Carbon emission accounting method for evaluating whole household garbage classification process

Abstract

The invention discloses a carbon emission accounting method for evaluating the whole household garbage classification process, and relates to the technical field of garbage classification carbon emission accounting. The system is realized based on a carbon emission accounting system, and specifically comprises a data acquisition module which is responsible for collecting original data related to garbage classification, including garbage generation amount, classification type, transportation distance and processing mode; the data processing module is used for cleaning, sorting and standardizing the collected data to ensure the accuracy and consistency of the data; and the carbon emission factor library comprises carbon emission factors in various garbage treatment processes. According to the invention, the detailed carbon emission factor library and the accounting module are combined, so that the carbon emission of each garbage treatment link can be accurately calculated, and managers are helped to understand main emission sources; by using optimization technologies such as particle swarm optimization, an optimal strategy for reducing carbon emission can be found under complex environmental conditions, and double benefits of economy and environmental protection are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission accounting for waste sorting, and in particular to a carbon emission accounting method for evaluating the entire process of domestic waste sorting. Background Art

[0002] In the face of global climate change and increasing pressure on environmental protection, the effective classification and treatment of urban domestic waste has become particularly important. Waste classification can not only reduce the waste of resources, but also significantly reduce environmental pollution and carbon emissions caused by improper waste disposal.

[0003] However, traditional waste management models often lack efficient data collection, processing and analysis capabilities, and are unable to cope with the growing data processing needs and accurate carbon emission accounting requirements.

[0004] After searching, the application scheme of Chinese patent application No. 202210685996.9 discloses a method for calculating carbon emissions of waste incineration power plants. This method specifically classifies the waste incinerated in waste-to-energy plants, determines the proportion of each type of waste, proposes a method for calculating carbon emissions of waste incineration power plants, and optimizes the model using the gradient reduction method. It can calculate the carbon emissions according to the type of waste incinerated, thereby obtaining the carbon emissions of the entire unit. The carbon emission calculation method in the above patent has the following shortcomings: insufficient comprehensive evaluation capabilities, and cannot predict future trends and provide emission reduction strategies, which needs to be improved. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a carbon emission accounting method for evaluating the entire process of domestic waste classification.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A carbon emission accounting method for evaluating the whole process of household waste classification is implemented based on a carbon emission accounting system, specifically including:

[0008] Data collection module: responsible for collecting raw data related to garbage classification, including garbage generation, classification type, transportation distance, and treatment method;

[0009] Data processing module: cleans, organizes and standardizes the collected data to ensure the accuracy and consistency of the data;

[0010] Carbon emission factor library: contains carbon emission factors for various waste treatment processes, including CO2 emission coefficients for different treatment methods such as incineration, landfill, composting, and recycling;

[0011] Carbon emission accounting module: Calculate the carbon emissions of each link based on the data processing results and the information in the carbon emission factor library;

[0012] Intelligent optimization algorithm module: introduces machine learning and optimization algorithms to simulate and optimize the waste sorting and treatment process to find carbon reduction potential;

[0013] Result output module: The accounting results are intuitively displayed in the form of charts and reports, including total carbon emissions, carbon emission ratios in each link, and emission reduction potential analysis;

[0014] Optimization suggestion module: Based on the calculation results and the simulation of intelligent optimization algorithm, it puts forward optimization suggestions for the garbage sorting process, including adjusting the transportation route and improving the treatment method to reduce carbon emissions;

[0015] System update and maintenance module: regularly update the carbon emission factor library to ensure the accuracy of accounting results;

[0016] Intelligent prediction module: Use time series analysis and machine learning models to predict future trends in waste sorting and carbon emissions;

[0017] User interaction module: Provides users with an interactive query and feedback platform through natural language processing and intelligent dialogue system.

[0018] Preferably: the data acquisition module comprises:

[0019] Real-time monitoring unit: uses sensor technology to monitor the amount of garbage generated and the classification in real time;

[0020] Manual input unit: provides a user interface for managers to enter data that is not automatically collected;

[0021] Third-party data access unit: integrates information from third-party data sources;

[0022] The data processing module comprises:

[0023] Data cleaning unit: Apply data cleaning algorithms to identify and correct errors and outliers in the data;

[0024] Data conversion unit: converts the collected data into a standardized format for easy analysis and processing;

[0025] Data fusion unit: Use data fusion technology to integrate data from different sources to improve data integrity and accuracy.

[0026] Preferably: the carbon emission factor library includes:

[0027] Static database unit: stores general carbon emission factor information;

[0028] Dynamic update unit: regularly update emission factor data based on the latest research results and policy changes;

[0029] User-defined unit: Upload customized emission factor data based on user operations.

[0030] Preferably: the carbon emission accounting module includes:

[0031] Direct emission calculation unit: calculates carbon emissions in the direct process of garbage disposal;

[0032] Indirect emission calculation unit: estimates the carbon emissions indirectly generated during waste treatment;

[0033] Comprehensive accounting unit: direct and indirect emissions are aggregated to obtain total carbon emissions.

[0034] Preferably: the intelligent optimization algorithm module comprises:

[0035] Algorithm selection unit: selects appropriate intelligent optimization algorithms according to different scenarios;

[0036] Parameter tuning unit: Use parameter tuning technology to optimize algorithm performance;

[0037] Optimization execution unit: runs the selected optimization algorithm to generate optimized garbage classification and treatment solutions;

[0038] The intelligent optimization algorithm module uses the particle swarm optimization algorithm to perform parameter tuning, and its basic steps are:

[0039] Initialization: Randomly generate a set of particle positions and velocities;

[0040] Evaluation: Calculate the fitness value of each particle;

[0041] Update: Update the velocity and position of particles according to individual and global optimal solutions;

[0042] The specific formula is:

[0043]

[0044] Among them, ω is the inertia factor, c1 and c2 are learning factors, r1 and r2 are random numbers, and p b,i is the individual optimal solution, q b It is the global optimal solution.

[0045] Preferably: the result output module comprises:

[0046] Report generation unit: to make accounting results into charts and reports;

[0047] Result visualization unit: Use data visualization technology to intuitively display carbon emissions and emission reduction effects;

[0048] The optimization suggestion module includes:

[0049] Emission reduction potential analysis unit: Analyze the emission reduction potential of different links and put forward improvement suggestions;

[0050] Cost-Benefit Assessment Unit: Evaluate the economic costs and environmental benefits of optimization proposals;

[0051] Recommendation output unit: Generates detailed optimization recommendation report.

[0052] Preferably: the intelligent prediction module comprises:

[0053] Time Series Analysis Unit: Apply time series analysis models to predict garbage volume and carbon emission trends;

[0054] Machine Learning Prediction Unit: Use machine learning models to perform pattern recognition and future prediction based on historical data;

[0055] Forecast result evaluation unit: evaluate the accuracy of forecast results and provide a scientific basis for decision-making.

[0056] Preferably, the intelligent prediction module uses ARIMA to predict the amount of garbage and carbon emission trends, as follows:

[0057] Φ(B)Δ d X t =Θ(B)∈ t

[0058] in:

[0059] Φ(B) is the autoregressive operator;

[0060] Θ(B) is the moving average operator;

[0061] X t is time series data;

[0062] ∈ t is the error term;

[0063] d is the difference order;

[0064] B is the backshift operator;

[0065] The time series data is predicted based on the ARIMA model which combines autoregression (AR), differencing (I) and moving average (MA).

[0066] Preferably, the intelligent prediction module uses a support vector machine to perform time series analysis, as follows:

[0067]

[0068] in:

[0069] w is the normal vector of the hyperplane;

[0070] b is the intercept of the hyperplane;

[0071] C is the regularization parameter;

[0072] Z i is a slack variable.

[0073] Preferably: the specific steps of the calculation method are:

[0074] S1: Collect relevant data through the data acquisition module;

[0075] S2: Data preprocessing is performed by the data processing module;

[0076] S3: The carbon emission accounting module calculates the carbon emissions of each link based on the processed data and the coefficients in the carbon emission factor library;

[0077] S4: The intelligent optimization algorithm module searches for the optimal solution through algorithm simulation to reduce overall carbon emissions;

[0078] S5: The result output module visualizes the calculation results to facilitate user understanding and decision-making;

[0079] S6: The optimization suggestion module proposes improvement measures based on the calculation results and the simulation of the intelligent optimization algorithm;

[0080] S7: The intelligent prediction module provides future trend predictions and feeds back to the user end.

[0081] The beneficial effects of the present invention are:

[0082] 1. The present invention combines a detailed carbon emission factor library and an accounting module to accurately calculate the carbon emissions of each garbage disposal link and help managers understand the main emission sources.

[0083] 2. The present invention utilizes optimization techniques such as particle swarm optimization to find the best strategy to reduce carbon emissions under complex environmental conditions, achieving both economic and environmental benefits.

[0084] 3. The present invention applies time series analysis and machine learning models such as ARIMA and neural networks to predict future trends in garbage classification and carbon emissions, providing a scientific basis for policy making and resource planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The present invention provides a flow chart of a carbon emission accounting method for evaluating the entire process of household waste classification. DETAILED DESCRIPTION

[0086] The technical solution of the present invention is further described in detail below in conjunction with specific implementation methods.

[0087] Embodiment 1:

[0088] A carbon emission accounting method for evaluating the whole process of household waste classification is implemented based on a carbon emission accounting system, specifically including:

[0089] Data collection module: responsible for collecting raw data related to garbage classification, including garbage generation, classification type, transportation distance, treatment method, etc.;

[0090] Data processing module: cleans, organizes and standardizes the collected data to ensure the accuracy and consistency of the data;

[0091] Carbon emission factor library: contains carbon emission factors for various waste treatment processes, including CO2 emission coefficients for different treatment methods such as incineration, landfill, composting, and recycling;

[0092] Carbon emission accounting module: Calculate the carbon emissions of each link based on the data processing results and the information in the carbon emission factor library;

[0093] Intelligent optimization algorithm module: introduces machine learning and optimization algorithms, including genetic algorithm and particle swarm optimization, to simulate and optimize the waste sorting and treatment process to find carbon reduction potential;

[0094] Result output module: The accounting results are intuitively displayed in the form of charts and reports, including total carbon emissions, carbon emission ratios in each link, emission reduction potential analysis, etc.;

[0095] Optimization suggestion module: Based on the calculation results and the simulation of intelligent optimization algorithm, it puts forward optimization suggestions for the garbage sorting process, including adjusting the transportation route, improving the treatment method, etc., to reduce carbon emissions;

[0096] System update and maintenance module: regularly update the carbon emission factor library to ensure the accuracy of accounting results; maintain the system to ensure stable operation;

[0097] Intelligent prediction module: Use time series analysis and machine learning models, including ARIMA and neural networks, to predict future trends in waste classification and carbon emissions, providing a scientific basis for policy making;

[0098] User interaction module: Provides users with an interactive query and feedback platform through natural language processing and intelligent dialogue system.

[0099] Wherein, the data acquisition module includes:

[0100] Real-time monitoring unit: uses sensor technology to monitor the amount of garbage generated and the classification in real time;

[0101] Manual input unit: provides a user interface for managers to enter data that is not automatically collected;

[0102] Third-party data access unit: Integrate information from third-party data sources.

[0103] Wherein, the data processing module includes:

[0104] Data cleaning unit: Apply data cleaning algorithms to identify and correct errors and outliers in the data;

[0105] Data conversion unit: converts the collected data into a standardized format for easy analysis and processing;

[0106] Data fusion unit: Use data fusion technology to integrate data from different sources to improve data integrity and accuracy.

[0107] Wherein, the carbon emission factor library includes:

[0108] Static database unit: stores general carbon emission factor information;

[0109] Dynamic update unit: regularly update emission factor data based on the latest research results and policy changes;

[0110] User-defined unit: Upload customized emission factor data based on user operations.

[0111] Wherein, the carbon emission accounting module includes:

[0112] Direct emission calculation unit: calculates carbon emissions in the direct process of garbage disposal;

[0113] Indirect emission calculation unit: estimates the carbon emissions indirectly generated during the waste treatment process, such as fuel consumption during transportation;

[0114] Comprehensive accounting unit: direct and indirect emissions are aggregated to obtain total carbon emissions.

[0115] Wherein, the intelligent optimization algorithm module includes:

[0116] Algorithm selection unit: selects appropriate intelligent optimization algorithms according to different scenarios;

[0117] Parameter tuning unit: Use parameter tuning techniques, including grid search, random search, etc., to optimize algorithm performance;

[0118] Optimization execution unit: runs the selected optimization algorithm to generate optimized garbage classification and treatment solutions.

[0119] The intelligent optimization algorithm module uses a particle swarm optimization algorithm to perform parameter tuning, and its basic steps are as follows:

[0120] Initialization: Randomly generate a set of particle positions and velocities;

[0121] Evaluation: Calculate the fitness value of each particle;

[0122] Update: Update the velocity and position of particles according to individual and global optimal solutions;

[0123] The specific formula is:

[0124]

[0125] Among them, ω is the inertia factor, c1 and c2 are learning factors, r1 and r2 are random numbers, and p b,i is the individual optimal solution, q b It is the global optimal solution.

[0126] Wherein, the result output module includes:

[0127] Report generation unit: to make accounting results into charts and reports;

[0128] Result visualization unit: Use data visualization technology to intuitively display carbon emissions and emission reduction effects.

[0129] The optimization suggestion module includes:

[0130] Emission reduction potential analysis unit: Analyze the emission reduction potential of different links and put forward improvement suggestions;

[0131] Cost-Benefit Assessment Unit: Evaluate the economic costs and environmental benefits of optimization proposals;

[0132] Suggestion output unit: Generates detailed optimization suggestion report for decision makers' reference.

[0133] Wherein, the intelligent prediction module includes:

[0134] Time Series Analysis Unit: Apply ARIMA and other time series analysis models to predict garbage volume and carbon emission trends;

[0135] Machine Learning Prediction Unit: Uses machine learning models such as neural networks to perform pattern recognition and future prediction based on historical data;

[0136] Forecast result evaluation unit: evaluate the accuracy of forecast results and provide a scientific basis for decision-making.

[0137] The intelligent prediction module uses ARIMA to predict the amount of garbage and carbon emission trends, as follows:

[0138] Φ(B)Δ d X t =Θ(B)∈ t

[0139] in:

[0140] Φ(B) is the autoregressive operator;

[0141] Θ(B) is the moving average operator;

[0142] X t is time series data;

[0143] ∈ t is the error term;

[0144] d is the difference order;

[0145] B is the backshift operator;

[0146] The time series data is predicted based on the ARIMA model which combines autoregression (AR), differencing (I) and moving average (MA).

[0147] The specific steps of the calculation method are as follows:

[0148] S1: Collect relevant data through the data acquisition module;

[0149] S2: Data preprocessing is performed by the data processing module;

[0150] S3: The carbon emission accounting module calculates the carbon emissions of each link based on the processed data and the coefficients in the carbon emission factor library;

[0151] S4: The intelligent optimization algorithm module searches for the optimal solution through algorithm simulation to reduce overall carbon emissions;

[0152] S5: The result output module visualizes the calculation results to facilitate user understanding and decision-making;

[0153] S6: The optimization suggestion module proposes improvement measures based on the calculation results and the simulation of the intelligent optimization algorithm;

[0154] S7: The intelligent prediction module provides future trend predictions and feeds back to the user end.

[0155] Embodiment 2:

[0156] A carbon emission accounting method for evaluating the whole process of household waste classification. In this embodiment, based on the first embodiment, the intelligent prediction module uses a support vector machine to perform time series analysis, as follows:

[0157]

[0158] in:

[0159] w is the normal vector of the hyperplane;

[0160] b is the intercept of the hyperplane;

[0161] C is the regularization parameter;

[0162] Z i is a slack variable.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A carbon emission accounting method for evaluating the whole process of household waste classification, characterized in that: Based on the carbon emission accounting system, it includes: Data collection module: responsible for collecting raw data related to garbage classification, including garbage generation, classification type, transportation distance, and treatment method; Data processing module: cleans, organizes and standardizes the collected data to ensure the accuracy and consistency of the data; Carbon emission factor library: contains carbon emission factors for various waste treatment processes, including CO2 emission coefficients for different treatment methods such as incineration, landfill, composting, and recycling; Carbon emission accounting module: Calculate the carbon emissions of each link based on the data processing results and the information in the carbon emission factor library; Intelligent optimization algorithm module: introduces machine learning and optimization algorithms to simulate and optimize the waste sorting and treatment process to find carbon reduction potential; Result output module: The accounting results are intuitively displayed in the form of charts and reports, including total carbon emissions, carbon emission ratios in each link, and emission reduction potential analysis; Optimization suggestion module: Based on the calculation results and the simulation of intelligent optimization algorithm, it puts forward optimization suggestions for the garbage sorting process, including adjusting the transportation route and improving the treatment method to reduce carbon emissions; System update and maintenance module: regularly update the carbon emission factor library to ensure the accuracy of accounting results; Intelligent prediction module: Use time series analysis and machine learning models to predict future trends in waste sorting and carbon emissions; User interaction module: Provides users with an interactive query and feedback platform through natural language processing and intelligent dialogue system.

2. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The data acquisition module comprises: Real-time monitoring unit: uses sensor technology to monitor the amount of garbage generated and the classification in real time; Manual input unit: provides a user interface for managers to enter data that is not automatically collected; Third-party data access unit: integrates information from third-party data sources; The data processing module comprises: Data cleaning unit: Apply data cleaning algorithms to identify and correct errors and outliers in the data; Data conversion unit: converts the collected data into a standardized format for easy analysis and processing; Data fusion unit: Use data fusion technology to integrate data from different sources to improve data integrity and accuracy.

3. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The carbon emission factor library includes: Static database unit: stores general carbon emission factor information; Dynamic update unit: regularly update emission factor data based on the latest research results and policy changes; User-defined unit: Upload customized emission factor data based on user operations.

4. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The carbon emission accounting module includes: Direct emission calculation unit: calculates carbon emissions in the direct process of garbage disposal; Indirect emission calculation unit: estimates the carbon emissions indirectly generated during waste treatment; Comprehensive accounting unit: direct and indirect emissions are aggregated to obtain total carbon emissions.

5. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The intelligent optimization algorithm module includes: Algorithm selection unit: selects appropriate intelligent optimization algorithms according to different scenarios; Parameter tuning unit: Use parameter tuning technology to optimize algorithm performance; Optimization execution unit: runs the selected optimization algorithm to generate optimized garbage classification and treatment solutions; The intelligent optimization algorithm module uses the particle swarm optimization algorithm to perform parameter tuning, and its basic steps are: Initialization: Randomly generate a set of particle positions and velocities; Evaluation: Calculate the fitness value of each particle; Update: Update the velocity and position of particles according to individual and global optimal solutions; The specific formula is: Among them, ω is the inertia factor, c1 and c2 are learning factors, r1 and r2 are random numbers, and p b,i is the individual optimal solution, q b It is the global optimal solution.

6. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The result output module comprises: Report generation unit: to make accounting results into charts and reports; Result visualization unit: Use data visualization technology to intuitively display carbon emissions and emission reduction effects; The optimization suggestion module includes: Emission reduction potential analysis unit: Analyze the emission reduction potential of different links and put forward improvement suggestions; Cost-Benefit Assessment Unit: Evaluate the economic costs and environmental benefits of optimization proposals; Recommendation output unit: Generates detailed optimization recommendation report.

7. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The intelligent prediction module comprises: Time Series Analysis Unit: Apply time series analysis models to predict garbage volume and carbon emission trends; Machine Learning Prediction Unit: Use machine learning models to perform pattern recognition and future prediction based on historical data; Forecast result evaluation unit: evaluate the accuracy of forecast results and provide a scientific basis for decision-making.

8. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 7, characterized in that: The intelligent prediction module uses ARIMA to predict the amount of garbage and carbon emission trends as follows: F(B)D d X t =Θ(B)∈ t in: Φ(B) is the autoregressive operator; Θ(B) is the moving average operator; X t is time series data; ∈ t is the error term; d is the difference order; B is the backshift operator; The ARIMA model combines autoregression (AR), differencing (I) and moving average (MA) to predict time series data.

9. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 7, characterized in that: The intelligent prediction module uses support vector machine to perform time series analysis, as follows: s.t.y i (w T x i +b)≥1-Z i ,Z i ≥0,i=1,…,n in: w is the normal vector of the hyperplane; b is the intercept of the hyperplane; C is the regularization parameter; Z i is a slack variable.

10. A carbon emission accounting method for evaluating the whole process of household waste classification according to claim 1, characterized in that: The specific steps of the calculation method are: S1: Collect relevant data through the data acquisition module; S2: Data preprocessing is performed by the data processing module; S3: The carbon emission accounting module calculates the carbon emissions of each link based on the processed data and the coefficients in the carbon emission factor library; S4: The intelligent optimization algorithm module searches for the optimal solution through algorithm simulation to reduce overall carbon emissions; S5: The result output module visualizes the calculation results to facilitate user understanding and decision-making; S6: The optimization suggestion module proposes improvement measures based on the calculation results and the simulation of the intelligent optimization algorithm; S7: The intelligent prediction module provides future trend predictions and feeds back to the user end.

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