A method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition

Abstract

This application provides a method for calculating a dynamic road-level non-exhaust particulate matter emission inventory for electrification transition. The method includes: acquiring multi-source traffic data; localizing the basic non-exhaust particulate matter emission factors based on a local fleet dataset to obtain a dynamic emission factor library; establishing a vehicle speed-flow model based on real-time vehicle speed and traffic flow data from the multi-source traffic data, and obtaining target traffic flow data for each road segment within a target time period using the vehicle speed-flow model; acquiring the target traffic flow data, vehicle type proportion data from the local fleet dataset, and the dynamic emission factor library, and inputting them into a pre-established road vehicle emission model to obtain a road-level non-exhaust particulate matter dynamic emission inventory. This application significantly improves the spatiotemporal accuracy and local adaptability of the emission inventory through multi-source data fusion and precise quantification of electrification characteristics, providing technical support for traffic environment management in the context of electrification transition.

Description

Technical Field

[0001] This invention relates to the field of road emission monitoring and treatment, and specifically to a method for calculating a dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition. Background Technology

[0002] Although electric vehicles can significantly reduce exhaust emissions, they still generate non-exhaust particulate matter such as brake wear, tire wear, and road wear during operation. Existing research shows that with advancements in exhaust gas treatment technologies, the proportion of non-exhaust emissions in traffic-source particulate matter is gradually increasing. Especially in the context of electrification, the increased vehicle weight due to the inclusion of batteries in electric vehicles exacerbates tire and road wear, while regenerative braking systems further alter brake wear characteristics. Therefore, in the context of electrification, non-exhaust emissions exhibit significant scenario dependence and spatiotemporal heterogeneity, making the construction of a highly accurate inventory of non-exhaust particulate emissions a crucial issue in current atmospheric environmental research.

[0003] Motor vehicle emission inventories are a core tool for pollution source analysis and control strategy development. Existing methods for constructing motor vehicle emission inventories are mostly based on macro-level emission models (such as the COPERT model), combining parameters such as vehicle ownership, fuel consumption, mileage, and fleet structure to estimate total emissions at the annual or monthly scale for a region, and then spatially allocating them using indicators such as road density. However, these methods are typical static accounting models, suffering from low temporal resolution, coarse spatial characterization, and difficulty in reflecting traffic congestion and road differences. To address the need for dynamic emission accounting, existing technologies utilize big data on traffic (such as cross-sectional data, GPS trajectories, and manual sampling) to construct high-resolution emission inventories. For example, a patent with publication number CN111612670A, entitled "A Method, Apparatus and Computer Equipment for Constructing Motor Vehicle Emission Inventories," discloses an emission inventory construction method based on multi-source traffic data and a traffic flow spatiotemporal distribution prediction model. By integrating road network information with traffic flow, vehicle speed, and vehicle type distribution, it achieves rapid generation of a road-level high-resolution emission inventory. Another patent with publication number CN111696369A, entitled "A Method for Predicting Citywide Road Traffic Flow by Time and Vehicle Type Based on Multi-Source Geospatial Big Data," discloses a method for predicting citywide road traffic flow by time and vehicle type based on multi-source geospatial big data. This method uses multi-source big data to fit the traffic flow of the entire road network and obtains 24-hour vehicle flow data by vehicle type through a two-step model framework, and calculates road pollutant emissions by combining emission factors.

[0004] However, such models have not yet developed a road-level, high-temporal-resolution dynamic non-exhaust emission parameterization scheme that can synchronously respond to dynamic changes in traffic demand and the evolution of fleet electrification technology. This makes it difficult to accurately characterize the emission characteristics of non-exhaust particulate matter under complex urban traffic conditions during the electrification transition. Therefore, developing a road-level dynamic non-exhaust particulate matter emission inventory calculation method and device for the electrification transition has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition, which can effectively solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a method for calculating a dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition, comprising the following steps:

[0008] Acquire multi-source traffic data, preprocess the multi-source traffic data to obtain a local fleet basic dataset, and obtain basic non-exhaust particulate matter emission factors from a preset database.

[0009] Based on the local fleet dataset, the basic non-exhaust particulate emission factors are localized to obtain a dynamic emission factor library. Specifically, this includes: constructing a localized non-exhaust particulate emission factor library: using emission factors from an internationally authoritative emission factor database as a foundation, establishing a vehicle weight regression model based on the curb weight data of each vehicle model to achieve parameterized expression of particulate emission factors related to brake wear and tire wear for different vehicle models; combining local vehicle fleet composition data, vehicle weight data, and average load rate data to localize and expand the emission factors; introducing a regenerative braking correction coefficient for the regenerative braking system of electric vehicles, constructing a speed response function based on real-time traffic speed data, and building a dynamic emission factor library that varies with vehicle model, electrification rate, and driving speed.

[0010] Based on the real-time vehicle speed data and traffic flow data in the multi-source traffic data, a vehicle speed and traffic flow model is established, and the target traffic flow data of each road segment within the target time period is obtained through the vehicle speed and traffic flow model.

[0011] The target traffic flow data, the vehicle type proportion data in the local fleet basic dataset, and the dynamic emission factor library are obtained and input into a pre-established road vehicle emission model to obtain a road-level non-exhaust particulate matter dynamic emission inventory.

[0012] Preferably, as one possible implementation; the step of acquiring multi-source traffic data and preprocessing the multi-source traffic data to obtain a local fleet basic dataset includes:

[0013] Acquire road traffic situation data, local motor vehicle fleet composition data, vehicle weight data, average load rate data, and real-time road monitoring video data;

[0014] The road traffic situation data is subjected to coordinate transformation and missing data completion processing to obtain a standardized road traffic situation dataset;

[0015] The road monitoring video data is processed by a pre-trained convolutional neural network model to detect and classify vehicle targets, thereby obtaining the proportion of each type of vehicle.

[0016] Based on the local motor vehicle fleet composition data, the vehicle weight data, and the average load rate data, an effective vehicle weight database by vehicle type is established, and the standardized road traffic situation dataset, the proportion information of each type of vehicle, and the effective vehicle weight database are determined as the basic dataset of the local fleet.

[0017] Preferably, as an feasible implementation, the following approach is adopted: Based on emission factors from an internationally authoritative emission factor database, a vehicle weight regression model is established using curb weight data for each vehicle model to achieve parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models; localized corrections and extensions are performed on the emission factors by combining local vehicle fleet composition data, vehicle weight data, and average load rate data; a regenerative braking correction coefficient is introduced for the regenerative braking system of electric vehicles; a speed response function is constructed based on real-time traffic speed data; and a dynamic emission factor library is built that varies with vehicle model, electrification rate, and driving speed, including:

[0018] Based on the average curb weight data of each vehicle model in the effective vehicle weight database, a regression model is used to establish a functional relationship between the basic non-exhaust particulate matter emission factor and vehicle weight, resulting in the first corrected emission factor. Specifically, based on the baseline emission factor dataset extracted from the EMEP / EEA database, and according to the average curb weight data of each vehicle model, a regression model is used to establish a functional relationship between emission factors from different sources and particle sizes and vehicle weight, achieving a parameterized expression of brake wear and tire wear particulate matter emission factors. The regression model formula is as follows, where EF represents the first corrected emission factor. This represents the vehicle weight, and parameters b and c are constants obtained after fitting.

[0019]

[0020] For new energy vehicle models in the local fleet basic dataset, the braking energy recovery ratio of these models is obtained, and a regenerative braking correction coefficient is determined based on this ratio. Specifically, a fitting formula is used to classify local vehicle models (distinguishing between fuel vehicles and electric vehicles) and perform emission factor completion calculations. For electric vehicles, considering that their regenerative braking system can reduce the frequency of mechanical brake use, thereby reducing brake wear particulate matter emissions, a regenerative braking correction coefficient k is introduced to differentiate the emission factors for different vehicle models, establishing a local vehicle non-particulate matter emission factor library considering electric vehicles. The regenerative braking correction coefficient k is related to the vehicle's braking energy recovery ratio ŋ, and is calculated as follows:

[0021]

[0022] Where ŋ represents the regenerative braking ratio under typical operating conditions. When ŋ = 0, it indicates no energy recovery. Given the significant uncertainties in vehicle design and driving conditions, the regenerative braking ratio under typical operating conditions can be determined based on vehicle announcement parameters, chassis dynamometer test results, or publicly available research reports.

[0023] A vehicle weight regression model was established based on the curb weight data of various vehicle models to achieve a parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models. This includes introducing load correction to quantify the impact of vehicle load on non-exhaust emissions, as shown in the following formula:

[0024]

[0025]

[0026]

[0027] in and These represent the particulate matter emission factors from tire and brake wear of heavy-duty vehicles, respectively. and This indicates the emission factor for the corresponding passenger vehicle model; Indicates the number of axles. and Indicated by load rate The calculated correction factor, i.e., the load correction factor;

[0028] A speed response function is constructed based on the real-time vehicle speed data. A speed correction factor is determined through the speed response function. The first corrected emission factor, regenerative braking correction coefficient, load correction factor, and speed correction factor are stored to obtain the dynamic emission factor library.

[0029] Preferably, as one possible implementation, the speed response function is a piecewise function, which sets different speed correction factor values ​​or linear relationships according to different speed ranges.

[0030] Preferably, as one possible implementation; the step of obtaining the regenerative braking correction coefficient for the new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking correction coefficient, includes:

[0031] Obtain the vehicle announcement parameters and chassis dynamometer test results of the new energy vehicle from the local fleet basic dataset;

[0032] Based on the vehicle announcement parameters and the chassis dynamometer test results, the braking energy recovery ratio of the new energy vehicle under the target operating conditions is determined;

[0033] Determine whether the braking energy recovery ratio is less than a preset threshold;

[0034] If the braking energy recovery ratio is less than a preset threshold, then when the new energy vehicle is in a state of no energy recovery, the regenerative braking correction coefficient is set to a first preset value.

[0035] If the regenerative braking ratio is not less than a preset threshold, the regenerative braking correction coefficient is calculated according to the regenerative braking ratio and the preset correction coefficient calculation formula. The regenerative braking correction coefficient is used to characterize the degree to which the regenerative braking system of the new energy vehicle reduces the frequency of mechanical brake use.

[0036] Preferably, as one possible implementation; the step of obtaining the regenerative braking correction coefficient for the new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking correction coefficient, includes:

[0037] The regenerative braking correction coefficient k is related to the vehicle's braking energy recovery ratio ŋ, and is calculated as follows: .

[0038] Preferably, as one possible implementation; the step of establishing a vehicle speed-flow model based on real-time vehicle speed data and flow data from the multi-source traffic data, and obtaining target traffic flow data for each road segment within the target time period through the vehicle speed-flow model, includes:

[0039] Obtain real traffic flow data for different time periods and different road types, as well as the corresponding real-time vehicle speed data;

[0040] The actual traffic flow data and the real-time vehicle speed data are obtained, and the parameters are fitted by inputting them into a preset function model framework to obtain the congestion density parameter and the free flow velocity parameter.

[0041] The vehicle speed-flow rate model is established based on the blockage density parameter and the free flow velocity parameter.

[0042] The real-time vehicle speed data of each road segment within the target time period is obtained, and the data is input into the vehicle speed-flow model for inversion calculation to obtain the target traffic flow data of each road segment within the target time period.

[0043] Preferably, as one possible implementation, the vehicle type ratio data for each road segment is obtained through the recognition results of the road monitoring images by the convolutional neural network model.

[0044] Preferably, as one possible implementation, the step of acquiring the target traffic flow data, the vehicle type proportion data in the local fleet basic dataset, and the dynamic emission factor library, inputting them into a pre-established road vehicle emission model, and obtaining a road-level non-exhaust particulate matter dynamic emission inventory includes:

[0045] For each road segment, obtain the length of the road segment;

[0046] Acquire the target traffic flow data, the vehicle type ratio data, the dynamic emission factor library, and the road segment length, and input them into the road vehicle emission model;

[0047] The pollutant emissions of each type of vehicle at each time point are calculated using the road vehicle emission model to obtain the initial emission results;

[0048] The initial emission results are processed by merging adjacent road segments and repairing missing data to obtain the dynamic emission inventory of non-exhaust particulate matter at the road level.

[0049] Preferably, as one possible implementation; the step of merging adjacent road segments and repairing missing data on the initial emission results to obtain the road-level non-exhaust particulate matter dynamic emission inventory includes:

[0050] Determine whether there is at least one missing or abnormal data in the initial emission results;

[0051] If the initial emission results contain missing or abnormal data, then obtain statistical characteristic data of roads of the same type in the same area.

[0052] Based on the statistical characteristic data, the missing or abnormal data are supplemented and corrected to obtain the corrected emission results;

[0053] The emission data belonging to adjacent road segments in the corrected emission results are merged to obtain the road-level non-exhaust particulate matter dynamic emission inventory, wherein the road-level non-exhaust particulate matter dynamic emission inventory includes at least a first-size particulate matter emission inventory and a second-size particulate matter emission inventory.

[0054] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition.

[0055] The present invention provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the aforementioned method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] This invention addresses the characteristics of increased vehicle weight and the application of regenerative braking systems in the context of electrification. It constructs a parameterized expression for braking and tire wear emission factors based on a vehicle weight regression model, introduces a regenerative braking correction coefficient based on the braking energy recovery ratio, and builds a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed. This fills the gap in existing technologies regarding the lack of quantitative correction mechanisms for electrification-specific factors, enabling emission estimation results to more accurately reflect the actual impact of the electrification transition. Furthermore, existing methods directly use internationally recognized emission factor databases without fully considering local fleet structure characteristics and differences in road traffic conditions. This invention, based on an authoritative international emission factor database, incorporates local vehicle fleet composition data, vehicle weight data, and average load rate data for localized correction. It introduces load rate and speed corrections to reflect local road traffic characteristics, and the established localized dynamic emission factor library effectively improves the accuracy and representativeness of emission estimation results.

[0058] This invention integrates road traffic situation data, fleet composition data, vehicle weight data, road monitoring video data, and international authoritative emission factor databases through a multi-source data acquisition and preprocessing unit. Based on a convolutional neural network model, it achieves intelligent vehicle type identification and statistics, and completes the collaborative coupling calculation of multi-source data within the framework of a near-real-time road motor vehicle emission model, forming a complete technical solution from data acquisition to inventory generation. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the overall process of the dynamic emission inventory calculation method for road-level non-exhaust particulate matter for electrification transformation proposed in this invention.

[0060] Figure 2 This is a schematic diagram of the architecture principle of the road-level non-exhaust particulate matter dynamic emission inventory calculation device for electrification transformation proposed in this invention.

[0061] Labels: Multi-source data acquisition and preprocessing unit 410, emission factor localization unit 420, emission inventory construction unit 430. Detailed Implementation

[0062] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0064] Example 1

[0065] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0066] This invention discloses a method for calculating a dynamic road-level non-exhaust particulate matter emission inventory for electrification transition. The method mainly includes: collecting multi-source traffic data and preprocessing it; constructing a localized non-exhaust particulate matter emission factor library based on fleet characteristics; and generating a road-level non-exhaust particulate matter emission inventory by integrating multi-source traffic datasets and emission factor databases and using a traffic emission model.

[0067] Furthermore, for the method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transformation, the multi-source data includes road traffic situation data obtained through the Gaode Open Platform API, local vehicle fleet composition data, vehicle weight data, average load rate data, vehicle type and reference brake wear data from EMEP / EEA reports, tire wear emission factor data, and real-time collected road monitoring video data. Preprocessing includes coordinate transformation, missing data completion, intelligent identification and statistical analysis of vehicle type proportions based on a convolutional neural network (CNN) model, and processing into a standardized format to provide data input for subsequent emission calculations.

[0068] Furthermore, for the aforementioned method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition, based on the EMEP / EEA emission factor database, local fleet characteristic parameters and corrections for the characteristics of new energy vehicles are introduced. This includes establishing vehicle model mapping, vehicle weight regression fitting modeling, incorporating regenerative braking correction, load rate correction, and speed correction, dynamically adjusting tire wear and brake wear particulate matter emission factors, and establishing a localized dynamic emission factor database suitable for the study area, thereby improving the accuracy and representativeness of emission estimation results.

[0069] Furthermore, for the method for calculating the dynamic non-exhaust particulate matter emission inventory for road-level electrification transformation, vehicle speed-flow modeling for different road types is performed by combining real-time vehicle speed and traffic flow results from traffic monitoring images. Within the framework of the near-real-time road vehicle emission model (ROE), traffic activity data input is coupled with a localized emission factor library for calculation, ultimately generating a dynamic non-exhaust particulate matter emission inventory dataset with hourly temporal resolution and road-level spatial resolution.

[0070] This invention provides a method for calculating a dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition, comprising the following steps:

[0071] S1. Acquire multi-source traffic data, preprocess the multi-source traffic data to obtain a local fleet basic dataset, and obtain basic non-exhaust particulate matter emission factors from a preset database.

[0072] S2. Based on the local fleet basic dataset, the basic non-exhaust particulate emission factors are locally modified to obtain a dynamic emission factor library. Specifically, this includes: constructing a localized non-exhaust particulate emission factor library: based on emission factors in an internationally authoritative emission factor database, a vehicle weight regression model is established based on the curb weight data of each vehicle model to achieve parameterized expression of particulate emission factors for brake wear and tire wear of different vehicle models; combined with local motor vehicle fleet composition data, vehicle weight data, and average load rate data, the emission factors are locally modified and expanded; a regenerative braking correction coefficient is introduced for the regenerative braking system of electric vehicles, and a speed response function is constructed based on real-time traffic speed data to build a dynamic emission factor library that varies with vehicle model, electrification rate, and driving speed.

[0073] S3. Based on the real-time vehicle speed data and traffic flow data in the multi-source traffic data, establish a vehicle speed and traffic flow model, and obtain the target traffic flow data of each road segment within the target time period through the vehicle speed and traffic flow model.

[0074] S4. Obtain the target traffic flow data, the vehicle type ratio data in the local fleet basic dataset, and the dynamic emission factor library, input them into the pre-established road vehicle emission model, and obtain the road-level non-exhaust particulate matter dynamic emission inventory.

[0075] This invention integrates road traffic situation data, fleet composition data, vehicle weight data, road monitoring video data, and international authoritative emission factor databases through a multi-source data acquisition and preprocessing unit. Based on a convolutional neural network model, it achieves intelligent vehicle type identification and statistics, and completes the collaborative coupling calculation of multi-source data within the framework of a near-real-time road motor vehicle emission model, forming a complete technical solution from data acquisition to inventory generation.

[0076] Preferably, as one possible implementation; the step of acquiring multi-source traffic data and preprocessing the multi-source traffic data to obtain a local fleet basic dataset includes:

[0077] Acquire road traffic situation data, local motor vehicle fleet composition data, vehicle weight data, average load rate data, and real-time road monitoring video data;

[0078] The road traffic situation data is subjected to coordinate transformation and missing data completion processing to obtain a standardized road traffic situation dataset;

[0079] The road monitoring video data is processed by a pre-trained convolutional neural network model to detect and classify vehicle targets, thereby obtaining the proportion of each type of vehicle.

[0080] Based on the local motor vehicle fleet composition data, the vehicle weight data, and the average load rate data, an effective vehicle weight database by vehicle type is established, and the standardized road traffic situation dataset, the proportion information of each type of vehicle, and the effective vehicle weight database are determined as the basic dataset of the local fleet.

[0081] Preferably, as an feasible implementation, the following approach is adopted: Based on emission factors from an internationally authoritative emission factor database, a vehicle weight regression model is established using curb weight data for each vehicle model to achieve parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models; localized corrections and extensions are performed on the emission factors by combining local vehicle fleet composition data, vehicle weight data, and average load rate data; a regenerative braking correction coefficient is introduced for the regenerative braking system of electric vehicles; a speed response function is constructed based on real-time traffic speed data; and a dynamic emission factor library is built that varies with vehicle model, electrification rate, and driving speed, including:

[0082] Based on the average curb weight data of each vehicle model in the effective vehicle weight database, a regression model is used to establish a functional relationship between the basic non-exhaust particulate matter emission factor and vehicle weight, resulting in the first corrected emission factor. Specifically, based on the baseline emission factor dataset extracted from the EMEP / EEA database, and according to the average curb weight data of each vehicle model, a regression model is used to establish a functional relationship between emission factors from different sources and particle sizes and vehicle weight, achieving a parameterized expression of brake wear and tire wear particulate matter emission factors. The regression model formula is as follows, where EF represents the first corrected emission factor. This represents the vehicle weight, and parameters b and c are constants obtained after fitting.

[0083]

[0084] For new energy vehicle models in the local fleet basic dataset, the braking energy recovery ratio of these models is obtained, and a regenerative braking correction coefficient is determined based on this ratio. Specifically, a fitting formula is used to classify local vehicle models (distinguishing between fuel vehicles and electric vehicles) and perform emission factor completion calculations. For electric vehicles, considering that their regenerative braking system can reduce the frequency of mechanical brake use, thereby reducing brake wear particulate matter emissions, a regenerative braking correction coefficient k is introduced to differentiate the emission factors for different vehicle models, establishing a local vehicle non-particulate matter emission factor library considering electric vehicles. The regenerative braking correction coefficient k is related to the vehicle's braking energy recovery ratio ŋ, and is calculated as follows:

[0085]

[0086] Where ŋ represents the regenerative braking ratio under typical operating conditions. When ŋ = 0, it indicates no energy recovery. Given the significant uncertainties in vehicle design and driving conditions, the regenerative braking ratio under typical operating conditions can be determined based on vehicle announcement parameters, chassis dynamometer test results, or publicly available research reports.

[0087] A vehicle weight regression model was established based on the curb weight data of various vehicle models to achieve a parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models. This includes introducing load correction to quantify the impact of vehicle load on non-exhaust emissions, as shown in the following formula:

[0088]

[0089]

[0090]

[0091]

[0092] in and These represent the particulate matter emission factors from tire and brake wear of heavy-duty vehicles, respectively. and This indicates the emission factor for the corresponding passenger vehicle model; Indicates the number of axles. and Indicated by load rate The calculated correction factor, i.e., the load correction factor;

[0093] A speed response function is constructed based on the real-time vehicle speed data. A speed correction factor is determined through the speed response function. The first corrected emission factor, regenerative braking correction coefficient, load correction factor, and speed correction factor are stored to obtain the dynamic emission factor library.

[0094] By comprehensively applying the aforementioned vehicle weight regression modeling, regenerative braking correction, load rate correction, and speed correction, a dynamic emission factor library is constructed that varies with vehicle type, electrification rate, and driving speed. The data structure of this dynamic emission factor library includes multiple dimensions such as vehicle type, powertrain type, speed range, load state, particulate matter size, and emission source. Each cell stores the corresponding emission factor correction value. The output format of the dynamic emission factor library supports data interface integration with traffic emission models, facilitating coupled calculations in subsequent steps.

[0095] Preferably, as one possible implementation, the speed response function is a piecewise function, which sets different speed correction factor values ​​or linear relationships according to different speed ranges.

[0096] Preferably, as one possible implementation; the step of obtaining the regenerative braking correction coefficient for the new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking correction coefficient, includes:

[0097] Obtain the vehicle announcement parameters and chassis dynamometer test results of the new energy vehicle from the local fleet basic dataset;

[0098] Based on the vehicle announcement parameters and the chassis dynamometer test results, the braking energy recovery ratio of the new energy vehicle under the target operating conditions is determined;

[0099] Determine whether the braking energy recovery ratio is less than a preset threshold;

[0100] If the braking energy recovery ratio is less than a preset threshold, then when the new energy vehicle is in a state of no energy recovery, the regenerative braking correction coefficient is set to a first preset value.

[0101] If the regenerative braking ratio is not less than a preset threshold, the regenerative braking correction coefficient is calculated according to the regenerative braking ratio and the preset correction coefficient calculation formula. The regenerative braking correction coefficient is used to characterize the degree to which the regenerative braking system of the new energy vehicle reduces the frequency of mechanical brake use.

[0102] Preferably, as one possible implementation; the step of obtaining the regenerative braking correction coefficient for the new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking correction coefficient, includes:

[0103] The regenerative braking correction coefficient k is related to the vehicle's braking energy recovery ratio ŋ, and is calculated as follows: .

[0104] Preferably, as one possible implementation; the step of establishing a vehicle speed-flow model based on real-time vehicle speed data and flow data from the multi-source traffic data, and obtaining target traffic flow data for each road segment within the target time period through the vehicle speed-flow model, includes:

[0105] Obtain real traffic flow data for different time periods and different road types, as well as the corresponding real-time vehicle speed data;

[0106] The actual traffic flow data and the real-time vehicle speed data are obtained, and the parameters are fitted by inputting them into a preset function model framework to obtain the congestion density parameter and the free flow velocity parameter.

[0107] The vehicle speed-flow rate model is established based on the blockage density parameter and the free flow velocity parameter.

[0108] The real-time vehicle speed data of each road segment within the target time period is obtained, and the data is input into the vehicle speed-flow model for inversion calculation to obtain the target traffic flow data of each road segment within the target time period.

[0109] Preferably, as one possible implementation, the vehicle type ratio data for each road segment is obtained through the recognition results of the road monitoring images by the convolutional neural network model.

[0110] Preferably, as one possible implementation, the step of acquiring the target traffic flow data, the vehicle type proportion data in the local fleet basic dataset, and the dynamic emission factor library, inputting them into a pre-established road vehicle emission model, and obtaining a road-level non-exhaust particulate matter dynamic emission inventory includes:

[0111] For each road segment, obtain the length of the road segment;

[0112] Acquire the target traffic flow data, the vehicle type ratio data, the dynamic emission factor library, and the road segment length, and input them into the road vehicle emission model;

[0113] The pollutant emissions of each type of vehicle at each time point are calculated using the road vehicle emission model to obtain the initial emission results;

[0114] The initial emission results are processed by merging adjacent road segments and repairing missing data to obtain the dynamic emission inventory of non-exhaust particulate matter at the road level.

[0115] Preferably, as one possible implementation; the step of merging adjacent road segments and repairing missing data on the initial emission results to obtain the road-level non-exhaust particulate matter dynamic emission inventory includes:

[0116] Determine whether there is at least one missing or abnormal data in the initial emission results;

[0117] If the initial emission results contain missing or abnormal data, then obtain statistical characteristic data of roads of the same type in the same area.

[0118] Based on the statistical characteristic data, the missing or abnormal data are supplemented and corrected to obtain the corrected emission results;

[0119] The emission data belonging to adjacent road segments in the corrected emission results are merged to obtain the road-level non-exhaust particulate matter dynamic emission inventory, wherein the road-level non-exhaust particulate matter dynamic emission inventory includes at least a first-size particulate matter emission inventory and a second-size particulate matter emission inventory.

[0120] Specifically, the following provides a detailed example of the specific processing method of Embodiment 1 of the present invention:

[0121] In step S1, traffic situation big data and intelligent vehicle type and traffic flow are acquired to establish a local fleet basic dataset and extract non-exhaust particulate emission factors from the EMEP / EEA database.

[0122] As a concrete example, traffic situation and basic data can be collected based on the city being studied. Real-time urban road traffic operation data can be obtained by calling the Gaode Open Platform API (https: / / lbs.amap.com / ), including road segment names, latitude and longitude information, road type, and traffic speed at the corresponding time. The latitude and longitude of the acquired raw data are converted to the commonly used WGS-1984 coordinate system, and the data is categorized and organized according to road type. Missing road segment data is supplemented by the average speed of similar roads within the same administrative region at the same time, forming a standardized road traffic situation dataset.

[0123] Monitoring equipment is installed near roads or on overpasses, using monocular cameras to capture road images. A convolutional neural network (CNN)-based vehicle type recognition and traffic flow statistics device is used to analyze the traffic monitoring images frame by frame, achieving vehicle target detection, classification, and quantity statistics. Vehicle types include: small passenger cars, taxis, medium-sized passenger cars, large passenger cars, buses, light trucks, medium trucks, and heavy trucks. Traffic flow is statistically analyzed, and the proportion of each vehicle type is output, providing data support for traffic structure analysis.

[0124] Based on the vehicle curb weight data in the "Announcement of Road Motor Vehicle Manufacturers and Products" issued by the Ministry of Industry and Information Technology, combined with relevant literature and local traffic survey results, the passenger load rate and load factor of typical local vehicles were obtained, and an effective vehicle weight database by vehicle type was established.

[0125] Total suspended particulate matter (TSP) emission factors from brake wear and tire wear particles of the base vehicle were extracted from the EMEP / EEA database. 10 PM 2.5 Emission factors are calculated from the TSP based on a baseline ratio, forming a standardized emission factor dataset. The vehicle type, vehicle weight mapping, and baseline TSP emission factors extracted from the EMEP / EEA database are as follows:

[0126] Two-wheel vehicles 0.2 3.7 1.932 Passenger cars–ICE–Mini 0.9 8.2 3.57 Passenger cars–ICE–Small 1.2 10.2 4.032 Passenger cars–ICE–Medium 1.6 12.2 4.494 Passenger cars–ICE–Large 2 14.3 4.956 Light-Commercial Vehicles (N1–II,III) 3 17.3 6.216

[0127] In subsequent steps, based on the EMEP / EEA Tier 2 methodology framework and combined with the COPERT 5.8 model, non-exhaust particulate emission factors are locally corrected. The Tier 2 methodology framework proposed in the *EMEP / EEA Air Pollutant Emission Inventory Guidebook 2023* not only distinguishes between different vehicle categories but also introduces speed and load factor corrections, reflecting the impact of road conditions on non-exhaust emissions. By introducing corrections based on new energy characteristics and local traffic features, a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed can be constructed.

[0128] Based on the baseline emission factor dataset extracted from the EMEP / EEA database, and using the average curb weight data of various vehicle models, a regression model is employed to establish a functional relationship between emission factors from different sources and particle sizes and vehicle weight. This achieves a parameterized expression of particulate matter emission factors related to brake wear and tire wear. The regression model formula is as follows, where EF represents the emission factor. This represents the vehicle weight, and parameters b and c are constants obtained after fitting.

[0129]

[0130] Emission factor calculations were performed to complete the classification of local vehicle types (distinguishing between gasoline and electric vehicles) using a fitted formula. For electric vehicles, considering that their regenerative braking system can reduce the frequency of mechanical brake use, thereby reducing brake wear particulate emissions, a regenerative braking correction coefficient k was introduced to differentiate the emission factors for different vehicle types, establishing a local vehicle non-particulate emission factor library that considers electric vehicles. The regenerative braking correction coefficient k is related to the vehicle's brake energy recovery ratio ŋ, and is calculated as follows:

[0131]

[0132] Where ŋ represents the regenerative braking ratio under typical operating conditions. When ŋ = 0, it indicates no energy recovery. Given the significant uncertainties in vehicle design and driving conditions, the regenerative braking ratio under typical operating conditions can be determined based on vehicle announcement parameters, chassis dynamometer test results, or publicly available research reports.

[0133] A load correction is introduced based on the COPERT 5.8 model to quantify the impact of vehicle load on non-exhaust emissions, as shown in the following formula:

[0134]

[0135]

[0136]

[0137] in and These represent the particulate matter emission factors from tire and brake wear of heavy-duty vehicles, respectively. and This indicates the emission factor of the corresponding passenger vehicle model. Indicates the number of axles. and Indicated by load rate The calculated correction factor.

[0138] A speed response function for non-exhaust emission factors is constructed based on real-time traffic speed data. The emission factors are adjusted piecewise or continuously according to different speed ranges to quantify changes in emission characteristics under conditions of frequent acceleration / deceleration, start-stop, and congestion on urban roads. Speed ​​correction factors for tire and brake wear emission factors are also included. and The formula is as follows:

[0139]

[0140]

[0141] The correction described here is embedded in the emission calculation model and linked with traffic flow data to form a dynamic emission factor library that changes with driving conditions and participates in emission calculation.

[0142] In subsequent steps, a near-real-time road vehicle emission model is used to couple road traffic condition data with dynamic emission factors to generate a dynamic emission inventory of road-level non-exhaust particulate matter with hourly resolution.

[0143] The main steps to achieve refined calculation and inventory construction of non-exhaust particulate matter emissions are as follows:

[0144] Using real traffic flow data for different time periods and road types (including highways, urban arterial roads, and secondary roads) obtained through traffic monitoring image recognition, combined with road traffic speed information obtained from the Gaode Open Platform at the corresponding times, parameter fitting is performed within the Underwood function model framework. The formula for road traffic flow V (veh / lane / h) at the corresponding speed is as follows:

[0145]

[0146] In the formula The congestion density parameter (veh / km) represents the number of standard vehicles passing through a kilometer of road at the corresponding speed. Free-flow velocity parameter (km / h) represents the average operating speed that vehicles can reach when the road is in a near-empty state. The speed is (km / h). The speed-flow function obtained through fitting can reflect the flow rate variation pattern under real traffic conditions quite well.

[0147] By combining the speed-flow relationship, the traffic flow of each road segment within the target study period is calculated based on the real-time road segment speed data obtained in step S1, and a road-level high spatiotemporal resolution traffic flow database is constructed.

[0148] Using road-level traffic flow data, vehicle type proportion data, and local dynamic non-emission factors as core inputs, this study performs coupled calculations within the framework of a near-real-time on-road emission model (ROE). The ROE model is a bottom-up road mobile source emission inventory model based on real-time traffic big data, primarily used for estimating motor vehicle pollutant emissions at the street scale. The emission calculation formula is shown below. Parallel computing is employed to improve the efficiency and stability of emission calculations for large-scale road networks.

[0149]

[0150] In the formula It is a pollutant exist Emissions at any time Motor vehicle type pollutants Emission factors, Motor vehicle type At any moment Traffic flow, It refers to the length of the road segment.

[0151] The initial emission results obtained from the calculations are further optimized, including the reasonable merging of adjacent road segments and the repair of missing data. Specifically, for missing or abnormal data, statistical characteristics of roads of the same type and within the same region are used for supplementation and correction to ensure the completeness and continuity of the emission inventory. The final output includes particulate matter emission inventories for brake wear and tire wear, including PM2.5. 2.5 and PM 10 .

[0152] This invention discloses a method and apparatus for calculating a dynamic road-level non-exhaust particulate matter emission inventory for electrification transformation. It constructs a basic traffic dataset by acquiring big data on traffic conditions and intelligently identifying vehicle types and traffic flow. Based on the EMEP / EEA Tier 2 methodology framework and combined with the COPERT 5.8 model, the baseline non-exhaust particulate matter emission factors are locally corrected by introducing vehicle load and speed correction coefficients to reflect local road traffic characteristics. Considering the characteristics of new energy vehicles under electrification, correction coefficients are set for tire wear emission factors based on vehicle mass and brake wear emission factors based on kinetic energy recovery systems, thereby constructing a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed. Based on this, a near-real-time road vehicle emission model is used to couple road traffic condition data with dynamic emission factors for calculation, achieving hourly and segment-by-segment calculation of non-exhaust particulate matter emissions, ultimately generating a road-level dynamic non-exhaust particulate matter emission inventory with hourly resolution.

[0153] As a test example of the technical effect of the method and device for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transformation in the above embodiment, traffic situation data of Beijing were collected, covering road sections within and around the Sixth Ring Road, and covering the time range from August 1, 2020 to July 31, 2021. At the same time, a traffic flow and vehicle type recognition device based on convolutional neural network (CNN) was used to conduct on-site sampling analysis of the proportion of vehicle types and traffic flow on roads inside and outside the Fifth Ring Road. The road types included national highways, trunk roads and local roads.

[0154] The braking energy recovery rate under typical operating conditions within the target space range in the test instance is 95%. The non-exhaust particulate matter emission factors for different vehicle models after correction for new energy characteristics and load rate are as follows:

[0155] In the test instance, by combining road traffic flow information with road traffic speed information obtained from the Gaode Open Platform at the corresponding time, a speed-flow relationship conforming to the Underwood function was obtained. The free-flow speed parameters for national highways, arterial roads, and local roads in Beijing were 88 km / h, 54 km / h, and 35 km / h, respectively, and the congestion density parameters were 159 veh / km, 173 veh / km, and 210 veh / km, respectively. Traffic situation data was coupled with a locally corrected non-exhaust particulate matter emission factor and input into the traffic emission model to calculate the emission inventory of brake wear and tire wear particulate matter.

[0156] In summary, the method and apparatus for calculating road-level non-exhaust particulate matter dynamic emission inventories for electrification transformation provided by this invention obtains urban road-level hourly vehicle speed information by calling the Gaode Open Platform API, and uses a convolutional neural network (CNN) to automatically count traffic flow and vehicle type from traffic monitoring images to collect and establish a local fleet basic dataset. Basic emission factors for tire wear and brake wear are extracted from the EMEP / EEA database and mapped to local vehicle types. Based on the EMEP / EEA Tier 2 methodology framework and combined with the COPERT 5.8 model, non-exhaust particulate matter emission factors are locally corrected by introducing vehicle load coefficients and speed correction coefficients to reflect local road traffic characteristics. Furthermore, considering the characteristics of new energy vehicles, tire wear emission factor correction coefficients based on vehicle mass and brake wear emission factor correction coefficients based on kinetic energy recovery systems are set, thereby constructing a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed. By using a near-real-time road vehicle emission model, road traffic condition data and dynamic emission factors are coupled and calculated to achieve hourly and road segment-by-road non-exhaust particulate matter emissions calculation, and finally generate a road-level non-exhaust particulate matter dynamic emission inventory with hourly resolution.

[0157] Example 2

[0158] On the other hand, the present invention discloses a schematic diagram of a road-level non-exhaust particulate matter dynamic emission inventory construction device for electrification transformation.

[0159] like Figure 2As shown, this embodiment provides a road-level non-exhaust particulate matter dynamic emission inventory construction device 400 for electrification transformation, which includes a multi-source data acquisition and preprocessing unit 410, an emission factor localization unit 420, and an emission inventory construction unit 430.

[0160] The multi-source data acquisition and preprocessing unit 410 is used to acquire big data on traffic conditions and intelligently identify vehicle types and traffic flow, establish a local fleet basic dataset, and extract non-exhaust particulate emission factors from the EMEP / EEA database. The operation of the multi-source data acquisition and preprocessing unit 410 can be referenced above. Figure 1 The operation described in step S1.

[0161] The emission factor localization unit 420 is used to localize non-exhaust particulate emission factors. By introducing corrections based on new energy characteristics and local traffic characteristics, it constructs a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed. The operation of the emission factor localization unit 420 can be referenced above. Figure 1 The operation described in step S2.

[0162] Emission inventory construction unit 430 is used to couple multi-source traffic data and a localized emission factor library to generate a road-level high-resolution dynamic emission inventory of non-exhaust particulate matter using a traffic emission model. The operation of emission inventory construction unit 430 can be referenced above. Figure 1 The operation described in step S3.

[0163] Example 3

[0164] The present invention provides a 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 steps of the method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition provided in Embodiment 1.

[0165] Example 4

[0166] The present invention provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method for calculating the dynamic emission inventory of road-level non-exhaust particulate matter for electrification transformation provided in Embodiment 1 above.

[0167] In summary, the method for calculating road-level non-exhaust particulate matter dynamic emission inventories for electrification transformation provided in this invention is based on the EMEP / EEA Tier 2 framework and incorporates the COPERT 5.8 model. It localizes non-exhaust particulate matter emission factors by introducing vehicle load and speed correction coefficients to reflect local road traffic characteristics. Furthermore, considering the characteristics of new energy vehicles, it sets correction coefficients for tire wear emission factors based on vehicle mass and brake wear emission factors based on kinetic energy recovery systems, thereby constructing a dynamic emission factor library that varies with vehicle type, electrification rate, and driving speed. By using a near-real-time road vehicle emission model to couple road traffic condition data with dynamic emission factors, it achieves hourly and segment-by-segment calculation of non-exhaust particulate matter emissions, ultimately generating a road-level non-exhaust particulate matter dynamic emission inventory with hourly resolution.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0170] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for calculating a dynamic emission inventory of road-level non-exhaust particulate matter for electrification transition, characterized in that, include: Acquire multi-source traffic data, preprocess the multi-source traffic data to obtain a local fleet basic dataset, and obtain basic non-exhaust particulate matter emission factors from a preset database. Based on the local fleet dataset, the basic non-exhaust particulate emission factors are localized to obtain a dynamic emission factor library. Specifically, this includes: constructing a localized non-exhaust particulate emission factor library: using emission factors from an internationally authoritative emission factor database as a foundation, establishing a vehicle weight regression model based on the curb weight data of each vehicle model to achieve parameterized expression of particulate emission factors related to brake wear and tire wear for different vehicle models; combining local vehicle fleet composition data, vehicle weight data, and average load rate data to localize and expand the emission factors; introducing a regenerative braking correction coefficient for the regenerative braking system of electric vehicles, constructing a speed response function based on real-time traffic speed data, and building a dynamic emission factor library that varies with vehicle model, electrification rate, and driving speed. Based on the real-time vehicle speed data and traffic flow data in the multi-source traffic data, a vehicle speed and traffic flow model is established, and the target traffic flow data of each road segment within the target time period is obtained through the vehicle speed and traffic flow model. The target traffic flow data, the vehicle type proportion data in the local fleet basic dataset, and the dynamic emission factor library are obtained and input into a pre-established road vehicle emission model to obtain a road-level non-exhaust particulate matter dynamic emission inventory.

2. The method as described in claim 1, characterized in that, The process of acquiring multi-source traffic data and preprocessing the multi-source traffic data to obtain a local fleet basic dataset includes: Acquire road traffic situation data, local motor vehicle fleet composition data, vehicle weight data, average load rate data, and real-time road monitoring video data; The road traffic situation data is subjected to coordinate transformation and missing data completion processing to obtain a standardized road traffic situation dataset; The road monitoring video data is processed by a pre-trained convolutional neural network model to detect and classify vehicle targets, thereby obtaining the proportion of each type of vehicle. Based on the local motor vehicle fleet composition data, the vehicle weight data, and the average load rate data, an effective vehicle weight database by vehicle type is established, and the standardized road traffic situation dataset, the proportion information of each type of vehicle, and the effective vehicle weight database are determined as the basic dataset of the local fleet.

3. The method as described in claim 1, characterized in that, Based on emission factors from an internationally authoritative emission factor database, a vehicle weight regression model is established using curb weight data for various vehicle models to achieve parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models. Localized corrections and extensions are made to the emission factors by incorporating local vehicle fleet composition data, vehicle weight data, and average load rate data. A regenerative braking correction coefficient is introduced for the regenerative braking system of electric vehicles. A speed response function is constructed based on real-time traffic speed data, and a dynamic emission factor library is built that varies with vehicle model, electrification rate, and driving speed, including: Based on the average curb weight data of each vehicle model in the effective vehicle weight database, a regression model is used to establish a functional relationship between the basic non-exhaust particulate matter emission factor and vehicle weight, resulting in the first corrected emission factor. Using the baseline emission factor dataset extracted from the EMEP / EEA database as a basis, and based on the average curb weight data of each vehicle model, a regression model is used to establish a functional relationship between emission factors from different sources and with different particle sizes and vehicle weight, achieving a parameterized expression of brake wear and tire wear particulate matter emission factors. The regression model formula is as follows, where EF represents the first corrected emission factor. This represents the vehicle weight, and parameters b and c are constants obtained after fitting: ； For the new energy vehicle models in the local fleet basic dataset, obtain the braking energy recovery ratio of the new energy vehicle models, and determine the regenerative braking correction coefficient based on the braking energy recovery ratio; A vehicle weight regression model was established based on the curb weight data of various vehicle models to achieve a parameterized expression of particulate matter emission factors related to brake wear and tire wear for different vehicle models. This includes introducing load correction to quantify the impact of vehicle load on non-exhaust emissions, as shown in the following formula: ； ； ； ； in and These represent the particulate matter emission factors from tire and brake wear of heavy-duty vehicles, respectively. and This indicates the emission factor for the corresponding passenger vehicle model; Indicates the number of axles. and Indicated by load rate The calculated correction factor, i.e., the load correction factor; A speed response function is constructed based on the real-time vehicle speed data. A speed correction factor is determined through the speed response function. The first corrected emission factor, regenerative braking correction coefficient, load correction factor, and speed correction factor are stored to obtain the dynamic emission factor library.

4. The method as described in claim 3, characterized in that, The speed response function is a piecewise function, which sets different speed correction factor values ​​or linear relationships according to different speed ranges.

5. The method as described in claim 3, characterized in that, The step of obtaining the regenerative braking correction coefficient for new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking ratio, includes: Obtain the vehicle announcement parameters and chassis dynamometer test results of the new energy vehicle from the local fleet basic dataset; Based on the vehicle announcement parameters and the chassis dynamometer test results, the braking energy recovery ratio of the new energy vehicle under the target operating conditions is determined; Determine whether the braking energy recovery ratio is less than a preset threshold; If the braking energy recovery ratio is less than a preset threshold, then when the new energy vehicle is in a state of no energy recovery, the regenerative braking correction coefficient is set to a first preset value. If the regenerative braking ratio is not less than a preset threshold, the regenerative braking correction coefficient is calculated according to the regenerative braking ratio and the preset correction coefficient calculation formula. The regenerative braking correction coefficient is used to characterize the degree to which the regenerative braking system of the new energy vehicle reduces the frequency of mechanical brake use.

6. The method as described in claim 4, characterized in that, The step of obtaining the regenerative braking correction coefficient for new energy vehicle models in the local fleet basic dataset, and determining the regenerative braking correction coefficient based on the regenerative braking ratio, includes: The regenerative braking correction coefficient k is related to the vehicle's braking energy recovery ratio ŋ, and is calculated as follows: .

7. The method as described in claim 1, characterized in that, The step of establishing a vehicle speed-flow model based on real-time vehicle speed and flow data from the multi-source traffic data, and obtaining target traffic flow data for each road segment within a target time period through the vehicle speed-flow model, includes: Obtain real traffic flow data for different time periods and different road types, as well as the corresponding real-time vehicle speed data; The actual traffic flow data and the real-time vehicle speed data are obtained, and the parameters are fitted by inputting them into a preset function model framework to obtain the congestion density parameter and the free flow velocity parameter. The vehicle speed-flow rate model is established based on the blockage density parameter and the free flow velocity parameter. The real-time vehicle speed data of each road segment within the target time period is obtained, and the data is input into the vehicle speed-flow model for inversion calculation to obtain the target traffic flow data of each road segment within the target time period.

8. The method as described in claim 7, characterized in that, in, The vehicle type ratio data for each road segment is obtained by the recognition results of the road monitoring images using the convolutional neural network model.

9. The method as described in claim 1, characterized in that, The process involves acquiring the target traffic flow data, the vehicle type proportion data in the local fleet basic dataset, and the dynamic emission factor library, inputting them into a pre-established road vehicle emission model, and obtaining a road-level non-exhaust particulate matter dynamic emission inventory, including: For each road segment, obtain the length of the road segment; Acquire the target traffic flow data, the vehicle type ratio data, the dynamic emission factor library, and the road segment length, and input them into the road vehicle emission model; The pollutant emissions of each type of vehicle at each time point are calculated using the road vehicle emission model to obtain the initial emission results; The initial emission results are processed by merging adjacent road segments and repairing missing data to obtain the dynamic emission inventory of non-exhaust particulate matter at the road level.

10. The method as described in claim 9, characterized in that, The process of merging adjacent road segments and repairing missing data in the initial emission results yields the road-level non-exhaust particulate matter dynamic emission inventory, including: Determine whether there is at least one missing or abnormal data in the initial emission results; If the initial emission results contain missing or abnormal data, then obtain statistical characteristic data of roads of the same type in the same area. Based on the statistical characteristic data, the missing or abnormal data are supplemented and corrected to obtain the corrected emission results; The emission data belonging to adjacent road segments in the corrected emission results are merged to obtain the road-level non-exhaust particulate matter dynamic emission inventory, wherein the road-level non-exhaust particulate matter dynamic emission inventory includes at least a first-size particulate matter emission inventory and a second-size particulate matter emission inventory.

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