall optimization target can be effectively connected. The feedback matrix not only reflects the dynamic relationship between emission points, but also provides sufficient decision-making basis for constructing steady-state targets, thereby ensuring that the target setting is dynamically adaptable.

[0160] By combining the actual capabilities of emission points, the overall carbon emission target is refined to each emission point. At the operational level, it provides a flexible solution that combines strong and loose constraints. Because the decomposition process takes into account the actual capacity constraints of emission points (such as technical conditions and processing capacity), this target decomposition avoids the "unfairness" or "overload risk" that may occur in resource allocation, and ensures the feasibility of the optimized target and the value of practical application.

[0161] By decomposing the emission components corresponding to time step t of emission point i, the emission plans of emission points at different times can be dynamically planned. This time-dimensional decomposition makes the entire emission management more refined, while smoothing the path to achieving regional emission targets and avoiding systemic risks caused by excessive concentrated emission reduction within a certain period of time.

[0162] By adjusting emission plans based on the decomposed emission components, close coordination between regional total carbon emission targets and the emission behavior of individual emission points can be achieved. This mechanism not only ensures the achievement of carbon emission targets at the policy level, but also optimizes the cooperative relationship between emission points at the implementation level, giving the system stronger resistance to disturbances and adaptability, enabling the system to form a dynamic adaptation relationship between regional carbon emission targets and local technical capabilities and resource conditions.

[0163] This embodiment also provides an ESG-based carbon emission optimization system, including,

[0164] Data acquisition module: Collects regional carbon emission-related data based on the ESG framework, including emission point flow values, intensity difference values, and emission change rate data over time;

[0165] Collaboration matrix construction module: By analyzing the composition of emission flow values ​​and geometric optimization functions, and combining emission regional differences and average fitness values, the optimization index is calculated and a collaborative matrix with dynamic constraints is formed.

[0166] Geometric constraint matrix extension module: Extends the cooperation matrix through the time dimension and combines logistics flow information to construct a dynamic behavior matrix, which is used to define the transition probability between emission points and the initial steady-state emission distribution;

[0167] Iterative optimization module: The total emissions constraint is embedded into the optimization function using the Lagrange method, and the dynamic behavior matrix is ​​iteratively optimized to generate an optimization transition sequence;

[0168] Stability Analysis Module: Calculates principal eigenvalues, quantitatively measures the stability of flow behavior, and performs adjustments for abnormal over-aggregation to rebalance and optimize the transfer matrix;

[0169] Deviation Feedback Module: Constructs a monitoring matrix based on real-time logistics flow data, compares and analyzes the deviation magnitude, and updates the final feedback matrix through dynamic feedback time weighting factor;

[0170] Emissions decomposition module: Based on the actual capacity of emission points, the results of the feedback matrix are decomposed into specific emission components, and the emission plans of each emission point are dynamically adjusted to achieve the global carbon emission target.

[0171] This embodiment also provides a computer device applicable to ESG-based carbon emission optimization methods, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the ESG-based carbon emission optimization method proposed in the above embodiment.

[0172] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0173] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the ESG-based carbon emission optimization method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0174] In summary, this invention further enhances the analytical capability of differentiated cooperation among different emission points by calculating the regional emission intensity difference value and average adaptability parameter. Through standardization of the maximum optimization value and cooperation value constraints, all emission points in the region are ultimately associated with the geometric constraint matrix. The optimization value is used as the weight to guide the behavior regulation between points, effectively ensuring the optimization direction of the emission point cooperation behavior. By extending the geometric constraint matrix to the time dimension based on the dynamic growth rate matrix, the dynamic behavior matrix is ​​calculated and constructed step by step, making the geometric constraint matrix a dynamic model that can express the changes in emission point cooperation. Through real-time comparison of the principal eigenvalue and the eigenvalue threshold, abnormal points of flow behavior can be quickly located, thereby simplifying the broad monitoring needs and prioritizing the focus on abnormal areas. By analyzing the deviation amplitude and the rate of change over time, the introduction of the dynamic feedback time weight factor effectively combines the short-term fluctuations and long-term trends of logistics flow.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An ESG-based carbon emission optimization method, characterized in that, include: Data is collected based on the ESG framework, characteristic indicators are calculated, and the carbon emission flow values ​​at different emission points are analyzed to form a cooperation matrix. Based on the difference and average fitness values ​​of emission intensity in the analysis area, the optimization value is calculated using a geometric optimization function, and the geometric constraint matrix is ​​constructed by combining the cooperation matrix as a dynamic constraint. Data on the rate of change of emissions at emission points over time are collected. The geometric constraint matrix is ​​extended to the time dimension to construct a dynamic behavior matrix. Based on the information on the number of logistics flows, the transfer probability between emission points is defined in combination with the dynamic behavior matrix. An initial steady-state emission distribution vector is defined. The total emission constraint is embedded into the optimization objective function through the Lagrange method for iterative optimization to obtain the optimized transfer sequence. Calculate the principal eigenvalues ​​to quantitatively measure the stability of emission flow behavior, perform steady-state analysis and adjust for excessive clustering distribution, and rebalance and optimize the transfer matrix; A monitoring matrix is ​​constructed based on real-time logistics flow information to compare deviations, analyze the deviation magnitude and dynamic feedback time weight factor, update the final feedback matrix, and decompose it into emission components for different emission points based on the actual capacity of the emission points, thereby adjusting the emission plans of the emission points. The optimization value is calculated using a geometric optimization function based on the difference and average fitness values ​​of the emission intensity analysis area, and a geometric constraint matrix is ​​constructed using the cooperation matrix as a dynamic constraint. Data collection was conducted based on the ESG framework to address carbon emission intensity, energy efficiency, and emission control information. Based on the emissions at each emission point, characteristic indicators are calculated according to energy consumption, total energy per unit of output, emission limits, and emission reduction from capture and storage equipment. These indicators include emission intensity, carbon emission efficiency, and emission reduction capacity, and a characteristic matrix is ​​constructed. Collect carbon emission flow values ​​between different emission points, including mass flow and energy transfer records between each emission point, define the cooperation value between two emission points as the ratio of shared mass flow to maximum flow, and form a cooperation matrix. The difference in emission intensity within the region is calculated based on the emission intensity index of different emission points. At the same time, the average adaptability between the carbon emission efficiency index and the cooperation value of the emission points is analyzed based on the cooperation value of the emission points. The emission point indicators in the feature matrix are processed using a geometric optimization function, and the cooperation matrix is ​​used as a dynamic constraint for optimization. By calculating the emission intensity difference value and the average fitness parameter, the efficiency superposition of different emission points and the self-efficacy combination of the same emission point are analyzed. A dynamic geometric constant optimization function is defined, and the corresponding optimization value is calculated. The maximum optimal value of all emission points in the region is taken as the optimization result. It is then standardized by combining emission intensity and carbon emission efficiency, and the result is constrained according to the cooperation value to construct a geometric constraint matrix.

2. The ESG-based carbon emission optimization method as described in claim 1, characterized in that: The process involves constructing a dynamic behavior matrix, defining the transition probabilities between emission points based on logistics flow frequency information, defining an initial steady-state emission distribution vector, and iteratively optimizing the total emission constraint within the objective function using the Lagrange multiplier method. Collect emission change rate data at emission points over time and construct a dynamic growth rate matrix; The geometric constraint matrix is ​​extended to the time dimension based on the dynamic growth rate matrix, and time-step calculations are performed to construct a dynamic behavior matrix. Collect information on the number of logistics flows between emission points within the region, construct a regional logistics flow relationship matrix, and define the transfer probability between emission points in conjunction with a dynamic behavior matrix; The transition probability of each emission point is normalized and constructed into a Markov chain transition probability matrix. The transition matrices at all time steps are stored in time series data format. An initial steady-state emission distribution vector is defined based on historical data of emission points, and the average historical emissions of emission points are used as the initial share of each emission point in the steady-state emission distribution. Based on the steady-state emission distribution vector, the constraints are defined, including the optimized transition matrix. The cumulative result of each emission point should reach the predetermined steady-state emission ratio, while also meeting the global emission constraints. That is, the optimization process must ensure that the total emissions do not exceed the specified limit. The optimization objective function is defined in this way. The total emissions constraint is embedded into the optimization objective function using the Lagrange method to construct the Lagrange function; The transition probability matrix is ​​solved iteratively by using gradient descent. When the iterative change of the objective function is lower than a preset value, the iteration stops and the optimized transition matrix is ​​obtained. The optimization results at different time steps are stored as an optimized transition sequence.

3. The ESG-based carbon emission optimization method as described in claim 2, characterized in that: The calculation of principal eigenvalues ​​quantitatively measures the stability of emission flow behavior, performs steady-state analysis, adjusts for excessive clustering distribution, and rebalances and optimizes the transfer matrix. include, The stability of emission flow behavior is quantitatively measured by calculating the principal eigenvalues ​​based on the optimized transfer matrix. Based on the actual steady-state flow and the degree of deviation from the target, the characteristic value threshold is defined as the weighted average of the steady-state proportions of all emission points; If the principal eigenvalue is less than or equal to the eigenvalue threshold, it indicates a stable state. If the principal eigenvalue is greater than the eigenvalue threshold, an anomaly is marked, and an over-aggregated distribution is adjusted to rebalance and optimize the transition matrix.

4. The ESG-based carbon emission optimization method as described in claim 3, characterized in that: The process involves constructing a monitoring matrix based on real-time logistics flow information, comparing deviations, analyzing the deviation magnitude and dynamic feedback time weighting factor, and updating the final feedback matrix. include, Real-time logistics flow data between emission points is collected, a monitoring matrix is ​​constructed, and it is compared with the rebalanced optimized transfer matrix. Calculate the deviation matrix between the real-time monitoring value and the current optimization matrix, and analyze the deviation magnitude and the dynamic feedback time weight factor of the time change rate. The final feedback matrix is ​​obtained by updating the deviation matrix using dynamic feedback time weighting factors.

5. The ESG-based carbon emission optimization method as described in claim 4, characterized in that: The process involves combining the actual capacity of each emission point with the emission components for different emission points, and adjusting the emission plans for each emission point accordingly. Based on the feedback matrix, the optimal steady-state target ratio among different emission points is determined, and combined with the actual capacity of the emission points, it is decomposed into emission components for different emission points. Adjust the emission plans for different emission points based on the emission components at those emission points.

6. The ESG-based carbon emission optimization method as described in claim 1, characterized in that: The data collection based on the ESG framework includes, Historical carbon emissions within the framework area, as well as data on major equipment and carbon emission factors, are collected to determine timestamps and construct a matrix. Each row represents the annual emissions of a single emission point, and the average annual emission intensity is calculated based on the overall regional data. The energy benefits within the data collection framework area include total energy consumption and total industrial output as contribution benefits, and the total energy consumption per unit of output is calculated. The collection of emission control information within the framework area includes obtaining emission limits through publicly available policies and governance frameworks, and determining adjustment factors for emission reductions by obtaining the annual processing capacity of carbon capture and storage facilities.

7. An ESG-based carbon emission optimization system, based on the ESG-based carbon emission optimization method according to any one of claims 1 to 6, characterized in that: include, Data acquisition module: Collects regional carbon emission-related data based on the ESG framework, including emission point flow values, intensity difference values, and emission change rate data over time; Collaboration matrix construction module: By analyzing the composition of emission flow values ​​and geometric optimization functions, and combining emission regional differences and average fitness values, the optimization index is calculated and a collaborative matrix with dynamic constraints is formed. Geometric constraint matrix extension module: Extends the cooperation matrix through the time dimension and combines logistics flow information to construct a dynamic behavior matrix, which is used to define the transition probability between emission points and the initial steady-state emission distribution; Iterative optimization module: The total emissions constraint is embedded into the optimization function using the Lagrange method, and the dynamic behavior matrix is ​​iteratively optimized to generate an optimization transition sequence; Stability Analysis Module: Calculates principal eigenvalues, quantitatively measures the stability of flow behavior, and performs adjustments for abnormal over-aggregation to rebalance and optimize the transfer matrix; Deviation Feedback Module: Constructs a monitoring matrix based on real-time logistics flow data, compares and analyzes the deviation magnitude, and updates the final feedback matrix through dynamic feedback time weighting factor; Emissions decomposition module: Based on the actual capacity of emission points, the results of the feedback matrix are decomposed into specific emission components, and the emission plans of each emission point are dynamically adjusted to achieve the global carbon emission target.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the ESG-based carbon emission optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the ESG-based carbon emission optimization method according to any one of claims 1 to 6.

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