Fuel economy accounting method, carbon emission evaluation method and electronic equipment

Abstract

The invention relates to a fuel economy accounting method, a carbon emission evaluation method and electronic equipment, and the fuel economy accounting method comprises the steps: defining input parameters affecting the traffic and transportation fuel economy, and obtaining data corresponding to the input parameters, the parameters comprising a vehicle type, a fuel type, an engine displacement and a load rate; constructing an interval type-2 fuzzy membership function for each input parameter; the input parameters are mapped to an interval type-2 fuzzy membership function, reasoning is carried out through a designed fuzzy rule base, and a fuzzy output set of fuel economy is generated; and performing defuzzification processing on the output set to obtain a specific value and an efficiency grade of the fuel economy. Compared with the prior art, the method has the advantages of improving the accuracy and applicability of fuel economy accounting and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic fuel economy and carbon emission monitoring, and in particular, to a fuel economy accounting method, a carbon emission assessment method, and an electronic device. Background Art

[0002] Carbon footprint is an important indicator for evaluating greenhouse gas emissions during the life cycle of an activity or product. In the field of transportation, carbon footprint accounting faces many challenges due to multiple transportation modes, complex transportation routes, and variable operating conditions. Traditional accounting methods are usually based on linear models or simple empirical formulas, making it difficult to accurately capture complex non-linear relationships and uncertainties.

[0003] Fuel economy (unit: km / L) is an important indicator for measuring the fuel utilization efficiency of a vehicle, directly reflecting the vehicle's energy consumption level and carbon emission potential. Generally speaking, the higher the fuel economy, the lower the carbon emission per unit distance of the vehicle. Therefore, fuel economy can be used as an indirect assessment indicator for carbon emissions to help quantify and compare the environmental impacts under different vehicles and driving conditions.

[0004] Interval type-2 fuzzy logic has been widely applied to complex system modeling because its membership function itself has fuzziness and can better describe uncertainty and fuzziness. However, there is currently no effective solution to apply interval type-2 fuzzy logic to transportation carbon footprint accounting.

[0005] How to achieve fuel economy accounting and carbon emission assessment for the complex environment in road traffic has become a technical problem to be solved. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a fuel economy accounting method, a carbon emission assessment method, and an electronic device.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] According to one aspect of the present invention, a fuel economy accounting method is provided, and the method includes the following steps:

[0009] Step 1, define input parameters affecting transportation fuel economy and obtain data corresponding to the input parameters, where the parameters include vehicle type, fuel type, engine displacement, and load factor;

[0010] Step 2, construct an interval type-2 fuzzy membership function for each input parameter in Step 1;

[0011] Step 3: Map the input parameters in Step 1 to the interval type-2 fuzzy membership functions, and perform reasoning through the designed fuzzy rule base to generate a fuzzy output set of fuel economy;

[0012] Step 4: Defuzzify the output set, convert it into a crisp value, and output the specific value of fuel economy and the efficiency level.

[0013] Preferably, constructing the interval type-2 fuzzy membership functions includes constructing upper and lower membership functions;

[0014] The upper membership function represents the main fuzzy set of the input parameters;

[0015] The lower membership function is obtained by adding an uncertainty interval on the basis of the upper membership function, reflecting the uncertainty of parameter measurement or definition.

[0016] Preferably, the fuzzy rule base is constructed based on the correlation between vehicle type and fuel type, the difference in engine displacement, the dynamic influence of load factor, and the dimension of multi-parameter interaction relationship.

[0017] More preferably, constructing the fuzzy rule base based on the correlation between vehicle type and fuel type is specifically as follows: The fuel type used by small vehicles is gasoline, and its fuel economy is relatively high, and its membership degree is set to "high efficiency"; when medium and large vehicles use the same fuel, their fuel economy is lower than that of small vehicles, and their membership degrees are set to "medium efficiency" and "low efficiency" respectively; the membership degree of fuel economy of small hybrid vehicles is set to "very high efficiency".

[0018] More preferably, constructing the fuzzy rule base based on the difference in engine displacement is specifically as follows: The engine displacement is divided into three fuzzy sets of "low displacement", "medium displacement" and "large displacement", and the corresponding fuel economy membership degrees are set to "relatively high", "medium" and "relatively low" respectively.

[0019] More preferably, constructing the fuzzy rule base based on the difference in engine displacement further includes: Dynamically adjusting in combination with the influence of fuel type on engine displacement. When a large-displacement engine is paired with diesel fuel, the fuel economy membership degree is reduced to "low efficiency".

[0020] Preferably, constructing the fuzzy rule base based on the dynamic influence of the load factor is specifically as follows: The fuzzy set of the load factor is set to "no load", "half load" and "full load", and the corresponding fuel economy membership degrees are set to "high efficiency", "medium efficiency" and "low efficiency" respectively through a piecewise linear function;

[0021] In addition, non-linear adjustment is performed in combination with the influence of vehicle type and fuel type on the load factor.

[0022] Preferably, constructing a fuzzy rule base based on the multi-parameter interaction relationship is specifically as follows: for the combination of a small vehicle, a hybrid fuel, and a low-displacement engine, set the fuel economy membership degree to "very efficient", and strengthen the evaluation result of this combination through a high weight; for the full-load combination of a large vehicle, a diesel fuel, and a large-displacement engine, set the fuel economy membership degree to "inefficient", and assign it a lower weight; for the combination of a medium-sized vehicle, a gasoline fuel, and a medium-displacement engine, the fuel economy is between "medium efficient" and "less efficient", and the specific membership degree is dynamically calculated by fuzzy rule inference.

[0023] According to another aspect of the present invention, there is provided a carbon emission assessment method, which takes fuel economy as an indirect representation of carbon emissions. The higher the fuel economy, the lower the carbon emission level.

[0024] According to the third aspect of the present invention, there is provided an electronic device, including a memory and a processor. A computer program is stored on the memory, and when the processor executes the program, the method described above is implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1) By introducing interval type-2 fuzzy logic into the calculation of transportation fuel economy, including defining input parameters, constructing interval type-2 fuzzy membership functions for each parameter to express the uncertainty of input parameters, and reasoning through a designed fuzzy rule base to output the specific value and evaluation result of fuel economy, the present invention significantly improves the accuracy and applicability of fuel economy accounting in complex scenarios.

[0027] 2) Due to considering the uncertainty of input parameters, the present invention expands the applicable scenarios and is applicable to various transportation scenarios, including road freight, urban distribution, etc.

[0028] 3) The present invention takes fuel economy as an indirect representation of carbon emissions. The higher the fuel economy, the lower the carbon emission level, so as to achieve the indirect assessment of carbon emissions. Brief Description of the Drawings

[0029] Figure 1 It is a schematic flowchart of the fuel economy accounting method of the present invention. Detailed Embodiments

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Based on the above technical problems, the present invention proposes a method and system for calculating the carbon footprint of transportation based on interval type-2 fuzzy logic. By introducing interval type-2 fuzzy logic to model the uncertainties in the calculation of the carbon footprint of transportation, the accuracy and adaptability of the calculation are improved.

[0032] This embodiment relates to a method for calculating fuel economy based on interval type-2 fuzzy logic, which can evaluate fuel economy with high precision and use it as an indirect characterization index of carbon emission level. By modeling input parameters such as vehicle type, fuel type, engine displacement, and load rate, and using interval type-2 fuzzy logic reasoning, a fuel economy result is generated, and an efficiency rating is provided at the same time, providing a scientific basis for low-carbon transportation.

[0033] Such as Figure 1 , this method includes the following steps:

[0034] Step S1, input parameter definition. Define the main variables that affect the fuel economy and carbon footprint of transportation as the inputs of the fuzzy system, including:

[0035] Vehicle type (vehicle_type): Represents the size of the vehicle (small, medium, large).

[0036] Fuel type (fuel_type): Represents the type of fuel used (gasoline, diesel, hybrid, etc.).

[0037] Engine displacement (engine_size): Represents the displacement size of the engine (small, medium, large).

[0038] Load rate (load_rate): Represents the load rate of the vehicle (unloaded, half-loaded, fully-loaded).

[0039] Step S2, construction of interval type-2 fuzzy membership functions. Construct an upper membership function (UMF) and a lower membership function (LMF) for each input parameter:

[0040] Upper membership function (UMF): Use a triangular membership function to represent the main fuzzy set of parameter values.

[0041] UMF(x i ) = trimf(x i , a, b, c) (1)

[0042] Where x i is an input parameter that affects the carbon footprint of transportation. For example, vehicle type, fuel type, engine displacement, or load rate. In fuzzy logic, x i is the actual input value that needs to be fuzzified.

[0043] a represents the left endpoint, which indicates the minimum value where the membership degree starts to be non - zero (corresponding to the point with a membership degree of 0), and is the left boundary of the triangular membership function.

[0044] b represents the vertex, which indicates the position where the membership degree is 1 (i.e., the point with the highest possibility that the variable belongs to this fuzzy set).

[0045] c represents the right endpoint, which indicates the maximum value where the membership degree returns to 0, that is, the right boundary of the triangular membership function.

[0046] In addition, trimf(.) is the triangular membership function, as shown in formula (2).

[0047]

[0048] Lower membership function (LMF): By adding an uncertainty interval to the upper membership function, it reflects the uncertainty in parameter measurement or definition. The LMF is constructed based on the UMF and reflects the uncertainty of parameters by reducing the standard deviation or adjusting the membership degree range. All parameters are consistent with those in the UMF.

[0049] The lower membership function adds an uncertainty interval Δ(x i ) to the upper membership function:

[0050] Δ(x i ) = UMF(x i ) - LMF(x i ) (3)

[0051] Δ(x i ) represents the uncertainty range between the UMF and the LMF, and is used to describe the ambiguity in parameter measurement or definition.

[0052] Step S3, fuzzy rule - base design. Based on the historical data related to vehicle dynamics characteristics and fuel economy, the present invention designs a set of comprehensive and logically clear fuzzy rule - bases. These rules accurately reflect their impact on fuel economy by comprehensively considering key parameters such as vehicle type, fuel type, engine displacement, and load factor. The design of the fuzzy rule - base fully reflects the interaction relationship between parameters and combines the actual energy consumption performance under different scenarios, making it have strong applicability and scalability.

[0053] Specifically, the rule - base is constructed through the following core dimensions:

[0054] (1) The correlation between vehicle type and fuel type:

[0055] For combinations of different vehicle types (such as small, medium, large) and fuel types (such as gasoline, diesel, hybrid), the fuzzy rule base adjusts fuel economy through parametric models of power output and fuel efficiency. For example, small vehicles have lower power requirements and higher fuel economy when using gasoline, and the rule base sets their membership degree as "high efficiency"; while medium or large vehicles have higher power requirements and lower fuel economy when using the same fuel, and the rule base sets their membership degrees as "medium efficiency" or "low efficiency". In addition, for hybrid vehicles, their fuel efficiency is improved due to electric assistance, and the rule base adjusts the fuel economy assessment by assigning higher weights. For example, the membership degree of fuel economy for small hybrid vehicles may be closer to the fuzzy set of "very high efficiency".

[0056] (2) Differences in engine displacement:

[0057] The rule base reflects the non-linear change of fuel consumption with the increase of displacement by refining the fuzzy membership function of engine displacement. For example, the rule base divides engine displacement into three fuzzy sets: "low displacement" (such as below 1.0L), "medium displacement" (such as 1.0L to 2.5L), and "high displacement" (such as above 2.5L). For low-displacement engines, the membership degree of fuel economy assessment is set as "relatively high", while for medium and high displacements, they are set as "medium" and "low" respectively.

[0058] The rule base also dynamically adjusts in combination with the influence of fuel type on engine displacement. For example, when a high-displacement engine is paired with diesel fuel, the rule base further reduces the fuel economy, and the membership degree tends to be "low efficiency".

[0059] (3) Dynamic influence of load factor:

[0060] The fuzzy sets of load factor are set as "unloaded", "half-loaded", and "fully-loaded", and the rule base reflects the downward trend of fuel economy with the increase of load factor through a piecewise linear function. For example, for unloaded vehicles, the membership degree of fuel economy is set as "high efficiency"; while in the fully-loaded state, the membership degree gradually decreases to "low efficiency".

[0061] In addition, the rule base makes non-linear adjustments in combination with the influence of vehicle type and fuel type on load factor. For example, large diesel vehicles have a greater decrease in fuel economy when fully loaded, while small hybrid vehicles are less affected by load factor, and these differences are reflected through the non-linear membership functions of the rules.

[0062] (4) Interaction relationships of multiple parameters:

[0063] The rule base models the complexity of multi-parameter interactions and defines fuel economy evaluation rules for different parameter combinations. For example, for the combination of small vehicles, hybrid fuels, and low-displacement engines, the rule base sets the fuel economy membership degree to "very efficient" and strengthens the evaluation result of this combination with a high weight. On the contrary, for the full-load combination of large vehicles, diesel fuels, and large-displacement engines, the rule base sets the fuel economy membership degree to "inefficient" and assigns it a lower weight.

[0064] In addition, the rule base also processes intermediate combinations through a fuzzy inference mechanism. For example, when a medium-sized vehicle is paired with gasoline fuel and a medium-displacement engine, the fuel economy may be between "medium efficiency" and "lower efficiency", and the specific membership degree is dynamically calculated by fuzzy rule inference.

[0065] Step S4, interval type-2 fuzzy inference: Map the input parameters to the upper membership function (UMF) and the lower membership function (LMF), and perform inference through the fuzzy rule base to generate a fuzzy output set of fuel economy.

[0066] Step S5, defuzzification: Use the centroid method to convert the fuzzy output set into a crisp value to obtain the fuel economy (unit: km / L).

[0067]

[0068] where: μ B (y) is the fuzzy membership function of the output variable; y * is the crisp value after defuzzification; y is the value of the output variable, representing the possible value range of the fuel economy result after defuzzification in the formula.

[0069] Step S6, result output and rating: Output the specific value of the fuel economy and the efficiency rating ("very efficient", "efficient", "medium", "inefficient", "extremely inefficient"), and use the fuel economy as an indirect reflection of the carbon emission level.

[0070] This embodiment also relates to a fuel economy accounting method based on interval type-2 fuzzy logic, which provides an accurate and efficient accounting scheme for the complexity and uncertainty in the transportation field. The entire accounting process from data preprocessing to result output includes:

[0071] (1) Input parameter definition, data acquisition and preprocessing:

[0072] The definition of input parameters has been introduced in the previous embodiment and will not be repeated here.

[0073] Collect transportation-related data, including vehicle type, fuel type, engine displacement, load factor, etc.

[0074] Standardize the collected data and handle missing values.

[0075] (2) Construction of the interval type-2 fuzzy model:

[0076] Construct the upper membership function (UMF) and lower membership function (LMF) for each input parameter.

[0077] Upper membership function (UMF): Represents the upper bound of the membership degree of the input parameter, defined using a Gaussian distribution or triangular membership function. For example, using a Gaussian distribution, for the load factor "no load":

[0078]

[0079] Lower membership function (LMF): Represents the lower bound of the membership degree of the input parameter. Defined by reducing the standard deviation of the Gaussian distribution or adjusting the range of the triangular membership function. For example, using a Gaussian distribution, for the load factor "no load":

[0080]

[0081] The uncertainty interval Δ(x i ) is defined as follows:

[0082] Δ(x i ) = UMF(x i ) - LMF(x i )

[0083] Through the above construction, the interval type-2 fuzzy model can more accurately describe the fuzzy set of uncertain input parameters.

[0084] (3) Fuzzy rule matching and inference:

[0085] Calculate the activation degree of the fuzzy rules based on the input parameter values.

[0086] Generate the fuzzy output set of carbon emissions by integrating the activation contributions of all fuzzy rules through the inference system.

[0087] (4) Defuzzification and result output:

[0088] Use the centroid method to defuzzify the fuel economy fuzzy set and generate a crisp value of fuel economy (unit: km / L).

[0089] Output the fuel economy value and rating:

[0090] If the fuel economy > 20 km / L, the rating is "very efficient".

[0091] If the fuel economy is in the range of 16 - 20 km / L, the rating is "efficient".

[0092] The fuel economy is between 13-16km / L, rated as "medium".

[0093] Fuel economy is between 10-13km / L, which is rated as "inefficient".

[0094] Fuel economy is <10km / L, rated as “extremely inefficient”.

[0095] (5) Indirect assessment of carbon emissions:

[0096] Taking fuel economy as an indirect representation of carbon emissions, the higher the fuel economy, the lower the carbon emission level, reflecting low-carbon efficiency.

[0097] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0098] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0099] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method in any other appropriate manner (e.g., by means of firmware).

[0100] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0101] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A fuel economy calculation method, characterized in that: The method comprises the following steps: Step 1, defining input parameters that affect transportation fuel economy and obtaining data corresponding to the input parameters, wherein the parameters include vehicle type, fuel type, engine displacement and load factor; Step 2, constructing interval type-2 fuzzy membership functions for each input parameter in step 1; Step 3, mapping the input parameters of step 1 to interval type-2 fuzzy membership functions, performing reasoning through the designed fuzzy rule base, and generating a fuzzy output set of fuel economy; Step 4, defuzzify the output set, convert it into a clear value, and output the specific value and efficiency level of fuel economy.

2. A fuel economy calculation method according to claim 1, characterized in that: The construction of interval type II fuzzy membership function includes constructing an upper membership function and a lower membership function; The upper membership function represents the main fuzzy set of input parameters; The lower membership function is obtained by adding an uncertainty interval to the upper membership function, reflecting the uncertainty of parameter measurement or definition.

3. A fuel economy calculation method according to claim 1, characterized in that: The fuzzy rule base is constructed based on the correlation between vehicle type and fuel type, the difference in engine displacement, the dynamic impact of load factor, and the dimensions of multi-parameter interaction.

4. A fuel economy calculation method according to claim 3, characterized in that: The fuzzy rule base is constructed based on the association between the vehicle type and the fuel type. Specifically, the fuel type used by small vehicles is gasoline, which has high fuel economy, and its membership is set to "high efficiency"; when medium and large vehicles use the same fuel, their fuel economy is lower than that of small vehicles, and their memberships are set to "medium efficiency" and "low efficiency" respectively; the fuel economy membership of small hybrid vehicles is set to "very efficient".

5. A fuel economy calculation method according to claim 3, characterized in that: The fuzzy rule base is constructed based on the differences in the engine displacements as follows: the engine displacements are divided into three fuzzy sets of "low displacement", "medium displacement" and "large displacement", and the corresponding fuel economy memberships are set to "high", "medium" and "low", respectively.

6. A fuel economy calculation method according to claim 5, characterized in that: Constructing a fuzzy rule base based on the differences in the engine displacement also includes: dynamically adjusting the impact of the fuel type on the engine displacement, and when a large-displacement engine is used with diesel fuel, the fuel economy membership is reduced to "inefficient".

7. A fuel economy calculation method according to claim 1, characterized in that: The fuzzy rule base is constructed based on the dynamic influence of the load factor as follows: the fuzzy set of the load factor is set to "empty", "half-loaded" and "full-loaded", and the corresponding fuel economy membership is set to "high efficiency", "medium efficiency" and "low efficiency" respectively through a piecewise linear function; In addition, the impact of vehicle type and fuel type on load factor is combined to make nonlinear adjustments.

8. A fuel economy calculation method according to claim 1, characterized in that: The fuzzy rule base is constructed based on the multi-parameter interaction relationship as follows: for the combination of small vehicles, hybrid fuel and low-displacement engines, the fuel economy membership is set to "very efficient", and the evaluation result of the combination is strengthened by a high weight; for the full-load combination of large vehicles, diesel fuel and large-displacement engines, the fuel economy membership is set to "inefficient" and a lower weight is given to it; for the combination of medium-sized vehicles, gasoline fuel and medium-displacement engines, the fuel economy is between "medium efficiency" and "relatively efficient", and the specific membership is dynamically calculated by fuzzy rule reasoning.

9. A carbon emission assessment method using the fuel economy calculation method according to any one of claims 1 to 8, characterized in that: Taking fuel economy as an indirect representation of carbon emissions, the higher the fuel economy, the lower the carbon emissions level.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

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