Regional motor vehicle emission list method and system fusing multi-source data

Abstract

The invention discloses a regional motor vehicle emission list method and system fusing multi-source data, and the method comprises the steps: obtaining the driving data of urban heavy vehicles, the driving data comprises the driving mileage of the urban heavy-duty vehicle and the NOx basic emission of the urban heavy-duty vehicle, and T is the driving time of the urban heavy-duty vehicle, the longitude of the urban heavy-duty vehicle during driving and the latitude of the urban heavy-duty vehicle during driving; setting an urban heavy duty vehicle NOx emission correction model, and calculating an urban heavy duty vehicle NOx emission correction index according to the driving data; the initial NOx emission amount of the urban heavy duty vehicle during running is obtained, the urban heavy duty vehicle NOx emission amount correction index serves as a coefficient to be multiplied by the initial NOx emission amount, the result is normalized to be within the range of the urban heavy duty vehicle NOx emission amount, and urban heavy duty vehicle NOx emission accounting is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle exhaust emission monitoring, and more specifically, relates to a method and system for regional motor vehicle emission inventory integrating multi-source data. Background Art

[0002] The following are some current status of NOx emission monitoring technology for urban heavy-duty vehicles: Remote sensing monitoring technology: Remote sensing monitoring is a method of measuring pollutant emissions from vehicle exhaust from a distance by using sensors and spectroscopy technology. This method can be implemented under actual traffic conditions without affecting traffic flow. Remote sensing monitoring is usually used for large-scale monitoring, but the precise measurement of individual vehicles may be limited.

[0003] Vehicle inspection data: Vehicle inspections are another source of emission data. These data usually include vehicle emission levels and performance assessments. Inspection data can be used to infer emission trends for the entire vehicle fleet.

[0004] Traffic checkpoint data: Traffic checkpoint data can be used to monitor vehicle movement and density. Combined with other data such as vehicle type and speed, emission levels can be estimated.

[0005] However, the technical solutions of the prior art are not accurate enough in monitoring the NOx emissions of heavy-duty vehicles in cities. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a regional motor vehicle emission inventory method integrating multi-source data, including: Acquire driving data of urban heavy vehicles, wherein the driving data includes: driving mileage of urban heavy vehicles, basic NOx emissions of urban heavy vehicles, T is driving time of urban heavy vehicles, longitude of urban heavy vehicles when driving, and latitude of urban heavy vehicles when driving; Setting a correction model for NOx emissions of urban heavy vehicles, and calculating a correction index for NOx emissions of urban heavy vehicles according to the driving data; The initial NOx emissions of urban heavy-duty vehicles during driving are obtained, the urban heavy-duty vehicle NOx emissions correction index is used as a coefficient to multiply the initial NOx emissions, and the result is normalized to the range of urban heavy-duty vehicle NOx emissions to complete the NOx emissions accounting for urban heavy-duty vehicles.

[0007] Furthermore, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0008] Furthermore, it also includes calculating the comprehensive road condition index , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For road quality, is the weather condition weight, For weather conditions.

[0009] Further, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0010] Furthermore, the optimized urban heavy-duty vehicle NOx emission correction model includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, It is a comprehensive indicator of road conditions.

[0011] The present invention also proposes a regional motor vehicle emission inventory system integrating multi-source data, comprising: The data acquisition module is used to acquire the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; A model setting module is used to set a correction model for NOx emissions of urban heavy vehicles, and calculate a correction index for NOx emissions of urban heavy vehicles based on the driving data; The calculation module is used to obtain the initial NOx emissions of urban heavy-duty vehicles during driving, multiply the initial NOx emissions by the urban heavy-duty vehicle NOx emission correction index as a coefficient, and normalize the result to the range of urban heavy-duty vehicle NOx emissions to complete the NOx emission accounting of urban heavy-duty vehicles.

[0012] Furthermore, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0013] Furthermore, it also includes calculating the comprehensive road condition index , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For road quality, is the weather condition weight, For weather conditions.

[0014] Further, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0015] Furthermore, the optimized urban heavy-duty vehicle NOx emission correction model includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, It is a comprehensive indicator of road conditions.

[0016] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects: The present invention obtains the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy-duty vehicle are the driving time of the urban heavy-duty vehicle, and the latitude of the urban heavy-duty vehicle; a correction model for the NOx emissions of urban heavy-duty vehicles is set, and the correction index of the NOx emissions of urban heavy-duty vehicles is calculated according to the driving data; the initial NOx emissions of urban heavy-duty vehicles are obtained, and the correction index of the NOx emissions of urban heavy-duty vehicles is multiplied by the initial NOx emissions as a coefficient, and the result is normalized to the range of the NOx emissions of urban heavy-duty vehicles, so as to complete the NOx emission accounting of urban heavy-duty vehicles. Through the above technical scheme, the present invention can accurately calculate the NOx emissions of urban heavy-duty vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0018] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0020] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0021] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0022] The display screen is used to display the user interface of each application.

[0023] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0024] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0025] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a method for regional motor vehicle emission inventory integrating multi-source data, including: Step 101, obtaining the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emission of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; Step 102, setting a correction model for the NOx emissions of urban heavy vehicles, and calculating a correction index for the NOx emissions of urban heavy vehicles according to the driving data; Specifically, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0026] Step 103, obtaining the initial NOx emissions of urban heavy-duty vehicles during driving, multiplying the initial NOx emissions by the urban heavy-duty vehicle NOx emissions correction index as a coefficient, and normalizing the result to within the range of urban heavy-duty vehicle NOx emissions to complete the NOx emissions accounting for urban heavy-duty vehicles.

[0027] Specifically, it also includes calculating comprehensive road condition indicators , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For example, the road quality score can be determined according to the level of the road, for example, mountain roads are 5 points, urban roads are 4 points, and highways are 3 points. The better the road quality, the lower the score. is the weather condition weight, For example, the weather condition may include wind, and the score of the weather condition is determined according to the wind force level, for example, level 6 wind is 6 points, level 5 wind is 5 points, and so on.

[0028] Specifically, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0029] Specifically, the optimized correction model for urban heavy-duty vehicle NOx emissions includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, As a comprehensive indicator of road conditions, the optimized urban heavy-duty vehicle NOx emission correction model is used to calculate the optimized urban heavy-duty vehicle NOx emission correction index, making the final result more accurate.

[0030] Example 2 like Figure 2 As shown, the embodiment of the present invention also proposes a regional motor vehicle emission inventory system integrating multi-source data, including: The data acquisition module is used to acquire the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; A model setting module is used to set a correction model for NOx emissions of urban heavy vehicles, and calculate a correction index for NOx emissions of urban heavy vehicles based on the driving data; Specifically, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0031] The calculation module is used to obtain the initial NOx emissions of urban heavy-duty vehicles during driving, multiply the initial NOx emissions by the urban heavy-duty vehicle NOx emission correction index as a coefficient, and normalize the result to the range of urban heavy-duty vehicle NOx emissions to complete the NOx emission accounting of urban heavy-duty vehicles.

[0032] Specifically, it also includes calculating comprehensive road condition indicators , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For example, the road quality score can be determined according to the level of the road, for example, mountain roads are 5 points, urban roads are 4 points, and highways are 3 points. The better the road quality, the lower the score. is the weather condition weight, For example, the weather condition may include wind, and the score of the weather condition is determined according to the wind force level, for example, level 6 wind is 6 points, level 5 wind is 5 points, and so on.

[0033] Specifically, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0034] Specifically, the optimized correction model for urban heavy-duty vehicle NOx emissions includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, As a comprehensive indicator of road conditions, the optimized urban heavy-duty vehicle NOx emission correction model is used to calculate the optimized urban heavy-duty vehicle NOx emission correction index, making the final result more accurate.

[0035] Example 3 An embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the method for regional motor vehicle emission inventory integrating multi-source data.

[0036] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0037] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining driving data of urban heavy vehicles, wherein the driving data includes: mileage of urban heavy vehicles, basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; Step 102, setting a correction model for the NOx emissions of urban heavy vehicles, and calculating a correction index for the NOx emissions of urban heavy vehicles according to the driving data; Specifically, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0038] Step 103, obtaining the initial NOx emissions of urban heavy-duty vehicles during driving, multiplying the initial NOx emissions by the urban heavy-duty vehicle NOx emissions correction index as a coefficient, and normalizing the result to within the range of urban heavy-duty vehicle NOx emissions to complete the NOx emissions accounting for urban heavy-duty vehicles.

[0039] Specifically, it also includes calculating comprehensive road condition indicators , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For example, the road quality score can be determined according to the level of the road, for example, mountain roads are 5 points, urban roads are 4 points, and highways are 3 points. The better the road quality, the lower the score. is the weather condition weight, For example, the weather condition may include wind, and the score of the weather condition is determined according to the wind force level, for example, level 6 wind is 6 points, level 5 wind is 5 points, and so on.

[0040] Specifically, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0041] Specifically, the optimized correction model for urban heavy-duty vehicle NOx emissions includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, As a comprehensive indicator of road conditions, the optimized urban heavy-duty vehicle NOx emission correction model is used to calculate the optimized urban heavy-duty vehicle NOx emission correction index, making the final result more accurate.

[0042] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a regional motor vehicle emission inventory method that integrates multi-source data.

[0043] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0044] Among them, the storage medium can be used to store software programs and modules, such as a method for regional motor vehicle emission inventory integrating multi-source data in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for regional motor vehicle emission inventory integrating multi-source data. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0045] The processor can call the information and application stored in the storage medium through the transmission system to execute the steps: Step 101, obtaining the driving data of the urban heavy-duty vehicle, wherein the driving data includes: the mileage of the urban heavy-duty vehicle, the basic NOx emission of the urban heavy-duty vehicle, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; Step 102, setting a correction model for the NOx emissions of urban heavy vehicles, and calculating a correction index for the NOx emissions of urban heavy vehicles according to the driving data; Specifically, the urban heavy-duty vehicle NOx emission correction model includes: , in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

[0046] Step 103, obtaining the initial NOx emissions of urban heavy-duty vehicles during driving, multiplying the initial NOx emissions by the urban heavy-duty vehicle NOx emissions correction index as a coefficient, and normalizing the result to within the range of urban heavy-duty vehicle NOx emissions to complete the NOx emissions accounting for urban heavy-duty vehicles.

[0047] Specifically, it also includes calculating comprehensive road condition indicators , specifically: , in, is the traffic density weight, is the traffic density, is the road quality weight, For example, the road quality score can be determined according to the level of the road, for example, mountain roads are 5 points, urban roads are 4 points, and highways are 3 points. The better the road quality, the lower the score. is the weather condition weight, For example, the weather condition may include wind, and the score of the weather condition is determined according to the wind force level, for example, level 6 wind is 6 points, level 5 wind is 5 points, and so on.

[0048] Specifically, according to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

[0049] Specifically, the optimized correction model for urban heavy-duty vehicle NOx emissions includes: , in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, As a comprehensive indicator of road conditions, the optimized urban heavy-duty vehicle NOx emission correction model is used to calculate the optimized urban heavy-duty vehicle NOx emission correction index, making the final result more accurate.

[0050] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0051] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0052] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0053] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0055] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0056] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A regional vehicle emission inventory method integrating multi-source data, characterized in that: include: Acquire the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; Setting a correction model for NOx emissions of urban heavy vehicles, and calculating a correction index for NOx emissions of urban heavy vehicles according to the driving data; The initial NOx emissions of urban heavy-duty vehicles during driving are obtained, the urban heavy-duty vehicle NOx emissions correction index is used as a coefficient to multiply the initial NOx emissions, and the result is normalized to the range of urban heavy-duty vehicle NOx emissions to complete the NOx emissions accounting for urban heavy-duty vehicles.

2. A method for regional motor vehicle emission inventory integrating multi-source data as claimed in claim 1, characterized in that: The urban heavy-duty vehicle NOx emission correction model includes: ， in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

3. A method for regional motor vehicle emission inventory integrating multi-source data as claimed in claim 2, characterized in that: It also includes calculating comprehensive road condition indicators , specifically: ， in, is the traffic density weight, is the traffic density, is the road quality weight, For road quality, is the weather condition weight, For weather conditions.

4. A method for regional motor vehicle emission inventory integrating multi-source data as claimed in claim 3, characterized in that: According to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

5. A method for regional motor vehicle emission inventory integrating multi-source data as claimed in claim 4, characterized in that: The optimized correction model for urban heavy-duty vehicle NOx emissions includes: ， in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, It is a comprehensive indicator of road conditions.

6. A regional vehicle emission inventory system integrating multi-source data, characterized in that: include: The data acquisition module is used to acquire the driving data of urban heavy vehicles, wherein the driving data includes: the driving mileage of urban heavy vehicles, the basic NOx emissions of urban heavy vehicles, The driving time, longitude and latitude of the urban heavy vehicle when the urban heavy vehicle is driving; A model setting module is used to set a correction model for NOx emissions of urban heavy vehicles, and calculate a correction index for NOx emissions of urban heavy vehicles based on the driving data; The calculation module is used to obtain the initial NOx emissions of urban heavy-duty vehicles during driving, multiply the initial NOx emissions by the urban heavy-duty vehicle NOx emission correction index as a coefficient, and normalize the result to the range of urban heavy-duty vehicle NOx emissions to complete the NOx emission accounting of urban heavy-duty vehicles.

7. A regional vehicle emission inventory system integrating multi-source data as claimed in claim 6, characterized in that: The urban heavy-duty vehicle NOx emission correction model includes: ， in, is the correction index of NOx emissions from heavy-duty vehicles in cities, is the first adjustment factor, is the second adjustment factor, is the mileage of heavy vehicles in the city, is the third adjustment factor, is the basic NOx emission of heavy-duty vehicles in cities, is the fourth adjustment factor, is the fifth adjustment factor, The driving time of heavy vehicles in the city, is the sixth adjustment factor, is the seventh adjustment factor, is the eighth adjustment factor, is the longitude of the city heavy vehicle when traveling, is the ninth adjustment factor, It is the latitude at which heavy vehicles in the city travel.

8. A regional vehicle emission inventory system integrating multi-source data as claimed in claim 7, characterized in that: It also includes calculating comprehensive road condition indicators , specifically: ， in, is the traffic density weight, is the traffic density, is the road quality weight, For road quality, is the weather condition weight, For weather conditions.

9. A regional vehicle emission inventory system integrating multi-source data as claimed in claim 8, characterized in that: According to the comprehensive road condition index , optimizing the urban heavy-duty vehicle NOx emission correction model to generate an optimized urban heavy-duty vehicle NOx emission correction model.

10. A regional vehicle emission inventory system integrating multi-source data as claimed in claim 9, characterized in that: The optimized correction model for urban heavy-duty vehicle NOx emissions includes: ， in, It is the optimized correction index of NOx emission of urban heavy vehicles. is the tenth adjustment factor, It is a comprehensive indicator of road conditions.

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