Prediction method and system based on carbon dioxide emission

Abstract

The invention relates to a prediction method and system based on carbon dioxide emission. The system comprises a waste management mode collection module, a carbon emission simulation relation calculation module, a mode flow analysis module, a mode recognition and comparison module and a carbon emission prediction module. According to the method, the carbon emission change in different waste management modes is dynamically analyzed, a scientific basis is provided for policy making, a decision maker can evaluate the influence of the management modes on the environment, and implementation of a low-carbon management mode is promoted. Through life cycle analysis, a researcher quantitatively evaluates contribution of each link to carbon emission, and key data support is provided for waste management optimization. In addition, through comparison with other modes in a carbon emission database, similar management modes are identified, differences are deeply analyzed, improvement suggestions are proposed, and propagation of optimal practice is promoted. A prediction model is established by integrating similar modes, mutual influence is analyzed, the carbon emission trend in the future is predicted, support is provided for decision making, and implementation of waste management measures is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and particularly to a prediction method and system based on carbon dioxide emissions. Background Art

[0002] Carbon emission management technologies cover a series of technologies and methods aimed at monitoring, reducing, and managing greenhouse gas emissions. These technologies mainly 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 technologies capture carbon dioxide emissions from industrial processes or power generation and safely store them underground to reduce the carbon concentration in the atmosphere. Renewable energy technologies, such as solar, wind, and hydropower, provide low-carbon alternatives, helping to reduce dependence on fossil fuels and thus overall carbon emissions.

[0003] Existing technologies usually fail to comprehensively explore the complex relationship between different management modes and carbon emissions, resulting in inaccurate assessment of waste management effects. Moreover, during the prediction process, they often rely on static data, lacking consideration of the commonalities and differences between different regions or management methods, which limits the effective prediction of future carbon emission trends. In the absence of multi-dimensional data correlation analysis, existing technologies are difficult to provide sufficient basis for decision-makers, resulting in management strategies that may not be tailored to specific environmental and economic conditions. At the same time, in the face of successful waste management experiences, existing technologies often lack effective ways to identify and draw on, making it possible that the formulation of new policies may not learn from the successful experiences of existing models. Summary of the Invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a prediction method and system based on carbon dioxide emissions.

[0005] The technical solution of the present invention 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 the carbon emission data corresponding to different waste management modes, and establishing a carbon emission baseline for different regions;

[0008] According to the collected waste management modes and carbon emission data, establishing a carbon emission simulation model, simulating the relationship between different waste management modes and carbon emission data, and calculating the change in carbon emissions brought about by different waste management modes;

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

[0010] Identify the waste management model in the current region, define it as the local model, compare it with the content stored in the carbon emission database, identify the waste management model with the highest similarity, define it as the similar model, analyze the differences between the local model and the similar model one by one, and compare the differences with the content in the cycle database to identify similar processes;

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

[0012] As a further solution of the present invention, the establishment of the carbon emission database stores the carbon emission data corresponding to different waste management models, and establishes the carbon emission baseline of different regions, specifically including:

[0013] Integrate open data interfaces, automatically identify and capture waste management models in different regions, record all waste management model processes, and simultaneously identify the carbon emission data of each region;

[0014] Identify the similarity of waste management models in different regions. For completely identical waste management models, only retain any one set of data, and add the secondary labels of the regions that all use this waste treatment model to the retained data;

[0015] Establish a carbon emission database, store all waste management models with different waste management models as the primary label, and store the carbon emission data of each region in the secondary label.

[0016] As a further solution of the present invention, the establishment of the carbon emission simulation model simulates the relationship between different waste management models and carbon emission data, and calculates the change in carbon emissions brought by different waste management models, specifically including:

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

[0018] For the trained carbon emission simulation model, substitute each waste management model one by one, adjust the parameters in the waste management model one by one, and record them as simulation models, and respectively simulate the carbon emissions under each parameter adjustment, and record them as simulated emissions;

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

[0020] As a further solution of the present invention, the identification of the relationship between the simulated emissions and the simulation modes, and the analysis of the influence rate of each simulation mode on the simulated emissions are specifically as follows:

[0021] Establish a waste management mode matrix X:

[0022]

[0023] where n is the number of samples, that is, different waste management modes, m is the number of features, that is, the number of parameters in the waste management mode, and represents X ij The value of the j-th parameter in the i-th waste management mode;

[0024] Calculate the average value for each feature And obtain the matrix X through mean centering 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 obtain 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 features onto the principal components to obtain the principal component matrix Z, expressed as:

[0034] Z = X c ·V;

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

[0036]

[0037] Among them, S i is the sensitivity coefficient, which represents the influence degree of the i-th principal component on carbon emissions. E0 is the initial carbon emissions, ΔE is the change in carbon emissions, and Z i0 is the initial value of the principal component i, that is, the initial value of the parameter i, and ΔZ i is the change in the principal component i, that is, the change in the parameter i.

[0038] As a further solution of the present invention, analyze all processes of each waste management mode, quantitatively analyze the carbon emissions of different waste management modes in different life cycle stages, and analyze the influence of different life cycle stages on carbon emissions, specifically including:

[0039] Divide the life cycle stages of the waste management process, including the generation stage, transportation stage, treatment stage, and post-treatment stage;

[0040] Decompose each waste management mode into specific processes, and collect the parameters related to each process;

[0041] Establish a carbon emission formula according to different life cycle stages, and calculate the carbon emissions of each life cycle stage:

[0042] Establish a cycle database, package and store all processes of each waste management mode and their carbon emissions in the life cycle stage, and add labels to the waste management mode and each life cycle stage data;

[0043] Calculate the contribution rate of each life cycle stage to the total carbon emissions, and identify the life cycle stage with the greatest impact on carbon emissions according to the contribution rate:

[0044]

[0045] Among them, R x represents the carbon emission contribution rate of the x-th stage, and 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 solution of the present invention, the calculation of the carbon emissions of each life cycle stage is specifically as follows:

[0047] Generation stage:

[0048]

[0049] Among them, K 产生 represents the carbon emissions in the generation stage, and W lis the generation amount of the l-th type of waste, EF 产生,l is the carbon emission factor in the generation stage of the l-th type of waste;

[0050] Transportation stage:

[0051]

[0052] Among them, K 运输 represents the carbon emission in the transportation stage, D o represents the distance of the o-th transportation mode, T o is the waste transportation volume of the o-th transportation mode, EF 运输,l is the carbon emission factor per kilometer of transportation;

[0053] Treatment stage:

[0054]

[0055] Among them, K 处理 represents the carbon emission in the treatment stage, W p is the treatment volume of the p-th type of waste, EF 处理,p is the carbon emission factor of the p-th treatment method;

[0056] Post-treatment stage:

[0057]

[0058] Among them, K 后处理 represents the carbon emission in the post-treatment stage, W q is the treatment volume of the q-th type of by-product, EF 后处理,q is the carbon emission factor of the q-th by-product treatment method.

[0059] As a further solution of the present invention, the waste management mode with the highest similarity 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 content in the cycle database to identify the similar process, specifically including:

[0060] Compare the current regional mode with all other modes in the carbon emission database, calculate the similarity score between each waste management mode stored in the database and the current regional mode, and identify the management mode with the highest similarity, defined as the similar mode;

[0061] For the current regional mode and the similar mode, analyze the differences between their characteristics, processes and parameters one by one, and extract the processes corresponding to the differences;

[0062] Compare the extracted processes with all the processes in the cycle database, identify the process with the highest similarity, and define it as the similar process.

[0063] As a further solution of the present invention, integrating all the identified similar processes and similar patterns, establishing a prediction model, analyzing the mutual influence between the similar processes and similar patterns, and outputting the carbon emission prediction data of the current region specifically include:

[0064] Combining the obtained similar patterns and similar processes to form a simulated management mode;

[0065] Establishing a prediction model, substituting the relevant parameters of the combined simulated management mode into the prediction model, and predicting the carbon emission data of the current region at a future moment:

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

[0067] Wherein, K t+1 represents the predicted carbon emission amount at the future moment t + 1, K t represents the carbon emission amount at the current moment t, α is a smoothing coefficient, β is a weighting coefficient, P t represents the influencing factor of the waste treatment method at the current moment t, T t represents the influencing factor of the transportation method at the current moment t, Q t represents the influencing factor of the resource recovery rate at the current moment t, S t represents other influencing factors at the current moment t.

[0068] Another object of the present invention is to provide a prediction system based on carbon dioxide emissions, and the system includes:

[0069] A waste management mode collection module, which is used to collect the 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 for different regions;

[0070] A carbon emission simulation relationship calculation module, which 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 change in carbon emission amount brought by different waste management modes;

[0071] A mode process analysis module, which is used to analyze all the processes of each waste management mode, quantitatively analyze the carbon emission amounts of different waste management modes in different life cycle stages, analyze the influence of different life cycle stages on the carbon emission amount, and establish a cycle database for storage;

[0072] The pattern recognition and comparison module is used to identify the waste management pattern in the current region, define it as the local pattern, compare it with the content stored in the carbon emission database, identify the waste management pattern with the highest similarity, define it as the similar pattern, 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 the similar processes;

[0073] The 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 invention are:

[0075] This 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 environmental impacts of different management patterns, 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, quantitative analysis is carried out on each stage of different waste management patterns, enabling researchers to understand which links contribute the most to carbon emissions in different links such as collection, transportation, treatment, and recycling. This in-depth whole-process analysis provides key data support for the optimization of waste management, promotes the research and development and application of low-carbon technologies, and thus realizes more effective waste management.

[0077] In addition, by identifying the waste management pattern in 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, put forward targeted improvement suggestions, promote the sharing of experiences and the dissemination of best practices, and thus promote local governments and enterprises to adopt successful management patterns.

[0078] Finally, integrating the identified similar processes and patterns to establish a prediction model can analyze the mutual influence between the similar processes and the patterns. This forward-looking analysis can predict the future carbon emission trends in the current region, provide forward-looking support for decision-making, help formulate long-term carbon emission reduction strategies, promote the implementation of various waste management measures, and thus promote the sustainable development of society. Brief Description of the Drawings

[0079] Figure 1 It is a flowchart of the prediction method based on carbon dioxide emissions provided by an embodiment of the present invention;

[0080] Figure 2 Flow chart of establishing a carbon emission database provided by an embodiment of the present invention;

[0081] Figure 3 Flow chart of establishing a carbon emission simulation model provided by an embodiment of the present invention;

[0082] Figure 4 Flow chart of calculating the contribution rate of each life cycle stage to the total carbon emission provided by an embodiment of the present invention;

[0083] Figure 5 Flow chart of identifying the waste management mode with the highest similarity provided by an embodiment of the present invention;

[0084] Figure 6 Flow chart of integrating all identified similar processes and similar modes provided by an embodiment of the present invention;

[0085] Figure 7 Block diagram of a prediction system based on carbon dioxide emissions provided by an embodiment of the present invention. Detailed implementation manners

[0086] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0087] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality of" means two or more unless otherwise specifically defined.

[0088] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that the present invention may be practiced without these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in the present application.

[0089] Figure 1 The flowchart of the prediction method based on carbon dioxide emissions provided by an embodiment of the present invention is as Figure 1 shown, and the prediction method includes:

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

[0091] This step automatically identifies and grabs the waste management modes of each region by integrating open data interfaces, and at the same time details the waste generation amount, classification data, distribution of treatment facilities and their treatment capabilities in these regions. 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, identifying the similarity of waste management modes in different regions can eliminate redundant data and avoid duplicate data storage, thus 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, it is convenient for subsequent analysis and comparison, ensuring the structuring and systematization of the data.

[0092] In addition, establishing a carbon emission database stores different waste management modes as first-level labels, making data retrieval and call more efficient. This structured database design not only facilitates subsequent data analysis and model establishment, but also provides an intuitive reference basis for policymakers, helping them formulate corresponding carbon emission reduction strategies according to the management modes of different regions.

[0093] This step lays the foundation for accurate carbon emission prediction through systematic data collection and collation. It can provide in-depth insights into waste management models in different regions, revealing the direct impact of different management models on carbon emissions, thus supporting decision-makers to make more reasonable choices based on scientific data when implementing green policies. This data-driven approach not only improves the scientificity and effectiveness of decision-making but also promotes the sharing and learning of experiences in waste management and carbon emission control among different regions, thereby driving the improvement of the overall environmental management level.

[0094] As Figure 2 shown, the establishment of a carbon emission database stores the carbon emission data corresponding to different waste management models and establishes a carbon emission baseline for different regions, specifically including:

[0095] S110, integrating open data interfaces, automatically identifying and capturing waste management models in different regions, recording all waste management model processes, and separately identifying the carbon emission data of each region;

[0096] S120, identifying the similarity of waste management models in different regions. For exactly the same waste management models, only retain any one set of data and add the secondary labels of the regions that all use this waste treatment model to the retained data;

[0097] S130, establishing a carbon emission database, storing all waste management models with different waste management models as the primary labels, and storing the carbon emission data of each region in the secondary labels.

[0098] S200, based on the collected waste management models and carbon emission data, establish a carbon emission simulation model to simulate the relationship between different waste management models and carbon emission data, and calculate the changes in carbon emissions brought about by different waste management models;

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

[0100] Next, by calculating the covariance matrix, the correlations between management mode features can be identified. This process will help analyze the direct impact of each feature variable on carbon emissions. In addition, by extracting eigenvectors, 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 scenarios can be obtained, and the relationships 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, thus providing targeted suggestions for policymakers.

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

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

[0103] S210, establish a carbon emission simulation model, and substitute the waste management modes and corresponding carbon emission data in the carbon emission database for 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 them as simulation modes, and simulate the carbon emissions under each parameter adjustment respectively, and record them as simulated emissions;

[0105] S230, identify the relationship between the simulated emissions and the simulation modes, analyze the influence rate of each simulation mode on the simulated emissions, identify the simulation mode with the highest influence rate, and record the parameters corresponding to the adjusted simulation mode as the highest influencing factors.

[0106] In this step, the identification of the relationship between the simulated emissions and the simulation modes, and the analysis of the influence rate of each simulation mode on the simulated emissions are specifically as follows:

[0107] Establish a waste management mode matrix X:

[0108]

[0109] where \(n\) is the number of samples, i.e., different waste management modes, and \(m\) is the number of features, i.e., the number of parameters in the waste management mode, then \(X\) represents ij the value of the \(j\)-th parameter in the \(i\)-th waste management mode;

[0110] Calculate the mean value for each feature and obtain the matrix \(X\) through mean centering c :

[0111]

[0112] Calculate the covariance matrix \(C\):

[0113]

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

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

[0116] \(Cv\) j \(=\lambda\) j \(v\) j ;

[0117] \(V = [v_1\ v_2\ \cdots\ v\) j ;

[0118] where \(v\) j is the eigenvector corresponding to the eigenvalue \(\lambda\) j ;

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

[0120] \(Z = X\) c \(\cdot V\);

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

[0122]

[0123] where \(S\) i is the sensitivity coefficient, i.e., represents the influence degree of the \(i\)-th principal component on carbon emissions, \(E_0\) is the initial carbon emissions, \(\Delta E\) is the change in carbon emissions, \(Z\) i0 is the initial value of the \(i\)-th principal component, i.e., the initial value of the parameter \(i\), \(\Delta Z\) i is the change in the \(i\)-th principal component, i.e., the change in the parameter \(i\).

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

[0125] In this step, the waste management process is first divided 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 the overall carbon emissions. In each stage, by establishing corresponding carbon emission calculation formulas, it is possible to quantitatively evaluate the contribution of different management modes to carbon emissions in a specific stage, providing data support for subsequent management decisions.

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

[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 affecting carbon emissions but also provides important reference for decision-makers and managers, so that when formulating emission reduction strategies, they can specifically select the most effective measures.

[0128] This step provides a comprehensive and in-depth understanding of the carbon emissions of 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, emission reduction strategies can be effectively formulated, concentrating resources on the stage with the greatest impact on carbon emissions, thereby achieving more efficient emission reduction effects. This systematic method helps to promote the realization of sustainable development goals and improve the overall environmental management level.

[0129] As Figure 4 shown, the ·, and quantitatively analyze the carbon emissions of different waste management modes in different life cycle stages, analyze the impact of different life cycle stages on carbon emissions, 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 the parameters related to each process;

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

[0133] S340, establish a cycle database, package and store all processes of each waste management mode and their carbon emissions in the life cycle stage, and add labels to the waste management mode and data of each life cycle stage;

[0134] S350, calculate the contribution rate of each life cycle stage to the total carbon emissions, and identify the life cycle stage with the greatest impact on carbon emissions according to the contribution rate:

[0135]

[0136] where R x represents the carbon emission contribution rate of the x-th stage, and E 阶段,x represents the carbon emissions of the x-th stage, represents the total carbon emissions of all stages.

[0137] In this step, the calculation of the carbon emissions of each life cycle stage is specifically as follows:

[0138] Generation stage:

[0139]

[0140] where K 产生 represents the carbon emissions in the generation stage, W l is the generation amount of the l-th type of waste, and EF 产生,l is the carbon emission factor in the generation stage of the l-th type of waste;

[0141] Transportation stage:

[0142]

[0143] where K 运输 represents the carbon emissions in the transportation stage, D o represents the distance of the o-th transportation mode, T o is the waste transportation volume of the o-th transportation mode, and EF 运输,l is the carbon emission factor per kilometer of transportation;

[0144] Treatment stage:

[0145]

[0146] where K 处理 represents the carbon emissions in the treatment stage, W p is the treatment volume of the p-th type of waste, and EF 处理,pThe carbon emission factor for the p-th type of treatment method

[0147] Post-treatment stage:

[0148]

[0149] Among them, K 后处理 represents the carbon emissions in the post-treatment stage, and W q is the treatment volume of the q-th type of by-product, and EF 后处理,q is the carbon emission factor for the treatment method of the q-th type of by-product.

[0150] S400. Identify the waste management mode of the current region, define it as the local mode, compare it with the content stored in the carbon emission database, identify the waste management mode with the highest similarity, define it as the similar mode, 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 the 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 management mode with the highest similarity, that is, the "similar mode", can be identified. This method not only helps to confirm the characteristics of the current management mode but also makes comparative analysis possible, enabling the extraction of differences from the similar mode. Such analysis helps to reveal the uniqueness of the local mode and potential improvement areas.

[0153] The following steps include a detailed comparison of the current mode and the similar mode, analyzing the differences between their characteristics, processes, and parameters one by one. Such refined analysis can help decision-makers identify the key factors affecting carbon emissions, thus 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 the sharing and application of relevant experiences and best practices.

[0154] This method not only enhances data-driven decision-making ability but also promotes the continuous improvement of management modes. By identifying similar modes, decision-makers can draw on the successful experiences of other regions, implement effective management measures, and thus 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] Such as Figure 5As shown, the waste management mode with the highest identified similarity is defined as the similar mode. The differences between the local mode and the similar mode are analyzed one by one, and the differences are compared with the content in the cycle database to identify similar processes, specifically including:

[0156] S410. Compare the current regional mode with all other modes in the carbon emission database. For each waste management mode stored in the database, calculate its similarity score with the current regional mode, and identify the management mode with the highest similarity, which is defined as the similar mode.

[0157] S420. For the current regional mode and the similar mode, analyze the differences in their characteristics, processes, and parameters one by one, and extract the processes corresponding to the differences.

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

[0159] S500. Integrate all the identified similar processes and similar modes, establish a prediction model, analyze the mutual influence between the similar processes and the 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 be able to more comprehensively reflect the waste management situation in the current region and its impact on carbon emissions.

[0161] By adopting a dynamic prediction model, historical data and identified influencing factors can be used to accurately predict future carbon emissions. The parameters involved in the model, such as waste generation volume, treatment methods, transportation efficiency, etc., can be continuously 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 is not only a retrospective analysis based on historical data, but also capable of making forward-looking predictions. This two-way thinking mode enables decision-makers to not only understand the effects of the current management mode, but also foresee the possible future carbon emission trends. This is crucial for formulating long-term carbon emission reduction strategies, which can help decision-makers make more scientific and reasonable choices when implementing new policies, and maximize the emission reduction effect.

[0163] As Figure 6 shown, the integration of all the identified similar processes and similar modes, the establishment of a prediction model, the analysis of the mutual influence between the similar processes and the similar modes, and the output of the carbon emission prediction data of the current region specifically include:

[0164] S510, combine the obtained similar patterns and similar processes to form a simulation management model;

[0165] S520, establish a prediction model, substitute the relevant parameters of the combined simulation management model into the prediction model, and predict the carbon emission data at future moments in 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 predicted carbon emission amount at future moment t + 1, K t represents the carbon emission amount at the current moment t, α is the smoothing coefficient, β is the weighting coefficient, P t represents the influencing factor of the waste treatment method at the current moment t, T t represents the influencing factor of the transportation method at the current moment t, Q t represents the influencing factor of the resource recovery rate at the current moment t, S t represents other influencing factors at the current moment t.

[0168] Figure 7 is the structural block diagram of the prediction system based on carbon dioxide emissions provided by the embodiments of the present invention. As Figure 7 shown, the system includes:

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

[0170] A carbon emission simulation relationship calculation module 200, configured 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 change in carbon emission amount brought by different waste management modes;

[0171] A mode process analysis module 300, configured to analyze all processes of each waste management mode, quantitatively analyze the carbon emission amounts of different waste management modes in different life cycle stages, analyze the influence of different life cycle stages on the carbon emission amount, and establish a cycle database for storage;

[0172] The pattern recognition and comparison module 400 is used to identify the waste management pattern in the current region, define it as the local pattern, compare it with the content stored in the carbon emission database, identify the waste management pattern with the highest similarity, define it as the similar pattern, 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 the similar processes;

[0173] The carbon emission prediction module 500 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.

[0174] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0176] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0177] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0178] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 or steps for implementing the functions specified in one block or a plurality of blocks.

[0179] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0180] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A prediction method based on carbon dioxide emissions, characterized in that: The prediction method comprises: Collect waste management models and carbon emission data from different regions, establish a carbon emission database, store the carbon emission data corresponding to different waste management models, and establish carbon emission baselines for different regions; Based on the collected waste management models and carbon emission data, a carbon emission simulation model is established to simulate the relationship between different waste management models and carbon emission data, and calculate the changes in carbon emissions caused by different waste management models; Analyze the entire process of each waste management model, and quantitatively analyze the carbon emissions of different waste management models at different life cycle stages, analyze the impact of different life cycle stages on carbon emissions, and establish a cycle database for storage; Identify the waste management mode in the current area and define it as the local mode, compare it with the content stored in the carbon emission database, identify the waste management mode with the highest similarity, define it as a 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; Integrate all the similar processes and similar patterns identified, establish a prediction model, analyze the mutual influence between similar processes and similar patterns, and output the carbon emission prediction data for the current region.

2. The prediction method according to claim 1, characterized in that: The carbon emission database is established to store carbon emission data corresponding to different waste management modes and establish carbon emission baselines for different regions, including: Integrate open data interfaces to automatically identify and capture waste management models in different regions, record the entire waste management model process, and identify carbon emission data for each region; Identify the similarity of waste management models in different regions. For completely identical waste management models, only retain one set of data and add secondary labels of all regions that use the waste treatment model to the retained data. A carbon emission database is established to store all waste management modes with different waste management modes as the first-level tags, and the carbon emission data of each region is stored in the second-level tags.

3. The prediction method according to claim 2, characterized in that: The carbon emission simulation model is established to simulate the relationship between different waste management modes and carbon emission data, and calculate the changes in carbon emissions caused by different waste management modes, specifically including: Establish a carbon emission simulation model and substitute the waste management model and corresponding carbon emission data in the carbon emission database into the training; For the trained carbon emission simulation model, each waste management mode is substituted, and the parameters in the waste management mode are adjusted one by one, which are recorded as simulation modes, and the carbon emissions under each parameter adjustment are simulated, which are recorded as simulated emissions; Identify the relationship between simulated emissions and simulation modes, analyze the impact rate of each simulation mode on simulated emissions, identify the simulation mode with the highest impact rate, and record the adjusted parameters corresponding to the simulation mode as the highest impact factor.

4. The prediction method according to claim 3, characterized in that: The identification of the relationship between the simulated emissions and the simulation mode, and the analysis of the impact rate of each simulation mode on the simulated emissions, are specifically: Establish waste management model matrix X: Where n is the number of samples, i.e., different waste management models, and m is the number of features, i.e., the number of parameters in the waste management model, then X ij the value of the jth parameter in the ith waste management model; Calculate the mean for each feature And get the matrix X by mean centering c : Calculate the covariance matrix C: in Represents the matrix X c The transposed matrix of Calculate the eigenvalues ​​and eigenvectors of the covariance matrix and obtain the eigenvector matrix V: Cv j =λ j v j ; V=[v1 v2 … v j ]; Among them, v j is the eigenvalue corresponding to j The eigenvector of Based on the eigenvector matrix, the original features are projected onto the principal components to obtain the principal component matrix Z, which is expressed as: Z=X c ·V; Analyze the impact of each main component on carbon emissions: Among them, S i is the sensitivity coefficient, which indicates the influence of the ith principal component on carbon emissions, E0 is the initial carbon emissions, ΔE is the change in carbon emissions, and Z i0 is the initial value of the principal component i, that is, the initial value of parameter i, ΔZ i is the change of principal component i, that is, the change of parameter i.

5. The prediction method according to claim 3, characterized in that: The above analysis includes the entire process of each waste management model, quantitative analysis of the carbon emissions of different waste management models at different life cycle stages, and analysis of the impact of different life cycle stages on carbon emissions, including: Divide the life cycle stages of the waste management process, including generation, transportation, treatment, and post-processing; Disassemble each waste management model into specific processes and collect parameters related to each process; A carbon emission formula is established according to different life cycle stages, and the carbon emissions at each life cycle stage are calculated: Establish a life cycle database to package and store all processes of each waste management model and the carbon emissions of each life cycle stage, and add labels to the waste management model 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 based on the contribution rate: Where R x represents the carbon emission contribution rate in the xth stage, E 阶段,x represents the carbon emissions in stage x, Represents the total carbon emissions in all stages.

6. The prediction method according to claim 5, characterized in that: The calculation of carbon emissions at each life cycle stage is as follows: Generation phase: Among them, K 产生 represents the carbon emissions during the production phase, W l is the amount of waste generated in category l, EF 产生,l is the carbon emission factor at the generation stage of Category 1 waste; Transportation stage: Among them, K 运输 represents the carbon emissions in the transportation stage, D o represents the distance of the oth mode of transportation, T o is the waste transportation volume of the oth mode of transportation, EF 运输,l is the carbon emission factor per kilometer of transportation; Processing stage: Among them, K 处理 represents the carbon emissions in the processing stage, W p is the treatment amount of the pth type of waste, EF 处理,p is the carbon emission factor for the pth type of treatment; Post-processing stage: Among them, K 后处理 represents the carbon emissions in the post-processing stage, W q is the processing capacity of the qth type of by-product, EF 后处理,q is the carbon emission factor for the qth type of by-product treatment method.

7. The prediction method according to claim 5, characterized in that: The waste management mode with the highest similarity is identified as a 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 content in the cycle database to identify similar processes, specifically including: Compare the current regional model with all other models in the carbon emission database, calculate the similarity score between each waste management model stored in the database and the current regional model, and identify the management model with the highest similarity, which is defined as the similar model; Analyze the differences in characteristics, processes and parameters between the current regional model and similar models one by one, and extract the processes corresponding to the differences; The extracted processes are compared with all the processes in the cycle database to identify the processes with the highest similarity, which are defined as similar processes.

8. The prediction method according to claim 7, characterized in that: The method integrates all the similar processes and similar patterns identified, establishes a prediction model, analyzes the mutual influence between similar processes and similar patterns, and outputs the carbon emission prediction data for the current region, including: Combine the obtained similar patterns and similar processes to form a simulation management model; A prediction model is established, and the relevant parameters of the combined simulation management model are substituted into the prediction model to predict the carbon emission data of the current region at future times: K t+1 =αK t +β(P t +T t +Q t +S t ); Among them, K t+1 represents the predicted carbon emissions at the future time t+1, K t represents the carbon emissions at the current time t, α is the smoothing coefficient, β is the weighting coefficient, P t represents the factors affecting the waste treatment method at the current time t, T t represents the factors affecting the mode of transport at the current time t, Q t represents the factors affecting the resource recovery rate at the current time t, S t Represents other influencing factors at the current time t.

9. A prediction system based on carbon dioxide emissions, characterized in that: The prediction system comprises: The waste management model collection module is used to collect waste management models and carbon emission data from different regions, establish a carbon emission database, store the carbon emission data corresponding to different waste management models, and establish carbon emission baselines for different regions; The carbon emission simulation relationship calculation module is used to establish a carbon emission simulation model based on the collected waste management mode and carbon emission data, simulate the relationship between different waste management modes and carbon emission data, and calculate the changes in carbon emissions caused by different waste management modes; The model process analysis module is used to analyze the entire process of each waste management model, and quantitatively analyze the carbon emissions of different waste management models at different life cycle stages, analyze the impact of different life cycle stages on carbon emissions, and establish a cycle database for storage; The pattern recognition and comparison module is used to identify the waste management pattern in the current area and define it as the local pattern. It is then compared with the content stored in the carbon emission database to identify the waste management pattern with the highest similarity and define it as a similar pattern. The module also analyzes the differences between the local pattern and the similar pattern one by one and compares the differences with the content in the cycle database to identify similar processes. The carbon emission prediction module is used to integrate all the similar processes and similar patterns identified, establish a prediction model, analyze the mutual influence between similar processes and similar patterns, and output the carbon emission prediction data for the current region.

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