Method and system for estimating carbon emission of road during construction period and electronic equipment

Abstract

The invention provides a construction period highway carbon emission estimation method and system and electronic equipment, and relates to the technical field of carbon emission, and the method comprises the steps: collecting special data based on construction project information, setting a corresponding carbon emission factor based on the obtained data, carrying out the LCA evaluation modeling based on the processed data, and synchronously carrying out the input and output analysis modeling, thereby achieving the estimation of the carbon emission of a highway during a construction period. Carrying out fusion modeling on the two models, carrying out explanation and evaluation based on the fused model, combining the collected data with corresponding carbon emission factors, calculating the carbon emission of each link, considering key parameters in the model, carrying out sensitivity analysis, evaluating the influence degree of the key parameters on an evaluation result, optimizing and improving the model, and carrying out analysis on the carbon emission of each link based on the improved model. And performing prediction effect evaluation, verifying the actual prediction capability, performing fine adjustment and correction on the model in time, and performing carbon emission prediction on newly collected highway special sample data based on the fine-adjusted and corrected fusion model to obtain a final prediction result.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a method, system and electronic device for estimating carbon emission of highways during construction. Background Art

[0002] Highway carbon emissions during construction refer to the carbon emissions generated during highway construction. Highway construction usually requires a large amount of machinery and equipment, transportation vehicles and energy consumption, which will lead to a large amount of carbon emissions. During construction, it may involve excavation, paving and stone processing activities, all of which consume energy and release greenhouse gases such as carbon dioxide;

[0003] When actually estimating and calculating carbon emissions, the carbon emission factor needs to be confirmed, and then the empirical formula is used to calculate based on the confirmed carbon emission factor to finally obtain the carbon emissions. Through the above calculation steps, since the carbon emission factor is no longer updated in real time after confirmation, there is a data lag. During the construction period, the project progresses step by step, and the carbon emission factor cannot progress with the project. The subsequent use of empirical formulas for calculations will produce erroneous calculation results, and it will not be possible to accurately estimate carbon emissions.

[0004] Therefore, it is urgent to propose a method, system and electronic equipment for estimating carbon emissions of highways during the construction period. Summary of the invention

[0005] The purpose of the present invention is to address the shortcomings of the existing technology and provide a method for estimating carbon emissions of highways during the construction period. The method aims to achieve regular updating of the carbon emission factor as the project progresses based on the carbon emission factor obtained through evaluation, and to use an improved and optimized fusion model to achieve accurate estimation of carbon emissions.

[0006] To this end, this application provides a method for estimating carbon emissions of highways during construction, including the following steps:

[0007] Step 100: collect special data based on the construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data;

[0008] Step 200: Based on the processed data, LCA assessment modeling is performed, input and output analysis modeling is performed simultaneously, and the two models are fused to achieve data fusion, model fusion and result fusion, and interpretation and evaluation are performed based on the fused model;

[0009] Step 300: Based on the fused model, carbon emission calculation is performed, and the collected data is combined with the corresponding carbon emission factors to calculate the carbon emission of each link;

[0010] Step 400: Consider the key parameters in the model, conduct sensitivity analysis, evaluate their influence on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model;

[0011] Step 500: Based on the improved model, the prediction effect is evaluated to verify its actual prediction ability, and based on its prediction results, its reliability and accuracy are checked, and the model is fine-tuned and corrected in a timely manner;

[0012] Step 600: Based on the fine-tuned and corrected fusion model, the newly collected highway-specific sample data is subjected to carbon emission prediction to obtain a final prediction result.

[0013] In some specific implementations, special data is collected based on construction project information, corresponding carbon emission factors are established based on the collected data, and the collected data are standardized, specifically including:

[0014] Step 100.1, collecting data related to the highway project, specifically including vehicle type information, fuel type information, travel distance information and vehicle load information;

[0015] Step 100.2: Preliminarily calculate the specific carbon emissions of each information and establish the carbon emission factor, specifically:

[0016] Vehicle type information:

[0017] The carbon emission factor for small passenger cars is 0.15 kg CO2 / km;

[0018] The carbon emission factor for large trucks is 0.3 kg CO2 / km;

[0019] The carbon emission factor for buses is 0.2 kg CO2 / km;

[0020] Fuel Combustion Emissions Calculation:

[0021] Carbon emissions = fuel consumption × fuel carbon content × carbon oxidation rate

[0022] If we substitute specific numerical values, assuming that the vehicle uses diesel to travel 100 kilometers, the carbon content of the diesel is 0.85, and the carbon oxidation rate is 0.99, then the calculation formula is:

[0023] Carbon emissions = 100 × 0.85 × 0.99

[0024] The above calculation formula can be used to obtain the specific carbon emissions;

[0025] Calculation of vehicle load:

[0026] The carbon emissions per kilometer of a single bus trip can be adjusted according to the actual number of passengers carried to reflect the actual transportation efficiency;

[0027] The influence of driving speed:

[0028] Driving at high speeds will increase the vehicle's aerodynamic drag and tire rolling resistance, leading to higher fuel consumption and carbon emissions. The impact assessment formula is based on the cubic method of speed, which is:

[0029] Carbon emissions = baseline carbon emissions × (actual speed / baseline speed) ^3

[0030] In the formula, the benchmark carbon emissions are the carbon emissions measured at a specific speed, and the benchmark speed is the corresponding reference speed. When the vehicle increases its speed, the increase in aerodynamic drag and tire rolling resistance leads to an increase in fuel consumption and carbon emissions. Since aerodynamic drag is proportional to the square of speed and tire rolling resistance is proportional to speed, using the cube of speed as an approximation can better reflect the impact of speed on carbon emissions.

[0031] Step 100.3: For different types of vehicles and different transportation situations, standardization is performed to facilitate comparison and aggregation of data. The standardization is based on classification based on vehicle type, driving distance and load situation factors, and the corresponding average carbon emission factor is calculated. The average carbon emission per kilometer of a small passenger car can be calculated;

[0032] Step 100.4: Update the assessment factors regularly. As the project progresses and time passes, the vehicles and fuel efficiency involved in the project may change. Therefore, it is necessary to reassess the carbon emission factors based on the changes in the vehicles and fuel efficiency. Based on the carbon emission factors obtained from the assessment, the carbon emission factors can be regularly updated as the project progresses.

[0033] In some specific implementations, based on the processed data, LCA evaluation modeling is performed, input and output analysis modeling is performed simultaneously, and the two models are fused to achieve data fusion, model fusion and result fusion, and interpretation and evaluation are performed based on the fused model, specifically including:

[0034] Step 200.1: Data fusion: integrating data from different sources and in different formats into a unified data set to ensure data consistency and integrity;

[0035] Step 200.2: LCA assessment modeling, using the life cycle assessment method to assess and model carbon emissions;

[0036] Step 200.2.1. Clarify the objectives and scope of the assessment, including defining the system boundaries, functional units, and number of functional units to be studied;

[0037] Step 200.2.2. Collect all processes, inputs and outputs related to the system boundary, including raw material acquisition, production, transportation, use and disposal;

[0038] Step 200.2.3 conducts an environmental impact assessment for each process in the inventory. The specific assessment includes carbon emissions, energy consumption and carbon emissions of each process. For energy consumption, the energy balance formula can be used as follows:

[0039] E=∑ i (E in，i -E out，i )

[0040] Where E is energy consumption, E in，i is the energy input of the ith process, E out，i is the energy output;

[0041] Carbon emissions are calculated based on energy consumption and the corresponding carbon emission coefficient. The carbon emission coefficient is estimated based on the energy type and source, specifically:

[0042] C=∑ i (E i ×CF i )

[0043] In the formula, C is carbon emissions, E is i is the energy input of the ith process, CF i is the carbon emission factor.

[0044] Step 200.2.4, interpret and evaluate the model calculation results, including uncertainty analysis and credibility assessment of the results, and obtain the LCA model after the evaluation;

[0045] Step 200.3: Input-output analysis modeling can determine the carbon emission transfer and indirect emissions between industries;

[0046] Step 200.3.1, obtain the input and output table of the economic information of the construction highway project, which can be obtained from the project finance department. The input and output table can show the direct and indirect transactions between the various departments in the project;

[0047] Step 200.3.2, use the input-output table to build an input-output model, which can describe the economic situation in the project information;

[0048] Step 200.3.3, calculate the carbon emission coefficient matrix, based on the carbon emissions and economic output of each sector, calculate the carbon emission coefficient matrix, based on the carbon emission coefficient matrix and the industry output vector, calculate the direct and indirect carbon emissions of each industry;

[0049] Step 200.3.4: Calculate total carbon emissions, specifically:

[0050] C total =∑ i C i

[0051] In the formula, C total is the total carbon emissions, C i is the carbon emissions of the ith sector;

[0052] Step 200.4, using the weighted average method to integrate the LCA model with the input-output model;

[0053] Step 200.4.1: Calculate using the weighted average method. The results of the two models are weighted averaged. The weights may be determined based on the accuracy and reliability factors of the models. Specifically,

[0054] Fusion result = w1×LCA model result + w2×input and output model result

[0055] Where w1 is the weight of the LCA model results and the input-output model results, satisfying w1+w2=1.

[0056] In some specific implementations, carbon emission calculation is performed based on the fused model, and the collected data is combined with the corresponding carbon emission factors to calculate the carbon emission of each link, specifically:

[0057] Step 300.1: Calculate the carbon emissions of energy consumption, specifically:

[0058] Assuming that energy consumption E = 100 units and carbon emission factor CF = 0.5 units / energy unit, the carbon emissions are:

[0059] Carbon emissions = 100 × 0.5 = 50

[0060] In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 50;

[0061] Step 300.2: Calculation of carbon emissions from material use, specifically:

[0062] Assuming that the material usage is E = 200 units and the carbon emission factor CF = 0.2 / material unit, the carbon emissions are:

[0063] Carbon emissions = 200 × 0.4 = 40

[0064] In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 40;

[0065] Step 300.3: Calculate the total carbon emissions, specifically:

[0066] Assuming that the carbon emissions from energy consumption are 50 and the carbon emissions from material use are 40, the total carbon emissions are:

[0067] Total carbon emissions = 50 + 40 = 90

[0068] By substituting the specific values ​​calculated in S300.1 and S300.2, the specific value of total carbon emissions can be obtained as 90.

[0069] In some specific embodiments, key parameters in the model are considered, sensitivity analysis is performed, the degree of their influence on the evaluation results is evaluated, the model is optimized and improved, and the uncertainty of the model is reduced, specifically including:

[0070] Step 400.1, using variance analysis method as a benchmark to confirm key parameters in the fusion model;

[0071] Step 400.1.1: Assuming there are k parameters, each parameter has r levels, and there are n observations, the specific calculation is as follows:

[0072] Grand mean calculation:

[0073]

[0074] Where N = k × r × n is the number of total observations;

[0075] Calculation of the mean within the group:

[0076]

[0077] Where, X ijl is the lth observation at the jth level of the ith factor;

[0078] Calculation of mean between groups:

[0079]

[0080] Total sum of squares calculation:

[0081]

[0082] Based on the variance analysis method, the contribution of different parameters to the variance of the model output can be evaluated, and then which parameters are key parameters can be determined;

[0083] Step 400.2, using a multi-parameter sensitivity analysis method to confirm the parameter range and determine its possible value range, such as a to b;

[0084] Step 400.2.1: For parameter P i Sample N values, specifically P i1 , P i2, ..., P iN ;

[0085] Step 400.2.2: For each parameter combination, run the fusion model and obtain the corresponding result, y j1 ,y j2 , ..., y k1 ;

[0086] Step 400.2.3: Based on the obtained results, analyze the influence of each parameter on the model.

[0087] In some specific embodiments, based on the model after sensitivity analysis, the prediction effect is evaluated to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results, and the model is fine-tuned and corrected in time, specifically including:

[0088] Step 500.1, based on the results of multi-parameter sensitivity analysis, optimize and improve the model and adjust the parameter value range for the parameters with greater influence;

[0089] Step 500.2: Use the mean square error calculation method to evaluate the difference between the prediction result of the fusion model and the actual value, specifically:

[0090]

[0091] In the formula, the smaller the MSE value is, the smaller the gap between the model prediction result and the actual value is, and the higher the accuracy of the model prediction is;

[0092] Step 500.3: Verify the accuracy and reliability of the model, compare the model prediction results with the actual observations, evaluate the reliability of the model, and fine-tune and correct the model based on the evaluation and verification results to improve the accuracy and reliability of the model.

[0093] This application also provides a system for estimating highway carbon emissions during construction, including:

[0094] A pre-processing unit is used to collect special data based on construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data;

[0095] The fusion modeling unit is used to perform LCA assessment modeling based on the processed data, simultaneously perform input and output analysis modeling, and fuse the two models to achieve data fusion, model fusion and result fusion, and interpret and evaluate based on the fused model;

[0096] The calculation unit is used to calculate carbon emissions based on the fused model, combining the collected data with the corresponding carbon emission factors to calculate the carbon emissions of each link;

[0097] The analysis unit is used to consider the key parameters in the model, conduct sensitivity analysis, evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model;

[0098] The evaluation unit is used to evaluate the prediction effect based on the improved model, verify its actual prediction ability, check its reliability and accuracy based on its prediction results, and make fine-tuning and corrections to the model in a timely manner;

[0099] The prediction unit is used to predict carbon emissions for the newly collected highway-specific sample data based on the fine-tuned and revised fusion model to obtain the final prediction result.

[0100] In some specific implementations, the prediction effect is evaluated based on the improved model to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results, and the model is fine-tuned and corrected in a timely manner, specifically:

[0101] Based on the results of multi-parameter sensitivity analysis, the model is optimized and improved and the parameter value range is adjusted for the parameters with greater influence;

[0102] The mean square error calculation method is used to evaluate the gap between the prediction results of the fusion model and the actual values, specifically:

[0103]

[0104] In the formula, the smaller the MSE value is, the smaller the gap between the model prediction result and the actual value is, and the higher the accuracy of the model prediction is;

[0105] Verify the accuracy and reliability of the model, compare the model prediction results with the actual observations, evaluate the reliability of the model, and fine-tune and correct the model based on the evaluation and verification results to improve the accuracy and reliability of the model.

[0106] The present application further provides an electronic device, comprising: one or more processors; a storage system for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0107] The method for estimating carbon emissions of highways during the construction period provided in the present application collects special data based on construction project information, establishes corresponding carbon emission factors based on the collected data, and standardizes the collected data. Based on the processed data, LCA assessment modeling is performed, input-output analysis modeling is performed simultaneously, and the two models are integrated to achieve data fusion, model fusion and result fusion, and interpretation and evaluation are performed based on the integrated model. Based on the integrated model, carbon emissions are calculated, the collected data are combined with the corresponding carbon emission factors, and the carbon emissions of each link are calculated. The key parameters in the model are considered, and sensitivity analysis is performed to evaluate their influence on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model. Based on the improved model, a prediction effect evaluation is performed to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results, and the model is fine-tuned and corrected in time. Based on the fine-tuned and corrected fusion model, carbon emissions are predicted for the newly collected highway special sample data to obtain the final prediction result. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0109] Figure 1 A flow chart of a method for estimating carbon emissions of highways during construction provided in an embodiment of the present application. DETAILED DESCRIPTION

[0110] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0111] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0112] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0113] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0114] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0115] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0116] Please refer to Figure 1 , which shows the process of an embodiment of the method for estimating carbon emissions of highways during construction period according to the present disclosure. The method for estimating carbon emissions of highways during construction period can be applied to the transmission system of an automobile, a hydraulic pouring system, an industrial robot, a material transportation system, etc. Correspondingly, the corresponding method can also be called a transmission system working condition intelligent monitoring method, a hydraulic pouring system working condition intelligent monitoring method, an industrial robot working condition intelligent monitoring method, and a material transportation system working condition intelligent monitoring method, but it is not limited thereto.

[0117] like Figure 1 As shown, the method for estimating carbon emissions of highways during the construction period includes the following steps:

[0118] Step 100: collect special data based on the construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data;

[0119] Here, specifically include:

[0120] Step 100.1, collecting data related to the highway project, specifically including vehicle type information, fuel type information, travel distance information and vehicle load information;

[0121] Step 100.2: Preliminarily calculate the specific carbon emissions of each information and establish the carbon emission factor, specifically:

[0122] Vehicle type information:

[0123] The carbon emission factor for small passenger cars is 0.15 kg CO2 / km;

[0124] The carbon emission factor for large trucks is 0.3 kg CO2 / km;

[0125] The carbon emission factor for buses is 0.2 kg CO2 / km;

[0126] Fuel Combustion Emissions Calculation:

[0127] Carbon emissions = fuel consumption × fuel carbon content × carbon oxidation rate

[0128] If we substitute specific numerical values, assuming that the vehicle uses diesel to travel 100 kilometers, the carbon content of the diesel is 0.85, and the carbon oxidation rate is 0.99, then the calculation formula is:

[0129] Carbon emissions = 100 × 0.85 × 0.99

[0130] The above calculation formula can be used to obtain the specific carbon emissions;

[0131] Calculation of vehicle load:

[0132] The carbon emissions per kilometer of a single bus trip can be adjusted according to the actual number of passengers carried to reflect the actual transportation efficiency;

[0133] The influence of driving speed:

[0134] Driving at high speeds will increase the vehicle's aerodynamic drag and tire rolling resistance, leading to higher fuel consumption and carbon emissions. The impact assessment formula is based on the cubic method of speed, which is:

[0135] Carbon emissions = baseline carbon emissions × (actual speed / baseline speed) ^3

[0136] In the formula, the benchmark carbon emissions are the carbon emissions measured at a specific speed, and the benchmark speed is the corresponding reference speed. When the vehicle increases its speed, the increase in aerodynamic drag and tire rolling resistance leads to an increase in fuel consumption and carbon emissions. Since aerodynamic drag is proportional to the square of speed and tire rolling resistance is proportional to speed, using the cube of speed as an approximation can better reflect the impact of speed on carbon emissions.

[0137] Step 100.3: For different types of vehicles and different transport situations, standardization is performed to facilitate comparison and aggregation of data. Standardization is based on classification based on vehicle type, travel distance and load situation factors, and the corresponding average carbon emission factor is calculated. The average carbon emission per kilometer of a small passenger car can be calculated;

[0138] Step 100.4: Update the assessment factors regularly. As the project progresses and time passes, the vehicles and fuel efficiency involved in the project may change. Therefore, it is necessary to reassess the carbon emission factors based on the changes in the vehicles and fuel efficiency. Based on the carbon emission factors obtained from the assessment, the carbon emission factors can be regularly updated as the project progresses.

[0139] Step 200: Based on the processed data, LCA assessment modeling is performed, input and output analysis modeling is performed simultaneously, and the two models are fused to achieve data fusion, model fusion and result fusion, and interpretation and evaluation are performed based on the fused model;

[0140] Here, specifically include:

[0141] Step 200.1: Data fusion: integrating data from different sources and in different formats into a unified data set to ensure data consistency and integrity;

[0142] Step 200.2: LCA assessment modeling, using the life cycle assessment method to assess and model carbon emissions;

[0143] Step 200.2.1. Clarify the objectives and scope of the assessment, including defining the system boundaries, functional units, and number of functional units to be studied;

[0144] Step 200.2.2. Collect all processes, inputs and outputs related to the system boundary, including raw material acquisition, production, transportation, use and disposal;

[0145] Step 200.2.3 conducts an environmental impact assessment for each process in the inventory. The specific assessment includes carbon emissions, energy consumption and carbon emissions of each process. For energy consumption, the energy balance formula can be used as follows:

[0146] E=∑ i (E in，i -E out，i )

[0147] Where E is energy consumption, E in，i is the energy input of the ith process, E out，i is the energy output;

[0148] Carbon emissions are calculated based on energy consumption and the corresponding carbon emission coefficient. The carbon emission coefficient is estimated based on the energy type and source, specifically:

[0149] C=∑ i (E i ×CF i )

[0150] In the formula, C is carbon emissions, E is iis the energy input of the ith process, CF i is the carbon emission factor.

[0151] Step 200.2.4, interpret and evaluate the model calculation results, including uncertainty analysis and credibility assessment of the results, and obtain the LCA model after the evaluation;

[0152] Step 200.3: Input-output analysis modeling can determine the carbon emission transfer and indirect emissions between industries;

[0153] Step 200.3.1, obtain the input and output table of the economic information of the construction highway project, which can be obtained from the project finance department. The input and output table can show the direct and indirect transactions between the various departments in the project;

[0154] Step 200.3.2, use the input-output table to build an input-output model, which can describe the economic situation in the project information;

[0155] Step 200.3.3, calculate the carbon emission coefficient matrix, based on the carbon emissions and economic output of each sector, calculate the carbon emission coefficient matrix, based on the carbon emission coefficient matrix and the industry output vector, calculate the direct and indirect carbon emissions of each industry;

[0156] Step 200.3.4: Calculate total carbon emissions, specifically:

[0157] C total =∑ i C i

[0158] In the formula, C total is the total carbon emissions, C i is the carbon emissions of the ith sector;

[0159] Step 200.4, using the weighted average method to integrate the LCA model with the input-output model;

[0160] Step 200.4.1: Calculate using the weighted average method. The results of the two models are weighted averaged. The weights may be determined based on the accuracy and reliability factors of the models. Specifically,

[0161] Fusion result = w1×LCA model result + w2×input and output model result

[0162] Where w1 is the weight of the LCA model results and the input-output model results, satisfying w1+w2=1.

[0163] Step 300: Based on the fused model, carbon emission calculation is performed, and the collected data is combined with the corresponding carbon emission factors to calculate the carbon emission of each link;

[0164] Here, specifically include:

[0165] Assuming that energy consumption E = 100 units and carbon emission factor CF = 0.5 units / energy unit, the carbon emissions are:

[0166] Carbon emissions = 100 × 0.5 = 50

[0167] In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 50;

[0168] Step 300.2: Calculation of carbon emissions from material use, specifically:

[0169] Assuming that the material usage is E = 200 units and the carbon emission factor CF = 0.2 / material unit, the carbon emissions are:

[0170] Carbon emissions = 200 × 0.4 = 40

[0171] In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 40;

[0172] Step 300.3: Calculate the total carbon emissions, specifically:

[0173] Assuming that the carbon emissions from energy consumption are 50 and the carbon emissions from material use are 40, the total carbon emissions are:

[0174] Total carbon emissions = 50 + 40 = 90

[0175] By substituting the specific values ​​calculated in S300.1 and S300.2, the specific value of total carbon emissions can be obtained as 90.

[0176] Step 400: Consider the key parameters in the model, conduct sensitivity analysis, evaluate their influence on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model;

[0177] Here, specifically include:

[0178] Step 400.1, using variance analysis method as a benchmark to confirm key parameters in the fusion model;

[0179] Step 400.1.1: Assuming there are k parameters, each parameter has r levels, and there are n observations, the specific calculation is as follows:

[0180] Grand mean calculation:

[0181]

[0182] Where N = k × r × n is the number of total observations;

[0183] Calculation of the mean within the group:

[0184]

[0185] Where, X ijl is the lth observation at the jth level of the ith factor;

[0186] Calculation of mean between groups:

[0187]

[0188] Total sum of squares calculation:

[0189]

[0190] Based on the variance analysis method, the contribution of different parameters to the variance of the model output can be evaluated, and then which parameters are key parameters can be determined;

[0191] Step 400.2, using a multi-parameter sensitivity analysis method to confirm the parameter range and determine its possible value range, such as a to b;

[0192] Step 400.2.1: For parameter P i Sample N values, specifically P i1 , P i2 , ..., P iN ;

[0193] Step 400.2.2: For each parameter combination, run the fusion model and obtain the corresponding result, y j1 ,y j2 , ..., y k1 ;

[0194] Step 400.2.3: Based on the obtained results, analyze the influence of each parameter on the model.

[0195] Step 500: Based on the improved model, the prediction effect is evaluated to verify its actual prediction ability, and based on its prediction results, its reliability and accuracy are checked, and the model is fine-tuned and corrected in a timely manner;

[0196] Here, specifically include:

[0197] Step 500.1, based on the results of multi-parameter sensitivity analysis, optimize and improve the model and adjust the parameter value range for the parameters with greater influence;

[0198] Step 500.2: Use the mean square error calculation method to evaluate the difference between the prediction result of the fusion model and the actual value, specifically:

[0199]

[0200] In the formula, the smaller the MSE value is, the smaller the gap between the model prediction result and the actual value is, and the higher the accuracy of the model prediction is;

[0201] Step 500.3: Verify the accuracy and reliability of the model, compare the model prediction results with the actual observations, evaluate the reliability of the model, and fine-tune and correct the model based on the evaluation and verification results to improve the accuracy and reliability of the model.

[0202] Step 600: Based on the fine-tuned and corrected fusion model, the newly collected highway-specific sample data is subjected to carbon emission prediction to obtain a final prediction result.

[0203] The method for estimating carbon emissions of highways during the construction period collects special data based on construction project information, establishes corresponding carbon emission factors based on the collected data, and standardizes the collected data. Based on the processed data, LCA assessment modeling is performed, input-output analysis modeling is performed simultaneously, and the two models are integrated to achieve data fusion, model fusion and result fusion, and the integrated model is interpreted and evaluated. Based on the integrated model, carbon emissions are calculated, the collected data are combined with the corresponding carbon emission factors, and the carbon emissions of each link are calculated. The key parameters in the model are considered, and sensitivity analysis is performed to evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model. Based on the improved model, a prediction effect evaluation is performed to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results. The model is fine-tuned and corrected in time, and carbon emissions are predicted for the newly collected highway special sample data based on the fine-tuned and corrected fusion model to obtain the final prediction result.

[0204] This application also provides a highway carbon emissions estimation system during the construction period, including:

[0205] A pre-processing unit is used to collect special data based on construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data;

[0206] The fusion modeling unit is used to perform LCA assessment modeling based on the processed data, simultaneously perform input and output analysis modeling, and fuse the two models to achieve data fusion, model fusion and result fusion, and interpret and evaluate based on the fused model;

[0207] The calculation unit is used to calculate carbon emissions based on the fused model, combining the collected data with the corresponding carbon emission factors to calculate the carbon emissions of each link;

[0208] The analysis unit is used to consider the key parameters in the model, conduct sensitivity analysis, evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model;

[0209] The evaluation unit is used to evaluate the prediction effect based on the improved model, verify its actual prediction ability, check its reliability and accuracy based on its prediction results, and make fine-tuning and corrections to the model in a timely manner;

[0210] The prediction unit is used to predict carbon emissions for the newly collected highway-specific sample data based on the fine-tuned and revised fusion model to obtain the final prediction result.

[0211] The present application further provides an electronic device, which may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc.

[0212] Electronic devices may include processing systems (e.g., central processing units, graphics processing units, etc.), which may perform various appropriate actions and processes according to programs stored in read-only memory or programs loaded from storage systems into random access memory. Various programs and data required for electronic device operation are also stored in the memory. The processing system, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0213] Typically, the following systems can be connected to the I / O interface: input systems including, for example, touch screens, touch pads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output systems including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage systems including, for example, magnetic tapes, hard disks, etc.; and communication systems. The communication system can allow an electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0214] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0215] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0216] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for estimating carbon emissions of highways during construction period, characterized in that: The steps include: S100. Collect special data based on construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data; S200, based on the processed data, perform LCA assessment modeling, simultaneously perform input and output analysis modeling, and fuse the two models to achieve data fusion, model fusion and result fusion, and perform interpretation and evaluation based on the fused model; S300, based on the fusion model, perform carbon emission calculations, combine the collected data with the corresponding carbon emission factors, and calculate the carbon emissions of each link; S400, consider the key parameters in the model, conduct sensitivity analysis, evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model; S500. Based on the improved model, conduct forecasting effect evaluation to verify its actual forecasting ability, check its reliability and accuracy based on its forecasting results, and make fine-tuning and corrections to the model in a timely manner; S600, based on the fine-tuned and revised fusion model, the carbon emissions of the newly collected highway special sample data are predicted to obtain the final prediction results.

2. The method for estimating carbon emissions of highways during construction period as claimed in claim 1, characterized in that: Collect special data based on construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data, including: S100.

1. Collect data related to the highway project, including vehicle type information, fuel type information, travel distance information and vehicle load information; S100.

2. Preliminary calculation of the specific carbon emissions of each information and establishment of carbon emission factors, specifically: Vehicle type information: The carbon emission factor for small passenger cars is 0.15 kg CO2 / km; The carbon emission factor for large trucks is 0.3 kg CO2 / km; The carbon emission factor for buses is 0.2 kg CO2 / km; Fuel Combustion Emissions Calculation: Carbon emissions = fuel consumption × fuel carbon content × carbon oxidation rate If we substitute specific numerical values, assuming that the vehicle uses diesel to travel 100 kilometers, the carbon content of the diesel is 0.85, and the carbon oxidation rate is 0.99, then the calculation formula is: Carbon emissions = 100 × 0.85 × 0.99 The above calculation formula can be used to obtain the specific carbon emissions; Calculation of vehicle load: The carbon emissions per kilometer of a single bus trip can be adjusted according to the actual number of passengers carried to reflect the actual transportation efficiency; The influence of driving speed: Driving at high speeds will increase the vehicle's aerodynamic drag and tire rolling resistance, leading to higher fuel consumption and carbon emissions. The impact assessment formula is based on the cubic method of speed, which is: Carbon emissions = baseline carbon emissions × (actual speed / baseline speed) ^3 In the formula, the benchmark carbon emissions are the carbon emissions measured at a specific speed, and the benchmark speed is the corresponding reference speed. When the vehicle increases its speed, the increase in aerodynamic drag and tire rolling resistance leads to an increase in fuel consumption and carbon emissions. Since aerodynamic drag is proportional to the square of speed and tire rolling resistance is proportional to speed, using the cube of speed as an approximation can better reflect the impact of speed on carbon emissions. S100.

3. For different types of vehicles and different transport situations, standardization is performed to facilitate comparison and aggregation of data. The standardization is based on classification based on vehicle type, travel distance and load situation factors, and the corresponding average carbon emission factor is calculated. The average carbon emission per kilometer of a small passenger car can be calculated; S100.

4. Update assessment factors regularly. As the project progresses and time passes, the vehicles and fuel efficiency involved in the project may change. Therefore, it is necessary to reassess the carbon emission factors based on the changes in the vehicles and fuel efficiency. Based on the carbon emission factors obtained from the assessment, the carbon emission factors can be regularly updated as the project progresses.

3. The method for estimating carbon emissions of highways during construction period as claimed in claim 2, characterized in that: Based on the processed data, LCA assessment modeling is carried out, input and output analysis modeling is carried out simultaneously, and the two models are integrated to achieve data fusion, model fusion and result fusion, and interpretation and evaluation are carried out based on the integrated model, including: S200.1, data fusion, integrating data from different sources and in different formats into a unified data set to ensure data consistency and integrity; S200.2, LCA assessment modeling, using the life cycle assessment method to assess and model carbon emissions; S200.2.

1. Clarify the objectives and scope of the assessment, including defining the system boundaries, functional units, and number of functional units to be studied; S200.2.

2. Collect all processes, inputs and outputs related to the system boundary, including raw material acquisition, production, transportation, use and disposal; S200.2.3 Conduct environmental impact assessments on each process in the inventory. The specific assessment includes carbon emissions, energy consumption and carbon emissions of each process. For energy consumption, the energy balance formula can be used as follows: And=∑ i (AND in，i -AND out，i ) Where E is energy consumption, E in，i is the energy input of the ith process, E out，i is the energy output; Carbon emissions are calculated based on energy consumption and the corresponding carbon emission coefficient. The carbon emission coefficient is estimated based on the energy type and source, specifically: C=∑ i (AND i ×CF i ) In the formula, C is carbon emissions, E is i is the energy input of the ith process, CF i is the carbon emission factor. S200.2.

4. Explain and evaluate the model results, including uncertainty analysis and credibility assessment of the results, and derive the LCA model after the evaluation; S200.3, input-output analysis modeling, can determine the carbon emissions transfer and indirect emissions between industries; S200.3.

1. Obtain the input and output tables of economic information for the highway construction project. This table can be obtained from the project finance department. The input and output tables can show the direct and indirect transactions between the various departments in the project; S200.3.

2. Use the input-output table to construct an input-output model that can describe the economic conditions in the project information; S200.3.

3. Calculate the carbon emission coefficient matrix based on the carbon emissions and economic output of each sector. Calculate the direct and indirect carbon emissions of each industry based on the carbon emission coefficient matrix and the industry output vector. S200.3.

4. Calculation of total carbon emissions, specifically: C total =∑ i C i In the formula, C total is the total carbon emissions, C i is the carbon emissions of the ith sector; S200.

4. Use the weighted average method to integrate the LCA model with the input-output model; S200.4.

1. Weighted average calculation: The results of the two models are weighted average. The weights may be determined based on the accuracy and reliability factors of the models, specifically: Fusion result = w1×LCA model result + w2×input and output model result Where w1 is the weight of the LCA model results and the input-output model results, satisfying w1+w2=1.

4. The method for estimating carbon emissions of highways during construction period as claimed in claim 3, characterized in that: Based on the fusion model, carbon emissions are calculated, and the collected data is combined with the corresponding carbon emission factors to calculate the carbon emissions of each link. Specifically: S300.

1. Calculation of carbon emissions from energy consumption, specifically: Assuming that energy consumption E = 100 units and carbon emission factor CF = 0.5 units / energy unit, the carbon emissions are: Carbon emissions = 100 × 0.5 = 50 In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 50; S300.

2. Calculation of carbon emissions from material use, specifically: Assuming that the material usage is E = 200 units and the carbon emission factor CF = 0.2 / material unit, the carbon emissions are: Carbon emissions = 200 × 0.4 = 40 In the formula, by substituting specific numerical values, it can be concluded that the carbon emissions are 40; S300.

3. Calculation of total carbon emissions, specifically: Assuming that the carbon emissions from energy consumption are 50 and the carbon emissions from material use are 40, the total carbon emissions are: Total carbon emissions = 50 + 40 = 90 By substituting the specific values ​​calculated in S300.1 and S300.2, the specific value of total carbon emissions can be obtained as 90.

5. The method for estimating carbon emissions of highways during construction period as claimed in claim 4, characterized in that: Consider the key parameters in the model, conduct sensitivity analysis, evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model, including: S400.

1. Use variance analysis as a benchmark to confirm the key parameters in the fusion model; S400.1.

1. Assuming there are k parameters, each parameter has r levels, and there are n observations, the specific calculation is as follows: Grand mean calculation: Where N = k × r × n is the number of total observations; Calculation of the mean within the group: Where, X ijl is the lth observation at the jth level of the ith factor; Calculation of mean between groups: Total sum of squares calculation: Based on the variance analysis method, the contribution of different parameters to the variance of the model output can be evaluated, and then which parameters are key parameters can be determined; S400.

2. Use multi-parameter sensitivity analysis methods to confirm the parameter range and determine its possible value range, such as a to b; S400.2.

1. For parameter P i Sample N values, specifically P i1 , P i2 , ..., P iN ; S400.2.

2. For each parameter combination, run the fusion model and obtain the corresponding results, y j1 ,y j2 , ..., y k1 ; S400.2.

3. Based on the results obtained, analyze the influence of each parameter on the model.

6. The method for estimating carbon emissions of highways during construction period as claimed in claim 5, characterized in that: Based on the model after sensitivity analysis, the prediction effect is evaluated to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results, and the model is fine-tuned and corrected in a timely manner, including: S500.

1. Based on the results of multi-parameter sensitivity analysis, optimize and improve the model and adjust the parameter value range for the parameters with greater influence; S500.

2. The mean square error calculation method is used to evaluate the gap between the prediction results of the fusion model and the actual values. Specifically: In the formula, the smaller the MSE value is, the smaller the gap between the model prediction result and the actual value is, and the higher the accuracy of the model prediction is; S500.

3. Verify the accuracy and reliability of the model, compare the model prediction results with the actual observations, evaluate the reliability of the model, and fine-tune and correct the model based on the evaluation and verification results to improve the accuracy and reliability of the model.

7. The highway carbon emission estimation system during construction period is characterized by: include: A pre-processing unit is used to collect special data based on construction project information, establish corresponding carbon emission factors based on the collected data, and standardize the collected data; The fusion modeling unit is used to perform LCA assessment modeling based on the processed data, simultaneously perform input and output analysis modeling, and fuse the two models to achieve data fusion, model fusion and result fusion, and interpret and evaluate based on the fused model; The calculation unit is used to calculate carbon emissions based on the fused model, combining the collected data with the corresponding carbon emission factors to calculate the carbon emissions of each link; The analysis unit is used to consider the key parameters in the model, conduct sensitivity analysis, evaluate their impact on the evaluation results, optimize and improve the model, and reduce the uncertainty of the model; The evaluation unit is used to evaluate the prediction effect based on the improved model, verify its actual prediction ability, check its reliability and accuracy based on its prediction results, and make fine-tuning and corrections to the model in a timely manner; The prediction unit is used to predict carbon emissions for the newly collected highway-specific sample data based on the fine-tuned and revised fusion model to obtain the final prediction result.

8. The system for estimating carbon emissions of highways during construction period as claimed in claim 7, characterized in that: Based on the improved model, the prediction effect is evaluated to verify its actual prediction ability, and its reliability and accuracy are checked based on its prediction results, and the model is fine-tuned and corrected in a timely manner. Specifically: Based on the results of multi-parameter sensitivity analysis, the model is optimized and improved and the parameter value range is adjusted for the parameters with greater influence; The mean square error calculation method is used to evaluate the gap between the prediction results of the fusion model and the actual values, specifically: In the formula, the smaller the MSE value is, the smaller the gap between the model prediction result and the actual value is, and the higher the accuracy of the model prediction is; Verify the accuracy and reliability of the model, compare the model prediction results with the actual observations, evaluate the reliability of the model, and fine-tune and correct the model based on the evaluation and verification results to improve the accuracy and reliability of the model.

9. An electronic device, characterized in that include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

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