A method and device for dynamic emission analysis of motor vehicles based on Monte Carlo simulation

Abstract

The present invention discloses a method and device for analyzing dynamic emissions of motor vehicles based on Monte Carlo simulation, comprising: firstly obtaining multiple emission index factors of a target area, calling a dynamic emission calculation model of a motor vehicle based on Monte Carlo simulation, and inputting these index factors to obtain the calculation results of motor vehicle emissions in the target area. Based on the results, the key factors affecting the emissions in the target area are determined. Such a design improves the accuracy and timeliness of emission analysis, and helps to formulate targeted traffic environmental protection measures by region.

Description

Technical Field

[0001] The present invention relates to the technical field of motor vehicle emission management, and in particular to a motor vehicle dynamic emission analysis method and device based on Monte Carlo simulation. Background Art

[0002] With the acceleration of urbanization, motor vehicle emissions have become an important source of air pollution.

[0003] In order to accurately assess and control motor vehicle emissions, dynamic emission analysis of the target area is required.

[0004] Traditional emission analysis methods are often based on fixed parameters and simplified models, which are difficult to fully reflect the actual dynamic emission situation. Summary of the invention

[0005] The object of the present invention is to provide a method and device for analyzing dynamic emissions of a motor vehicle based on Monte Carlo simulation.

[0006] In a first aspect, an embodiment of the present invention provides a method for analyzing dynamic emissions of a motor vehicle based on Monte Carlo simulation, comprising:

[0007] Obtain multiple emission indicators for target areas;

[0008] Calling a motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation;

[0009] Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation results of the target area;

[0010] Based on the motor vehicle emission calculation results, influencing factors of multiple emission indicators for the target area are determined.

[0011] In a possible implementation manner, inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation result of the target area includes:

[0012] Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model: Obtaining calculation results of motor vehicle emissions in the target area;

[0013] in, It represents the total emission of pollutants in province m at a specific time t, which is the calculated result of motor vehicle emissions in the target area; It represents the total emission of pollutants from all types of motor vehicles in province m that meet emission standard j at a specific time t; It represents the total emission of pollutants from type i motor vehicles in province m under all emission standards at a specific time t; It refers to the number of type i motor vehicles that meet j emission standards in province m at a specific time t; is the mileage of type i motor vehicles that meet j emission standards in province m at a specific time t; It refers to the emission factor of type i motor vehicle that meets j emission standard in province m at a specific time t.

[0014] In a possible implementation, the Calculated by, among other things:

[0015] in, , is the environmental correction factor for type i motor vehicle under emission standard j in province m at a specific time t; is the degradation factor of vehicle type i under emission standard j in province m at a specific time t; is the temperature correction factor; is the humidity correction factor; is the altitude correction factor.

[0016] In a possible implementation, the Calculated by, among other things: in, represents the number of i-type vehicles scrapped at a certain time t-1 in the j emission standard of province m, is the number of newly added type i motor vehicles with type j emission standard in province m at a certain time t.

[0017] In a possible implementation, the method further includes:

[0018] Based on the uncertainty range of the motor vehicle emission calculation results of Monte Carlo simulation, the formula is: Simulating uncertainty ranges for the calculated emissions of the motor vehicle;

[0019] Among them, RVC is the relative coefficient of variation, Q 25 represents the 25% quantile, Q 50 represents the 50% quantile, Q 75 Represents the 75% quantile.

[0020] In a possible implementation, based on the motor vehicle emission calculation result, determining the influencing factors of multiple emission indicators for the target area includes:

[0021] Using the formula: ; Determining factors affecting multiple emission indicators for the target area;

[0022] in, Calculation results for motor vehicle emissions; is the weight coefficient; for The i-th influencing factor among the influencing factors; is an empirical constant; is the regression factor The F test statistic value of It is the maximum value of the statistic of the regression F test.

[0023] In a possible implementation, the method further includes: using the formula: Calculate the impact of urbanization on non-conventional organic pollutant emissions from motor vehicles in different regions;

[0024] in, For Region The impact of unconventional organic pollutants emitted by motor vehicles, represents the coordinates of sample point i, Refers to the spatial geographic location function, which represents the jth regression coefficient at sampling point i; is the jth unconventional organic pollutant factor at sample point i; is the empirical constant at sample point i.

[0025] In a second aspect, an embodiment of the present invention provides a motor vehicle dynamic emission analysis device based on Monte Carlo simulation, comprising:

[0026] An acquisition module, used for acquiring multiple emission indicators for a target area;

[0027] A calculation module is used to call a motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation; input the multiple emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation results of the target area; based on the motor vehicle emission calculation results, determine the influencing factors of the multiple emission indicators for the target area.

[0028] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor and a non-volatile memory storing computer instructions, wherein when the computer instructions are executed by the processor, the computer device executes the motor vehicle dynamic emission analysis method based on Monte Carlo simulation described in at least one possible implementation of the first aspect.

[0029] In a fourth aspect, an embodiment of the present invention provides a readable storage medium, wherein the readable storage medium includes a computer program, and when the computer program is running, the computer device where the readable storage medium is located is controlled to execute the motor vehicle dynamic emission analysis method based on Monte Carlo simulation described in at least one possible implementation manner of the first aspect.

[0030] Compared with the prior art, the beneficial effects provided by the present invention include: adopting a motor vehicle dynamic emission analysis method and device based on Monte Carlo simulation disclosed in the present invention, by obtaining multiple emission index factors of a target area, and calling a motor vehicle dynamic emission calculation model based on Monte Carlo simulation, after inputting these index factors, the motor vehicle emission calculation results of the target area are obtained.

[0031] Based on the results, the key factors affecting emissions in the target area were identified.

[0032] This design improves the accuracy and timeliness of emission analysis, and helps to formulate targeted transportation environmental protection measures by region. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.

[0034] It should be understood that the following drawings depict only certain embodiments of the present invention and therefore should not be viewed as limiting the scope.

[0035] For ordinary technicians in this field, other related drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 A schematic diagram of the steps of a method for dynamic emission analysis of a motor vehicle based on Monte Carlo simulation provided by an embodiment of the present invention;

[0037] Figure 2 A schematic block diagram of the structure of a motor vehicle dynamic emission analysis device based on Monte Carlo simulation provided by an embodiment of the present invention;

[0038] Figure 3 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

[0042] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] In order to solve the technical problems in the aforementioned background technology, Figure 1 A flow chart of a method for dynamic emission analysis of a motor vehicle based on Monte Carlo simulation is provided in an embodiment of the present disclosure. The method for dynamic emission analysis of a motor vehicle based on Monte Carlo simulation is introduced in detail below.

[0044] Step S201, obtaining multiple emission indicators for a target area;

[0045] Step S202, calling a motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation;

[0046] Step S203, inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation result of the target area;

[0047] Step S204: determining influencing factors of multiple emission indicators for the target area based on the motor vehicle emission calculation results.

[0048] In the embodiment of the present invention, illustratively, the server first determines a target area - a prosperous commercial area in Beijing.

[0049] The server then connected to the environmental protection department database to obtain multiple emission index factors of motor vehicles in the commercial area, including but not limited to carbon monoxide (CO), nitrogen oxides (NOx), particulate matter (PM 2.5 and PM 10 ) and emission factors, emission time distribution, vehicle type distribution, etc. of unconventional organic pollutants (such as polycyclic aromatic hydrocarbons).

[0050] These data are the basis for subsequent analysis.

[0051] After obtaining the emission index factors, the server automatically calls the pre-built motor vehicle dynamic emission calculation model based on Monte Carlo simulation.

[0052] This model is trained using a large amount of historical data and statistical methods, and can simulate the emissions of motor vehicles under different scenarios.

[0053] The server passes the multiple emission indicators obtained in step 1 as input parameters to the Monte Carlo simulation calculation model.

[0054] Based on these parameters, the model combines built-in algorithms and random processes to simulate the emissions of motor vehicles in the target area at different times, road conditions, and weather conditions, and outputs detailed emission calculation results.

[0055] These results include emission forecasts of various pollutants, emission hotspot distribution, emission trend analysis, etc.

[0056] After obtaining the motor vehicle emission calculation results, the server further conducted in-depth analysis and mining of these data.

[0057] By comparing emission data from different time periods, the server found that peak hours in the morning and evening are the times with the highest emissions, which is related to heavy traffic and slow vehicle speeds.

[0058] At the same time, the server also found that under certain specific weather conditions (such as low temperature and high humidity), emissions from certain types of vehicles would increase significantly.

[0059] Based on these findings, the server identified several key factors that affect motor vehicle emissions in the target area, including traffic volume, vehicle speed, weather conditions, vehicle type, etc.

[0060] The determination of these factors provides an important reference for subsequent emission control and formulation of relevant regulations.

[0061] In a possible implementation, the aforementioned step S203 may be implemented through the following example execution.

[0062] Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model: Obtaining calculation results of motor vehicle emissions in the target area;

[0063] in, It represents the total emission of pollutants in province m at a specific time t, which is the calculated result of motor vehicle emissions in the target area; It represents the total emission of pollutants from all types of motor vehicles in province m that meet emission standard j at a specific time t; It represents the total emission of pollutants from type i motor vehicles in province m under all emission standards at a specific time t; It refers to the number of type i motor vehicles that meet j emission standards in province m at a specific time t; is the mileage of type i motor vehicles that meet j emission standards in province m at a specific time t; It refers to the emission factor of type i motor vehicle that meets j emission standard in province m at a specific time t.

[0064] In the embodiment of the present invention, the server first obtains a plurality of key emission indicators, including the number of motor vehicles under different emission standards. , Mileage and emission factors .

[0065] These data cover detailed situations in the target area, Guangdong Province, under different time periods, different motor vehicle types and emission standards.

[0066] Next, the server passes these emission indicators as input parameters to the motor vehicle dynamic emission calculation model based on Monte Carlo simulation.

[0067] This model can comprehensively consider various factors, such as vehicle type, emission standards, mileage and emission factors, to simulate motor vehicle emissions.

[0068] After multiple emission indicators are input into the model, the server begins to perform Monte Carlo simulation calculations.

[0069] Based on the input parameters and built-in algorithms, the model simulates the emissions of motor vehicles in Guangdong Province at different times and under different conditions. Through calculation, the server obtained the following results: : The total emissions of a certain pollutant (such as polycyclic aromatic hydrocarbons (PAHs)) in Guangdong Province at a specific time t (such as 2:00 p.m. on May 1, 2023).

[0070] This result reflects the overall emission pressure of motor vehicles on the environment during this period.

[0071] : At the same time t, the total emissions of all motor vehicle types in Guangdong Province that meet a certain emission standard j (such as the National VI standard).

[0072] This result helps to understand the impact of different emission standards on motor vehicle emissions.

[0073] : At the same time t, the total emissions of a certain type of motor vehicle i (such as diesel trucks) under all emission standards in Guangdong Province.

[0074] This result reveals the extent to which different types of motor vehicles contribute to emissions.

[0075] These emission calculation results provide important data support and scientific basis for the subsequent formulation of environmental protection regulations, emission control strategies and motor vehicle management.

[0076] For example, the results could be used to develop stricter emission standards, promote clean energy vehicles, or optimize traffic flow to reduce the environmental impact of motor vehicle emissions.

[0077] In the embodiment of the present invention, the Calculated by, among other things: in, , is the environmental correction factor for type i motor vehicle under emission standard j in province m at a specific time t; is the degradation factor of vehicle type i under emission standard j in province m at a specific time t; is the temperature correction factor.

[0078] is the humidity correction factor; is the altitude correction factor.

[0079] In an embodiment of the present invention, exemplarily, the server first obtains the meteorological data of the target area, Jiangsu Province, at a specific time t (such as 10 am on July 1, 2023), including temperature, humidity and altitude.

[0080] These data are critical for calculating environmental correction factors.

[0081] Next, the server calculated the temperature correction factors according to the preset algorithms and formulas. , humidity correction factor and height correction factor .

[0082] For example, a temperature correction factor might be calculated based on the difference between the current temperature and the vehicle's optimal operating temperature, and similar correction factors for humidity and altitude.

[0083] Finally, the server multiplies these three correction factors to get the environmental correction factor .

[0084] This factor reflects the changes in motor vehicle emission performance under current environmental conditions.

[0085] The server obtains the degradation factor of a certain type i (such as gasoline sedan) in Jiangsu Province under a specific emission standard j (such as National V standard) from the motor vehicle management database. This degradation factor takes into account the gradual degradation of the emission performance of a motor vehicle as it is used over time.

[0086] For example, a 5-year-old car may have a higher degradation factor than a new car.

[0087] After obtaining the environmental correction factor and degradation factor, the server calculates the emission factor of a certain type i of motor vehicle in Jiangsu Province under a specific emission standard j and a specific time t according to the formula.

[0088] in, It is the basic emission factor, which can be obtained from data provided by the motor vehicle manufacturer or relevant standards.

[0089] The emission factors calculated The server can more accurately simulate the emission conditions of motor vehicles during actual use, and provide more precise data support for subsequent emission control and relevant regulation formulation.

[0090] For example, more refined motor vehicle management regulations can be formulated based on emission factors of different vehicle models, different emission standards and different environmental conditions to reduce the impact of motor vehicle emissions on the environment.

[0091] In the embodiment of the present invention, the Calculated by, among other things: in, represents the number of i-type vehicles scrapped at a certain time t-1 in the j emission standard of province m, is the number of newly added type i motor vehicles with type j emission standard in province m at a certain time t.

[0092] In the embodiment of the present invention, the server first accesses the motor vehicle registration database, and obtains the number of type i motor vehicles (such as diesel trucks) that meet the specific emission standard j (such as the National VI standard) in the target area, Shandong Province, at the last moment (for example, at 24:00 on June 30, 2023). .

[0093] This data is the basis for calculating the number of motor vehicles at the current moment. Subsequently, the server accessed the motor vehicle sales database and obtained the number of newly added diesel trucks that meet the National VI emission standards in Shandong Province during a certain time t (for example, from July 1, 2023 to July 31, 2023). .

[0094] This data reflects the growth in the number of motor vehicles during that period.

[0095] Next, the server obtains the number of diesel trucks that meet the National VI emission standards that were scrapped in Shandong Province before the previous time t-1 (i.e. 24:00 on June 30, 2023) from the scrapped motor vehicle database. .

[0096] This figure takes into account the reduction in motor vehicles due to scrapping.

[0097] Finally, the server calculated the number of diesel trucks that meet the National VI emission standards in Shandong Province at the current time t (i.e. 24:00 on July 31, 2023) according to the formula.

[0098] This result comprehensively considers the growth and scrapping of the number of motor vehicles, and can more accurately reflect the actual situation of the number of motor vehicles in the target area.

[0099] Through this series of calculation steps, the server can dynamically grasp the changes in the number of motor vehicles in the target area, providing important data support for subsequent motor vehicle emission analysis and relevant regulation formulation.

[0100] In the embodiments of the present invention, the following implementation modes are also provided.

[0101] Based on the uncertainty range of the motor vehicle emission calculation results of Monte Carlo simulation, the formula is: Simulating uncertainty ranges for the calculated emissions of the motor vehicle;

[0102] Among them, RVC is the relative coefficient of variation, Q25 represents the 25% quantile, Q50 represents the 50% quantile, and Q75 represents the 75% quantile.

[0103] In the embodiment of the present invention, illustratively, the server first calls the Monte Carlo simulation algorithm to simulate the previously calculated motor vehicle emission results multiple times.

[0104] During this process, the server took into account the uncertainty of various input parameters (such as the number of motor vehicles, mileage, emission factors, etc.) and generated a large number of simulated samples through random sampling and probability distribution functions.

[0105] After completing the Monte Carlo simulation, the server obtains a sample set containing multiple simulation results.

[0106] Next, the server performed a statistical analysis on this sample set and calculated the 25% quantile (Q 25 ), 50% quantile (Q 50 ) and the 75% quantile (Q 75 ).

[0107] These quantiles represent the values ​​of the simulation results at different probability levels.

[0108] For example, Q 50 (i.e., median) means that among all the simulation results, half of the results are lower than this value and the other half are higher than this value.

[0109] Likewise, Q 25 It means that 25% of the simulation results are lower than this value, and Q 75 It means that 75% of the simulation results are lower than this value.

[0110] After obtaining each quantile, the server calculates the relative coefficient of variation RVC according to the formula.

[0111] This coefficient is used to quantify the uncertainty range of the simulation results.

[0112] Specifically, the server first calculates Q 50 With Q 25 The difference between 50 get ; Then Q is calculated 75 With Q 50 The difference between 50 get .

[0113] Finally, the server will and Combine into an interval , that is, relative coefficient of variation RVC.

[0114] Finally, the server interprets the calculated relative coefficient of variation RVC.

[0115] This coefficient represents the degree of variability or uncertainty range of the simulation results.

[0116] For example, if the RVC value is large, it means that the uncertainty of the simulation results is high and may be affected by multiple factors; conversely, if the RVC value is small, it means that the simulation results are relatively stable and the uncertainty is low.

[0117] Through this series of steps, the server can quantify the uncertainty range of motor vehicle emission calculation results based on the Monte Carlo simulation method, providing more comprehensive and reliable data support for subsequent relevant regulation formulation and decision-making.

[0118] In the embodiment of the present invention, the aforementioned step S204 may be implemented through the following example execution.

[0119] Using the formula: ; Determining factors affecting multiple emission indicators for the target area;

[0120] in, Calculation results for motor vehicle emissions; is the weight coefficient; for The i-th influencing factor among the influencing factors; is an empirical constant; is the regression factor The F test statistic value of It is the maximum value of the statistic of the regression F test.

[0121] In an embodiment of the present invention, illustratively, the server first collects the calculation results of motor vehicle emissions in a single target area (such as Beijing) or multiple target areas (different provinces or cities). These data include the total emissions of motor vehicles of different models and under different emission standards.

[0122] At the same time, the server also collects data on multiple possible influencing factors, such as the number of motor vehicles, mileage, average speed, road congestion, meteorological conditions, regional urbanization level, etc.

[0123] These data will serve as the basis for subsequent analysis.

[0124] Next, the server constructed a multivariate linear regression model using the collected data.

[0125] In this model, the calculated results of motor vehicle emissions (Y) are used as the dependent variable, and the various influencing factors is taken as the independent variable.

[0126] At the same time, the server also assigns a weight coefficient to each influencing factor , to reflect its impact on motor vehicle emissions.

[0127] In addition, an empirical constant is introduced into the model , to account for other influencing factors that are not explicitly included in the model.

[0128] After building the regression model, the server performed an F test to evaluate the significance of the model.

[0129] Specifically, the server calculates each influencing factor The F test statistic value , and find the maximum value among them .

[0130] These F-test statistic values ​​reflect the degree of influence of each influencing factor on the calculated results of motor vehicle emissions, and the maximum value represents the most influential factor.

[0131] Finally, based on the results of the F test, the server determined the influencing factors of multiple emission indicators for the target area.

[0132] Specifically, the server determines those influencing factors with larger F test statistic values ​​(ie, higher significance) as key influencing factors.

[0133] For example, if the F-test statistic value of a certain influencing factor is much larger than other factors, then it is likely to be one of the main reasons for the change in motor vehicle emissions.

[0134] Through this series of steps, the server can accurately identify the main influencing factors of motor vehicle emissions in the target area, and provide strong data support for the subsequent formulation of relevant regulations and emission control.

[0135] For example, traffic management strategies can be adjusted, urban planning can be optimized, and clean energy can be promoted based on these influencing factors to reduce the impact of motor vehicle emissions on the environment.

[0136] In the embodiments of the present invention, the following implementation modes are also provided.

[0137] Using the formula: Calculate the impact of urbanization on non-conventional organic pollutant emissions from motor vehicles in different regions;

[0138] in, For Region The impact of unconventional organic pollutants emitted by motor vehicles, represents the coordinates of sample point i, Refers to the spatial geographic location function, which represents the jth regression coefficient at sampling point i; is the jth unconventional organic pollutant factor at sample point i; is the empirical constant at sample point i.

[0139] In the embodiment of the present invention, illustratively, the server first determines the target area for research, such as one or more cities (provinces) and their surrounding areas.

[0140] Within this area, the server selected a series of representative sampling points, which may be located in different locations such as city centers, suburbs, transportation hubs, etc., to fully reflect the impact of urbanization on the emission of unconventional organic pollutants from motor vehicles.

[0141] Each sampling point has a clear coordinate , which is used for subsequent spatial analysis.

[0142] Next, the server collected factor data related to the emission of unconventional organic pollutants from motor vehicles at each sampling point. .

[0143] These data may include the distribution of emission sources, traffic flow, road types, economic development status, urbanization level, etc.

[0144] These data are the basis for calculating the impact and can reflect the characteristics and intensity of motor vehicle emissions in different regions. The server constructs a spatial geographic location function based on the coordinates of the sampling points and the collected data. .

[0145] This function takes into account the impact of geographical location on motor vehicle emissions and can reflect the spatial differences between different regions.

[0146] For example, for a specific city, the city center may lead to higher pollutant emissions due to traffic congestion and dense buildings, while the suburbs may have relatively low pollutant emissions due to less traffic.

[0147] For many cities, pollutant emissions may be higher in cities with high urbanization levels and dense transportation networks, while pollutant emissions may be relatively low in cities with low urbanization levels and scattered transportation networks.

[0148] Therefore, using the constructed spatial geographic location function and the collected unconventional organic pollutant factor data, the server conducted a geographically weighted regression analysis.

[0149] Through regression analysis, the server was able to quantify each factor Emissions of unconventional organic pollutants from motor vehicles degree of impact.

[0150] This process takes into account the interactions and combined effects of multiple factors.

[0151] Finally, based on the results of the regression analysis, the server calculated the impact of urbanization on the emission of unconventional organic pollutants from motor vehicles in different regions. .

[0152] This result is a specific value or indicator that can intuitively reflect the pressure of motor vehicle emissions on the environment during the urbanization process.

[0153] For example, in a certain area The higher values ​​indicate that motor vehicle emissions in this area contribute more to unconventional organic pollutants and that more stringent emission control measures may be needed.

[0154] Through this series of steps, the server can accurately evaluate the impact of urbanization on the emission of unconventional organic pollutants from motor vehicles in different regions, and provide strong data support for environmental protection and urban planning.

[0155] In order to more clearly describe the solution provided by the embodiments of the present application, a relatively complete implementation scheme is provided below.

[0156] The present invention takes into account emission standards, vehicle types, correction factors and vehicle retirement.

[0157] It can simulate the dynamic emission process of unconventional organic pollutants (such as polycyclic aromatic hydrocarbons) better than existing motor vehicle emission models, focuses on the physical and chemical properties of unconventional organic pollutants, expands the sample size through Monte Carlo simulation, and improves the calculation accuracy of unconventional pollutants in motor vehicles.

[0158] When calculating motor vehicle pollutant emissions, three key factors should be considered: the average annual mileage of motor vehicles (VKT), emission factors (EFs) and the number of motor vehicles (VP).

[0159] The basic formula of the model is as follows: In the formula, Represents the total amount of pollutant emissions; is the serial number of the motor vehicle of category i; is the average annual mileage of the i-th category of motor vehicles (km), is the emission factor of the i-th type of motor vehicle (g.km -1 ).

[0160] However, this equation cannot analyze the impact of specific vehicle models, motor vehicle emission standards and dynamic changes in the number of motor vehicles (increases and scrapping) on ​​the total amount of pollutant emissions, nor does it take into account time and location restrictions on emissions. Most importantly, the above model does not yet consider persistent organic pollutants emitted by motor vehicles.

[0161] Therefore, this study proposes a new estimation method that comprehensively considers all the above factors, namely the Dynamic Vehicle Emission Model (DVEM).

[0162] Monte Carlo simulation is used to perform random simulation on all parameters in the equation. The calculation formula for motor vehicle pollutant emissions is as follows: In the formula, It represents the total emission of pollutants in province m at a specific time t (t); It represents the total emission of pollutants from all types of motor vehicles in province m that meet emission standard j at a specific time t (t); It represents the total emission of pollutants from type i motor vehicles in province m under all emission standards at a specific time t (t); It refers to the number of type i motor vehicles that meet j emission standards in province m at a specific time t; is the mileage (km) of type i motor vehicles that meet j emission standards in province m at a specific time t, which is mainly derived from existing research papers and reports; It refers to the emission factor (g.km) of type i motor vehicles in province m that meet j emission standards at a specific time t. -1).

[0163] The changes in the DVEM model parameters are as follows: ; Refers to the baseline (reference) emission factor (g.km) of type i motor vehicle under the emission standard of province j at a specific time t. -1 ).

[0164] For conventional pollutants, the source can be open source databases; for unconventional organic pollutants (such as polycyclic aromatic hydrocarbons), the source can be published papers and research reports.

[0165] is the environmental correction factor for type i motor vehicle under emission standard j in province m at a specific time t.

[0166] is the degradation factor of type i motor vehicle under emission standard j in province m at a specific time t.

[0167] is the temperature correction factor.

[0168] is the humidity correction factor; is the altitude correction factor.

[0169] and The data comes from the "Technical Guidelines for the Compilation of Road Motor Vehicle Emission Inventories (Trial)".

[0170] In order to calculate the number of motor vehicles in each province by fleet type and emission standard, the following steps were adopted: First, based on the motor vehicle registration information of each province in the National Bureau of Statistics database, the number of vehicles by vehicle type and emission standard in the 31 provinces, autonomous regions, and municipalities (excluding Hong Kong, Macao, and Taiwan) across the country was obtained.

[0171] Motor vehicles are classified into nine types based on the availability of emission factor data.

[0172] At the same time, according to the open source motorcycle database, the number of motor vehicles eliminated or increased under different models and emission standards in each province can be obtained. The specific calculation formula is as follows: In the formula, represents the number of i-type vehicles scrapped at a certain time t-1 in the j emission standard of province m, is the number of newly added type i motor vehicles with type j emission standard in province m at a certain time t.

[0173] It is worth noting that in the calculation of emissions of unconventional organic pollutants (such as polycyclic aromatic hydrocarbons), since the emission factors are derived from measured data in published literature from various places, the input data may be uncertain, resulting in certain differences between the simulation results and the actual values.

[0174] Typically, model parameters and independent variables follow their own probability density distributions.

[0175] Therefore, the key to improving the assessment accuracy is to integrate these probability distributions into the calculation of unconventional organic pollutant emissions.

[0176] In this regard, the advantage of Monte Carlo simulation is that it fully considers the uncertainty of model parameters and independent variables, which can make the calculation results of unconventional organic pollutant emissions more accurate.

[0177] Specifically, the parameters and independent variables of each model are randomly sampled from their theoretical probability distributions in a Monte Carlo simulation.

[0178] In this study, the benchmark emission factors (BEFs) of the main variables conform to the Weibull distribution and the Gamma distribution, and the VP and VKT conform to the uniform distribution.

[0179] According to the law of large numbers, the more Monte Carlo simulations are performed, the richer the possible parameter values ​​generated will be, and their frequencies will be closer to probability.

[0180] In all the above equations, m represents 31 provinces, autonomous regions and municipalities; i represents the type of motor vehicle; j represents the national motor vehicle pollutant emission limit standard (National I to National VI); t represents the statistical time.

[0181] The present invention optimizes the dynamic emission model of a motor vehicle, and the overall uncertainty of the model is derived from the uncertainty set of 10,000 samples of Monte Carlo simulation.

[0182] Therefore, this study obtained the probability distribution function of each important parameter in the form of statistical distribution, and used the Q25 (25% quantile) and Q75 (75% quantile) values ​​of its distribution curve as the lower and upper limits of the parameter uncertainty range.

[0183] The sources of uncertainty mainly include the following:

[0184] (1) Under different emission standards, the emission factors of different vehicle models conform to their corresponding probability distribution functions.

[0185] Emission factor data mainly come from different literature.

[0186] However, in different documents, the experimental methods, morphological distribution, vehicle operating conditions, driving conditions, etc. for testing motor vehicle pollutant emission factors are different.

[0187] Therefore, we simulated the emission factor data of each vehicle model 10,000 times through Monte Carlo simulation, calculated and fitted the probability distribution function of the emission factors of different vehicle models, and selected the optimal function for calculation.

[0188] However, there will be slight differences in the random numbers selected for each Monte Carlo simulation, so the median value of the parameter is not fixed, which is also one of the uncertainties of the model.

[0189] However, as the number of simulations increases, the differences in the median values ​​of the parameters become almost negligible.

[0190] In addition, expanding the sample size through Monte Carlo simulation may indirectly amplify the uncertainty of the emission factors themselves.

[0191] (2) When calculating the emissions of unconventional organic pollutants (such as polycyclic aromatic hydrocarbons), due to the lack of an accurate emission factor database, the emission factors are mainly derived from existing references. This leads to uncertainty in model parameters and input data, and the model simulation results may differ from the actual values.

[0192] Monte Carlo simulations can also quantify the uncertainty of the DVEM model in estimating PAHs, which is based on the probability distribution and relative coefficient of variation (RVC) of total PAHs emissions, thereby simulating the uncertainty range of the dependent variable.

[0193] Among them, Q25 represents the 25% quantile, Q50 represents the 50% quantile, and Q75 represents the 75% quantile.

[0194] After calculating the emissions of different models under different emission standards and the total emissions of a region, we believe that changes in emission standards and related regulations, the increase in the number of motor vehicles and rapid urbanization are key factors affecting motor vehicle pollutant emissions.

[0195] Therefore, this study used stepwise regression to evaluate the specific influencing factors of urbanization on inter-provincial motor vehicle pollutant emissions.

[0196] Stepwise regression analysis strongly relies on the degree of curve fit and requires parameters to be set in advance.

[0197] The specific calculation formula is as follows: First, for independent variables and dependent variables Establish a univariate regression model and calculate the regression factor The F test statistic value of ,Will Set to maximum value.

[0198] For a given significance level , the corresponding critical value is expressed as in, .

[0199] Then the independent variable was introduced into the regression model and use Represents the collection of selected variables.

[0200] The specific expression is as follows: The above equation establishes the dependent variable and the subset of independent variables , The multiple regression model of .

[0201] Calculate the F-test statistic for the regression factor of the variable and express it as , select the maximum value and mark it as .

[0202] and label the corresponding independent variable footnotes as .

[0203] Next, repeat the above steps, each time reselecting an independent variable that is not introduced into the regression model, until the model passes the verification and no new variables are introduced.

[0204] This study uses the observation data of motor vehicle pollutant emissions and social economy as sampling units according to certain spatial locations. As the geographic spatial coordinates of the sampling points change, the structure and relationship between the independent variables and the dependent variables may change, which will produce "spatial non-stationarity".

[0205] Therefore, if we want to analyze the impact of urbanization on motor vehicle pollutant emissions in different regions, we innovatively use the Geographically Weighted Regression (GWR) method. The core calculation formula of Geographically Weighted Regression is as follows: In the above formula, represents the coordinates of sample point i, Refers to the spatial geographic location function, which represents the j-th regression coefficient at sampling point i and can be calculated through spatial weight analysis.

[0206] Please refer to Figure 2 , Figure 2 A motor vehicle dynamic emission analysis device 110 based on Monte Carlo simulation provided in an embodiment of the present invention includes:

[0207] An acquisition module 1101 is used to acquire multiple emission indicators for a target area;

[0208] The calculation module 1102 is used to call the motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation; input the multiple emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation results of the target area; based on the motor vehicle emission calculation results, determine the influencing factors of the multiple emission indicators for the target area.

[0209] It should be noted that the implementation principle of the aforementioned vehicle dynamic emission analysis device 110 based on Monte Carlo simulation can refer to the implementation principle of the aforementioned vehicle dynamic emission analysis method based on Monte Carlo simulation, which will not be repeated here. It should be understood that the division of the various modules of the above device is only a division of logical functions, and in actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated.

[0210] And these modules can all be implemented in the form of software calling through processing elements; or all be implemented in the form of hardware; or some modules can be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware.

[0211] For example, the vehicle dynamic emission analysis device 110 based on Monte Carlo simulation can be a separate processing element, or can be integrated into a chip of the above device, or can be stored in the memory of the above device in the form of program code, and can be called and executed by a processing element of the above device. The implementation of other modules is similar.

[0212] In addition, all or part of these modules can be integrated together or implemented independently.

[0213] The processing element described herein may be an integrated circuit having signal processing capabilities.

[0214] In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in a processor element or an instruction in software form.

[0215] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more microprocessors (digital signal processors, DSPs), or, one or more field programmable gate arrays (FPGAs), etc.

[0216] For another example, when a certain module above is implemented in the form of a processing element scheduling a program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program codes.

[0217] For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0218] An embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned motor vehicle dynamic emission analysis device 110 based on Monte Carlo simulation.

[0219] like Figure 3 As shown, Figure 3 A structural block diagram of a computer device 100 provided in an embodiment of the present invention.

[0220] The computer device 100 includes a vehicle dynamic emission analysis device 110 based on Monte Carlo simulation, a memory 111 , a processor 112 and a communication unit 113 .

[0221] To achieve data transmission or interaction, the memory 111 , the processor 112 , and the communication unit 113 are electrically connected to each other directly or indirectly.

[0222] For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0223] The vehicle dynamic emission analysis device 110 based on Monte Carlo simulation includes at least one software function module which can be stored in the memory 111 in the form of software or firmware or fixed in the operating system (OS) of the computer device 100 .

[0224] The processor 112 is used to execute the vehicle dynamic emission analysis device 110 based on Monte Carlo simulation stored in the memory 111 , such as the software function modules and computer programs included in the vehicle dynamic emission analysis device 110 based on Monte Carlo simulation.

[0225] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program is executed, the computer device where the readable storage medium is located is controlled to execute the aforementioned motor vehicle dynamic emission analysis method based on Monte Carlo simulation.

[0226] In summary, Monte Carlo simulation can comprehensively account for motor vehicle emissions, and is especially suitable for unconventional organic pollutants (such as polycyclic aromatic hydrocarbons) with insufficient emission factor data. The dynamic emission model takes into account multiple factors to achieve multi-scale analysis in time and space, and can be combined with data for continuous optimization, thereby effectively exploring the emission driving mechanisms of motor vehicle pollutants in different regions and updating the emission database.

[0227] The foregoing description, for purposes of illustration, has been presented with reference to specific embodiments.

[0228] However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed.

[0229] Numerous modifications and variations are possible in light of the above teaching.

[0230] The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and utilize various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A motor vehicle dynamic emission analysis method based on Monte Carlo simulation, characterized in that: include: Obtain multiple emission indicators for target areas; Calling a motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation; Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation results of the target area; Determining influencing factors of a plurality of emission indicators for the target area based on the motor vehicle emission calculation results; The determining, based on the motor vehicle emission calculation result, influencing factors of multiple emission indicators for the target area includes: Using the formula: Determining factors affecting multiple emission indicators for the target area; Among them, Y is the calculated result of motor vehicle emissions; β is the weight coefficient; ∈ is the empirical constant; is the regression factor X i The F test statistic value of It is the maximum value of the F test statistic of the regression factor.

2. The method according to claim 1, characterized in that: The step of inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain the motor vehicle emission calculation result of the target area includes: Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model: Obtaining calculation results of motor vehicle emissions in the target area; Among them, Q m,t It represents the total emission of pollutants in province m at a specific time t, which is the calculated result of motor vehicle emissions in the target area; Q m,j,t It represents the total emission of pollutants by all types of motor vehicles in province m that meet emission standard j at a specific time t; Q m,i,t It represents the total emission of pollutants from type i motor vehicles in province m under all emission standards at a specific time t; VP m,i,j,t VKT refers to the number of type i vehicles that meet j emission standards in province m at a specific time t; m,i,j,t is the mileage of type i motor vehicles that meet j emission standards in province m at a specific time t; EF m,i,j,t It refers to the emission factor of type i motor vehicle that meets j emission standard in province m at a specific time t.

3. The method according to claim 2, characterized in that The EF m,i,j,t Calculated by, among other things: EF m,i,j,t =BEF m,i,j,t ×λ m,i,j,t ×θ m,i,j,t Among them, λ m,i,j,t =λ Temp ×λ RH ×λ Height ,λ m,i,j,t is the environmental correction factor for type i motor vehicle under emission standard j in province m at a specific time t; θ m,i,j,t is the degradation factor of vehicle type i under emission standard j in province m at a specific time t; Temp is the temperature correction factor; RH is the humidity correction factor; Height is the altitude correction factor.

4. The method according to claim 2, characterized in that: The VP m,i,j,t Calculated by, among other things: VP m,i,j,t =VP m,i,j,t-1 +ΔVP m,i,j,t -S m,i,j,t-1 Among them, S m,i,j,t-1 represents the number of i-type vehicles scrapped at a certain time t-1 in the j emission standard of province m, ΔVP m,i,j,t is the number of newly added type i motor vehicles with type j emission standard in province m at a certain time t.

5. The method according to claim 1, characterized in that The method further comprises: Based on the uncertainty range of the motor vehicle emission calculation results of Monte Carlo simulation, the formula is: Simulating uncertainty ranges for the calculated emissions of the motor vehicle; Among them, RVC is the relative coefficient of variation, Q 25 represents the 25% quantile, Q 50 represents the 50% quantile, Q 75 Indicates the 75% quantile.

6. The method according to claim 1, characterized in that The method further comprises: Using the formula: Calculate the impact of urbanization on non-conventional organic pollutant emissions from motor vehicles in different regions; Among them, Y I is the impact of non-conventional organic pollutant emissions from motor vehicles in region I, (μ i ,v i ) represents the coordinates of sample point i, β j (μ i ,ν i )(j=0,1,…,p) refers to the spatial geographic location function, which represents the jth regression coefficient at sampling point i; X ij is the jth unconventional organic pollutant factor at sampling point i; ε i is the empirical constant at sampling point i.

7. A motor vehicle dynamic emission analysis device based on Monte Carlo simulation, characterized in that: include: An acquisition module, used for acquiring multiple emission indicators for a target area; A calculation module, used for calling a motor vehicle dynamic emission calculation model determined based on Monte Carlo simulation; Inputting the plurality of emission indicators into the motor vehicle dynamic emission calculation model to obtain motor vehicle emission calculation results for the target area; determining influencing factors for the plurality of emission indicators for the target area based on the motor vehicle emission calculation results; The calculation module is specifically used for: Using the formula: Determining factors affecting multiple emission indicators for the target area; Among them, Y is the calculated result of motor vehicle emissions; β is the weight coefficient; ∈ is the empirical constant; is the regression factor X i The F test statistic value of It is the maximum value of the F test statistic of the regression factor.

8. A computer device, characterized in that: The computer device includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device executes the motor vehicle dynamic emission analysis method based on Monte Carlo simulation as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium includes a computer program, and when the computer program is executed, the computer device where the readable storage medium is located is controlled to execute the motor vehicle dynamic emission analysis method based on Monte Carlo simulation as described in any one of claims 1 to 6.

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