Manufacturing industry low-carbon technology modeling and carbon reduction path planning method and system

Abstract

The invention discloses a manufacturing industry low-carbon technology modeling and carbon reduction path planning method and system, and belongs to the field of manufacturing industry carbon reduction path prediction. The method comprises the following steps: determining a future carbon emission development path of an enterprise under a reference scene; determining a future carbon emission development path of the enterprise under the planning scene; screening all low-carbon technologies of which the investment cost is less than or equal to the total investment cost of annual planning of an enterprise from a low-carbon technology library by utilizing a minimum heap optimization method; according to the screened low-carbon technology, a carbon reduction path multi-objective optimization model is constructed and solved, and an optimal low-carbon technology combination is obtained; based on the optimal low-carbon technology combination, establishing an enterprise low-carbon technology dynamic carbon reduction accounting model; and calculating the future carbon emission development path of the enterprise under the low-carbon technology scene according to the future carbon emission development path of the enterprise under the reference scene and the dynamic carbon reduction accounting model of the low-carbon technology of the enterprise. According to the invention, enterprises can be helped to utilize the low-carbon technology to the maximum extent, and effective implementation of a carbon reduction path is ensured.

Description

Technical Field

[0001] The present invention relates to the field of predicting carbon reduction paths in the manufacturing industry, and particularly to a method and system for modeling low-carbon technologies and planning carbon reduction paths in the manufacturing industry. Background Art

[0002] Under the urgent situation of global climate change and environmental protection, as the main field of energy consumption and carbon emissions, the low-carbon transformation of the manufacturing industry has become a broad consensus in the international community. Traditional manufacturing industries rely on high-energy-consuming and high-emission production models, which not only cause great pressure on the environment but also limit their own sustainable development. Therefore, exploring and applying low-carbon technologies and designing effective carbon reduction paths are of great significance for promoting the green transformation of the manufacturing industry.

[0003] There are many low-carbon technologies in the manufacturing industry, including the utilization of clean energy, the development of low-carbon manufacturing processes, the promotion of highly energy-efficient equipment, and the recycling of waste. However, the application of these technologies still faces many challenges. On the one hand, there are differences in the pollution reduction and carbon reduction effects and economic costs of different low-carbon technologies. How to balance the carbon reduction effect of the technology and the economic cost in actual application and select the optimal low-carbon technology solution is a major problem in the low-carbon transformation of the manufacturing industry. On the other hand, technological progress and scale effects will have a dynamic impact on the performance of low-carbon technologies, but existing technologies often ignore this dynamic change, resulting in inaccurate and costly carbon reduction paths planned; more importantly, there is no prediction of carbon reduction paths under different scenarios in existing technologies, the gap between the basic current situation and the planned target cannot be quantified, and a technology carbon reduction path design oriented by the planned target cannot be carried out, and the design effect of diversified technology carbon reduction paths cannot be achieved. Therefore, a comprehensive and diverse analysis and consideration are required for the design of carbon reduction paths.

[0004] Currently, carbon reduction solutions mainly include two carbon reduction processes based on target design and path planning. Methods such as the KAYA model, LEAP model, and STIRPAT model are used to predict and set the total amount of energy-saving and carbon reduction targets, and then the actual carbon reduction path is planned based on the set targets. Although the target design method is mature, most of the determination of the targets is based on speculation of prediction data, which may cause a large deviation between the path planning and the actual situation and cannot guide the implementation of the actual specific carbon reduction design plan of enterprises.

[0005] For example, the Chinese patent application "A Method, System, Medium and Device for Pollutant Reduction and Carbon Emission Reduction Path Planning" with the publication number CN118114928A constructs a scale-emission-cost pollutant reduction and carbon emission reduction path planning model that changes dynamically under technological progress and solves it from various green and low-carbon technology pollutant emission intensity models, green and low-carbon technology economic cost models, and scale dynamic change models between various green and low-carbon technologies to obtain the optimal pollutant reduction and carbon emission reduction path. However, the types and quantities of design parameters of this method model are relatively large, it is difficult to obtain relevant indicators of low-carbon technologies in the whole industry, and the pollutant reduction and carbon emission reduction path is single, which is not conducive to the implementation of the carbon emission reduction path of enterprises.

[0006] The Chinese patent application "A Method and System for Designing Regional Carbon Peak Targets and Planning Paths" with the publication number CN117852735A mainly conducts planning and design for the carbon emissions of the power system. It uses logistic regression and grey theory to carry out highly adaptable prediction of the influencing factors of regional carbon emissions, then conducts regional carbon emissions prediction based on a convolutional neural network, and finally conducts target design based on spatial mapping with the carbon peak target and solves to obtain the carbon peak implementation path. However, this method involves a relatively large number of types of carbon emission influencing factors, it is difficult to obtain the corresponding data of the influencing factors, and the requirement for the amount of data during prediction is large. There is an obvious inappropriateness in the design of the carbon emission reduction path for the manufacturing industry, which is not conducive to supporting the actual diverse carbon emission reduction needs of enterprises, and is even less conducive to guiding enterprises to accurately implement the requirements of the dual control targets for the total amount and intensity of carbon emissions. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for modeling low-carbon technologies and planning carbon emission reduction paths in the manufacturing industry to solve one or more of the above technical problems.

[0008] To achieve the above purpose, the present invention adopts the following technical solutions:

[0009] In the first aspect, the present invention provides a method for modeling low-carbon technologies and planning carbon emission reduction paths in the manufacturing industry, including:

[0010] Determine the future carbon emission development path of the enterprise under the baseline scenario;

[0011] According to the manufacturing industry carbon emission prediction model, determine the future carbon emission development path of the enterprise under the planned scenario and obtain the total annual planned carbon emission reduction of the enterprise;

[0012] Taking the total annual planned investment cost of the enterprise as a constraint condition, use the minimum heap optimization method to screen out all low-carbon technologies with investment costs less than or equal to the total annual planned investment cost of the enterprise from the low-carbon technology library;

[0013] Taking the investment cost and carbon emission reduction amount of the selected low-carbon technologies as the optimization objectives, and the total annual planned investment cost and total annual planned carbon emission reduction amount of the enterprise as the constraints, a multi-objective optimization model for the carbon emission reduction path is constructed, and the multi-objective optimization model for the carbon emission reduction path is solved to obtain the optimal low-carbon technology portfolio;

[0014] Based on the optimal low-carbon technology portfolio selected each year, a dynamic carbon emission reduction accounting model for the enterprise's low-carbon technologies is established;

[0015] According to the enterprise's future carbon emission development path under the baseline scenario and the dynamic carbon emission reduction accounting model of the enterprise's low-carbon technologies, the enterprise's future carbon emission development path under the low-carbon technology scenario is calculated.

[0016] Furthermore, taking the average carbon emission intensity of the enterprise's carbon inventory results in historical years as the benchmark, the enterprise's future carbon emission development path under the baseline scenario is determined, and the calculation formula is:

[0017] EP1 k =I 平均 *GDP forecast k

[0018] where k is the year corresponding to the predicted carbon emissions, and EP1 k is the predicted value of the enterprise's carbon emissions under the baseline scenario in the kth year, I 平均 is the average carbon emission intensity of the enterprise's carbon inventory results in historical years, and GDP forecast k is the predicted value of the enterprise's future output value plan in the kth year.

[0019] Furthermore, the determination of the enterprise's future carbon emission development path under the planned scenario according to the manufacturing carbon emission prediction model includes:

[0020] Dividing the end-use energy according to the actual situation of the manufacturing industry, constructing a manufacturing carbon emission prediction model based on LEAP. The manufacturing carbon emission prediction model includes a basic assumption module and a demand module. According to relevant policies and plans, the basic assumption conditions in the basic assumption module are determined. On the premise of meeting the basic assumption conditions, the carbon emissions of energy demand and non-energy demand in the demand module are predicted to obtain the enterprise's future carbon emission development path under the planned scenario.

[0021] Furthermore, the use of the minimum heap optimization method to screen out all low-carbon technologies with investment costs less than or equal to the total annual planned investment cost of the enterprise from the low-carbon technology library includes:

[0022] Create an empty minimum heap, traverse the investment cost values of all low-carbon technologies in the low-carbon technology library. For the investment cost of each low-carbon technology, if it is less than the total annual planned investment cost of the enterprise, insert it into the minimum heap. After the traversal, the values stored in the minimum heap are all those less than or equal to the total annual planned investment cost of the enterprise, and the selected low-carbon technologies are obtained.

[0023] Furthermore, the multi-objective optimization model for the carbon reduction path is:

[0024] Max(∑E i )

[0025] Min(∑C i )

[0026] s.t.∑E i ≤E 规划 ∑C i ≤C 规划

[0027] i = 1,…,x

[0028] where i is the type of low-carbon technology, E i is the carbon reduction amount of the i-th low-carbon technology, C i is the investment cost of the i-th low-carbon technology, E 规划 is the total annual planned investment cost of the enterprise, C 规划 is the total annual planned carbon reduction amount of the enterprise, and x is all the low-carbon technologies whose investment costs are less than or equal to the total annual planned investment cost of the enterprise selected.

[0029] Furthermore, based on the optimal low-carbon technology portfolio selected each year, establish an enterprise low-carbon technology dynamic carbon reduction accounting model, including:

[0030] Based on the optimal low-carbon technology portfolio, by determining the relevant model parameters of each low-carbon technology in the construction period, upgrade period, and saturation period, construct an enterprise low-carbon technology dynamic carbon reduction accounting model.

[0031] Furthermore, the enterprise low-carbon technology dynamic carbon reduction accounting model is:

[0032]

[0033] where i is the type of low-carbon technology, i is selected from the optimal low-carbon technology portfolio; E0 i is the carbon reduction amount of the i-th low-carbon technology in the low-carbon technology library, E k is the carbon reduction amount in the k-th year, k is the time corresponding to the accounting year, k1 i is the starting time of the application of the i-th low-carbon technology, k2 i is the time corresponding to the maximum expansion range achieved by the i-th low-carbon technology; m iis the scale correction coefficient of the i-th low-carbon technology, n i is the annual expansion coefficient of the i-th low-carbon technology. The construction period is the k1-th year and n = 1. The upgrade period is the period corresponding to k1 < k ≤ k2, and the saturation period is the period corresponding to k > k2.

[0034] Furthermore, the future carbon emission development path of the enterprise under the low-carbon technology scenario is calculated by the following formula:

[0035] EP3 k = EP1 k - E k

[0036] where E k is the carbon emission reduction amount in the k-th year; EP1 k is the predicted carbon emission value under the baseline scenario in the k-th year, and EP3 k is the predicted carbon emission value under the low-carbon technology scenario in the k-th year.

[0037] On the second aspect, the present invention provides a manufacturing low-carbon technology modeling and carbon emission reduction path planning system, including:

[0038] The first prediction module is used to determine the future carbon emission development path of the enterprise under the baseline scenario;

[0039] The second prediction module is used to determine the future carbon emission development path of the enterprise under the planned scenario according to the manufacturing carbon emission prediction model, and obtain the total annual planned carbon emission reduction amount of the enterprise;

[0040] The first screening module is used to screen out all low-carbon technologies with investment costs less than or equal to the total annual planned investment cost of the enterprise from the low-carbon technology library by using the minimum heap optimization method with the total annual planned investment cost of the enterprise as the constraint condition;

[0041] The second screening module is used to construct a multi-objective optimization model for the carbon emission reduction path with the investment costs and carbon emission reduction amounts of the screened low-carbon technologies as the optimization objectives and the total annual planned investment cost and the total annual planned carbon emission reduction amount of the enterprise as the constraint conditions, and solve the multi-objective optimization model for the carbon emission reduction path to obtain the optimal low-carbon technology portfolio;

[0042] The carbon emission reduction accounting model establishment module is used to establish a dynamic carbon emission reduction accounting model for the enterprise's low-carbon technology based on the optimal low-carbon technology portfolio screened out every year;

[0043] The carbon emission reduction path prediction module is used to calculate the future carbon emission development path of the enterprise under the low-carbon technology scenario according to the future carbon emission development path of the enterprise under the baseline scenario and the dynamic carbon emission reduction accounting model of the enterprise's low-carbon technology.

[0044] In a third aspect, the present invention provides an electronic device, including one or more processors, a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in the first aspect.

[0045] In a fourth aspect, the present invention provides a readable storage medium, on which one or more programs are stored, and the one or more programs include instructions that, when executed by a computing device, cause the computing device to execute the instructions of any of the methods described in the first aspect.

[0046] Compared with the prior art, the beneficial technical effects achieved by the present invention are as follows:

[0047] 1. Constrained by the expected investment cost of enterprise low-carbon technologies, the present invention uses the minimum heap optimization method to screen out low-carbon technologies that meet the constraints, and then combines the carbon reduction potential of each technology. Using a multi-objective optimization algorithm to calculate the Pareto optimal solution, the optimal carbon reduction technology plan is selected, taking into account the design requirements of minimizing cost and maximizing carbon reduction, and solving the dilemma that the investment cost of low-carbon technologies and the carbon reduction amount cannot be balanced.

[0048] 2. Based on the low-carbon technologies that are maturely applied in the manufacturing industry and their key indicators such as related investment costs and carbon reduction amounts, a low-carbon technology library is constructed and continuously optimized. By considering the influence of scale effect factors and expansion effect factors, the carbon reduction accounting models of technologies in different stages such as the construction period, upgrade period, and saturation period are dynamically corrected, and a distributed and diversified carbon reduction design is established to quantify the carbon emission reduction and carbon reduction effects of technologies, ensuring the accuracy of the carbon reduction path design plan and solving the problem that the carbon reduction path cannot be supported by quantitative means.

[0049] 3. Using the carbon reduction accounting model of specific technologies, the carbon reduction effects of different carbon reduction paths are quantified, and based on the carbon reduction planning goals of the manufacturing industry, a dynamic evolution curve between the carbon reduction planning goals and specific technical paths is constructed to ensure the effective implementation of the carbon reduction path design plan.

[0050] 4. Through scenario analysis, the carbon reduction path predictions under the baseline scenario, technology scenario, and planning scenario are analyzed, the gap between the baseline status quo and future planning goals is quantified, and the technology carbon reduction design plan gradually approaches the planning goals to achieve diversified and visual carbon reduction path predictions.

[0051] 5. Use low-carbon technology for dynamic modeling to provide a quantitative model for the carbon reduction path in the technical scenario, and form predictions of the carbon reduction paths under three different scenarios: the baseline scenario, the planning scenario, and the technical scenario. Dynamically quantify the gap between the technical carbon reduction design plan and the carbon reduction planning goal. The model parameter factors are comprehensively considered, the carbon reduction path logic is clear, the principle is simple, and it is closer to the actual carbon reduction path planning of enterprises. It has strong technical popularization in achieving dual control of carbon emissions, precise emission reduction, and effective carbon reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a method for manufacturing low-carbon technology modeling and carbon reduction path planning of the present invention;

[0053] Figure 2 It is another flowchart of a method for manufacturing low-carbon technology modeling and carbon reduction path planning of the present invention;

[0054] Figure 3 It is a schematic diagram of the structure of the LEAP model for manufacturing;

[0055] Figure 4 It is a schematic diagram of the trend change of the carbon reduction path under the baseline scenario, the planning scenario, and the low-carbon technology scenario;

[0056] Figure 5 It is a construction drawing of the low-carbon technology carbon reduction path planning of the embodiment of the present invention;

[0057] Figure 6 It is a system structure diagram of a manufacturing low-carbon technology modeling and carbon reduction path planning of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The present invention will be further described below in conjunction with specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0059] As Figures 1 to 2 shown, the present invention provides a method for manufacturing low-carbon technology modeling and carbon reduction path planning, including the following steps:

[0060] Step 1, determine the future carbon emission development path of the enterprise under the baseline scenario;

[0061] Based on the average carbon emission intensity of the enterprise's historical carbon inventory results, determine the future carbon emission development path P1 of the enterprise under the baseline scenario.

[0062] In some embodiments, in combination with Figure 4As shown in the figure, the basic parameters of the base scenario are set through scenario analysis. In the base scenario, the average value of the carbon emission intensity in the historical carbon inventory years is taken as the benchmark value. Without considering the influence of other factors, assuming the benchmark value remains unchanged, the carbon emissions change with the development of the output value, and thus the future carbon emission development path P1 of the enterprise under the benchmark scenario is obtained.

[0063] Specifically, the future carbon emission development path P1 of the enterprise under the benchmark scenario is obtained according to the following formula

[0064] EP1 k = I 平均 * GDP forecast k

[0065] where k is the year corresponding to the predicted carbon emissions, and EP1 k is the predicted value of the enterprise's carbon emissions in the k-th year under the benchmark scenario, and I 平均 is the average carbon emission intensity of the enterprise's historical carbon inventory results, and GDP forecast k is the predicted value of the enterprise's future output value plan for the k-th year.

[0066] Step 2: According to the manufacturing industry carbon emission prediction model, determine the future carbon emission development path of the enterprise under the planned scenario, and obtain the total annual planned carbon emission reduction of the enterprise;

[0067] By fully considering the constraints of relevant government policies, the requirements of the manufacturing industry's dual-carbon plan, and the impact of future new development technologies, multiple tools such as the KAYA model, LEAP model, and STIRPAT model are used to achieve carbon emission prediction.

[0068] In the embodiment of the present invention, a manufacturing industry carbon emission prediction model based on LEAP is adopted to simulate and predict the future carbon emission development path P2 of the enterprise under the planned scenario, which is used as the target basis for technological carbon reduction.

[0069] Specifically, as Figure 3 shown, according to the actual situation of the manufacturing industry, a reasonable division of end-use energy is carried out, and a LEAP prediction model including a basic assumption module and a demand module is established. Among them, basic assumption conditions such as GDP output value, energy consumption intensity, carbon emission intensity, and new energy usage ratio need to be determined in combination with relevant policies and plans. On the premise of meeting the basic assumption conditions, the carbon emissions of energy demand and non-energy demand are predicted, and finally the carbon emission prediction value under the planned scenario based on the LEAP model is obtained, that is, the future carbon emission development path P2 of the enterprise under the planned scenario, as Figure 4 shown.

[0070] Step 3: Taking the total annual planned investment cost of the enterprise as a constraint condition, use the minimum heap optimization method to screen out all low-carbon technologies whose investment costs are less than or equal to the total annual planned investment cost of the enterprise from the low-carbon technology library;

[0071] Based on the existing mature low-carbon technologies in the manufacturing industry, collect key indicators such as their investment costs and carbon reduction amounts, construct and continuously optimize to form a low-carbon technology library (assuming it contains z technologies), continuously incorporate emerging low-carbon technologies and update the upgrading effects of existing technologies to ensure the advancement of the included technology information, and provide a database foundation for the design of the technology carbon reduction path.

[0072] In some embodiments, using the minimum heap optimization method, with the total planned annual investment cost of the enterprise as the constraint condition, traverse the investment costs of all technologies using a complete binary tree, and screen out x low-carbon technologies from the low-carbon technology library whose investment costs are less than or equal to the total planned annual investment cost of the enterprise for the enterprise to select carbon reduction technologies.

[0073] More specifically, using the minimum heap optimization method, screen out x low-carbon technologies from the low-carbon technology library whose investment costs are less than or equal to the total planned annual investment cost of the enterprise, including:

[0074] Create an empty minimum heap, traverse the investment cost values of the low-carbon technologies of the given z technologies in the low-carbon technology library. For the investment cost of each low-carbon technology, if it is less than the total planned annual investment cost of the enterprise, insert it into the minimum heap. After the traversal, the values stored in the minimum heap are all those less than the total planned annual investment cost of the enterprise, which are the x low-carbon technologies screened out.

[0075] Step 4, taking the investment costs and carbon reduction amounts of the screened low-carbon technologies as the optimization objectives, and the total planned annual investment cost and the total planned annual carbon reduction amount of the enterprise as the constraint conditions, construct a multi-objective optimization model for the carbon reduction path, solve the multi-objective optimization model for the carbon reduction path, and obtain the optimal low-carbon technology combination;

[0076] Using optimization algorithms such as the multi-objective particle swarm optimization algorithm (MOPSO) and the multi-objective artificial hummingbird algorithm (MOAHA), construct a multi-objective optimization model for the manufacturing industry carbon reduction path. Taking the investment costs and carbon reduction amounts of the low-carbon technologies screened in step 3 as the optimization objectives, and the total planned annual investment cost and the total planned annual carbon reduction amount as the constraint conditions, construct a multi-objective optimization problem, solve the Pareto optimal solution set of the carbon reduction path, and realize the automatic screening of the optimal low-carbon technology combination.

[0077] The constructed multi-objective optimization model for the manufacturing industry carbon reduction path is:

[0078] Max(∑E i )

[0079] Min(∑C i )

[0080] s.t.∑E i ≤E规划 ∑C i ≤C 规划

[0081] i = 1, …, x

[0082] where i is the type of low - carbon technology, e i is the carbon reduction amount of the i - th low - carbon technology, C i is the investment cost of the i - th low - carbon technology, E 规划 is the total investment cost of the enterprise's annual plan, C 规划 is the total carbon reduction amount of the enterprise's annual plan, and x is all the low - carbon technologies whose investment costs are less than or equal to the total investment cost of the enterprise's annual plan selected.

[0083] Step 5: Based on the optimal low - carbon technology portfolio selected each year, establish a dynamic carbon reduction accounting model for the enterprise's low - carbon technology;

[0084] Based on the optimal low - carbon technology portfolio selected each year, by comparing the basic information of any one of these low - carbon technologies, determine the relevant information of the technology investment and application in this enterprise, and starting from the dynamic impacts of the technology in different periods such as the construction period, upgrade period, and saturation period, determine a series of parameters such as the starting time k1 of technology application, the time k2 corresponding to the maximum expansion range of the technology, the scale correction coefficient m, and the expansion coefficient n, and construct a carbon reduction accounting model for the dynamic changes of the enterprise's low - carbon technology, accurately quantify the contribution value of different technologies to the reduction of the enterprise's carbon emissions in different periods, and finally achieve the balance between technology investment costs and carbon reduction potential.

[0085] Among them, the scale correction coefficient m is to make a certain correction and adjustment by comparing the scale information of the case technology in the low - carbon technology library of different enterprises, and the expansion coefficient n is the annual expansion ability of the enterprise for a certain low - carbon technology.

[0086] Specifically, the dynamic carbon reduction accounting model of the enterprise's low - carbon technology is calculated by the following formula:

[0087]

[0088] where i is the type of low - carbon technology, and i is selected from the optimal low - carbon technology portfolio; E0 i is the carbon reduction amount of the i - th low - carbon technology in the low - carbon technology library, E k is the carbon reduction amount in the k - th year; k is the time corresponding to the accounting year, k1 i is the starting time of the application of the i - th low - carbon technology, k2 i is the time corresponding to the maximum expansion range of the i - th low - carbon technology; m i is the scale correction coefficient of the i - th low - carbon technology, n iis the annual expansion coefficient of the i-th low-carbon technology. The construction period is the k1-th year and n = 1. The upgrade period is the period corresponding to k1 < k ≤ k2, and the saturation period is the period corresponding to k > k2.

[0089] Step 6: Calculate the future carbon emission development path of the enterprise under the low-carbon technology scenario according to the future carbon emission development path of the enterprise under the baseline scenario and the dynamic carbon emission reduction accounting model of the enterprise's low-carbon technology.

[0090] Based on the determination of the optimal low-carbon technology portfolio plan in Step 4 and the establishment of the enterprise's low-carbon technology dynamic carbon emission reduction accounting model in Step 5, according to the enterprise's own low-carbon development needs, a top-down carbon emission reduction path accounting model is constructed, so as to obtain the future carbon emission development path P3 of the enterprise under the low-carbon technology scenario, as Figure 4 shown.

[0091] The future carbon emission development path P3 of the enterprise under the low-carbon technology scenario is calculated by the following formula:

[0092]

[0093] where E k is the carbon emission reduction amount in the k-th year; EP1 k is the predicted carbon emission value under the baseline scenario (P1 path) in the k-th year, and EP3 k is the predicted carbon emission value under the low-carbon technology scenario (P3 path) in the k-th year.

[0094] Figure 5 This is the construction drawing of the low-carbon technology carbon emission reduction path planning in the embodiment of the present invention. Assume that the scale correction coefficient m of all technologies in the optimal low-carbon technology portfolio is 0.9, the expansion coefficient n takes 1 in the first year, n expands at a speed of 1.2 / year in the upgrade period, and n does not change in the saturation period. i represents a certain low-carbon technology, k is the corresponding time of the accounting year, and the times of each technology k1 and k2 are as Figure 5 shown.

[0095] Then, according to the calculation formula of the future carbon emission development path P3 of the enterprise under the low-carbon technology scenario, we get:

[0096] Calculation of the carbon emission reduction amount in 2020: E = E01 * 0.9 + E02 * 0.9 (construction period)

[0097] Calculation of the carbon emission reduction amount in 2021:

[0098]

[0099] Calculation of the carbon emission reduction amount in 2022:

[0100] Calculation of the carbon emission reduction amount in 2023:

[0101] In a further embodiment, the method of the present invention further includes:

[0102] Step 7, combining statistical analysis methods such as residual analysis and R 2 to perform fitting test analysis on the data of the P3 path, and finally obtaining a qualified model to ensure that the enterprise realizes the maximum emission reduction with the minimum investment cost, so that EP3 k is infinitely close to EP2 k , and promoting the sustainable development of the enterprise.

[0103] Calculate the carbon emissions in the predicted year through the tested model. Specifically, taking the planned investment cost of the enterprise in low-carbon technologies as the cost ceiling constraint, a series of parameters such as k1, k2, scale correction coefficient m, and expansion coefficient n of the technology are determined, and a distributed and diversified dynamic carbon reduction accounting model for low-carbon technologies is constructed to establish the relationship between the planning goal and the specific technical path, quantify the emission reduction contributions of different low-carbon technologies, and obtain the carbon emission prediction value EP3 under the low-carbon technology scenario k ; further calculate the carbon emission prediction value EP1 under the baseline scenario k and the carbon emission prediction value EP2 under the planned scenario k . By comparing the carbon emission prediction values of different scenarios, taking the carbon emission prediction value of EP2 k as the planning goal, a dynamically adjustable technical carbon reduction path planning method is formed, providing diversified technical carbon reduction path plans to support the effective implementation of the enterprise's dual-carbon planning goal.

[0104] The present invention sets the basic parameters of the base scenario and the planning scenario through scenario analysis, and uses methods such as the KAYA model, the LEAP model, and the STIRPAT model to calculate the future carbon emission development paths of the enterprise under the baseline scenario (P1 path) and the planning scenario (P2 path). The predicted carbon emission value under the P2 path provides a reference for the enterprise's future planned carbon reduction target. Further, based on the low-carbon technologies that are currently maturely applied in the manufacturing industry, key indicators such as their investment costs and carbon reduction amounts are collected, and a low-carbon technology library system is built. Based on the minimum heap optimization and multi-objective optimization algorithm models, design schemes that conform to the enterprise's carbon reduction path are selected for the enterprise to choose low-carbon technologies. By determining the relevant model parameters of low-carbon technologies during the construction period, upgrade period, and saturation period, an emission reduction accounting model for the dynamic change of the enterprise's low-carbon technology is constructed to quantify the contribution value of different technologies to the enterprise's carbon emission reduction. By designing a carbon reduction path design system under three scenarios: the baseline scenario, the planning scenario, and the technology scenario, the trend changes of the baseline scenario (P1 path), the planning scenario (P2 path), and the technology scenario (P3 path) are dynamically displayed, and the gap between the technology carbon reduction path and the planned target carbon reduction is quantified, helping the enterprise to maximize the use of low-carbon technologies, ensuring the effective implementation of the carbon reduction path, timely restricting the development of high-carbon and high-energy consumption, providing scientific, reasonable, comprehensive and diverse carbon reduction path suggestions for the enterprise, and realizing green and low-carbon development.

[0105] As Figure 6 shown, the present invention provides a low-carbon technology modeling and carbon reduction path planning system for the manufacturing industry, including:

[0106] The first prediction module is used to determine the future carbon emission development path of the enterprise under the baseline scenario;

[0107] The second prediction module is used to determine the future carbon emission development path of the enterprise under the planning scenario according to the manufacturing industry carbon emission prediction model, and obtain the total annual planned carbon reduction amount of the enterprise;

[0108] The first screening module is used to use the minimum heap optimization method with the total annual planned investment cost of the enterprise as the constraint condition to screen out all low-carbon technologies with investment costs less than or equal to the total annual planned investment cost of the enterprise from the low-carbon technology library;

[0109] The second screening module is used to construct a multi-objective optimization model for the carbon reduction path with the total investment cost and total carbon reduction amount of the screened low-carbon technologies as the optimization objectives and the total annual planned investment cost and total annual planned carbon reduction amount of the enterprise as the constraint conditions, solve the multi-objective optimization model for the carbon reduction path, and obtain the optimal low-carbon technology combination;

[0110] The carbon reduction accounting model establishment module is used to establish a dynamic carbon reduction accounting model for the enterprise's low-carbon technology based on the optimal low-carbon technology combination screened out every year;

[0111] The carbon emission reduction path prediction module is used to calculate the future carbon emission development path of the enterprise under the low-carbon technology scenario according to the future carbon emission development path of the enterprise under the baseline scenario and the dynamic carbon emission reduction accounting model of the enterprise's low-carbon technology.

[0112] The present invention also provides an electronic device, including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods according to the foregoing methods.

[0113] The present invention also provides a readable storage medium, on which one or more programs are stored, and the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device is caused to execute any of the foregoing methods.

[0114] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 means for implementing the functions specified in one block or multiple blocks.

[0116] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 means for implementing the functions specified in one block or multiple blocks.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps specified in one process or multiple processes and / or boxes Figure 1 one process or multiple processes and / or boxes Figure 1 steps for implementing the functions specified in one box or multiple boxes.

[0118] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval of the application.

Claims

1. A method for low-carbon technology modeling and carbon reduction path planning in manufacturing industry, characterized in that: include: Determine the future carbon emission development path of the enterprise under the baseline scenario; Based on the manufacturing carbon emission prediction model, determine the future carbon emission development path of the enterprise under the planning scenario and obtain the total carbon reduction amount of the enterprise's annual plan; Taking the total investment cost of the enterprise's annual plan as the constraint condition, the minimum stack optimization method is used to screen out all low-carbon technologies whose investment costs are less than or equal to the total investment cost of the enterprise's annual plan from the low-carbon technology library; Taking the investment cost and carbon reduction amount of the selected low-carbon technologies as optimization targets, and the annual planned total investment cost and annual planned total carbon reduction amount of the enterprise as constraints, a multi-objective optimization model of carbon reduction path is constructed, and the multi-objective optimization model of carbon reduction path is solved to obtain the optimal low-carbon technology combination; Based on the optimal low-carbon technology combination screened out each year, a dynamic carbon reduction accounting model for corporate low-carbon technologies is established; Based on the future carbon emission development path of enterprises under the baseline scenario and the dynamic carbon reduction accounting model of enterprises' low-carbon technology, the future carbon emission development path of enterprises under the low-carbon technology scenario is calculated.

2. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: Taking the average carbon emission intensity of the company's historical carbon inventory results as the benchmark, determine the company's future carbon emission development path under the baseline scenario. The calculation formula is: EP1 k =I 平均 *GDP forecast k Among them, k is the corresponding year of predicted carbon emissions, EP1 k is the predicted value of enterprise carbon emissions under the baseline scenario in year k, I 平均 is the average carbon emission intensity of the company's historical carbon inventory results, GDP forecast k It is the planned forecast value of the enterprise's future output value in the kth year.

3. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: According to the manufacturing carbon emission prediction model, the future carbon emission development path of the enterprise under the planning scenario is determined, including: According to the actual situation of the manufacturing industry, terminal energy consumption is divided, and a manufacturing carbon emission prediction model based on LEAP is constructed. The manufacturing carbon emission prediction model includes a basic assumption module and a demand module. The basic assumption conditions in the basic assumption module are determined according to relevant policies and plans. On the premise of meeting the basic assumption conditions, the carbon emissions of energy demand and non-energy demand in the demand module are predicted to obtain the future carbon emission development path of the enterprise under the planning scenario.

4. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: The minimum heap optimization method is used to screen out all low-carbon technologies whose investment costs are less than or equal to the total investment cost of the enterprise's annual plan from the low-carbon technology library, including: Create an empty minimum heap and traverse the investment cost values ​​of all low-carbon technologies in the low-carbon technology library. For each low-carbon technology, if the investment cost is less than the total investment cost of the enterprise's annual plan, insert it into the minimum heap. After the traversal is completed, the minimum heap stores all values ​​that are less than or equal to the total investment cost of the enterprise's annual plan, and obtains the screened low-carbon technologies.

5. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: The multi-objective optimization model of carbon reduction path is: Max(∑E i ) Min(∑C i ) s.t.∑E i ≤E 规划 ∑C i ≤C 规划 i=1,…,x Among them, i is the type of low-carbon technology, E i is the carbon reduction amount of the i-th low-carbon technology, C i is the investment cost of the ith low-carbon technology, E 规划 is the total investment cost of the enterprise's annual plan, C 规划 is the total carbon reduction amount planned for the enterprise in the annual plan, and x is all the low-carbon technologies selected whose investment cost is less than or equal to the total investment cost planned for the enterprise in the annual plan.

6. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: The above mentioned low-carbon technology dynamic carbon reduction accounting model for enterprises is established based on the optimal low-carbon technology combination screened out each year, including: Based on the optimal low-carbon technology combination, a dynamic carbon reduction accounting model for enterprise low-carbon technologies is constructed by determining the relevant model parameters of each low-carbon technology in the construction period, upgrade period and saturation period.

7. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 6 is characterized in that: The dynamic carbon reduction accounting model of the enterprise's low-carbon technology is: Where i is the type of low-carbon technology, i is selected from the optimal low-carbon technology combination; E0 i is the carbon reduction amount of the i-th low-carbon technology in the low-carbon technology pool, E k is the carbon reduction amount in the kth year, k is the time corresponding to the year of calculation, k1 i is the starting time of the application of the i-th low-carbon technology, k2 i is the time corresponding to the maximum expansion scope of the i-th low-carbon technology; m i is the scale correction factor of the i-th low-carbon technology, n i is the annual expansion coefficient of the i-th low-carbon technology, the construction period is the k1th year and n = 1, the upgrade period is the period corresponding to k1<k≤k2, and the saturation period is the period corresponding to k>k2.

8. The method for manufacturing low-carbon technology modeling and carbon reduction path planning according to claim 1 is characterized in that: The future carbon emission development path of enterprises under the low-carbon technology scenario is calculated by the following formula: EP3 k =EP1 k -AND k Among them, E k is the carbon reduction in the kth year; EP1 k is the carbon emission forecast value under the baseline scenario in the kth year, EP3 k is the predicted carbon emission value under the low-carbon technology scenario in year k.

9. A low-carbon technology modeling and carbon reduction path planning system for manufacturing industry, characterized in that: include: The first prediction module is used to determine the future carbon emission development path of the enterprise under the baseline scenario; The second prediction module is used to determine the future carbon emission development path of the enterprise under the planning scenario based on the manufacturing carbon emission prediction model, and obtain the total carbon reduction amount of the enterprise's annual plan; The first screening module is used to use the enterprise's annual planned total investment cost as a constraint condition and use the minimum heap optimization method to screen out all low-carbon technologies whose investment costs are less than or equal to the enterprise's annual planned total investment cost from the low-carbon technology library; The second screening module is used to construct a multi-objective optimization model for carbon reduction paths with the investment cost and carbon reduction amount of the selected low-carbon technologies as optimization targets, and the annual planned total investment cost and annual planned total carbon reduction amount of the enterprise as constraints, and solve the multi-objective optimization model for carbon reduction paths to obtain the optimal low-carbon technology combination; The carbon reduction accounting model establishment module is used to establish a dynamic carbon reduction accounting model for enterprise low-carbon technologies based on the optimal low-carbon technology combination screened each year; The carbon reduction path prediction module is used to calculate the future carbon emission development path of the enterprise under the low-carbon technology scenario based on the future carbon emission development path of the enterprise under the baseline scenario and the dynamic carbon reduction accounting model of the enterprise's low-carbon technology.

10. A computing device, characterized in that: It comprises a processor and a memory, wherein the processor is used to implement the steps of the method for low-carbon technology modeling and carbon reduction path planning in the manufacturing industry as described in any one of claims 1 to 8 when executing the computer program stored in the memory.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method for low-carbon technology modeling and carbon reduction path planning for the manufacturing industry as described in any one of claims 1 to 8 are implemented.

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