Strategy optimization method and device based on carbon leakage identification, storage medium, program product and computer equipment

Abstract

The application discloses a strategy optimization method and device based on carbon leakage identification, a storage medium, a program product and computer equipment. The method comprises the following steps: acquiring historical vehicle data; determining the vehicle life cycle cost based on the historical vehicle data and the blending strategy; determining the vehicle model share prediction information of various vehicles based on the vehicle life cycle cost; determining the actual carbon emission based on the historical vehicle data, the vehicle model share prediction information and the blending strategy, determining the ideal carbon emission based on the historical vehicle data, the vehicle model ideal share information and the blending strategy, and determining the carbon leakage based on the actual carbon emission and the ideal carbon emission; constructing an optimization model based on the carbon leakage, the vehicle life cycle cost, the vehicle model share prediction information and the actual carbon emission; and solving the optimization model to obtain an optimization strategy, wherein the optimization strategy comprises an optimized blending strategy and is used for reducing carbon leakage, the vehicle fuel blending strategy can be optimized, and therefore the energy saving and carbon reduction efficiency and effectiveness in the transportation field can be improved.

Description

Technical Field

[0001] This application relates to the field of transportation energy technology, and in particular to a strategy optimization method, apparatus, storage medium, program product and computer equipment based on carbon leakage identification. Background Technology

[0002] Carbon leakage refers to the actual amount of carbon emissions being higher than the ideal amount.

[0003] With the increasing demand for carbon emission reduction in the transportation industry, various low-carbon technologies in the transportation sector are gradually being promoted. For example, some vehicle power technologies allow users to choose the fuel blending ratio, which can reduce carbon emissions by increasing the proportion of clean energy. However, due to cost differences between different low-carbon technologies and fuels, and the potential for fuel costs to change over time, vehicle user behavior (e.g., the choice of vehicle fuel blending strategies) can easily change with varying vehicle usage costs. This could lead to carbon leakage within the transportation sector, hindering precise energy conservation and carbon reduction, and reducing the efficiency and effectiveness of energy conservation and carbon reduction in the transportation sector. Summary of the Invention

[0004] To address the aforementioned technical problems, this application proposes a strategy optimization method, apparatus, storage medium, program product, and computer equipment based on carbon leakage identification. This method can optimize vehicle fuel blending strategies for user reference, thereby improving the efficiency and effectiveness of energy conservation and carbon reduction in the transportation sector.

[0005] In a first aspect, embodiments of this application provide a strategy optimization method based on carbon leakage identification, including: Obtain historical vehicle data; Based on the historical vehicle data and blending strategy, the total life cycle cost of the vehicle is determined, wherein the blending strategy includes the vehicle fuel blending ratio. Based on the vehicle's total lifecycle cost, predict the market share of various vehicle types. The actual carbon emissions are determined based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy. The ideal carbon emissions are determined based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy. Furthermore, the carbon leakage is determined based on the actual carbon emissions and the ideal carbon emissions. An optimization model is constructed based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions. Solving the optimization model yields an optimization strategy, which includes an optimized blending strategy for reducing carbon leakage.

[0006] Optionally, determining the vehicle segment share prediction information based on the vehicle's total lifecycle cost includes: Based on the vehicle's total lifecycle cost and non-cost attributes, the vehicle utility information for each type of vehicle is obtained through weighted calculation. Based on the vehicle utility information of the various types of vehicles, the vehicle share prediction information is determined by constructing a discrete choice model.

[0007] Optionally, determining the vehicle type share prediction information by constructing a discrete choice model based on the vehicle utility information of the various types of vehicles includes: Based on the sensitivity coefficient and the vehicle utility information of the various types of vehicles, a discrete choice model is constructed to determine the vehicle share prediction information.

[0008] Optionally, the historical vehicle data includes vehicle mileage information; The process of determining actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy, and determining ideal carbon emissions based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy, includes: Based on the blending strategy, the carbon emission factors of the various types of vehicles under the blending strategy are determined; The actual carbon emissions are determined based on the vehicle mileage information, the vehicle model share prediction information, and the carbon emission factor. The ideal carbon emissions are determined based on the vehicle mileage information, the ideal share information of the vehicle model, and the carbon emission factor.

[0009] Optionally, the step of constructing an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: Based on the vehicle's total life cycle cost and the vehicle model share prediction information, the total usage cost corresponding to the blending strategy is determined. Construct an objective function with the goal of minimizing the total usage cost; Based on the aforementioned carbon leakage amount, a carbon leakage constraint is constructed; Based on the actual carbon emissions, carbon emission compliance constraints are constructed. The optimization model is constructed based on the objective function, the carbon leakage constraint, and the carbon emission compliance constraint.

[0010] Optionally, the method further includes: Obtain fuel subsidy information; The step of determining the total life cycle cost of a vehicle based on the historical vehicle data and blending strategy includes: determining the total life cycle cost of a vehicle based on the fuel subsidy information, the historical vehicle data, and the blending strategy. The step of constructing an optimization model based on the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: constructing an optimization model based on the fuel subsidy information, the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, wherein the optimization strategy also includes a subsidy strategy.

[0011] Secondly, embodiments of this application provide a strategy optimization device based on carbon leakage identification, comprising: The data acquisition module is used to acquire historical vehicle data; The cost determination module is used to determine the total life cycle cost of a vehicle based on the historical vehicle data and the blending strategy, wherein the blending strategy includes the vehicle fuel blending ratio. The market share determination module is used to determine the predicted market share information for various types of vehicles based on the total life cycle cost of the vehicles. A carbon leakage identification module is used to determine the actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information and the blending strategy, determine the ideal carbon emissions based on the historical vehicle data, the vehicle model ideal share information and the blending strategy, and determine the carbon leakage amount based on the actual carbon emissions and the ideal carbon emissions. An optimization model building module is used to build an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions. A carbon leakage elimination module is used to solve the optimization model to obtain an optimization strategy, wherein the optimization strategy includes an optimized blending strategy and is used to reduce carbon leakage.

[0012] Thirdly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above-mentioned embodiments.

[0013] Fourthly, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described in any of the above-described embodiments.

[0014] Fifthly, embodiments of this application provide a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the preceding claims.

[0015] In summary, the embodiments of this application have at least the following beneficial effects: This application employs an embodiment to acquire historical vehicle data; based on the historical vehicle data and a blending strategy, determine the vehicle's total life cycle cost, wherein the blending strategy includes the vehicle's fuel blending ratio; based on the vehicle's total life cycle cost, determine the predicted market share information for various vehicle types; based on the historical vehicle data, the predicted market share information, and the blending strategy, determine the actual carbon emissions; based on the historical vehicle data, the ideal market share information for each vehicle type, and the blending strategy, determine the ideal carbon emissions; and based on the actual carbon emissions and the ideal carbon emissions, determine the carbon leakage; based on the carbon leakage, the vehicle's total life cycle cost, the predicted market share information, and the actual carbon emissions, construct an optimization model; solve the optimization model to obtain an optimization strategy, wherein the optimization strategy includes optimizing the blending strategy and is used to reduce carbon leakage. Thus, vehicle fuel blending strategies can be optimized for user reference, thereby improving the efficiency and effectiveness of energy conservation and carbon reduction in the transportation sector. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the strategy optimization method based on carbon leakage identification provided in an embodiment of this application; Figure 2 This is a schematic diagram of the strategy optimization device based on carbon leakage identification provided in an embodiment of this application; Figure 3 This is a schematic diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments / examples are only a part of the embodiments / examples of this application, and not all of the embodiments / examples. Based on the embodiments / examples in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more. In the description of this application, the term "comprising" and its variations are open-ended, meaning "including but not limited to." The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment / example" means "at least one embodiment / example"; the term "another embodiment / example" means "at least one additional embodiment / example"; the term "some embodiments / examples" means "at least some embodiments / examples."

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this application is for the purpose of describing specific embodiments only and is not intended to limit the application. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0021] Firstly, see [the following] Figure 1 The diagram shows a flowchart of a strategy optimization method based on carbon leakage identification provided in an embodiment of this application. This strategy optimization method based on carbon leakage identification can be applied to a computer device with data processing capabilities. The method includes S101-S106, as detailed below.

[0022] S101, Obtain historical vehicle data.

[0023] In some examples, the historical vehicle data may include at least one of the following: vehicle purchase cost, fuel / energy price, annual energy consumption, maintenance cost, and carbon emission factor (e.g., carbon emission factor under different blending strategies) for vehicles using different technologies (various types of vehicles).

[0024] For example, vehicles using different technologies may include at least one of the following: E-fuel vehicles, electric vehicles, hydrogen fuel cell vehicles, etc., where e-fuel refers to a liquid fuel formed by producing hydrogen and oxygen through the electrolysis of water using renewable electricity, and then combining the hydrogen with carbon dioxide captured from the air through a catalytic reaction process.

[0025] S102, Based on the historical vehicle data and blending strategy, determine the vehicle's total life cycle cost, wherein the blending strategy includes the vehicle's fuel blending ratio.

[0026] In some examples, the vehicle fuel blending ratio can be used to characterize the mass / volume proportion of each type of fuel added to the vehicle for its use.

[0027] In some examples, total life cycle cost of a vehicle can be used to characterize the costs incurred by a vehicle throughout its entire life cycle (i.e., from purchase to scrapping). For example, total life cycle cost of a vehicle can include the cost of purchasing the vehicle, the fuel costs incurred for the fuel used throughout its life cycle, and the maintenance costs throughout its life cycle.

[0028] S103, Based on the vehicle's total lifecycle cost, determine the predicted market share information for each type of vehicle.

[0029] In some examples, the probability of a user choosing a particular vehicle can be predicted based on the total lifecycle cost of that vehicle, thus generating vehicle market share prediction information that includes that probability.

[0030] In some examples, the total lifecycle cost of a vehicle can be calculated using the following formula.

[0031] in, It can represent the total lifecycle cost of a vehicle. It can represent the one-time cost of a vehicle. It can represent the annual cost of a vehicle. It can represent the discount rate. It can represent the vehicle's life cycle.

[0032] S104, determine the actual carbon emissions based on the historical vehicle data, the model share prediction information and the blending strategy, determine the ideal carbon emissions based on the historical vehicle data, the ideal model share information and the blending strategy, and determine the carbon leakage based on the actual carbon emissions and the ideal carbon emissions.

[0033] In some examples, vehicle model ideal share information may include the market share of various types of vehicles under a baseline / ideal scenario, which may refer to a type of scenario set by the user, such as a scenario set to adapt to a certain carbon emission target.

[0034] In some examples, the difference between actual carbon emissions and ideal carbon emissions can be calculated as the amount of carbon leakage.

[0035] S105, Based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, an optimization model is constructed.

[0036] In some examples, the optimization model constructed can be the optimal model.

[0037] S106, Solve the optimization model to obtain the optimization strategy, wherein the optimization strategy includes an optimized blending strategy and is used to reduce carbon leakage.

[0038] In some examples, the optimized blending strategy can be used to indicate the target fuel blending ratio of a vehicle. Furthermore, if the user has pre-authorized the automatic adjustment permission for the fuel blending ratio, the optimized blending strategy corresponding to the target vehicle can be configured to the target vehicle and / or the cloud so that when the target vehicle needs to be refueled, the target vehicle and / or the cloud can automatically select the blending ratio of the fuel to be replenished according to the optimized blending strategy.

[0039] In some examples, intelligent optimization algorithms, optimization algorithms, etc., can be used to solve the optimization model to obtain the optimization strategy. The intelligent optimization algorithm may include differential evolution algorithm.

[0040] In one optional implementation, determining the vehicle segment share prediction information based on the vehicle's total lifecycle cost includes: Based on the vehicle's total lifecycle cost and non-cost attributes, the vehicle utility information for each type of vehicle is obtained through weighted calculation. Based on the vehicle utility information of the various types of vehicles, the vehicle share prediction information is determined by constructing a discrete choice model.

[0041] In some examples, vehicle utility information can be calculated using the following formula.

[0042] in, This represents the first utility sensitivity coefficient. This represents the second utility sensitivity coefficient. This represents the vehicle utility information corresponding to the i-th type of vehicle. This represents the total lifecycle cost of the vehicle corresponding to the i-th type of vehicle. This represents the non-cost attribute of the vehicle corresponding to the i-th type of vehicle.

[0043] In some examples, the discrete choice model can be expressed by the following formula.

[0044] in, This represents the probability that a user / consumer will choose the i-th type of vehicle. The vehicle type share prediction information can include this probability for each type of vehicle (the probability that each type of vehicle will be chosen by the user / consumer), that is, it can include... ; This represents the vehicle utility information corresponding to the i-th type of vehicle. It is a natural constant. This refers to each type of vehicle among all vehicle types.

[0045] In one optional implementation, determining the vehicle type share prediction information by constructing a discrete choice model based on the vehicle utility information of the various types of vehicles includes: Based on the sensitivity coefficient and the vehicle utility information of the various types of vehicles, a discrete choice model is constructed to determine the vehicle share prediction information.

[0046] In some examples, the discrete choice model can be constructed by combining the sensitivity coefficient, which can be expressed as the following formula.

[0047] in, Represents the sensitivity coefficient (for example, and They can be equal or unequal.

[0048] In one optional implementation, the historical vehicle data includes vehicle mileage information; The process of determining actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy, and determining ideal carbon emissions based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy, includes: Based on the blending strategy, the carbon emission factors of the various types of vehicles under the blending strategy are determined; The actual carbon emissions are determined based on the vehicle mileage information, the vehicle model share prediction information, and the carbon emission factor. The ideal carbon emissions are determined based on the vehicle mileage information, the ideal share information of the vehicle model, and the carbon emission factor.

[0049] In some examples, vehicle mileage information may include the mileage of a single vehicle within a preset time period, such as the annual mileage of a single vehicle.

[0050] In some examples, different blending strategies can correspond to different carbon emission factors for the same type of vehicle, so the corresponding carbon emission factor can be determined based on the current blending strategy.

[0051] In some examples, the general carbon emissions can be calculated using the following formula.

[0052] in, Indicates carbon emissions. This represents the market share of vehicle class i. Indicates the annual mileage of a single vehicle. This represents the carbon emission factor of vehicle type i.

[0053] Therefore, the actual carbon emissions can be calculated using the following formula.

[0054] in, Let represent the actual carbon emissions in year t. This represents the market share of vehicle type i in year t (e.g., it can be represented by...). (Determined by the probability corresponding to year t). This represents the annual mileage of a single vehicle of type i in year t (for example, it could be the average annual mileage of type i vehicles in year t). This represents the carbon emission factor of vehicle type i under the blending strategy.

[0055] Furthermore, the ideal carbon emissions can be calculated using the following formula.

[0056] in, This represents the ideal market share information for vehicle type i. Let represent the ideal carbon emissions for year t.

[0057] In one optional implementation, the step of constructing an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: Based on the vehicle's total life cycle cost and the vehicle model share prediction information, the total usage cost corresponding to the blending strategy is determined. Construct an objective function with the goal of minimizing the total usage cost; Based on the aforementioned carbon leakage amount, a carbon leakage constraint is constructed; Based on the actual carbon emissions, carbon emission compliance constraints are constructed. The optimization model is constructed based on the objective function, the carbon leakage constraint, and the carbon emission compliance constraint.

[0058] In some examples, the total cost of use can be expressed by the following formula.

[0059] in, This represents the market share of vehicle type i in year t (e.g., it can be represented by...). (Determined by the probability corresponding to year t). This represents the total lifecycle cost of the vehicle corresponding to the i-th type of vehicle. This indicates that in year t and the blending ratio is Total cost of use at any time It can represent the blending ratio in year t.

[0060] Thus, the constructed objective function can be expressed by the following formula: In some examples, carbon leakage constraints may include a zero carbon leakage constraint, which can be expressed by the following formula: In other words, this zero carbon leakage constraint can be used to indicate that the difference between actual carbon emissions and ideal carbon emissions is zero. This can be represented as the mixture in year t with a blending ratio of . Actual carbon emissions at that time This can be represented as the mixture in year t with a blending ratio of . The ideal carbon emissions at that time.

[0061] In some examples, carbon emission compliance constraints may include carbon emission compliance constraints expressed by the following formula: ,in, It can represent carbon emission targets.

[0062] In an optional implementation, the method further includes: Obtain fuel subsidy information; The step of determining the total life cycle cost of a vehicle based on the historical vehicle data and blending strategy includes: determining the total life cycle cost of a vehicle based on the fuel subsidy information, the historical vehicle data, and the blending strategy. The step of constructing an optimization model based on the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: constructing an optimization model based on the fuel subsidy information, the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, wherein the optimization strategy also includes a subsidy strategy.

[0063] In some examples, fuel subsidy information may include the amount of fuel subsidy corresponding to different types of fuel. In this way, the fuel subsidy information can be used to influence the total life cycle cost of the vehicle. Furthermore, when building the optimization model, the final objective function can be constructed based on the objective function that aims to minimize the total amount of fuel subsidies and the objective function that aims to minimize the total usage cost.

[0064] Secondly, correspondingly, the embodiments of this application also provide a strategy optimization device based on carbon leakage identification, which can implement all the processes of the strategy optimization method based on carbon leakage identification provided in the above embodiments.

[0065] See Figure 2 The diagram shows a schematic of the structure of a strategy optimization device based on carbon leakage identification provided in an embodiment of this application. The strategy optimization device 200 based on carbon leakage identification includes: Data acquisition module 201 is used to acquire historical vehicle data; The cost determination module 202 is used to determine the total life cycle cost of a vehicle based on the historical vehicle data and the blending strategy, wherein the blending strategy includes the vehicle fuel blending ratio. The share determination module 203 is used to determine the vehicle model share prediction information based on the total life cycle cost of the vehicle. The carbon leakage identification module 204 is used to determine the actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information and the blending strategy, determine the ideal carbon emissions based on the historical vehicle data, the vehicle model ideal share information and the blending strategy, and determine the carbon leakage amount based on the actual carbon emissions and the ideal carbon emissions. The optimization model building module 205 is used to build an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions. The carbon leakage elimination module 206 is used to solve the optimization model to obtain an optimization strategy, wherein the optimization strategy includes an optimized blending strategy and is used to reduce carbon leakage.

[0066] In one optional implementation, determining the vehicle segment share prediction information based on the vehicle's total lifecycle cost includes: Based on the vehicle's total lifecycle cost and non-cost attributes, the vehicle utility information for each type of vehicle is obtained through weighted calculation. Based on the vehicle utility information of the various types of vehicles, the vehicle share prediction information is determined by constructing a discrete choice model.

[0067] In one optional implementation, determining the vehicle type share prediction information by constructing a discrete choice model based on the vehicle utility information of the various types of vehicles includes: Based on the sensitivity coefficient and the vehicle utility information of the various types of vehicles, a discrete choice model is constructed to determine the vehicle share prediction information.

[0068] In one optional implementation, the historical vehicle data includes vehicle mileage information; The process of determining actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy, and determining ideal carbon emissions based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy, includes: Based on the blending strategy, the carbon emission factors of the various types of vehicles under the blending strategy are determined; The actual carbon emissions are determined based on the vehicle mileage information, the vehicle model share prediction information, and the carbon emission factor. The ideal carbon emissions are determined based on the vehicle mileage information, the ideal share information of the vehicle model, and the carbon emission factor.

[0069] In one optional implementation, the step of constructing an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: Based on the vehicle's total life cycle cost and the vehicle model share prediction information, the total usage cost corresponding to the blending strategy is determined. Construct an objective function with the goal of minimizing the total usage cost; Based on the aforementioned carbon leakage amount, a carbon leakage constraint is constructed; Based on the actual carbon emissions, carbon emission compliance constraints are constructed. The optimization model is constructed based on the objective function, the carbon leakage constraint, and the carbon emission compliance constraint.

[0070] In one optional embodiment, the device further includes a subsidy processing module, which is used to: obtain fuel subsidy information; The step of determining the total life cycle cost of a vehicle based on the historical vehicle data and blending strategy includes: determining the total life cycle cost of a vehicle based on the fuel subsidy information, the historical vehicle data, and the blending strategy. The step of constructing an optimization model based on the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: constructing an optimization model based on the fuel subsidy information, the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, wherein the optimization strategy also includes a subsidy strategy.

[0071] Thirdly, embodiments of this application provide a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any of the above-mentioned embodiments.

[0072] Fourthly, embodiments of this application provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described in any of the above-described embodiments.

[0073] Fifthly, embodiments of this application provide a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the steps of the method described in any of the preceding claims.

[0074] See Figure 3 The computer device in this embodiment includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301, such as a strategy optimization program based on carbon leak identification. When the processor 301 executes the computer program, it implements the steps in the various embodiments of the strategy optimization method based on carbon leak identification described above, for example... Figure 1 The steps S101-S106 are shown.

[0075] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0076] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0077] The processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor 301 can be any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0078] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0079] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a non-transitory computer-readable storage medium. When the computer program is executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0080] In summary, the embodiments of this application have at least the following beneficial effects: This application employs an embodiment to acquire historical vehicle data; based on the historical vehicle data and a blending strategy, determine the vehicle's total life cycle cost, wherein the blending strategy includes the vehicle's fuel blending ratio; based on the vehicle's total life cycle cost, determine the predicted market share information for various vehicle types; based on the historical vehicle data, the predicted market share information, and the blending strategy, determine the actual carbon emissions; based on the historical vehicle data, the ideal market share information for each vehicle type, and the blending strategy, determine the ideal carbon emissions; and based on the actual carbon emissions and the ideal carbon emissions, determine the carbon leakage; based on the carbon leakage, the vehicle's total life cycle cost, the predicted market share information, and the actual carbon emissions, construct an optimization model; solve the optimization model to obtain an optimization strategy, wherein the optimization strategy includes optimizing the blending strategy and is used to reduce carbon leakage. Thus, vehicle fuel blending strategies can be optimized for user reference, thereby improving the efficiency and effectiveness of energy conservation and carbon reduction in the transportation sector.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware platforms, or it can be implemented entirely by hardware. Based on this understanding, all or part of the technical solutions of this application that contribute to the background technology can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM (Read-Only Memory) / RAM (Random Access Memory), magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0082] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A strategy optimization method based on carbon leakage identification, characterized in that, include: Obtain historical vehicle data; Based on the historical vehicle data and blending strategy, the total life cycle cost of the vehicle is determined, wherein the blending strategy includes the vehicle fuel blending ratio. Based on the vehicle's total lifecycle cost, predict the market share of various vehicle types. The actual carbon emissions are determined based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy. The ideal carbon emissions are determined based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy. Furthermore, the carbon leakage is determined based on the actual carbon emissions and the ideal carbon emissions. An optimization model is constructed based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions. Solving the optimization model yields an optimization strategy, which includes an optimized blending strategy for reducing carbon leakage.

2. The method according to claim 1, characterized in that, The determination of vehicle segment share prediction information based on the vehicle's total lifecycle cost includes: Based on the vehicle's total lifecycle cost and non-cost attributes, the vehicle utility information for each type of vehicle is obtained through weighted calculation. Based on the vehicle utility information of the various types of vehicles, the vehicle share prediction information is determined by constructing a discrete choice model.

3. The method according to claim 2, characterized in that, The determination of vehicle market share prediction information based on vehicle utility information of the various vehicle types, by constructing a discrete choice model, includes: Based on the sensitivity coefficient and the vehicle utility information of the various types of vehicles, a discrete choice model is constructed to determine the vehicle share prediction information.

4. The method according to claim 1, characterized in that, The historical vehicle data includes vehicle mileage information; The process of determining actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information, and the blending strategy, and determining ideal carbon emissions based on the historical vehicle data, the ideal vehicle model share information, and the blending strategy, includes: Based on the blending strategy, the carbon emission factors of the various types of vehicles under the blending strategy are determined; The actual carbon emissions are determined based on the vehicle mileage information, the vehicle model share prediction information, and the carbon emission factor. The ideal carbon emissions are determined based on the vehicle mileage information, the ideal share information of the vehicle model, and the carbon emission factor.

5. The method according to claim 1, characterized in that, The optimization model is constructed based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, including: Based on the vehicle's total life cycle cost and the vehicle model share prediction information, the total usage cost corresponding to the blending strategy is determined. Construct an objective function with the goal of minimizing the total usage cost; Based on the aforementioned carbon leakage amount, a carbon leakage constraint is constructed; Based on the actual carbon emissions, carbon emission compliance constraints are constructed. The optimization model is constructed based on the objective function, the carbon leakage constraint, and the carbon emission compliance constraint.

6. The method according to claim 1, characterized in that, The method further includes: Obtain fuel subsidy information; The step of determining the total life cycle cost of a vehicle based on the historical vehicle data and blending strategy includes: determining the total life cycle cost of a vehicle based on the fuel subsidy information, the historical vehicle data, and the blending strategy. The step of constructing an optimization model based on the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions includes: constructing an optimization model based on the fuel subsidy information, the carbon leakage, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions, wherein the optimization strategy also includes a subsidy strategy.

7. A strategy optimization device based on carbon leakage identification, characterized in that, include: The data acquisition module is used to acquire historical vehicle data; The cost determination module is used to determine the total life cycle cost of a vehicle based on the historical vehicle data and the blending strategy, wherein the blending strategy includes the vehicle fuel blending ratio. The market share determination module is used to determine the predicted market share information for various types of vehicles based on the total life cycle cost of the vehicles. A carbon leakage identification module is used to determine the actual carbon emissions based on the historical vehicle data, the vehicle model share prediction information and the blending strategy, determine the ideal carbon emissions based on the historical vehicle data, the vehicle model ideal share information and the blending strategy, and determine the carbon leakage amount based on the actual carbon emissions and the ideal carbon emissions. An optimization model building module is used to build an optimization model based on the carbon leakage amount, the vehicle's total life cycle cost, the vehicle model share prediction information, and the actual carbon emissions. A carbon leakage elimination module is used to solve the optimization model to obtain an optimization strategy, wherein the optimization strategy includes an optimized blending strategy and is used to reduce carbon leakage.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-6.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method described in any one of claims 1-6.

10. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-6.

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