Plateau mountain environment under the motor vehicle road emission test route selection method and system

Abstract

The application discloses a kind of highland mountain environment under motor vehicle road emission test route selection method, it is related to highland environment under motor vehicle road emission test route selection technical field, the method includes: selecting highland mountain environment under the remote sensing image of the region to be tested for testing motor vehicle road emission, the remote sensing image is grid division, each grid unit is as a path unit;State factor of each path unit is obtained, extract multiple induction factors for inducing motor vehicle driving behavior in the state factor, and the induction value of each induction factor is calculated, the state factor includes: road state factor and environmental state factor;Find out multiple induction factors in all grids in the same region simultaneously reach local peak value Overlapping area, any one overlapping area is as motor vehicle road emission test route, or multiple adjacent overlapping areas are connected as motor vehicle road emission test route.

Description

Technical Field

[0001] This invention belongs to the technical field of motor vehicle road emission test route selection in plateau environments, and more specifically, relates to a method and system for selecting motor vehicle road emission test routes in plateau mountain environments. Background Technology

[0002] The high altitude, low air pressure, and thin oxygen levels in plateau environments reduce the combustion efficiency of motor vehicle engines, leading to increased fuel consumption and carbon emissions. Furthermore, the complex terrain and frequent uphill driving on plateaus increase vehicle load, exacerbating carbon emission intensity. Therefore, carbon emissions per unit distance traveled by motor vehicles are generally higher in plateau environments than in plains areas, resulting in a more significant impact on the regional ecological environment and climate.

[0003] Currently, there is no technological solution that can intelligently select routes for carbon emission testing of motor vehicles in high-altitude environments, and the specific impact of various road conditions on carbon emissions in high-altitude environments is unclear. Summary of the Invention

[0004] To address the above technical problems, this invention proposes a method for selecting a road emission test route for motor vehicles in high-altitude mountainous environments, comprising:

[0005] Remote sensing images of the test area for testing motor vehicle road emissions in a plateau mountainous environment are selected, and the remote sensing images are divided into grids, with each grid cell serving as a path cell;

[0006] Obtain the state factors of each path unit, extract multiple induction factors used to guide motor vehicle driving behavior from the state factors, and calculate the induction value of each induction factor. The state factors include: road state factors and environmental state factors.

[0007] Identify overlapping areas in all grids where multiple inducing factors simultaneously reach local peaks in the same region. Use any one of these overlapping areas as a test route for motor vehicle road emissions, or connect multiple adjacent overlapping areas to form a test route for motor vehicle road emissions.

[0008] Furthermore, the road condition factors include: road slope, road friction, and road smoothness;

[0009] The environmental state factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0010] Furthermore, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0011] Furthermore, the inducing factors include: pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

[0012] Furthermore, calculating the induction value for each induction factor includes:

[0013]

[0014] Where F(x, y, t) is the induced value of the current point (x, y) in the path cell at time t, x is the abscissa of the current point, y is the ordinate of the current point, A is the maximum perturbation intensity of the current induced factor, ω is the perturbation frequency, x0 is the abscissa of the center point of the current induced factor, y0 is the ordinate of the center point of the current induced factor, and σ is the spatial diffusion radius of the current induced factor.

[0015] Furthermore, finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained.

[0016] Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

[0017] Furthermore, obtaining the surface disturbance factor includes: classifying the surface texture of the remote sensing image, extracting the surface category boundary using an image segmentation algorithm, calculating the boundary change rate and patch fragmentation degree, and summing the results as the surface disturbance factor.

[0018] This invention also proposes a vehicle road emission test route selection system for high-altitude mountainous environments, comprising:

[0019] The grid division module is used to select remote sensing images of the test area for testing motor vehicle road emissions in a plateau and mountainous environment, and to divide the remote sensing images into grids, with each grid cell serving as a path cell.

[0020] The guidance value calculation module is used to obtain the state factors of each path unit, extract multiple guidance factors for guiding motor vehicle driving behavior from the state factors, and calculate the guidance value of each guidance factor. The state factors include: road state factors and environmental state factors.

[0021] The route selection module is used to find overlapping areas in all grids where multiple inducing factors reach local peaks in the same region. Any one of these overlapping areas is used as a test route for motor vehicle road emissions, or multiple adjacent overlapping areas are connected to form a test route for motor vehicle road emissions.

[0022] Furthermore, the road condition factors include: road slope, road friction, and road smoothness;

[0023] The environmental state factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0024] Furthermore, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0025] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0026] Through the above technical solutions, this invention can divide the roads in the plateau environment into multiple areas and generate routes that can induce corresponding driving behaviors, thereby completing the intelligent selection of vehicle carbon emission test routes. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0028] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0029] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0030] The method provided by this invention can be implemented in a terminal environment that 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, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0031] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0032] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0033] The display screen is used to show the user interface of each application.

[0034] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0035] Example 1

[0036] like Figure 1 This embodiment proposes a method for selecting a test route for motor vehicle road emissions in a high-altitude mountainous environment, including:

[0037] Step 101: Select a remote sensing image of the test area for testing motor vehicle road emissions in a plateau mountainous environment, and divide the remote sensing image into a grid, with each grid cell serving as a path cell;

[0038] Specifically, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0039] Step 102: Obtain the state factors of each path unit, extract multiple induction factors used to guide motor vehicle driving behavior from the state factors, and calculate the induction value of each induction factor. The state factors include: road state factors and environmental state factors.

[0040] Specifically, the road condition factors include: road slope, road friction, and road smoothness; the environmental condition factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0041] Specifically, the inducing factors include: air pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

[0042] Specifically, obtaining surface disturbance factors includes: classifying surface textures in remote sensing images (based on NDVI, NDWI, thermal reflectance, and color texture heterogeneity), extracting surface category boundaries using image segmentation algorithms (such as GLCM texture analysis and U-Net semantic segmentation), calculating boundary change rate and patch fragmentation degree, and summing the results as surface disturbance factors.

[0043] Preferably, the boundary change rate refers to the complexity of the edges of surface patches, typically reflecting the "severity of transition" between landform / surface types. It is used to measure: the degree of transition between surface types in a given area (e.g., sandy land). The intensity of spatial transformation; whether the vehicle will encounter "sudden changes in adhesion" or induce behavioral disturbances (such as slippage or jumping); boundary change rate = boundary pixel length / total patch area;

[0044] Patch fragmentation describes whether the same surface type is highly fragmented or dispersed in space. In this embodiment, high patch fragmentation indicates frequent changes in surface adhesion (such as gravel mixed with grass), which may cause vehicle response to exhibit phenomena such as "frequent shaking and slip correction". Patch fragmentation = number of patches / total area.

[0045] Regarding the inducing factors, this embodiment provides examples, as shown in Tables 1 and 2 below:

[0046]

[0047] Table 1

[0048]

[0049]

[0050] Table 2

[0051] Specifically, calculating the induction value of each induction factor includes:

[0052]

[0053] Where F(x, y, t) is the induced value of the current point (x, y) in the path cell at time t, x is the abscissa of the current point, y is the ordinate of the current point, A is the maximum perturbation intensity of the current induced factor, ω is the perturbation frequency, x0 is the abscissa of the center point of the current induced factor, y0 is the ordinate of the center point of the current induced factor, and σ is the spatial diffusion radius of the current induced factor.

[0054] Specifically, as a "behavior," the vehicle's perception input is the nearby F(x, y, t) values. If the F(x, y, t) value near a location is particularly large, the vehicle will engage in a driving behavior, such as:

[0055] Encountering high-pressure turbulence areas → increased fuel injection compensation → increased emissions

[0056] Entering a high-slope shear zone → instantaneous gear shift → power disturbance

[0057] Entering the peak of temperature fluctuation → Cold start compensation → Rapid acceleration

[0058] The following is a specific example of calculating the induced value of each induced factor:

[0059] Suppose we simulate a trend-driven behavior:

[0060] Location: A windy pass in a high-altitude canyon;

[0061] Type: Pressure disturbance induced

[0062] Parameter settings:

[0063] A = 8 Pa / km (representing strong wind shear)

[0064] σ = 600 meters (local wind fluctuations, but not on an extremely small scale)

[0065] ω = 2π / 86400 (one per day / night disturbance period)

[0066] x0, y0 = 103.54, 31.23 (coordinates of the wind tunnel center)

[0067] The above parameters can be substituted into the formula for calculating the induced value of each induction factor, thereby calculating the induced value induced by air pressure disturbance.

[0068] Step 103: Identify overlapping areas in all grids where multiple inducing factors simultaneously reach local peaks in the same region. Use any one of these overlapping areas as a test route for motor vehicle road emissions, or connect multiple adjacent overlapping areas to form a test route for motor vehicle road emissions.

[0069] Specifically, finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window (such as a 3×3 path cell or a 5×5 path cell); if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained.

[0070] Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route. Preferably, delete isolated points that are too small (an area threshold can be set, such as >10 pixels). Output the boundary, center coordinates, state factors, and induction factors of the merged region of each path unit.

[0071] Example 2

[0072] like Figure 2 As shown in the figure, this embodiment proposes a vehicle road emission test route selection system for high-altitude mountainous environments, including:

[0073] The grid division module is used to select remote sensing images of the test area for testing motor vehicle road emissions in a plateau and mountainous environment, and to divide the remote sensing images into grids, with each grid cell serving as a path cell.

[0074] Specifically, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0075] The guidance value calculation module is used to obtain the state factors of each path unit, extract multiple guidance factors for guiding motor vehicle driving behavior from the state factors, and calculate the guidance value of each guidance factor. The state factors include: road state factors and environmental state factors.

[0076] Specifically, the road condition factors include: road slope, road friction, and road smoothness; the environmental condition factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0077] Specifically, the inducing factors include: air pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

[0078] Specifically, calculating the induction value of each induction factor includes:

[0079]

[0080] Where F(x, y, t) is the induced value of the current point (x, y) in the path cell at time t, x is the abscissa of the current point, y is the ordinate of the current point, A is the maximum perturbation intensity of the current induced factor, ω is the perturbation frequency, x0 is the abscissa of the center point of the current induced factor, y0 is the ordinate of the center point of the current induced factor, and σ is the spatial diffusion radius of the current induced factor.

[0081] The route selection module is used to find overlapping areas in all grids where multiple inducing factors reach local peaks in the same region. Any one of these overlapping areas is used as a test route for motor vehicle road emissions, or multiple adjacent overlapping areas are connected to form a test route for motor vehicle road emissions.

[0082] Specifically, finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained.

[0083] Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

[0084] Specifically, obtaining the surface disturbance factor includes: classifying the surface texture of the remote sensing image, extracting the surface category boundaries using an image segmentation algorithm, calculating the boundary change rate and patch fragmentation degree, and summing the results as the surface disturbance factor.

[0085] Example 3

[0086] This invention also proposes a storage medium storing multiple instructions for implementing the method for selecting a road emission test route for motor vehicles in a high-altitude mountainous environment.

[0087] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0088] Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: Step 101, selecting a remote sensing image of the test area for testing motor vehicle road emissions in a plateau mountainous environment, dividing the remote sensing image into a grid, with each grid cell serving as a path cell;

[0089] Specifically, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0090] Step 102: Obtain the state factors of each path unit, extract multiple induction factors used to guide motor vehicle driving behavior from the state factors, and calculate the induction value of each induction factor. The state factors include: road state factors and environmental state factors.

[0091] Specifically, the road condition factors include: road slope, road friction, and road smoothness; the environmental condition factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0092] Specifically, the inducing factors include: air pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

[0093] Specifically, calculating the induction value of each induction factor includes:

[0094]

[0095] Where F(x, y, t) is the induced value of the current point (x, y) in the path cell at time t, x is the abscissa of the current point, y is the ordinate of the current point, A is the maximum perturbation intensity of the current induced factor, ω is the perturbation frequency, x0 is the abscissa of the center point of the current induced factor, y0 is the ordinate of the center point of the current induced factor, and σ is the spatial diffusion radius of the current induced factor.

[0096] Step 103: Identify overlapping areas in all grids where multiple inducing factors simultaneously reach local peaks in the same region. Use any one of these overlapping areas as a test route for motor vehicle road emissions, or connect multiple adjacent overlapping areas to form a test route for motor vehicle road emissions.

[0097] Specifically, finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained.

[0098] Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

[0099] Specifically, obtaining the surface disturbance factor includes: classifying the surface texture of the remote sensing image, extracting the surface category boundaries using an image segmentation algorithm, calculating the boundary change rate and patch fragmentation degree, and summing the results as the surface disturbance factor.

[0100] Example 4

[0101] This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the method for selecting a road emission test route for motor vehicles in a high-altitude mountainous environment.

[0102] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0103] The storage medium can be used to store software programs and modules, such as the method for selecting a vehicle road emission test route in a high-altitude mountainous environment according to an embodiment of the present invention. The corresponding program instructions / modules allow the processor to execute various functional applications and data processing by running the software programs and modules stored in the storage medium, thus realizing the aforementioned method for selecting a vehicle road emission test route in a high-altitude mountainous environment. The storage medium may include high-speed random access 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 configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0104] The processor can call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, select the remote sensing image of the test area for testing motor vehicle road emissions in a plateau mountain environment, divide the remote sensing image into grids, and each grid cell is a path cell;

[0105] Specifically, the remote sensing image is divided into grids using Delaunay triangulation or the Voronoi algorithm.

[0106] Step 102: Obtain the state factors of each path unit, extract multiple induction factors used to guide motor vehicle driving behavior from the state factors, and calculate the induction value of each induction factor. The state factors include: road state factors and environmental state factors.

[0107] Specifically, the road condition factors include: road slope, road friction, and road smoothness; the environmental condition factors include: altitude, humidity, oxygen content, wind speed, and temperature.

[0108] Specifically, the inducing factors include: air pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

[0109] Specifically, calculating the induction value of each induction factor includes:

[0110]

[0111] Where F(x, y, t) is the induced value of the current point (x, y) in the path cell at time t, x is the abscissa of the current point, y is the ordinate of the current point, A is the maximum perturbation intensity of the current induced factor, ω is the perturbation frequency, x0 is the abscissa of the center point of the current induced factor, y0 is the ordinate of the center point of the current induced factor, and σ is the spatial diffusion radius of the current induced factor.

[0112] Step 103: Identify overlapping areas in all grids where multiple inducing factors simultaneously reach local peaks in the same region. Use any one of these overlapping areas as a test route for motor vehicle road emissions, or connect multiple adjacent overlapping areas to form a test route for motor vehicle road emissions.

[0113] Specifically, finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained.

[0114] Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

[0115] Specifically, obtaining the surface disturbance factor includes: classifying the surface texture of the remote sensing image, extracting the surface category boundaries using an image segmentation algorithm, calculating the boundary change rate and patch fragmentation degree, and summing the results as the surface disturbance factor.

[0116] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0118] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0119] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] If the integrated unit is implemented as 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, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0122] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for selecting a test route for motor vehicle road emissions in a plateau mountainous environment, characterized in that, include: Remote sensing images of the test area for testing motor vehicle road emissions in a plateau mountainous environment are selected, and the remote sensing images are divided into grids, with each grid cell serving as a path cell; Obtain the state factors of each path unit, extract multiple induction factors used to guide motor vehicle driving behavior from the state factors, and calculate the induction value of each induction factor. The state factors include: road state factors and environmental state factors. The calculation of the induction value for each induction factor includes: ， in, For time Current point in the time path unit Induced value, The x-coordinate of the current point. The ordinate of the current point. The maximum perturbation intensity of the current inducing factor. For the perturbation frequency, The x-coordinate of the current center point of the inducing factor. The ordinate of the current center point of the inducing factor. The spatial diffusion radius of the current inducing factor; Treating motor vehicles as actors, and according to The value determines the actor's driving behavior; Find the overlapping area in all grids where multiple inducing factors reach local peaks in the same region at the same time, and use any one of the overlapping areas as the test route for motor vehicle road emissions, or connect multiple adjacent overlapping areas as the test route for motor vehicle road emissions. Finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained. Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

2. The method for selecting a test route for motor vehicle road emissions in a plateau mountainous environment as described in claim 1, characterized in that, The road condition factors include: road slope, road friction, and road smoothness; The environmental state factors include: altitude, humidity, oxygen content, wind speed, and temperature.

3. The method for selecting a test route for motor vehicle road emissions in a plateau mountainous environment as described in claim 1, characterized in that, The remote sensing image is divided into grids using Delaunay triangulation or Voronoi algorithm.

4. The method for selecting a test route for motor vehicle road emissions in a plateau mountainous environment as described in claim 1, characterized in that, Inducing factors include: pressure disturbance factor, slope shear disturbance factor, temperature fluctuation factor, and surface disturbance factor.

5. The method for selecting a test route for motor vehicle road emissions in a plateau mountainous environment as described in claim 1, characterized in that, Obtaining surface disturbance factors includes: classifying surface textures in remote sensing images, extracting surface category boundaries using image segmentation algorithms, calculating boundary change rates and patch fragmentation, and summing the results as surface disturbance factors.

6. A vehicle road emission test route selection system for high-altitude mountainous environments, characterized in that, include: The grid division module is used to select remote sensing images of the test area for testing motor vehicle road emissions in a plateau and mountainous environment, and to divide the remote sensing images into grids, with each grid cell serving as a path cell. The guidance value calculation module is used to obtain the state factors of each path unit, extract multiple guidance factors for guiding motor vehicle driving behavior from the state factors, and calculate the guidance value of each guidance factor. The state factors include: road state factors and environmental state factors. The calculation of the induction value for each induction factor includes: ， in, For time Current point in the time path unit Induced value, The x-coordinate of the current point. The ordinate of the current point. The maximum perturbation intensity of the current inducing factor. For the perturbation frequency, The x-coordinate of the current center point of the inducing factor. The ordinate of the current center point of the inducing factor. The spatial diffusion radius of the current inducing factor; Treating motor vehicles as actors, and according to The value determines the actor's driving behavior; The route selection module is used to find the overlapping area where multiple inducing factors in all grids reach local peaks in the same area at the same time. Any overlapping area is used as the test route for motor vehicle road emissions, or multiple adjacent overlapping areas are connected to form the test route for motor vehicle road emissions. Finding the overlapping area where multiple induced factors in all grids simultaneously reach local peaks in the same region includes: scanning all path cells using a sliding window; if the induced value at the center point of the current window is the maximum value within the current window, then the induced value at the center point of the current window is marked as a local peak, and the peak point layer of each induced factor is obtained. Add all peak point layers pixel by pixel. If the number of local peaks at a point in the window is greater than or equal to 2, then the point in the window is a resonance point of at least two induction factors. Merge the path units containing consecutive adjacent resonance points into a final route.

7. The vehicle road emission test route selection system in a plateau mountain environment as described in claim 6, characterized in that, The road condition factors include: road slope, road friction, and road smoothness; The environmental state factors include: altitude, humidity, oxygen content, wind speed, and temperature.

8. The vehicle road emission test route selection system in a plateau mountain environment as described in claim 6, characterized in that, The remote sensing image is divided into grids using Delaunay triangulation or Voronoi algorithm.

Images