Method and system for predicting carbon dioxide emissions

Abstract

The present application relates to a prediction method and system based on carbon dioxide emissions, the system comprises: waste management mode collection module, carbon emission simulation relationship calculation module, mode process analysis module, mode recognition comparison module, carbon emission prediction module. The method dynamically analyzes the carbon emission changes under different waste management modes, provides a scientific basis for policy making, enables decision makers to assess the impact of management mode on the environment, and promotes the implementation of low-carbon management mode. Through life cycle analysis, researchers quantitatively evaluate the contribution of each link to carbon emissions, provide key data support for waste management optimization. In addition, by comparing with other modes in the carbon emission database, similar management modes are identified, the differences are analyzed in depth and improvement suggestions are put forward, and the spread of best practices is promoted. Integrate similar modes to establish a prediction model, analyze their mutual influence, predict future carbon emission trends, provide support for decision making, and promote the implementation of waste management measures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission management, specifically a prediction method and system based on carbon dioxide emissions. BACKGROUND

[0002] Carbon emission management technology encompasses a range of technologies and methods aimed at monitoring, reducing, and managing greenhouse gas emissions. These technologies primarily include carbon capture and storage (CCS), renewable energy technologies, energy efficiency improvement technologies, carbon trading systems, and carbon footprint calculation tools. Carbon capture and storage technology captures carbon dioxide emissions from industrial processes or power generation and stores them safely underground, reducing the concentration of carbon in the atmosphere. Renewable energy technologies, such as solar, wind, and hydro power, provide low-carbon alternatives that help reduce dependence on fossil fuels, thereby reducing overall carbon emissions.

[0003] Existing technologies generally fail to comprehensively explore the complex relationship between different management modes and carbon emissions, resulting in inaccurate assessments of waste management effectiveness. In the process of prediction, they often rely on static data, lack consideration of commonalities and differences between different regions or management methods, and limit effective prediction of future carbon emission trends. In the absence of multi-dimensional data correlation analysis, existing technologies cannot provide sufficient basis for decision-makers, leading to management strategies that may not be tailored to specific environmental and economic conditions. Meanwhile, in the face of successful waste management experiences, existing technologies often lack effective ways to identify and learn from them, making it difficult for new policies to draw on the successful experiences of existing models. SUMMARY

[0004] The present application provides a prediction method and system based on carbon dioxide emissions to address the technical problems in the prior art.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] A prediction method based on carbon dioxide emissions, comprising:

[0007] Collecting waste management modes and carbon emission data from different regions, establishing a carbon emission database, storing carbon emission data corresponding to different waste management modes, and establishing carbon emission baselines for different regions;

[0008] According to the collected waste management modes and carbon emission data, a carbon emission simulation model is established to simulate the relationship between different waste management modes and carbon emission data, and to calculate the change in carbon emissions caused by different waste management modes;

[0009] Analyze the entire process of each waste management mode, and quantitatively analyze the carbon emissions of different waste management modes in different life cycle stages, analyze the influence of different life cycle stages on carbon emissions, and establish a cycle database for storage;

[0010] Identify the current region's waste management mode, and define it as the local mode, and compare it with the stored content in the carbon emission database, identify the most similar waste management mode, define it as the similar mode, and analyze the differences between the local mode and the similar mode one by one, and compare the differences with the content in the cycle database, identify the similar process;

[0011] Integrate all the similar processes and similar modes identified, and establish a prediction model to analyze the mutual influence between similar processes and similar modes, and output the carbon emission prediction data of the current region.

[0012] As a further scheme of the present application, the carbon emission database is established to store the corresponding carbon emission data under different waste management modes, and the carbon emission baseline of different regions is established, specifically including:

[0013] Integrate open data interface to automatically identify and capture waste management modes in different regions, record all waste management mode processes, and identify carbon emission data for each region;

[0014] Identify the similarity of waste management modes in different regions, and for waste management modes that are exactly the same, only keep one set of data, and add a secondary tag for all regions using the waste management mode to the retained data;

[0015] Establish a carbon emission database to store all waste management modes with different waste management modes as the first level tag, and store the carbon emission data of each region in the second level tag.

[0016] As a further scheme of the present application, the carbon emission simulation model is established to simulate the relationship between different waste management modes and carbon emission data, and calculate the change in carbon emissions caused by different waste management modes, specifically including:

[0017] Establish a carbon emission simulation model, and input the waste management modes and corresponding carbon emission data in the carbon emission database for training;

[0018] For the trained carbon emission simulation model, each waste management mode is inputted, and the parameters in the waste management mode are adjusted one by one, and recorded as simulation mode, and the carbon emissions under each parameter adjustment are simulated, recorded as simulation emissions;

[0019] Identify the relationship between the simulated emission amount and the simulation mode, analyze the influence rate of each simulation mode on the simulated emission amount, identify the simulation mode with the highest influence rate, and record the adjusted parameter corresponding to the simulation mode as the highest influence factor.

[0020] As a further scheme of the present application, the identification of the relationship between the simulated emission amount and the simulation mode, the analysis of the influence rate of each simulation mode on the simulated emission amount, specifically comprises:

[0021] Establish a waste management mode matrix X:

[0022]

[0023] Where n is the sample size, i.e. different waste management modes, m is the number of characteristics, i.e. the number of parameters in the waste management mode, and X ij The value of the jth parameter in the ith waste management mode;

[0024] Calculate the average value for each characteristic And get the matrix X c :

[0025]

[0026] Calculate the covariance matrix C:

[0027]

[0028] Where represents the transpose matrix of matrix X c ;

[0029] Calculate the eigenvalues and eigenvectors of the covariance matrix to get the eigenvector matrix V:

[0030] Cv j = λ j v j ;

[0031] V = [v1 v2 … v j ];

[0032] Where v j is the eigenvector corresponding to the eigenvalue λ j ;

[0033] Based on the eigenvector matrix, project the original characteristics onto the principal components to get the principal component matrix Z, represented as:

[0034] Z = X c · V;

[0035] Analyze the influence degree of each principal component on carbon emission amount:

[0036]

[0037] wherein S i is a sensitivity coefficient, i.e. indicates the degree of influence of the i-th principal component on the carbon emissions, E0 is the initial carbon emissions, ΔE is the change in carbon emissions, Z i0 is the initial value of the principal component i, i.e. the initial value of the parameter i, ΔZ i is the change in the principal component i, i.e. the change in the parameter i.

[0038] As a further scheme of the present application, the entire process of each waste management mode is analyzed, and the carbon emissions of different waste management modes in different life cycle stages are quantitatively analyzed, the influence of different life cycle stages on carbon emissions is analyzed, and specifically includes:

[0039] The life cycle stages of the waste management process are divided, including the generation stage, the transportation stage, the treatment stage, and the post-treatment stage.

[0040] Each waste management mode is disassembled into specific processes, and parameters related to each process are collected.

[0041] Carbon emission formulas are established according to different life cycle stages, and the carbon emissions of each life cycle stage are calculated:

[0042] A cycle database is established, all processes of each waste management mode and their life cycle stage carbon emissions are packaged and stored, and labels are added to the waste management mode and each life cycle stage data.

[0043] The contribution rate of each life cycle stage to the total carbon emissions is calculated, and the life cycle stage with the greatest influence on carbon emissions is identified according to the contribution rate:

[0044]

[0045] wherein R x represents the carbon emission contribution rate of the x-th stage, E 阶段,x represents the carbon emissions of the x-th stage, represents the total carbon emissions of all stages.

[0046] As a further scheme of the present application, the carbon emissions of each life cycle stage are calculated, specifically:

[0047] Generation stage:

[0048]

[0049] wherein K 产生 represents the carbon emissions of the generation stage, W lEF represents the amount of waste generated in category l. 产生,l The carbon emission factor during the generation stage of Category I waste;

[0050] Transportation phase:

[0051]

[0052] Among them, K 运输 D represents the carbon emissions during the transportation phase. o T represents the distance of the o-th mode of transport. o For the waste transport volume of the o-th mode of transport, EF 运输,l Carbon emission factor per kilometer of transportation;

[0053] Processing phase:

[0054]

[0055] Among them, K 处理 W represents the carbon emissions during the treatment phase. p For the processing volume of waste type p, EF 处理,p The carbon emission factor for the p-th type of treatment method;

[0056] Post-processing stage:

[0057]

[0058] Among them, K 后处理 W represents the carbon emissions during the reprocessing stage. q EF represents the amount of by-product q to be processed. 后处理,q is the carbon emission factor for the treatment of the qth type of byproduct.

[0059] As a further aspect of the present invention, the step of identifying the waste management mode with the highest similarity is defined as a similar mode, and the differences between the local mode and the similar mode are analyzed one by one. The differences are then compared with the contents of the periodic database to identify similar processes. Specifically, this includes:

[0060] The current regional model is compared with all other models in the carbon emission database. For each waste management model stored in the database, its similarity score with the current regional model is calculated, and the management model with the highest similarity is identified and defined as the similar model.

[0061] For the current regional pattern and similar patterns, analyze the differences in their characteristics, processes and parameters, and extract the processes corresponding to the differences;

[0062] The extracted process is compared with all processes in the cycle database, and the process with the highest similarity is identified and defined as a similar process.

[0063] As a further scheme of the present application, the identified similar processes and similar patterns are integrated, a prediction model is established, the mutual influence between the similar processes and the similar patterns is analyzed, and the carbon emission prediction data of the current region is output, specifically comprising:

[0064] The obtained similar patterns and similar processes are combined to form a simulation management pattern.

[0065] A prediction model is established, and the related parameters of the combined simulation management pattern are substituted into the prediction model to predict the future time carbon emission data of the current region:

[0066] K t+1 = αK t + β (P t + T t + Q t + S t );

[0067] Wherein, K t+1 represents the carbon emission prediction quantity at future time t+1, K t represents the carbon emission quantity at current time t, α is a smoothing coefficient, β is a weighting coefficient, P t represents the waste treatment mode influence factor at current time t, T t represents the transportation mode influence factor at current time t, Q t represents the resource recovery rate influence factor at current time t, and S t represents other influence factors at current time t.

[0068] Another object of the present application is to provide a prediction system based on carbon dioxide emission, which comprises:

[0069] A waste management pattern collection module is used to collect waste management patterns and carbon emission data of different regions, establish a carbon emission database, store the corresponding carbon emission data under different waste management patterns, and establish a carbon emission baseline of different regions.

[0070] A carbon emission simulation relationship calculation module is used to establish a carbon emission simulation model according to the collected waste management patterns and carbon emission data, simulate the relationship between different waste management patterns and carbon emission data, and calculate the carbon emission quantity change brought by different waste management patterns.

[0071] A pattern process analysis module is used to analyze all processes of each waste management pattern, quantitatively analyze the carbon emission quantity of different waste management patterns in different life cycle stages, analyze the influence of different life cycle stages on the carbon emission quantity, and establish a cycle database for storage.

[0072] A pattern recognition comparison module is used to identify the waste management pattern of the current region and define it as the local pattern, and compare it with the stored content in the carbon emission database to identify the most similar waste management pattern, define it as the similar pattern, and analyze the differences between the local pattern and the similar pattern one by one, and compare the differences with the content in the cycle database to identify similar processes.

[0073] A carbon emission prediction module is used to integrate all the identified similar processes and similar patterns, establish a prediction model, analyze the mutual influence between the similar processes and the similar patterns, and output the carbon emission prediction data of the current region.

[0074] The beneficial effects of the present application are:

[0075] The method can dynamically analyze the carbon emission changes under different waste management patterns. The establishment of this model provides a scientific basis for policy making, enabling decision makers to evaluate the impact of different management patterns on the environment, thereby promoting the implementation of low-carbon management patterns, reducing overall carbon emissions, and promoting environmental protection. In this process, the development of policy evaluation tools helps to clarify which management strategies can more effectively reduce carbon emissions.

[0076] Through life cycle analysis, each stage of different waste management patterns is quantitatively analyzed, enabling researchers to understand which stages contribute most to carbon emissions in collection, transportation, treatment, and recycling. This in-depth whole-process analysis provides key data support for waste management optimization, promoting the development and application of low-carbon technologies, and thus achieving more effective waste management.

[0077] In addition, by identifying the waste management pattern of the current region and comparing it with other patterns in the carbon emission database, the most similar management pattern can be identified. This similarity analysis enables researchers to deeply analyze the differences between the local pattern and the similar pattern, propose targeted improvement suggestions, promote the sharing of experience and the dissemination of best practices, and thus promote local governments and enterprises to adopt successful management patterns.

[0078] Finally, the identified similar processes and patterns are integrated to establish a prediction model, which can analyze the mutual influence between similar processes and patterns. This forward-looking analysis can predict the future carbon emission trends of the current region, providing forward-looking support for decision making, helping to develop long-term carbon emission reduction strategies, and promoting the implementation of various waste management measures, thereby promoting the sustainable development of society. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 A flowchart of the prediction method based on carbon dioxide emissions provided by the embodiments of the present application is shown;

[0080] Figure 2 A flowchart for establishing a carbon emission database is provided for an embodiment of the present application;

[0081] Figure 3 A flowchart for establishing a carbon emission simulation model is provided for an embodiment of the present application;

[0082] Figure 4 A flowchart for calculating the contribution rate of each life cycle stage to total carbon emission is provided for an embodiment of the present application;

[0083] Figure 5 A flowchart for identifying the waste management mode with the highest similarity is provided for an embodiment of the present application;

[0084] Figure 6 A flowchart for integrating all the identified similar processes and similar modes is provided for an embodiment of the present application;

[0085] Figure 7 A structural block diagram of a prediction system based on carbon dioxide emission is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0086] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0087] In the description of the present application, the terms “first” and “second” are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with “first” and “second” can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of “multiple” is two or more, unless otherwise specifically limited.

[0088] In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration." Any embodiment described as "for example" in this application is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated upon in order to avoid unnecessary detail, which can obscure the description of the present application. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0089] Figure 1 The flowchart of the prediction method based on carbon dioxide emissions provided for the embodiments of the present application is shown in Figure 1 The prediction method comprises:

[0090] S100, collecting waste management modes and carbon emission data of different regions, establishing a carbon emission database, storing carbon emission data corresponding to different waste management modes, and establishing carbon emission baselines of different regions;

[0091] This step automatically identifies and extracts waste management modes in each region by integrating open data interfaces, while recording the waste generation, classification data, treatment facility distribution and their processing capacity in detail. This process not only improves the efficiency of data collection, but also ensures the comprehensiveness and accuracy of the data, laying a solid foundation for subsequent analysis. Secondly, similarity recognition of waste management modes in different regions can eliminate redundant data and avoid repeated storage of data, making the database more concise and easy to manage. By maintaining a set of representative waste management modes and adding secondary labels to the carbon emission data of each region, subsequent analysis and comparison are facilitated, ensuring the structured and systematic nature of the data.

[0092] In addition, the carbon emission database stores different waste management modes as primary labels, making data retrieval and calling more efficient. This structured database design not only facilitates subsequent data analysis and model establishment, but also provides policymakers with intuitive reference, helping them to develop appropriate carbon emission reduction strategies based on different management modes in different regions.

[0093] This step lays the foundation for accurate carbon emission prediction through systematic data collection and organization. It provides in-depth insights into different regional waste management models and reveals the direct impact of different management models on carbon emissions, supporting policymakers in making more rational choices when implementing green policies based on scientific data. This data-driven approach not only improves the scientificity and effectiveness of decision-making, but also promotes the sharing and learning of experiences between different regions in waste management and carbon emission control, thereby promoting the overall improvement of environmental management level.

[0094] As shown in Figure 2 , the establishment of the carbon emission database stores the corresponding carbon emission data under different waste management modes, and establishes the carbon emission baseline of different regions, specifically including:

[0095] S110, integrate open data interface, automatically identify and capture different regional waste management modes, record all waste management mode processes, and identify carbon emission data for each region respectively;

[0096] S120, similarity recognition of waste management modes in different regions, for completely identical waste management modes, only one set of data is retained, and all regions using this waste treatment mode are added to the secondary label in the retained data;

[0097] S130, establish a carbon emission database, store all waste management modes with different waste management modes as the first-level label, and store the carbon emission data of each region in the second-level label.

[0098] S200, according to the collected waste management mode and carbon emission data, establish a carbon emission simulation model, simulate the relationship between different waste management modes and carbon emission data, and calculate the change of carbon emission caused by different waste management modes;

[0099] The core of this step is to form a comprehensive model through the collected waste management mode and carbon emission data to simulate the relationship between different management modes and carbon emissions. Specifically, a waste management mode matrix needs to be constructed, where each row represents a waste management mode and each column represents the corresponding characteristic variable. This structured data representation enables the model to effectively capture the characteristics of different management modes and perform quantitative analysis.

[0100] Next, by calculating the covariance matrix, the correlation between the management mode features can be identified. This process will help analyze the direct impact of each feature variable on carbon emissions. In addition, through the extraction of feature vectors, the complexity of the model can be further simplified, highlighting the dominant features, which is very important for subsequent prediction and analysis. Then, by adjusting the parameters of the trained model, the simulated emissions under different conditions can be obtained, and the relationship between these simulated emissions and the corresponding management modes can be analyzed. This not only provides an assessment of the influence of each management mode, but also identifies the management mode with the largest change in carbon emissions, thereby providing targeted recommendations for policymakers.

[0101] This step uses systematic mathematical models and statistical analysis methods to make the prediction of carbon emissions more scientific and accurate. Through this method, the specific impact of different waste management modes on carbon emissions can be deeply understood, thereby providing a basis for optimizing management strategies. In addition, the simulation model can also be iteratively optimized as new data is continuously updated, thereby maintaining the accuracy and timeliness of the prediction. This dynamic adjustment capability ensures that policymakers can respond to environmental changes in a timely manner and implement effective emission reduction measures, ultimately promoting the realization of sustainable development goals.

[0102] As shown in Figure 3 , the method for establishing a carbon emission simulation model to simulate the relationship between different waste management modes and carbon emission data, and calculating the carbon emission changes brought about by different waste management modes, specifically includes:

[0103] S210, establishing a carbon emission simulation model and substituting the waste management modes and corresponding carbon emission data in the carbon emission database into the training;

[0104] S220, for the trained carbon emission simulation model, substitute each waste management mode respectively, and adjust the parameters in the waste management mode one by one, and record as simulation mode, and simulate the carbon emission under each parameter adjustment, record as simulation emission;

[0105] S230, identify the relationship between the simulation emission and the simulation mode, analyze the influence rate of each simulation mode on the simulation emission, identify the simulation mode with the highest influence rate, and record the adjusted parameter corresponding to the simulation mode as the highest influence factor.

[0106] In this step, the relationship between the simulation emission and the simulation mode is identified, and the influence rate of each simulation mode on the simulation emission is analyzed, specifically:

[0107] Establish a waste management mode matrix X:

[0108]

[0109] where n is the number of samples, i.e. different waste management modes, m is the number of features, i.e. the number of parameters in the waste management mode, and X represents X ij the value of the jth parameter in the ith waste management mode;

[0110] Calculate the average value for each feature and obtain the matrix X by mean centering c :

[0111]

[0112] Calculate the covariance matrix C:

[0113]

[0114] where represents the transpose matrix of matrix X c ;

[0115] Calculate the eigenvalues and eigenvectors of the covariance matrix to obtain the eigenvector matrix V:

[0116] Cv j = λ j v j ;

[0117] V = [v1 v2 … v j ];

[0118] where v j is the eigenvector corresponding to the eigenvalue λ j ;

[0119] Based on the eigenvector matrix, project the original features onto the principal components to obtain the principal component matrix Z, represented as:

[0120] Z = X c · V;

[0121] Analyze the degree of influence of each principal component on carbon emissions:

[0122]

[0123] where S i is the sensitivity coefficient, i.e. the degree of influence of the ith principal component on carbon emissions, E0 is the initial carbon emissions, ΔE is the change in carbon emissions, Z i0 is the initial value of the ith principal component, i.e. the initial value of parameter i, and ΔZ i is the change in the ith principal component, i.e. the change in parameter i.

[0124] S300, analyze the entire process of each waste management mode, and quantitatively analyze the carbon emissions of different waste management modes in different life cycle stages, analyze the influence of different life cycle stages on carbon emissions, and establish a cycle database for storage;

[0125] This step first divides the waste management process into several main life cycle stages, including the generation stage, transportation stage, treatment stage, and post-treatment stage. This detailed division helps to analyze the carbon emissions of each stage one by one, ensuring a comprehensive understanding of the contribution of each stage to overall carbon emissions. In each stage, by establishing the corresponding carbon emission calculation formula, the contribution of different management modes to carbon emissions in a specific stage can be quantitatively evaluated, providing data support for subsequent management decisions.

[0126] In each stage, the specific calculation process involves establishing the corresponding carbon emission formula. Through this detailed analysis, decision-makers can clearly identify the key sources of carbon emissions and develop targeted emission reduction strategies for specific stages. For example, if the carbon emissions of the transportation stage account for a significant proportion, managers can consider optimizing transportation routes, adopting low-carbon transportation methods, and other measures.

[0127] Finally, by integrating the carbon emissions of each life cycle stage, the total carbon emissions and the contribution rate of each stage to the total emissions are calculated. This calculation not only helps to identify the key stages that affect carbon emissions, but also provides important reference for decision-makers and managers, so that they can select the most effective measures when formulating emission reduction strategies.

[0128] This step provides a comprehensive and in-depth understanding of carbon emissions in waste management through detailed life cycle analysis and quantitative calculation methods. This method not only improves the scientific analysis of waste management modes, but also provides a reliable data basis for policy formulation. By identifying the contribution rate of each stage, effective emission reduction strategies can be developed, focusing resources on the stages with the greatest impact on carbon emissions, thereby achieving more efficient emission reduction results. This systematic approach helps to promote the achievement of sustainable development goals and improve overall environmental management.

[0129] As shown in Figure 4 , the carbon emissions of different waste management modes in different life cycle stages are analyzed, and the influence of different life cycle stages on carbon emissions is analyzed, specifically including:

[0130] S310, divide the life cycle stages of the waste management process, including the generation stage, transportation stage, treatment stage, and post-treatment stage;

[0131] S320, decompose each waste management mode into specific processes, and collect parameters related to each process;

[0132] S330, establish carbon emission formula according to different life cycle stages, and calculate carbon emission of each life cycle stage:

[0133] S340, establish cycle database, package and store all processes of each waste management mode and carbon emission of life cycle stages thereof, and add labels to waste management mode and data of each life cycle stage;

[0134] S350, calculate contribution rate of each life cycle stage to total carbon emission, and identify life cycle stage with greatest carbon emission influence according to contribution rate:

[0135]

[0136] wherein R x represents carbon emission contribution rate of xth stage, E 阶段,x represents carbon emission of xth stage, represents total carbon emission of all stages.

[0137] In this step, the carbon emission of each life cycle stage is calculated, specifically:

[0138] Generation stage:

[0139]

[0140] wherein K 产生 represents carbon emission of generation stage, W l represents generation amount of lth waste, EF 产生,l represents carbon emission factor of generation stage of lth waste;

[0141] Transport stage:

[0142]

[0143] wherein K 运输 represents carbon emission of transport stage, D o represents distance of oth transport mode, T o represents waste transport amount of oth transport mode, EF 运输,l represents carbon emission factor per kilometer of transport;

[0144] Treatment stage:

[0145]

[0146] wherein K 处理 represents carbon emission of treatment stage, W p represents treatment amount of pth waste, EF 处理,pCarbon emission factor for the pth treatment method;

[0147] Post-treatment stage:

[0148]

[0149] where K 后处理 represents the carbon emission amount of the post-treatment stage, W q is the treatment amount of the qth byproduct, EF 后处理,q is the carbon emission factor for the qth byproduct treatment method.

[0150] S400, identify the waste management mode of the current region, and define it as the local mode, and compare it with the stored content in the carbon emission database, identify the most similar waste management mode, define it as the similar mode, and analyze the differences between the local mode and the similar mode one by one, and compare the differences with the content in the cycle database to identify similar processes;

[0151] This step provides a comparison framework for waste management, enabling decision-makers to better understand the performance of the local management mode in a broader context.

[0152] Specifically, this step will first define the waste management mode of the current region, and compare it with all other modes in the database. By calculating the similarity score, the most similar management mode can be identified, i.e. the "similar mode". This method not only helps to confirm the characteristics of the current management mode, but also makes comparative analysis possible, which can extract the differences between the similar mode. This analysis helps to reveal the uniqueness of the local mode and potential improvement space.

[0153] The next step includes a detailed comparison of the current mode and the similar mode, analyzing the differences between their characteristics, processes and parameters one by one. This detailed analysis can help decision-makers identify key factors affecting carbon emissions, providing a basis for improving management strategies. At the same time, comparing the extracted processes with the processes in the cycle database can further refine the understanding of similar processes, ensuring that relevant experience and best practices are shared and applied.

[0154] This method not only enhances data-driven decision-making capabilities, but also promotes continuous improvement of management modes. By identifying similar modes, decision-makers can learn from the successful experiences of other regions and implement effective management measures to reduce carbon emissions. Such a comparison framework not only improves the scientificity and effectiveness of policies, but also provides a solid foundation for achieving sustainable development goals.

[0155] For example, Figure 5As shown, the most similar waste management mode is identified, defined as the similar mode, and the differences between the local mode and the similar mode are analyzed one by one, and the differences are compared with the contents in the cycle database to identify similar processes, specifically including:

[0156] S410, compare the current local mode with all other modes in the carbon emission database, calculate the similarity score of each waste management mode stored in the database with the current local mode, identify the most similar management mode, and define it as the similar mode;

[0157] S420, analyze the differences between the characteristics, processes and parameters of the current local mode and the similar mode one by one, and extract the processes corresponding to the differences;

[0158] S430, compare the extracted processes with all processes in the cycle database, identify the most similar process, and define it as the similar process.

[0159] S500, integrate all similar processes and similar modes identified, establish a prediction model, analyze the mutual influence between similar processes and similar modes, and output the carbon emission prediction data of the current region.

[0160] The core of this step is to combine each similar mode and similar process to form a comprehensive simulation management mode. This mode fully considers the characteristics and processes of each similar management mode, so as to more comprehensively reflect the waste management situation of the current region and its influence on carbon emissions.

[0161] By using a dynamic prediction model, historical data and identified influencing factors can be used to accurately predict future carbon emissions. Parameters involved in the model, such as waste generation, treatment methods, transportation efficiency, etc., can be updated and adjusted through real-time data. This flexibility enables the model to adapt to rapidly changing environments and management modes, providing timely and accurate information support for decision-makers.

[0162] In addition, this method not only performs retrospective analysis based on historical data, but also can make forward-looking predictions. This two-way thinking approach enables decision-makers to understand the effects of current management modes and anticipate future carbon emission trends. This is crucial for developing long-term carbon emission reduction strategies, helping decision-makers make more scientific and reasonable choices when implementing new policies, and maximizing emission reduction effects.

[0163] As Figure 6 shown, the identified similar processes and similar modes are integrated, a prediction model is established, the mutual influence between similar processes and similar modes is analyzed, and the carbon emission prediction data of the current region is output, specifically including:

[0164] S510, the obtained similar mode and similar flow are combined to form a simulation management mode;

[0165] S520, a prediction model is established, and the related parameters of the combined simulation management mode are substituted into the prediction model to predict the carbon emission data of the future moment of the current region:

[0166] K t+1 =αK t +β(P t +T t +Q t +S t );

[0167] Wherein, K t+1 represents the carbon emission prediction of the future moment t+1, K t represents the carbon emission at the current moment t, alpha is a smoothing coefficient, beta is a weighting coefficient, P t represents the waste treatment mode influencing factor at the current moment t, T t represents the transportation mode influencing factor at the current moment t, Q t represents the resource recovery rate influencing factor at the current moment t, S t represents other influencing factors at the current moment t.

[0168] Figure 7 The structure block diagram of the prediction system based on carbon dioxide emission provided by the embodiment of the application is shown in Figure 7 As shown in the figure, the system comprises:

[0169] A waste management mode collection module 100 is used to collect waste management modes and carbon emission data of different regions, establish a carbon emission database, store the carbon emission data corresponding to different waste management modes, and establish a carbon emission baseline of different regions;

[0170] A carbon emission simulation relationship calculation module 200 is used to establish a carbon emission simulation model according to the collected waste management modes and carbon emission data, simulate the relationship between different waste management modes and carbon emission data, and calculate the carbon emission changes brought by different waste management modes;

[0171] A mode flow analysis module 300 is used to analyze all the flows of each waste management mode, quantitatively analyze the carbon emission of different waste management modes in different life cycle stages, analyze the influence of different life cycle stages on the carbon emission, and establish a cycle database for storage;

[0172] The mode recognition comparison module 400 is used for recognizing the waste management mode of the current region, defining as the local mode, and comparing with the stored content in the carbon emission database to recognize the waste management mode with the highest similarity, defining as the similar mode, and analyzing the difference between the local mode and the similar mode one by one, and comparing the difference with the content in the cycle database to recognize the similar process.

[0173] The carbon emission prediction module 500 is used for integrating all the similar processes and similar modes recognized, establishing a prediction model, analyzing the mutual influence between the similar processes and the similar modes, and outputting the carbon emission prediction data of the current region.

[0174] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0175] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0176] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The function specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the function specified in one block or multiple blocks.

[0177] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The function specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the function specified in one block or multiple blocks.

[0178] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0179] While the preferred embodiments of the application have been described, it should be understood that various modifications and changes can be made by those skilled in the art which follow in the spirit of the application and the scope of the appended claims. Therefor, the description and figures are to be regarded as illustrative in nature and not as restrictive.

[0180] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.​​

Claims

1. A method for predicting carbon dioxide emissions based on, characterized by, The prediction method comprises: Collecting waste management modes and carbon emission data of different regions, establishing a carbon emission database, storing carbon emission data corresponding to different waste management modes, and establishing carbon emission baselines of different regions; According to the collected waste management modes and carbon emission data, a carbon emission simulation model is established to simulate the relationship between different waste management modes and carbon emission data, and to calculate the carbon emission changes brought by different waste management modes; Analyzing all the processes of each waste management mode, and quantitatively analyzing the carbon emission of different waste management modes in different life cycle stages, analyzing the influence of different life cycle stages on carbon emission, and establishing a cycle database for storage; Identifying the current waste management mode of the region, defining it as the local mode, and comparing it with the stored content in the carbon emission database to identify the waste management mode with the highest similarity, defining it as the similar mode, and analyzing the differences between the local mode and the similar mode one by one, and comparing the differences with the content in the cycle database to identify the similar process; Integrating all the similar processes and similar modes identified, establishing a prediction model, analyzing the mutual influence between the similar processes and the similar modes, and outputting the carbon emission prediction data of the current region.

2. The prediction method of claim 1, wherein, The establishment of the carbon emission database, the storage of the carbon emission data corresponding to the different waste management modes, and the establishment of the carbon emission baselines of different regions specifically comprises: Integrating an open data interface to automatically identify and capture waste management modes in different regions, record all waste management mode processes, and identify carbon emission data of each region respectively; Identifying the similarity of waste management modes in different regions, retaining only one set of data for completely identical waste management modes, and adding a secondary tag of all regions using the waste treatment mode to the retained data; Establishing a carbon emission database to store all waste management modes with different waste management modes as the first level tag, and storing carbon emission data of each region in the second level tag.

3. The prediction method of claim 2, wherein, The establishment of the carbon emission simulation model, the simulation of the relationship between different waste management modes and carbon emission data, and the calculation of the carbon emission changes brought by different waste management modes specifically comprises: Establishing a carbon emission simulation model and substituting the waste management modes and corresponding carbon emission data in the carbon emission database into the training; For the trained carbon emission simulation model, substitute each waste management mode into it, adjust the parameters in the waste management mode one by one, and record it as a simulation mode, and simulate the carbon emission under each parameter adjustment, and record it as a simulation emission; Identify the relationship between the simulation emission and the simulation mode, analyze the influence rate of each simulation mode on the simulation emission, identify the simulation mode with the highest influence rate, and record the corresponding adjusted parameters of the simulation mode as the highest influence factor.

4. The prediction method of claim 3, wherein, The identification of the relationship between the simulation emission and the simulation mode, and the analysis of the influence rate of each simulation mode on the simulation emission specifically comprises: Establishing a waste management mode matrix X: where n is the number of samples, i.e. different waste management modes, m is the number of features, i.e. the number of parameters in the waste management mode, and Xij represents X ij the value of the jth parameter in the ith waste management mode; Compute mean value for each feature And get matrix X by mean centering c : Calculating the covariance matrix C: wherein denotes the transpose of the matrix X c denotes the transpose of the matrix X Eigenvalues and eigenvectors of the covariance matrix are calculated to obtain an eigenvector matrix V: Cv j = λ j v j ; V=[v1 v2 … v j ]; where v j is the eigenvector corresponding to the eigenvalue λ j . Based on the eigenvector matrix, the original features are projected onto the principal components to obtain a principal component matrix Z, which is expressed as: Z = X c • V; The influence degree of each principal component on carbon emissions is analyzed: wherein S i is a sensitivity coefficient, i.e., indicates the degree of influence of the i-th principal component on the carbon emission, E0 is an initial carbon emission, ΔE is a change amount of the carbon emission, Z i0 is an initial value of the principal component i, i.e., an initial value of the parameter i, ΔZ i is a change amount of the principal component i, i.e., a change amount of the parameter i.

5. The prediction method of claim 3, wherein, The analysis of the entire process of each waste management mode, and the quantitative analysis of the carbon emissions of different waste management modes in different life cycle stages, the influence of different life cycle stages on carbon emissions, specifically including: Divide the life cycle stages of the waste management process, including the generation stage, transportation stage, treatment stage, post-treatment stage; Each waste management mode is disassembled into specific processes, and parameters related to each process are collected; According to different life cycle stages, carbon emission formulas are established, and the carbon emissions of each life cycle stage are calculated: Establish a cycle database to package and store all processes of each waste management mode and their life cycle stage carbon emissions, and add labels to the waste management mode and each life cycle stage data; Calculate the contribution rate of each life cycle stage to total carbon emissions, and identify the life cycle stage with the greatest impact on carbon emissions according to the contribution rate: wherein R x represents the carbon emission contribution rate of the xth stage, E 阶段,x represents the carbon emission amount of the xth stage, represents the total amount of carbon emissions of all stages.

6. The prediction method of claim 5, wherein, The calculation of the carbon emissions of each life cycle stage is specifically: Generation stage: wherein, K 产生 represents the carbon emission amount in the generation stage, W l is the amount of the lth waste, EF 产生,l is the carbon emission factor of the lth waste in the generation stage; Transportation stage: wherein K 运输 represents the carbon emissions of the transport phase, D o represents the distance of the othtransport mode, T o is the waste transport volume of the othtransport mode, EF 运输,l is the carbon emission factor per kilometre of transport; Treatment stage: wherein K 处理 represents the carbon emission amount of the treatment stage, W p is the treatment amount of the pth waste, EF 处理,p is the carbon emission factor of the pth treatment mode; Post-treatment stage: wherein K 后处理 represents the carbon emissions of the post-processing stage, W q is the processing amount of the qth by-product, EF 后处理,q is the carbon emission factor of the qth by-product processing method.

7. The prediction method of claim 5, wherein, The similarity mode with the highest similarity is identified, and the differences between the local mode and the similarity mode are analyzed one by one, and the differences are compared with the contents in the cycle database to identify similar processes, specifically including: Compare the current regional mode with all other modes in the carbon emission database, calculate the similarity score of each waste management mode stored in the database with the current regional mode, and identify the management mode with the highest similarity, defined as the similarity mode; Analyze the differences between the characteristics, processes and parameters of the current regional mode and the similarity mode one by one, and extract the processes corresponding to the differences; Compare the extracted processes with all processes in the cycle database to identify the process with the highest similarity, defined as the similar process.

8. The prediction method of claim 7, wherein, The identified similar processes and similar modes are integrated to establish a prediction model, analyze the mutual influence between similar processes and similar modes, and output the carbon emission prediction data of the current region, specifically including: Combine the obtained similar modes and similar processes to form a simulated management mode; Establish a prediction model, and input the parameters of the combined simulated management mode into the prediction model to predict the carbon emission data of the current region at a future time: K t+1 = aK t + b(P t + T t + Q t + S t ); wherein, K t+1 represents the carbon emission prediction at future time t+1, K t represents the carbon emission at current time t, a is a smoothing coefficient, β is a weighting coefficient, P t represents the waste disposal mode influencing factor at current time t, T t represents the transportation mode influencing factor at current time t, Q t represents the resource recycling rate influencing factor at current time t, S t represents other influencing factors at current time t.

9. A system for predicting carbon dioxide emissions based on, The prediction system includes: A waste management mode collection module for collecting waste management modes and carbon emission data in different regions, establishing a carbon emission database, storing carbon emission data corresponding to different waste management modes, and establishing carbon emission baselines in different regions; A carbon emission simulation relationship calculation module for establishing a carbon emission simulation model based on the collected waste management modes and carbon emission data, simulating the relationship between different waste management modes and carbon emission data, and calculating the carbon emission changes brought by different waste management modes; A mode flow analysis module is configured to analyze all flows of each waste management mode, quantitatively analyze carbon emissions of different waste management modes in different life cycle stages, analyze influences of different life cycle stages on carbon emissions, and establish a cycle database for storage; A mode recognition comparison module is configured to recognize a waste management mode of a current region, define the waste management mode as a local mode, compare the local mode with stored contents in the carbon emission database, recognize a waste management mode with the highest similarity, define the waste management mode as a similar mode, analyze differences between the local mode and the similar mode one by one, compare the differences with contents in the cycle database, and recognize similar flows; A carbon emission prediction module is configured to integrate all recognized similar flows and similar modes, establish a prediction model, analyze mutual influences between the similar flows and the similar modes, and output carbon emission prediction data of the current region.

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