A carbon emission reduction project development management and control method and system

Abstract

The present invention discloses a carbon emission reduction project development control method and system, which belongs to the technical field of carbon emission reduction project management, including S1, obtaining historical data and related data of carbon emission reduction projects, and preprocessing the historical data and related data to obtain a training set and a test set; S2, building and training a carbon emission reduction project development model based on the ESG indicator formula and the training set; S3, collecting real-time data and real-time related data of carbon emission reduction projects and preprocessing to obtain a verification set, and adjusting the trained carbon emission reduction project development model based on the verification set and the test set to obtain an optimal development model; synchronously, inputting the verification set into the optimal development model to obtain a development control strategy; S4, the carbon emission reduction project executes corresponding carbon emission reduction project development actions in response to the development control strategy; the present invention overcomes the problem that the prior art does not consider the mutual influence relationship between different ESG indicators and carbon emission reduction projects, resulting in poor timeliness of the development control strategy corresponding to the carbon emission reduction project.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction project management, and in particular to a carbon emission reduction project development management and control method and system. Background Art

[0002] With the development of new power system technologies, traditional carbon emission reduction evaluation and management methods are difficult to meet the increasing demand for corresponding carbon emission reduction projects. On the one hand, existing evaluation methods often focus on a single dimension and ignore the comprehensive impact of the location of the carbon emission reduction project, such as environmental impact, social acceptance, etc., resulting in the carbon emission reduction project being unsuitable for the local ecological environment. On the other hand, existing management methods lack a dynamic and intelligent control mechanism, and data collection is not comprehensive and analysis methods are backward, making it difficult to monitor project progress and performance in real time, and it is impossible to adjust the development strategy in time according to the actual situation of the project.

[0003] Chinese patent, publication number: CN117649137A, publication date: March 5, 2024, discloses an ESG evaluation system and evaluation method for the investment benefits of distribution network projects, the ESG evaluation system includes: three primary indicators: environment, society and governance; the evaluation method includes: obtaining the indicator scores and qualitative grades in the project to be evaluated, using the indicator scores and quantitative scores to calculate the subjective weights and objective weights of each secondary indicator, and calculating the combined weights of each secondary indicator through the subjective weights through consistency test and the objective weights through consistency test; determining the positive ideal solution and negative ideal solution of the secondary indicator, and calculating the degree of proximity of each secondary indicator to the positive ideal solution and the negative ideal solution respectively; using the corresponding degree of proximity of each secondary indicator, determining the closeness of each secondary indicator to the positive ideal solution, and obtaining the evaluation results of the project to be evaluated based on each closeness; however, the invention does not consider the mutual influence between different ESG indicators and carbon emission reduction projects, which makes it difficult for the evaluation method to be adjusted in time according to the actual working conditions of the carbon emission reduction project. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the existing technology does not take into account the mutual influence between different ESG indicators and carbon emission reduction projects, resulting in poor timeliness of development and control strategies corresponding to carbon emission reduction projects; a carbon emission reduction project development and control method and system are proposed, which constructs and trains a carbon emission reduction project development model based on the ESG indicator formula determined by the characteristics of the carbon emission reduction project and the ESG concept, and the historical data and related data of the carbon emission reduction project, and uses the real-time related data of the carbon emission reduction project to adjust the ESG indicators corresponding to the carbon emission reduction project development model to obtain the optimal development model, and then inputs the real-time data of the carbon emission reduction project into the optimal development model to obtain the development and control strategy. Finally, the carbon emission reduction project executes the corresponding carbon emission reduction project development action in response to the development and control strategy, which can not only timely adjust the corresponding carbon emission reduction project according to the actual working conditions of the carbon emission reduction project, significantly improve the timeliness of the development and control strategy, but also achieve the purpose of environmental protection while ensuring the benefits of the carbon emission reduction project.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a carbon emission reduction project development management and control method, comprising the following steps: S1. Obtain historical data and related data of carbon emission reduction projects, and preprocess the historical data and related data to obtain training sets and test sets; S2. Build and train a carbon reduction project development model based on the ESG indicator formula and training set; S3. Collect real-time data and real-time associated data of carbon emission reduction projects and perform preprocessing to obtain a validation set, and adjust the trained carbon emission reduction project development model based on the validation set and the test set to obtain the optimal development model; Simultaneously, the validation set is input into the optimal development model to obtain the development control strategy; S4. The carbon emission reduction project executes corresponding carbon emission reduction project development actions in response to the development management and control strategy.

[0006] In this solution, the historical data and related data of the carbon emission reduction project are preprocessed to obtain the training set and the test set, and the outliers and missing values ​​in the data are eliminated, so that the accuracy and completeness of the data can be improved. Then, the carbon emission reduction project development model is constructed and trained through the ESG indicator formula and the training set determined based on the characteristics of the carbon emission reduction project and the ESG concept. While predicting the operating status of the corresponding carbon emission reduction project, the ESG indicator can be combined to explore the optimal operating status of the carbon emission reduction project under specific environmental and business indicator conditions. However, the ESG indicator will change according to the changes in the environment where the carbon emission reduction project is located. In order to ensure that the ESG indicator meets the environmental conditions of the carbon emission reduction project at any time , business indicator requirements, the ESG indicators can be adjusted based on the real-time associated data of the carbon emission reduction project, that is, the carbon emission reduction project development model is adjusted based on the verification set and test set obtained by preprocessing the real-time data and real-time associated data of the carbon emission reduction project to obtain the optimal development model, and the real-time data of the carbon emission reduction project, that is, the verification set, is input into the optimal development model to obtain the development management and control strategy, which significantly improves the timeliness of the development management and control strategy. The carbon emission reduction project executes the corresponding carbon emission reduction project development actions in response to the development management and control strategy, which can not only timely adjust the corresponding carbon emission reduction project according to the actual working conditions of the carbon emission reduction project, but also achieve the purpose of environmental protection while ensuring the benefits of the carbon emission reduction project.

[0007] Preferably, in S1, the specific process of preprocessing the historical data and the associated data to obtain the training set and the test set is: S111. Cleaning historical data based on statistics and horizontal comparison and filling method to obtain complete historical data, and cleaning related data based on statistics and horizontal comparison and filling method to obtain complete related data; S112, classifying the complete historical data based on the historical data collection source to obtain classified historical data, and classifying the complete associated data based on the associated data collection source to obtain classified associated data; S113, integrating the classification history data and the classification association data to obtain an integrated data set; S114. Divide the data in the integrated data set into a training set and a test set based on a training-test allocation criterion.

[0008] In this solution, the horizontal comparison filling method can use the average value of the same data at the same historical moment to fill the missing values ​​in the data, thereby improving the overall data and making the data more in line with actual working conditions. Secondly, by classifying the data based on the data collection source, the correspondence between the data and the carbon emission reduction project links can be obtained, and the correspondence between historical data and related data can also be obtained to support subsequent steps. Then, based on the purpose of using the training set and the purpose of using the test set, the data in the integrated data set is divided to obtain a training set that contains both historical data and related data and a test set that is mainly related data. This can reasonably allocate data and corresponding computing resources, and effectively improve computing efficiency.

[0009] Preferably, before constructing the carbon emission reduction project development model based on the ESG indicator formula and the training set, S2 needs to determine the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept. The specific process of determining the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept is as follows: S011. Determine the ESG indicators of carbon emission reduction projects based on the characteristics of carbon emission reduction projects and ESG concepts, and determine the primary and secondary indicators of carbon emission reduction projects based on the ESG indicators and the process links of carbon emission reduction projects; Simultaneously, historical secondary indicators and historical primary indicators in the historical operation data of the corresponding carbon emission reduction projects are obtained; S012. Performing standardization processing based on historical secondary indicators and standardization formulas to obtain standard secondary indicators, and calculating secondary indicator weights based on the standard secondary indicators and entropy method; S013. Calculate the weight of the primary indicator based on the weight of the secondary indicator and the subordinate relationship between the primary indicator and the secondary indicator, and establish the ESG indicator formula based on the weight of the primary indicator and the primary indicator.

[0010] In this solution, the ESG indicators are divided into primary indicators and secondary indicators based on the corresponding data of ESG indicators, wherein the primary indicators directly affect the ESG indicators, the secondary indicators indirectly affect the ESG indicators, and the secondary indicators directly affect the primary indicators, so the ESG indicator formula needs to be established in combination with the calculation process of the secondary indicators and the primary indicators to improve the accuracy of the ESG indicators. Secondly, in order to achieve consistency in indicator types, the secondary indicators are standardized using a standardized formula, and the entropy method is used to analyze the degree of influence of different secondary indicators on the corresponding primary indicators, that is, the secondary indicator weights. Then, based on the subordinate relationship between the primary and secondary indicators, the secondary indicator weights can be summarized and calculated to obtain the primary indicator weights, and further the accurate calculation formula of the ESG indicators under the corresponding carbon emission reduction project environment and business indicator conditions can be obtained.

[0011] Preferably, in S012, the standardized formula is specifically: In the formula, +sx ij is the standard value of the jth positive secondary indicator corresponding to the ith primary indicator, x ij is the original value of the jth secondary indicator corresponding to the ith primary indicator, x min,j is the minimum value of all first-level indicators corresponding to the j-th second-level indicator, x max,j is the maximum value of all first-level indicators corresponding to the j-th second-level indicator, -sx ij It is the standard value of the i-th primary indicator corresponding to the j-th negative secondary indicator.

[0012] Preferably, in S012, the mathematical model corresponding to the entropy method is specifically: d j =1-e j ; In the formula, p ij is the weight of the j-th secondary indicator corresponding to the i-th primary indicator, sx ij is the standard value of the jth secondary indicator corresponding to the ith primary indicator, n is the total number of secondary indicators, e j is the information entropy value of the j-th secondary indicator, d j is the difference coefficient of the j-th secondary indicator, w j is the weight of the j-th secondary indicator.

[0013] Preferably, in S013, the ESG indicator formula is specifically: In the formula, Y is the ESG indicator, W j is the jth first-level indicator weight, X' j is the value of the jth first-level indicator, and m is the number of first-level indicators.

[0014] Preferably, in S2, the specific process of constructing and training the carbon emission reduction project development model based on the ESG indicator formula and the training set is: S211. Calculate ESG indicators according to the ESG indicator formula and training set, and establish objective functions based on the types of ESG indicators and carbon reduction project goals; Simultaneously, constraining conditions are constructed based on the ESG indicators and carbon reduction project targets; S212: construct a carbon emission reduction project development model based on the objective function and constraint conditions, and input the training set into the carbon emission reduction project development model for training.

[0015] In this solution, by calculating the ESG index based on the ESG index formula and the training set, the accurate ESG index of the carbon emission reduction project corresponding to the training set can be obtained, and the constraints are constructed based on the ESG index and the carbon emission reduction project goals. The working environment of the corresponding carbon emission reduction project development model can be comprehensively considered during the training process, which is helpful to improve the accuracy of the carbon emission reduction project development model. Secondly, the objective function and constraints of the carbon emission reduction project development model need to be reasonable, that is, in the actual environment of the corresponding carbon emission reduction project, it is necessary to meet the benefits of the enterprise, as well as the local ecological requirements, the needs of upstream and downstream enterprises and other conditions. Therefore, when constructing the objective function and constraints, it is necessary to comprehensively consider the ESG index and the carbon emission reduction project goals to prevent the carbon emission reduction project development model from falling into a single extreme target.

[0016] Preferably, in S212, the specific process of inputting the training set into the carbon emission reduction project development model for training is: A1, establishing a population corresponding to the carbon emission reduction project development model based on the training set, and calculating the fitness of individuals in the population based on the objective function of the carbon emission reduction project development model; Simultaneously, data association features are extracted based on multi-type data curve graphs drawn based on the training set; A2. Calculating the crowding degree of individuals in the population based on the data association characteristics, fitness and crowding degree criteria, and sorting the individuals based on the crowding degree; A3. Based on the ranking of the individuals and a preset new individual threshold, the individuals in the population are screened to obtain new individuals, and the population of the carbon emission reduction project development model is updated based on the new individuals; A4. Determine whether the training is finished based on the preset maximum number of iterations and the number of population updates. If the number of population updates is greater than the maximum number of iterations, the training is deemed to be finished. If the number of population updates is less than or equal to the maximum number of iterations, the training is deemed not to be finished. Calculate the fitness of the new individual based on the objective function of the carbon emission reduction project development model and execute A2.

[0017] In this scheme, in order to adapt to the multiple optimization objectives of the carbon emission reduction project development model, a multi-objective optimization algorithm can be used to train the carbon emission reduction project development model. Specifically, a corresponding population is established using a training set, and the objective function of the carbon emission reduction project development model is used as a fitness function in the training process to calculate the fitness of individuals in the population. Since the training needs to meet the purpose of optimizing multiple objectives of the carbon emission reduction project development model, there are multiple indicators corresponding to the fitness. In order to obtain a unified standard for judging the excellence of individuals, individuals in the population can be hierarchically divided based on data association characteristics to clarify the relationship between different individuals. Then, based on the relationship, the crowding of individuals is calculated using the fitness and crowding criteria to quantify the excellence of different individuals, so as to facilitate the judgment of the excellence of different individuals. Finally, in order to avoid overfitting and underfitting of the carbon emission reduction project development model, the maximum number of iterations and the number of population updates can be used to determine whether the training is completed.

[0018] Preferably, in S3, the specific process of adjusting the trained carbon emission reduction project development model based on the validation set and the test set to obtain the optimal development model is: S311. Extract verification indicator data based on the ESG indicators and verification set corresponding to the carbon emission reduction project development model, and extract test indicator data based on the ESG indicators and test set corresponding to the carbon emission reduction project development model; S312. Determine whether the corresponding ESG indicator needs to be adjusted based on the verification indicator data and the test indicator data. If the verification indicator data is equal to the test indicator data, then it is determined that the ESG indicator does not need to be adjusted. Otherwise, it is determined that the ESG indicator needs to be adjusted, and the ESG indicator corresponding to the carbon emission reduction project development model is adjusted based on the ESG indicator adjustment criteria and the ESG indicator adjustment coefficient; S313. Based on the adjusted ESG indicators, the corresponding carbon emission reduction project development model constraints are adjusted to obtain the optimal development model.

[0019] In this solution, when the data corresponding to the ESG indicator changes, the ESG indicator should also change accordingly to ensure that the carbon emission reduction project development model always meets the requirements of the corresponding carbon emission reduction project. Therefore, when it is determined that the data corresponding to the ESG indicator has changed, all ESG indicators are adjusted based on the ESG indicator adjustment coefficient, while ensuring the non-negativity of all weights and the sum of them is 1, so that the ESG indicator meets the real-time requirements of the corresponding carbon emission reduction project, thereby obtaining a timely carbon emission reduction project development model.

[0020] On the other hand, a technical solution also provided in an embodiment of the present invention is a carbon emission reduction project development management and control system, including: a data module, an ESG module, a development model module, and an optimization module; The first output end of the data module is electrically connected to the input end of the ESG module, the second output end of the data module is electrically connected to the input end of the open model module, the control end of the ESG module is electrically connected to the controlled end of the development model module, the output end of the ESG module is electrically connected to the input end of the optimization module, and the control end of the optimization module is electrically connected to the controlled end of the ESG module.

[0021] The present invention has at least the following substantial effects: This application preprocesses the historical data and related data of the carbon emission reduction project to obtain a training set and a test set, and removes outliers and missing values ​​in the data, which can improve the accuracy and completeness of the data. Then, the carbon emission reduction project development model is constructed and trained through the ESG indicator formula and training set determined based on the characteristics of the carbon emission reduction project and the ESG concept, so that while predicting the operating status of the corresponding carbon emission reduction project, the ESG indicator can be combined to explore the optimal operating status of the carbon emission reduction project under specific environmental and business indicator conditions. However, the ESG indicator will change according to changes in the environment where the carbon emission reduction project is located. In order to ensure that the ESG indicator meets the requirements of the environment and business indicators of the carbon emission reduction project at any time, the ESG indicator can be adjusted based on the real-time related data of the carbon emission reduction project, that is, the carbon emission reduction project development model is adjusted based on the verification set and test set obtained by preprocessing the real-time data and real-time related data of the carbon emission reduction project. Optimal development model. Secondly, in order to improve the accuracy of the ESG indicators, the ESG indicators are divided into primary indicators and secondary indicators based on the carbon emission reduction project process. The primary indicators directly affect the ESG indicators, and the secondary indicators indirectly affect the ESG indicators. Moreover, the secondary indicators directly affect the primary indicators. The changes in ESG indicators can be obtained through changes in indicators of different levels and corresponding data of indicators, which effectively improves the timeliness and accuracy of the ESG indicators, and thus improves the accuracy of the optimal development model. Finally, the real-time data of the carbon emission reduction project, that is, the verification set, is input into the optimal development model to obtain the development management and control strategy, which significantly improves the timeliness of the development management and control strategy. The carbon emission reduction project responds to the development management and control strategy to execute the corresponding carbon emission reduction project development actions, which can not only adjust the corresponding carbon emission reduction project in time according to the actual working conditions of the carbon emission reduction project, but also achieve the purpose of environmental protection while ensuring the benefits of the carbon emission reduction project. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0023] Figure 1Schematic diagram of the process for developing a control approach for a carbon reduction project; Figure 2 Schematic diagram of the architecture of a control system developed for a carbon reduction project. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] Embodiment 1: like Figure 1 As shown, this embodiment provides a carbon emission reduction project development management and control method, including the following steps: S1. Obtain historical data and related data of carbon emission reduction projects, and preprocess the historical data and related data to obtain training sets and test sets; S111. Cleaning historical data based on statistics and horizontal comparison and filling method to obtain complete historical data, and cleaning related data based on statistics and horizontal comparison and filling method to obtain complete related data; S112, classifying the complete historical data based on the historical data collection source to obtain classified historical data, and classifying the complete associated data based on the associated data collection source to obtain classified associated data; S113, integrating the classification history data and the classification association data to obtain an integrated data set; S114. Divide the data in the integrated data set into a training set and a test set based on a training-test allocation criterion.

[0027] In this embodiment, various types of relevant data of the carbon emission reduction project can be obtained from the data provided by the carbon emission reduction project developer and operator or the carbon emission reduction project owner, including historical project data, industry standard data, environmental and social data of the project location, etc. At the same time, the statistical method is used to count the abnormal values ​​and missing values ​​in the data, and the abnormal values ​​are deleted, so that only one abnormal data point type, namely the missing value, is retained in the data. Then, the historical average value of the missing value at the same time in different time periods is calculated, and the missing value is filled with the historical average value. However, the data has the characteristic of being massive. In order to reduce the complexity of the data, the data can be classified based on the data acquisition source, that is, the data acquisition device corresponding to the data, and invalid data and redundant data are removed. Finally, according to the functional requirements of calculating the ESG index and training the carbon emission reduction project development model in the training set, the historical data and related data in the data are divided to form a training set, and according to the functional requirements of calculating the ESG index in the test set, the related data in the data are divided to form a test set; secondly, the real-time data and real-time related data of the carbon emission reduction project also need to be preprocessed, but the real-time data and real-time related data only generate one verification set, so there is no need to divide them according to the training-test allocation criterion.

[0028] S2. Build and train a carbon reduction project development model based on the ESG indicator formula and training set; S211. Calculate ESG indicators according to the ESG indicator formula and training set, and establish objective functions based on the types of ESG indicators and carbon reduction project goals; Simultaneously, constraining conditions are constructed based on the ESG indicators and carbon reduction project targets; S212: construct a carbon emission reduction project development model based on the objective function and constraint conditions, and input the training set into the carbon emission reduction project development model for training.

[0029] In one embodiment, the specific process of inputting the training set into the carbon emission reduction project development model for training is as follows: A1. establishing a population corresponding to the carbon emission reduction project development model based on the training set, and calculating the fitness of individuals in the population based on the objective function of the carbon emission reduction project development model; Simultaneously, data association features are extracted based on multi-type data curve graphs drawn based on the training set; A2. Calculating the crowding degree of individuals in the population based on the data association characteristics, fitness and crowding degree criteria, and sorting the individuals based on the crowding degree; A3. Based on the ranking of the individuals and a preset new individual threshold, the individuals in the population are screened to obtain new individuals, and the population of the carbon emission reduction project development model is updated based on the new individuals; A4. Determine whether the training is finished based on the preset maximum number of iterations and the number of population updates. If the number of population updates is greater than the maximum number of iterations, the training is deemed to be finished. If the number of population updates is less than or equal to the maximum number of iterations, the training is deemed not to be finished. Calculate the fitness of the new individual based on the objective function of the carbon emission reduction project development model and execute A2.

[0030] In this embodiment, the ESG index is calculated based on the ESG index formula and the associated data in the training set, and the objective function is established based on the type of ESG index and the carbon emission reduction project target. The objective function is specifically: Maximize°R; Minimize℃; Maximize°S; In the formula, Maximize°R represents maximizing carbon emission reduction, Minimize℃ represents minimizing project cost, and Maximize°S represents maximizing social benefit. Constraints are constructed based on the ESG indicators and carbon emission reduction project goals. The constraints are actually thresholds set according to the environmental conditions corresponding to the carbon emission reduction project. It is necessary to ensure that the ESG indicators are less than or equal to the thresholds. At the same time, the ESG indicators must also be related to the goals of the carbon emission reduction project to effectively control the changes in related data. Then, the NSGA-II algorithm in the multi-objective optimization algorithm can be used to train the carbon emission reduction project development model based on the objective function and constraints. Specifically, the training set A population is established, and the objective function is used to calculate the fitness of individuals in the population. However, the carbon emission reduction project development model has multiple optimization objectives and multiple objective functions, so the fitness corresponding to the individual is not unique, which makes it difficult to distinguish the excellence of the individual. The crowding of the individual can be calculated based on the data association characteristics, fitness and crowding criteria to obtain a unified standard, which is convenient for distinguishing the excellence of the individual. It is necessary to first classify and sort the individuals in the population based on the data association characteristics, and divide the individuals that influence each other under the same objective function into the same level, that is, the data corresponding to the individuals are correlated under the same objective function, and then calculate the crowding of the individual based on the fitness and crowding criteria. The crowding criteria are specifically as follows: Where, Idis i is the crowding degree of the ith individual, f m(i+1) is the mth fitness of the next individual after the i-th individual, i.e. the i+1th individual, which has been sorted based on the data association feature. m(i-1) is the fitness of the previous individual of the i-th individual, i.e., the m-th individual, which has been sorted based on the data association feature. m(max) is the maximum individual fitness of the level where the ith individual is located, f m(min)is the minimum individual fitness of the level where the i-th individual is located; the maximum number of iterations is set to 800, which can be adjusted according to the actual working conditions of the carbon emission reduction project development model.

[0031] S3. Collect real-time data and real-time associated data of carbon emission reduction projects and perform preprocessing to obtain a validation set, and adjust the trained carbon emission reduction project development model based on the validation set and the test set to obtain the optimal development model; Simultaneously, the validation set is input into the optimal development model to obtain the development control strategy; S311. Extract verification indicator data based on the ESG indicators and verification set corresponding to the carbon emission reduction project development model, and extract test indicator data based on the ESG indicators and test set corresponding to the carbon emission reduction project development model; S312. Determine whether the corresponding ESG indicator needs to be adjusted based on the verification indicator data and the test indicator data. If the verification indicator data is equal to the test indicator data, then it is determined that the ESG indicator does not need to be adjusted. Otherwise, it is determined that the ESG indicator needs to be adjusted, and the ESG indicator corresponding to the carbon emission reduction project development model is adjusted based on the ESG indicator adjustment criteria and the ESG indicator adjustment coefficient; S313. Based on the adjusted ESG indicators, the corresponding carbon emission reduction project development model constraints are adjusted to obtain the optimal development model.

[0032] In this embodiment, based on the data collection equipment in the carbon emission reduction project, such as sensors, etc., combined with the Internet of Things technology, the corresponding energy consumption data, construction activity data, environmental parameter data, etc. are collected during the implementation of the carbon emission reduction project, and the collected data are processed using the same preprocessing method as the historical data to obtain a verification set. Then, the test indicator data corresponding to the ESG indicator in the test set is used to represent the expected target of the carbon emission reduction project development model, and the verification indicator data corresponding to the ESG indicator in the verification set is used to represent the actual target of the carbon emission reduction project development model. When there is a difference between the expected target and the actual target, it is necessary to adjust the ESG indicator based on the ESG indicator adjustment criterion and the ESG indicator adjustment coefficient. The ESG indicator adjustment coefficient is based on the increase ratio or fixed value set based on social attention. The ESG indicator adjustment criterion is specifically as follows: Δw=w i1 ×(1+α); In the formula, w i2 is the new weight of the i-th ESG indicator, Δw is the weight variable, and w i1 is the original weight of the ith ESG indicator, w is the weight variable at the last adjustment, and α is the ESG indicator adjustment coefficient; and during the adjustment of the ESG indicator, it is necessary to ensure that all weights are greater than 0 and the sum of all weights is 1.

[0033] In one embodiment, before constructing the carbon emission reduction project development model based on the ESG indicator formula and the training set, S2 needs to determine the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept. The specific process of determining the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept is as follows: S011. Determine the ESG indicators of carbon emission reduction projects based on the characteristics of carbon emission reduction projects and ESG concepts, and determine the primary and secondary indicators of carbon emission reduction projects based on the ESG indicators and the process links of carbon emission reduction projects; Simultaneously, historical secondary indicators and historical primary indicators in the historical operation data of the corresponding carbon emission reduction projects are obtained; S012. Performing standardization processing based on historical secondary indicators and standardization formulas to obtain standard secondary indicators, and calculating secondary indicator weights based on the standard secondary indicators and entropy method; S013. Calculate the weight of the primary indicator based on the weight of the secondary indicator and the subordinate relationship between the primary indicator and the secondary indicator, and establish the ESG indicator formula based on the weight of the primary indicator and the primary indicator.

[0034] In this embodiment, based on the characteristics of the carbon emission reduction project, such as region, project type, pollution level, etc., combined with the ESG concept, the ESG indicators are determined to include at least four dimensions: finance, environment, society and governance, and the primary and secondary indicators of the carbon emission reduction project are determined based on the ESG indicators and the carbon emission reduction project process. Specifically, the finance includes the total project cost, unit cost, investment recovery period, investment profit rate, etc. The environment includes the proportion of clean energy use, pollutant emission intensity, greenhouse gas emission intensity, energy utilization efficiency, resource utilization efficiency, energy-saving measures, etc. The society includes the safety accident rate, safety hazard rectification rate, stakeholder participation and satisfaction, community communication and harmonious co-construction, etc. The governance includes the perfection of the governance structure, decision-making transparency, risk management capabilities, project completion rate, number of days of construction delay, The achievement of key milestones, quality qualification rate, excellent rate, rework rate, innovation index, etc. Based on the subordinate relationship between the ESG index, primary index and secondary index, it can be inferred that the weight of the ESG index is related to the weight of the primary index, and the weight of the primary index is related to the weight of the secondary index. Therefore, the historical secondary index and historical primary index in the historical operation data of the corresponding carbon emission reduction project are obtained, and the historical secondary index is dimensionlessly processed through the standardized formula, that is, the standardized processing obtains the standard secondary index to achieve the consistency of the index type. It can also be decided whether to standardize the historical primary index according to actual needs, and calculate the weight of the secondary index according to the standard secondary index and the entropy method. Then, the weights of all secondary indicators belonging to the same type of primary index can be summarized to obtain the weight of the primary index.

[0035] In one embodiment, the normalization formula is specifically: In the formula, +sx ij is the standard value of the jth positive secondary indicator corresponding to the ith primary indicator, x ij is the original value of the jth secondary indicator corresponding to the ith primary indicator, x min,j is the minimum value of all first-level indicators corresponding to the j-th second-level indicator, x max,j is the maximum value of all first-level indicators corresponding to the j-th second-level indicator, -sx ij It is the standard value of the i-th primary indicator corresponding to the j-th negative secondary indicator.

[0036] In one embodiment, the mathematical model corresponding to the entropy method is specifically: d j =1-e j ; In the formula, p ij is the weight of the j-th secondary indicator corresponding to the i-th primary indicator, sx ij is the standard value of the jth secondary indicator corresponding to the ith primary indicator, n is the total number of secondary indicators, e j is the information entropy value of the j-th secondary indicator, d j is the difference coefficient of the j-th secondary indicator, w j is the weight of the j-th secondary indicator.

[0037] In one embodiment, the ESG indicator formula is specifically: In the formula, Y is the ESG indicator, W j is the jth first-level indicator weight, X' j is the value of the jth first-level indicator, and m is the number of first-level indicators.

[0038] S4. The carbon emission reduction project executes corresponding carbon emission reduction project development actions in response to the development management and control strategy.

[0039] like Figure 2 As shown, a technical solution also provided in the embodiment of the present invention is a carbon emission reduction project development management and control system, including: a data module, an ESG module, a development model module, and an optimization module; The first output end of the data module is electrically connected to the input end of the ESG module, the second output end of the data module is electrically connected to the input end of the open model module, the control end of the ESG module is electrically connected to the controlled end of the development model module, the output end of the ESG module is electrically connected to the input end of the optimization module, and the control end of the optimization module is electrically connected to the controlled end of the ESG module.

[0040] In this embodiment, the data module is responsible for collecting various data of the carbon emission reduction project, including basic project information, energy consumption data, environmental monitoring data, social impact data, corporate governance data, etc., and stores, manages and updates the collected data to ensure the security and availability of the data; the ESG module evaluates the ESG performance of the carbon emission reduction project based on the ESG indicator formula, which is specifically reflected in the form of ESG indicators. The module can calculate the value of each evaluation indicator based on the data collected in real time, and pass the calculated ESG indicators to the development model module; the development model module obtains the data collected by the data module and the ESG indicators calculated by the ESG module through the ESG module, and constructs and trains the carbon emission reduction project development model and the optimal development model, and then can generate a development control strategy based on the input data; the optimization module obtains the data collected by the data module through the ESG module, and evaluates and analyzes the ESG indicators of the carbon emission reduction project to determine whether the ESG indicators meet the actual working conditions of the carbon emission reduction project.

[0041] This embodiment has at least the following substantial effects: In this embodiment, the historical data and related data of the carbon emission reduction project are preprocessed to obtain a training set and a test set, and outliers and missing values ​​in the data are eliminated, so that the accuracy and completeness of the data can be improved. Then, the carbon emission reduction project development model is constructed and trained through the ESG indicator formula and training set determined based on the characteristics of the carbon emission reduction project and the ESG concept, so that while predicting the operating status of the corresponding carbon emission reduction project, the ESG indicator can be combined to explore the optimal operating status of the carbon emission reduction project under specific environmental and business indicator conditions. However, the ESG indicator will change according to changes in the environment where the carbon emission reduction project is located. In order to ensure that the ESG indicator meets the requirements of the environment and business indicators of the carbon emission reduction project at any time, the ESG indicator can be adjusted based on the real-time related data of the carbon emission reduction project, that is, the carbon emission reduction project development model is adjusted based on the verification set and test set obtained by preprocessing the real-time data and real-time related data of the carbon emission reduction project. Optimal development model. Secondly, in order to improve the accuracy of the ESG indicators, the ESG indicators are divided into primary indicators and secondary indicators based on the carbon emission reduction project process. The primary indicators directly affect the ESG indicators, and the secondary indicators indirectly affect the ESG indicators. Moreover, the secondary indicators directly affect the primary indicators. The changes in ESG indicators can be obtained through changes in indicators of different levels and corresponding data of indicators, which effectively improves the timeliness and accuracy of the ESG indicators, and thus improves the accuracy of the optimal development model. Finally, the real-time data of the carbon emission reduction project, that is, the verification set, is input into the optimal development model to obtain the development management and control strategy, which significantly improves the timeliness of the development management and control strategy. The carbon emission reduction project responds to the development management and control strategy to execute the corresponding carbon emission reduction project development actions, which can not only adjust the corresponding carbon emission reduction project in time according to the actual working conditions of the carbon emission reduction project, but also achieve the purpose of environmental protection while ensuring the benefits of the carbon emission reduction project.

[0042] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A carbon emission reduction project development management and control method, characterized in that: The method comprises the following steps: S1. Obtain historical data and related data of carbon emission reduction projects, and preprocess the historical data and related data to obtain training sets and test sets; S2. Build and train a carbon reduction project development model based on the ESG indicator formula and training set; S3. Collect real-time data and real-time related data of carbon emission reduction projects and perform preprocessing to obtain a validation set, and adjust the trained carbon emission reduction project development model based on the validation set and the test set to obtain the optimal development model; Simultaneously, the validation set is input into the optimal development model to obtain the development control strategy; S4. The carbon emission reduction project executes corresponding carbon emission reduction project development actions in response to the development management and control strategy.

2. A carbon emission reduction project development management and control method according to claim 1, characterized in that: In S1, the specific process of preprocessing the historical data and related data to obtain the training set and the test set is: S111. Cleaning historical data based on statistics and horizontal comparison and filling method to obtain complete historical data, and cleaning related data based on statistics and horizontal comparison and filling method to obtain complete related data; S112, classifying the complete historical data based on the historical data collection source to obtain classified historical data, and classifying the complete associated data based on the associated data collection source to obtain classified associated data; S113, integrating the classification history data and the classification association data to obtain an integrated data set; S114. Divide the data in the integrated data set into a training set and a test set based on a training-test allocation criterion.

3. A carbon emission reduction project development management and control method according to claim 1, characterized in that: Before constructing the carbon emission reduction project development model based on the ESG indicator formula and training set, S2 needs to determine the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept. The specific process of determining the ESG indicator formula according to the characteristics of the carbon emission reduction project and the ESG concept is as follows: S011. Determine the ESG indicators of carbon emission reduction projects based on the characteristics of carbon emission reduction projects and ESG concepts, and determine the primary and secondary indicators of carbon emission reduction projects based on the ESG indicators and the process links of carbon emission reduction projects; Simultaneously, historical secondary indicators and historical primary indicators in the historical operation data of the corresponding carbon emission reduction projects are obtained; S012. Performing standardization processing based on historical secondary indicators and standardization formulas to obtain standard secondary indicators, and calculating secondary indicator weights based on the standard secondary indicators and entropy method; S013. Calculate the weight of the primary indicator based on the weight of the secondary indicator and the subordinate relationship between the primary indicator and the secondary indicator, and establish the ESG indicator formula based on the weight of the primary indicator and the primary indicator.

4. A carbon emission reduction project development management and control method according to claim 3, characterized in that: In S012, the standardized formula is specifically: In the formula, +sx ij is the standard value of the jth positive secondary indicator corresponding to the ith primary indicator, x ij is the original value of the jth secondary indicator corresponding to the ith primary indicator, x min,j is the minimum value of all first-level indicators corresponding to the j-th second-level indicator, x max,j is the maximum value of all first-level indicators corresponding to the j-th second-level indicator, -sx ij It is the standard value of the i-th primary indicator corresponding to the j-th negative secondary indicator.

5. A carbon emission reduction project development management and control method according to claim 3, characterized in that: In S012, the mathematical model corresponding to the entropy method is specifically: d j =1-e j ; In the formula, p ij is the weight of the j-th secondary indicator corresponding to the i-th primary indicator, sx ij is the standard value of the jth secondary indicator corresponding to the ith primary indicator, n is the total number of secondary indicators, e j is the information entropy value of the j-th secondary indicator, d j is the difference coefficient of the j-th secondary indicator, w j is the weight of the j-th secondary indicator.

6. A carbon emission reduction project development management and control method according to claim 3, characterized in that: In S013, the ESG indicator formula is specifically: In the formula, Y is the ESG indicator, W j is the jth first-level indicator weight, X' j is the value of the jth first-level indicator, and m is the number of first-level indicators.

7. A carbon emission reduction project development management and control method according to claim 1, characterized in that: In S2, the specific process of constructing and training the carbon emission reduction project development model based on the ESG indicator formula and training set is as follows: S211. Calculate ESG indicators according to the ESG indicator formula and training set, and establish objective functions based on the types of ESG indicators and carbon reduction project goals; Simultaneously, constraining conditions are constructed based on the ESG indicators and carbon reduction project targets; S212: construct a carbon emission reduction project development model based on the objective function and constraint conditions, and input the training set into the carbon emission reduction project development model for training.

8. A carbon emission reduction project development management and control method according to claim 7, characterized in that: In S212, the specific process of inputting the training set into the carbon emission reduction project development model for training is as follows: A1. establishing a population corresponding to the carbon emission reduction project development model based on the training set, and calculating the fitness of individuals in the population based on the objective function of the carbon emission reduction project development model; Simultaneously, data association features are extracted based on multi-type data curve graphs drawn based on the training set; A2. Calculating the crowding degree of individuals in the population based on the data association characteristics, fitness and crowding degree criteria, and sorting the individuals based on the crowding degree; A3. Based on the ranking of the individuals and a preset new individual threshold, the individuals in the population are screened to obtain new individuals, and the population of the carbon emission reduction project development model is updated based on the new individuals; A4. Determine whether the training is finished based on the preset maximum number of iterations and the number of population updates. If the number of population updates is greater than the maximum number of iterations, the training is deemed to be finished. If the number of population updates is less than or equal to the maximum number of iterations, the training is deemed not to be finished. Calculate the fitness of the new individual based on the objective function of the carbon emission reduction project development model and execute A2.

9. A carbon emission reduction project development management and control method according to claim 1, characterized in that: In S3, the specific process of adjusting the trained carbon emission reduction project development model based on the validation set and the test set to obtain the optimal development model is as follows: S311. Extract verification indicator data based on the ESG indicators and verification set corresponding to the carbon emission reduction project development model, and extract test indicator data based on the ESG indicators and test set corresponding to the carbon emission reduction project development model; S312. Determine whether the corresponding ESG indicator needs to be adjusted based on the verification indicator data and the test indicator data. If the verification indicator data is equal to the test indicator data, then it is determined that the ESG indicator does not need to be adjusted. Otherwise, it is determined that the ESG indicator needs to be adjusted, and the ESG indicator corresponding to the carbon emission reduction project development model is adjusted based on the ESG indicator adjustment criteria and the ESG indicator adjustment coefficient; S313. Based on the adjusted ESG indicators, the corresponding carbon emission reduction project development model constraints are adjusted to obtain the optimal development model.

10. A carbon emission reduction project development management and control system, applicable to a carbon emission reduction project development management and control method as claimed in any one of claims 1 to 9, characterized in that: Includes: data module, ESG module, development model module, and optimization module; The first output end of the data module is electrically connected to the input end of the ESG module, the second output end of the data module is electrically connected to the input end of the open model module, the control end of the ESG module is electrically connected to the controlled end of the development model module, the output end of the ESG module is electrically connected to the input end of the optimization module, and the control end of the optimization module is electrically connected to the controlled end of the ESG module.

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