Energy-saving and carbon-reducing multi-objective optimization method for road transportation industry based on analytic hierarchy process

Abstract

The invention relates to the technical field of target optimization, and discloses an energy-saving and carbon-reducing multi-target optimization method for the road transportation industry based on an analytic hierarchy process, which comprises the following steps of: constructing a preliminary judgment matrix, calculating an initial consistency ratio, and when the initial consistency ratio is greater than or equal to 0.1, considering that the consistency of the judgment matrix is relatively poor; the method not only depends on the initial consistency ratio, but also comprehensively considers indexes such as the characteristic value, the characteristic mean value, the variance and the overall consistency deviation, and recalculates and improves the consistency ratio. If the improved consistency ratio is less than 0.1, taking the iteration weight vector as a final weight vector; when the improved consistency ratio is greater than or equal to 0.1, the expert assignment error is very large, and the final weight vector is corrected by using the ratio, so that the rationality and accuracy of weight allocation are improved; based on the finally obtained final weight vector, a multi-objective optimization function is constructed, and systematic energy-saving and carbon-reducing path optimization is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of target optimization, and in particular to a multi-target optimization method for energy conservation and carbon reduction in the highway transportation industry based on a hierarchical analysis method. Background Art

[0002] Transportation carbon emissions are an open, nonlinear, and complex system that is bound to be affected by a combination of factors. As the economy develops, it attracts a large number of workers and enterprises, and is accompanied by an increase in the number of cars, travel demand, and transportation demand, which leads to a significant increase in carbon emissions. In order to achieve energy conservation and carbon reduction, existing technologies have conducted a lot of research on transportation carbon emission reduction paths, and identified a variety of optimized paths and scenarios with significant emission reduction potential. These paths directly reduce transportation carbon emissions while also At the same time, it also has an indirect spillover effect on the overall carbon emission reduction in the region. There are many emission reduction paths related to transportation infrastructure, mainly including the optimization of transportation structure and technology. Existing technologies have also proposed the overall idea of ​​carbon peaking and carbon neutrality in the transportation field based on the study of future carbon emission reduction scenarios, based on the overall idea of ​​classified facilities, tiered peaking, easy first and difficult later, and incremental first and then stock. Based on the total carbon emissions in the transportation field in the future, four types of emission reduction measures are proposed, namely: optimizing the transportation structure, improving energy efficiency, new energy and clean energy substitution, and traffic demand management. The emission reduction benefits brought about by the management, after observing the emission reduction measures and future carbon emissions, the emission reduction measures that play a major role in the transportation field at different stages of carbon emissions are different: In the steady growth stage: Improving energy efficiency is the main measure to achieve carbon emission reduction. Large-scale substitution of new energy still requires time to actively promote the simultaneous efforts of upstream and downstream industries.

[0003] During the peak plateau period: it is necessary to rely on large-scale new energy substitution and extremely effective energy efficiency improvement measures to jointly achieve the carbon peak in the transportation industry; during the decline period: it mainly relies on new energy and renewable energy substitution to achieve a steady decline in carbon emissions in the transportation sector.

[0004] During the rapid decline period: mainly rely on the large-scale and stable use of new energy and renewable energy, as well as the vigorous promotion of transportation and energy integration, to minimize carbon emissions from transportation. Optimizing the transportation structure will play a stable role in carbon emission reduction in the development of the transportation industry, but the emission reduction potential is limited. Demand management can only play a small role in carbon emission reduction, and it is still necessary to consider the huge changes in future transportation modes and the new forms of social and economic development that may bring about a sharp drop in demand.

[0005] Therefore, it is necessary to match different energy-saving and emission-reduction measures and corresponding emission-reduction paths for different time periods. The existing method combines the analytic hierarchy process with target optimization to construct an analytic hierarchy process framework, assign weights to each indicator through expert scoring, and then construct an optimization function. The optimal solution is obtained with the help of the optimization algorithm to obtain a combination of energy-saving and emission-reduction measures and emission-reduction measures. Due to the different experiences and backgrounds of experts, they may assign different weights to the same indicator, resulting in poor consistency of the judgment matrix, which affects the final weight distribution and optimization results, and produces errors in the optimization results. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a multi-objective optimization method for energy saving and carbon reduction in the highway transportation industry based on the analytic hierarchy process, comprising: Step 1, construct a hierarchical model, which includes: target layer, criterion layer and solution layer, and each of the target layer, criterion layer and solution layer contains several indicators; Step 2: construct a preliminary judgment matrix based on the indicators of the target layer and / or the criterion layer and / or the solution layer; obtain a preliminary weight vector through the preliminary judgment matrix; and calculate the maximum eigenvalue of the preliminary judgment matrix; Step 3, calculate the initial consistency index value based on the maximum eigenvalue, obtain the random consistency index value, and calculate the initial consistency ratio according to the initial consistency index value and the random consistency index value; when the initial consistency ratio is less than 0.1, execute step 6; when the initial consistency ratio is greater than or equal to 0.1, execute step 4; Step 4, obtaining the eigenvalue, eigenmean, variance, initial consistency index value and overall consistency deviation value of each element in the preliminary judgment matrix, and calculating the improved consistency ratio according to the initial consistency ratio, initial consistency index value, random consistency index value, eigenvalue, eigenmean, variance and overall consistency deviation value; Step 5: When the improved consistency ratio is less than 0.1, execute step 6; if the improved consistency ratio is greater than or equal to 0.1, return to execute step 2; Step 6, using the power iteration method to update the preliminary weight vector to obtain an iterative weight vector; when the initial consistency ratio in step 3 is less than 0.1, or the improved consistency ratio in step 5 is less than 0.1, the iterative weight vector is used as the final weight vector; when the improved consistency ratio in step 5 is greater than or equal to 0.1, the correction term is calculated based on the improved consistency ratio; the final weight vector is obtained based on the iterative weight vector and the correction term; Step 7: construct a multi-objective optimization function based on the final weight vector, use a multi-objective optimization algorithm to solve the multi-objective optimization function, and obtain the optimal combination of development paths and emission reduction measures.

[0007] Furthermore, the indicators of the target layer are: carbon neutrality; the indicators of the criterion layer are: environmental impact, economic feasibility, social acceptance and support; the indicators of the plan layer are: energy efficiency improvement plan, clean energy substitution plan, transportation structure adjustment plan, demand management plan and green transportation infrastructure construction plan.

[0008] Furthermore, the calculation process of the maximum eigenvalue is: Step 21, multiplying the preliminary judgment matrix and the preliminary weight vector to obtain a new vector; Step 22, calculate the ratio of each element of the new vector to each element of the preliminary weight vector, take the average of all ratios, and obtain the maximum eigenvalue.

[0009] Further, obtaining the overall consistency deviation value includes: obtaining two elements in opposite rows and columns in the preliminary judgment matrix, and defining a judgment standard: , Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and the ith column; calculates the consistency deviation value of the current two elements based on the judgment standard, and obtains the overall consistency deviation value according to the consistency deviation value.

[0010] Furthermore, the calculation formula of the improved consistency ratio is: ; In the formula, represents the improved consistency ratio, represents the initial consistency index value, CR represents the initial consistency ratio, represents the variance, represents the eigenvalue of the kth element, represents the feature average, Represents the maximum value operation, represents the overall consistency deviation value, RI represents the random consistency index value, , , , Represent adjustment factors respectively.

[0011] Further, for: ; In the formula, m represents the dimension of the preliminary judgment matrix, Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and ith column.

[0012] Furthermore, the calculation formula of the final weight vector is: ; In the formula, represents the final weight vector, represents the iteration weight vector for iteration n+1 times, represents the correction function, Represents a correction item.

[0013] Further, for: .

[0014] Furthermore, the optimization objectives of the multi-objective optimization function are: minimizing carbon emissions, minimizing the cost of meeting carbon emission standards, and minimizing the difficulty of implementation.

[0015] Furthermore, the multi-objective optimization function is: ; In the formula, represents the multi-objective optimization function, represents the function of minimizing carbon emissions, represents the cost function of minimizing carbon emission compliance, represents the minimization implementation difficulty function, represents the first element of the final weight vector, represents the second element of the final weight vector, Represents the third element of the final weight vector.

[0016] The embodiments of the present invention have the following technical effects: The present invention constructs a preliminary judgment matrix by introducing an expert scoring method, and calculates the initial consistency ratio to ensure that the judgment matrix has strong consistency. When the initial consistency ratio is less than 0.1, it indicates that the expert scoring is relatively accurate and the judgment matrix is ​​consistent, and the iterative weight vector is used as the final weight vector; when the initial consistency ratio is greater than or equal to 0.1, it is considered that there is an error in the expert scoring, and the consistency of the judgment matrix is ​​poor. In order to further improve the accuracy and consistency of the judgment matrix, the present invention not only relies on the initial consistency ratio, but also comprehensively considers indicators such as eigenvalues, eigenmeans, variances, and overall consistency deviations, and recalculates the improved consistency ratio. If the improved consistency ratio is less than 0.1, the iterative weight vector is used as the final weight vector; when the improved consistency ratio is greater than or equal to 0.1, it indicates that the expert scoring error is very large, and the ratio is used to correct the final weight vector, thereby improving the rationality and accuracy of the weight distribution; based on the final weight vector obtained, the present invention constructs a multi-objective optimization function, combining multiple goals such as minimizing carbon emissions, minimizing carbon emission compliance costs, and minimizing implementation difficulties, to achieve systematic energy-saving and carbon reduction path optimization. This method not only effectively improves the scientificity and rationality of weight allocation, but also can more comprehensively evaluate the effects of different emission reduction measures, ensure the transparency and fairness of the decision-making process, and ultimately provide strong support for the carbon neutrality goals of the transportation industry. Through this systematic and multi-level analysis method, the present invention can better cope with complex and changing real-world situations, ensure the feasibility and effectiveness of the optimization scheme in practical applications, and significantly improve the overall effect and adaptability of energy-saving and emission reduction measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a multi-objective optimization method for energy conservation and carbon reduction in the highway transportation industry based on the analytic hierarchy process provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0020] Figure 1 Flow chart of a multi-objective optimization method for energy conservation and carbon reduction in the highway transportation industry based on the analytic hierarchy process provided by an embodiment of the present invention. Figure 1 , including: Step 1: construct a hierarchical model, which includes: a target layer, a criterion layer, and a solution layer, each of which contains several indicators. These indicators will help this embodiment to systematically evaluate and select the optimal combination of energy-saving and emission-reduction measures and development paths.

[0021] Furthermore, the target layer indicator is: carbon neutrality. The criteria layer indicator is: environmental impact, economic feasibility, social acceptance and support. The program layer indicator is: energy efficiency improvement program, clean energy substitution program, transportation structure adjustment program, demand management program, green transportation infrastructure construction program and development path.

[0022] At the solution level, each indicator can be further broken down into a variety of specific implementation plans: Solutions to improve energy efficiency: Vehicle lightweighting: Reduce vehicle weight by using lightweight materials; Engine optimization: Improve fuel efficiency by using efficient combustion technology and intelligent control systems; Tire optimization: Use low rolling resistance tires to reduce fuel consumption.

[0023] Clean energy alternatives: Electric vehicle promotion: increase the number of electric vehicles and charging facilities; hydrogen fuel cell vehicles: promote the use of hydrogen fuel cells as a power source; biofuel application: use biomass fuels instead of traditional fossil fuels.

[0024] Transport structure adjustment plan: Multimodal transport: integrating multiple modes of transport such as railways, roads, and waterways; Logistics center optimization: establishing an efficient logistics distribution center to reduce intermediate links; Freight route optimization: optimizing cargo transportation routes through an intelligent scheduling system.

[0025] Demand management plan: Travel mode adjustment: encourage low-carbon travel modes such as public transportation, walking and cycling; Shared travel: promote shared travel modes such as carpooling and shared bicycles; Flexible work system: implement flexible work schedules to reduce traffic pressure during peak hours.

[0026] Green transportation infrastructure construction plan: Charging pile construction: large-scale deployment of electric vehicle charging piles; intelligent transportation system: construction of intelligent transportation management system to improve road traffic efficiency; green building standards: adoption of green building standards in transportation facility construction to reduce energy consumption.

[0027] Development Path: Path 1: Integrated development First, the integration of urban space. Create an intensive, compact, and mixed land development model to avoid disorderly urban expansion and reduce ineffective transportation demand, such as creating mixed-function areas, reshaping street space resources, and marking low-emission or zero-emission areas. Second, the integration of urban public transportation and inter-city public transportation. In the context of urbanization and regionalization, attention should be paid to the integrated development of urban public transportation and external public transportation. Large cities need to focus on the construction of medium-capacity public transportation. Small and medium-sized cities should pay special attention to public transportation services for external passenger flows, organize the layout of the urban public transportation network based on external passenger transport hubs, and build an integrated public transportation service system. Third, the integrated development of new and traditional business formats. Clarify their respective positioning, give play to their comparative advantages, and improve the overall operation efficiency of provincial transportation.

[0028] Path 2: Electrification Development Traffic electrification is the least difficult and least costly of the road traffic "carbon neutrality goals". Due to the rapid development of power batteries, the scope of traffic electrification is getting wider and wider, from the original railways and urban rail transit to include new energy buses, cars, logistics distribution vehicles and sanitation vehicles. Pure electric vehicles will become the mainstream of new sales vehicles, public vehicles will be fully electrified, fuel cell vehicles will be commercialized, charging and swapping service networks will be convenient and efficient, and hydrogen fuel supply systems will be more efficient. The construction of the system is progressing steadily, effectively promoting the improvement of energy conservation and emission reduction levels and social operation efficiency.

[0029] Path 3: Shared Development Due to the large population in large cities, it is crucial to adhere to the "urban bus + walking / bicycle" as the dominant transportation system. The rapid development of new business forms such as online car-hailing and shared bicycles has provided diversified options to meet the travel needs of urban residents, reducing the travel mileage and willingness to buy private cars. Compared with traditional cruising taxis, online car-hailing has reduced the empty mileage of vehicles and improved operational efficiency. The development of Internet rental bicycles has solved the problem of short-distance travel for residents and replaced some motorized travel.

[0030] Path 4: Intelligent Development With the rapid development of Internet technology and communication technology, new technologies and new forms of urban transportation are emerging one after another. As the largest application scenario of new technologies, in recent years, the trend of urban transportation networking has been obvious, and new models and technologies such as shared travel and autonomous driving are being continuously cultivated and implemented. Autonomous driving vehicles have achieved large-scale application. Autonomous driving and scheduled travel make it possible to establish a "no traffic jam" transportation system, vigorously develop shared transportation, create a service system based on mobile intelligent terminal technology, and realize travel as a service. Develop "Internet +" efficient logistics, innovate smart logistics operation models, and accelerate the promotion of creating an efficient transportation system.

[0031] Path 5: Market-oriented development By giving full play to the main role of the market, improving the carbon trading market mechanism, and establishing a carbon emission rights trading system. Give full play to the role of individuals and actively participate in low-carbon travel. Enterprises with relatively low emission reduction costs that are economically motivated will take the lead in emission reduction, and sell excess carbon emission rights to enterprises with relatively high emission reduction costs and obtain additional benefits. Enterprises with high emission reduction costs can reduce the cost of meeting carbon emission standards by purchasing carbon emission rights.

[0032] Furthermore, CO2 is the main indicator for measuring environmental impact. Therefore, the environmental impact degree in the criterion layer of this embodiment evaluates carbon dioxide emissions through emission factors of various vehicles at different altitudes, and the average of the increase in all emission factors is used as the environmental impact degree. For example, in the four altitude intervals of [2000, 2500m), [2500, 3000m), [3000, 3500m), and [3500, 4000m), the emission factors show different degrees of differences. The CO2 emission factors of diesel vehicles in the four altitude intervals are: 161.83g / km, 169.44 g / km, 191.84 g / km, 195.54 g / km; the CO emission factors are: 0.59 g / km, 0.57 g / km, 0.64 g / km, 0.77 g / km; NO X The emission factors are: 4.61 g / km, 5.63 g / km, 5.66 g / km, 6.58 g / km. The CO2 emission factors of gasoline vehicles in the four altitude ranges are: 161.66 g / km, 164.86 g / km, 180.26 g / km, 181.98 g / km; CO emission factors are: 0.96 g / km, 0.76 g / km, 1.06 g / km, 0.95 g / km; NO X The emission factors are: 0.16 g / km, 0.15 g / km, 0.25 g / km, 0.12 g / km. Among them, the CO2 emission factors of the two vehicles show an upward trend with the increase in altitude. The increases of diesel vehicles are: 4.5%, 11.6% and 1.9%, and the increases of gasoline vehicles are: 1.9%, 8.5% and 9.5%. Therefore, the environmental impact is 6.3%.

[0033] The economic feasibility, social acceptance and support of each plan are comprehensively considered through expert scoring.

[0034] Step 2: construct a preliminary judgment matrix based on the indicators of the target layer and / or the criterion layer and / or the solution layer; obtain a preliminary weight vector through the preliminary judgment matrix; and calculate the maximum eigenvalue of the preliminary judgment matrix.

[0035] Step 21, multiplying the preliminary judgment matrix and the preliminary weight vector to obtain a new vector; Step 22, calculate the ratio of each element of the new vector to each element of the preliminary weight vector, take the average of all ratios, and get the maximum eigenvalue .

[0036] Step 3, calculate the initial consistency index value based on the maximum eigenvalue, and obtain the random consistency index value, and calculate the initial consistency ratio CR according to the initial consistency index value CI and the random consistency index value RI; when the initial consistency ratio is less than 0.1, execute step 6; when the initial consistency ratio is greater than or equal to 0.1, execute step 4.

[0037] CR=CI / RI.

[0038] . m is the dimension of the preliminary judgment matrix.

[0039] Step 4, obtain the eigenvalue, characteristic mean, variance, initial consistency index value and overall consistency deviation value of each element in the preliminary judgment matrix, and calculate the improved consistency ratio based on the initial consistency ratio, initial consistency index value, random consistency index value, eigenvalue, characteristic mean, variance and overall consistency deviation value.

[0040] Obtaining the overall consistency deviation value includes: obtaining two elements of opposite rows and columns in the preliminary judgment matrix and defining a judgment standard: , Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and the ith column; calculates the consistency deviation value of the current two elements based on the judgment standard, and obtains the overall consistency deviation value according to the consistency deviation value.

[0041] ; In the formula, m represents the dimension of the preliminary judgment matrix, Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and ith column.

[0042] Considering the consistency deviation value to calculate the improved consistency ratio can significantly improve the consistency of the judgment matrix, thereby enhancing the reliability and accuracy of decision-making. By calculating the consistency deviation value of each element in the preliminary judgment matrix and finding its average as the overall consistency deviation value, further adjustments and optimizations can be made on the basis of the initial consistency ratio. Specifically, the overall consistency deviation value reflects the degree of consistency between the elements in the matrix. When the value is low, it means that the elements in the matrix are relatively consistent, otherwise it means that there is a large inconsistency. Incorporating this indicator into the calculation of the improved consistency ratio can more comprehensively evaluate the consistency level of the matrix and ensure that the final weight distribution is more reasonable and credible.

[0043] ; In the formula, represents the improved consistency ratio, represents the initial consistency index value, CR represents the initial consistency ratio, represents the variance, represents the eigenvalue of the kth element, represents the feature average, Represents the maximum value operation, represents the overall consistency deviation value, RI represents the random consistency index value, , , , Represent adjustment factors respectively.

[0044] Step 5: When the improved consistency ratio is less than 0.1, execute step 6; if the improved consistency ratio is greater than or equal to 0.1, return to execute step 2.

[0045] Step 6, use the power iteration method to update the preliminary weight vector to obtain the iterative weight vector; when the initial consistency ratio in step 3 is less than 0.1, or the improved consistency ratio in step 5 is less than 0.1, the iterative weight vector is used as the final weight vector; when the improved consistency ratio in step 5 is greater than or equal to 0.1, the correction term is calculated based on the improved consistency ratio; based on the iterative weight vector and the correction term, the final weight vector is obtained.

[0046] ; In the formula, represents the final weight vector, represents the iteration weight vector for iteration n+1 times, represents the correction function, Represents a correction item.

[0047] .

[0048] The correction term is of great significance in the process of calculating the final weight vector, ensuring the consistency and accuracy of the final weight vector. When it is less than 0.1, it means that the initial weight matrix has high consistency and the iterative weight vector hardly changes, indicating that the current weight distribution is reasonable. When it is greater than or equal to 0.1, The larger the value, the smaller the correction term, resulting in a larger adjustment of the iterative weight vector to improve consistency. This dynamic adjustment mechanism ensures that even when the initial consistency ratio is high, the weight vector can be gradually optimized through iteration, and a more consistent and reliable weight distribution is finally obtained.

[0049] Step 7: construct a multi-objective optimization function based on the final weight vector, use a multi-objective optimization algorithm to solve the multi-objective optimization function, and obtain the optimal combination of development paths and emission reduction measures.

[0050] The optimization objectives of the multi-objective optimization function are: minimizing carbon emissions, minimizing the cost of meeting carbon emission standards, and minimizing the difficulty of implementation.

[0051] ; ; ; ; In the formula, represents the multi-objective optimization function, represents the function of minimizing carbon emissions, represents the cost function of minimizing carbon emission compliance, represents the minimization implementation difficulty function, represents the first element of the final weight vector, represents the second element of the final weight vector, represents the third element of the final weight vector, represents the application proportion of the z-th emission reduction measure, represents the carbon emission coefficient of the z-th emission reduction measure, represents the carbon emission compliance cost of the zth emission reduction measure, Represents the difficulty coefficient of implementing the zth emission reduction measure.

[0052] The accurate weights obtained through consistency check are applied to the objective function of multi-objective optimization. This method significantly improves the scientificity and rationality of the decision-making process. In the traditional multi-objective optimization process, the weight coefficients of the objective function often rely on human definition, which may lead to subjective bias and inconsistency. However, in this embodiment, the final weight vector is obtained through the analytic hierarchy process (AHA) and its consistency check, ensuring that each weight can truly reflect the importance of the corresponding goal. Applying these verified weight coefficients to the objective function not only reduces the uncertainty caused by artificially setting weights, but also ensures that the optimization results are more in line with the actual situation and the expectations of stakeholders. Therefore, the optimal solution obtained by this method can better reflect the balance between various goals, and provide solid data support and technical guarantee for the formulation of emission reduction measures and development paths.

[0053] Count the proportion of emission reduction measures used in the past, and calculate the average proportion of each emission reduction measure as the above formula. The carbon emission coefficient is calculated by collecting energy consumption data during highway construction. The energy consumption statistics are shown in Table 1: Table 1 Statistics of energy consumption during highway construction

[0054] Using the corresponding energy consumption data of all known classified engineering assessment workloads, the comprehensive energy consumption per unit classified engineering assessment workload during the highway construction period in Qinghai Province was calculated according to different classifications of construction machinery.

[0055] .

[0056] Specifically, the converted carbon emission coefficient is shown in Table 2: Table 2 Carbon emission coefficient statistics

[0057] According to the unit carbon emission conversion coefficient corresponding to the classification of construction machinery The carbon emission compliance cost of emission reduction measures is obtained through historical data statistics. The real-time difficulty coefficient is obtained by experts scoring the implementation process of each emission reduction measure, and the average score is selected as the implementation difficulty coefficient.

[0058] Finally, a multi-objective optimization algorithm is used to solve the multi-objective optimization function, with the goal of finding the optimal development path and combination of emission reduction measures on the basis of minimizing carbon emissions, minimizing the cost of carbon emission compliance, and minimizing the difficulty of implementation. Through this method, this embodiment can determine a series of optimization schemes, each of which is the best practice after comprehensively considering environmental impact, carbon emission compliance cost, and implementation feasibility.

Claims

1. A multi-objective optimization method for energy conservation and carbon reduction in the highway transportation industry based on the analytic hierarchy process, characterized by: include: Step 1, construct a hierarchical model, which includes: target layer, criterion layer and solution layer, and each of the target layer, criterion layer and solution layer contains several indicators; Step 2: construct a preliminary judgment matrix based on the indicators of the target layer and / or the criterion layer and / or the solution layer; obtain a preliminary weight vector through the preliminary judgment matrix; and calculate the maximum eigenvalue of the preliminary judgment matrix; Step 3, calculate the initial consistency index value based on the maximum eigenvalue, obtain the random consistency index value, and calculate the initial consistency ratio according to the initial consistency index value and the random consistency index value; when the initial consistency ratio is less than 0.1, execute step 6; when the initial consistency ratio is greater than or equal to 0.1, execute step 4; Step 4, obtaining the eigenvalue, eigenmean, variance, initial consistency index value and overall consistency deviation value of each element in the preliminary judgment matrix, and calculating the improved consistency ratio according to the initial consistency ratio, initial consistency index value, random consistency index value, eigenvalue, eigenmean, variance and overall consistency deviation value; Step 5: When the improved consistency ratio is less than 0.1, execute step 6; if the improved consistency ratio is greater than or equal to 0.1, return to execute step 2; Step 6, using the power iteration method to update the preliminary weight vector to obtain an iterative weight vector; when the initial consistency ratio in step 3 is less than 0.1, or the improved consistency ratio in step 5 is less than 0.1, the iterative weight vector is used as the final weight vector; when the improved consistency ratio in step 5 is greater than or equal to 0.1, the correction term is calculated based on the improved consistency ratio; the final weight vector is obtained based on the iterative weight vector and the correction term; Step 7: construct a multi-objective optimization function based on the final weight vector, use a multi-objective optimization algorithm to solve the multi-objective optimization function, and obtain the optimal combination of development paths and emission reduction measures.

2. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: The indicators of the target layer are: carbon neutrality; the indicators of the criterion layer are: environmental impact, economic feasibility, social acceptance and support; the indicators of the plan layer are: energy efficiency improvement plan, clean energy substitution plan, transportation structure adjustment plan, demand management plan and green transportation infrastructure construction plan.

3. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: In step 2, the calculation process of the maximum eigenvalue is: Step 21, multiplying the preliminary judgment matrix and the preliminary weight vector to obtain a new vector; Step 22, calculate the ratio of each element of the new vector to each element of the preliminary weight vector, take the average of all ratios, and obtain the maximum eigenvalue.

4. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: In step 4, obtaining the overall consistency deviation value includes: obtaining two elements in opposite rows and columns in the preliminary judgment matrix, and defining a judgment standard: , Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and the ith column; calculates the consistency deviation value of the current two elements based on the judgment standard, and obtains the overall consistency deviation value according to the consistency deviation value.

5. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: In step 4, the calculation formula for improving the consistency ratio is: ； In the formula, represents the improved consistency ratio, represents the initial consistency index value, CR represents the initial consistency ratio, represents the variance, represents the eigenvalue of the kth element, represents the feature average, Represents the maximum value operation, represents the overall consistency deviation value, RI represents the random consistency index value, , , , Represent adjustment factors respectively.

6. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 5 is characterized in that: Overall consistency deviation for: ； In the formula, m represents the dimension of the preliminary judgment matrix, Represents the element in the i-th row and j-th column of the preliminary judgment matrix, Represents the element in the jth row and ith column.

7. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: In step 6, the calculation formula of the final weight vector is: ； In the formula, represents the final weight vector, represents the iteration weight vector for iteration n+1 times, represents the correction function, represents the correction term, where .

8. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 1 is characterized in that: In step 7, the optimization objectives of the multi-objective optimization function are: minimizing carbon emissions, minimizing the cost of meeting carbon emission standards, and minimizing the difficulty of implementation.

9. The multi-objective optimization method for energy saving and carbon reduction in highway transportation industry based on analytic hierarchy process according to claim 8 is characterized in that: The multi-objective optimization function is: ； In the formula, represents the multi-objective optimization function, represents the function of minimizing carbon emissions, represents the cost function of minimizing carbon emission compliance, represents the minimization implementation difficulty function, represents the first element of the final weight vector, represents the second element of the final weight vector, Represents the third element of the final weight vector.

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