Method and device for detecting vehicle pollutant emission data in high-altitude extreme environment

Abstract

The application relates to the technical field of transportation, in particular to a detection method and device for vehicle pollutant emission data in a high-altitude extreme environment, wherein the method comprises the following steps: obtaining actual driving data of a vehicle in different driving states, and calculating motion characteristic data and environment characteristic data in different target driving segments; outputting emission characteristic data corresponding to a certain emission amount by using a pre-constructed pollutant emission classification and identification model; determining boundary conditions of the vehicle in a drum test working condition, and constructing the drum test working condition to determine emission data of the pollutants in the corresponding driving state. Therefore, the problems that the high-altitude working condition constructed by directly depending on the actual driving emission data in a low-altitude environment cannot accurately reflect the real emission performance of the vehicle in the high-altitude environment, that the actual driving emission test technology is difficult to implement in the high-altitude environment and costs a lot, and that it is difficult to accumulate a large amount of data in a short time are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transportation, and in particular relates to a method and device for detecting vehicle pollutant emission data in a high-altitude extreme environment. BACKGROUND

[0002] With the increasingly stringent global regulatory standards for vehicle pollutant emissions, higher requirements are placed on the emission performance of vehicles under different environmental conditions. Due to the special natural conditions such as thin air, low ambient temperature, and large road slope in high-altitude areas, the engine combustion characteristics and emission behavior are significantly different from those in plain areas.

[0003] In related technologies, for a medium-low altitude environment, the actual emission level can be obtained through an emission test method of a standard dynamometer test cycle; or the emission characteristics of a vehicle under real road conditions can be captured through an actual driving emission test.

[0004] However, in related technologies, the environmental parameters of a medium-low altitude environment are essentially different from those of a high-altitude environment. Directly relying on actual driving emission data of a medium-low altitude environment to construct a high-altitude test cycle is likely to cause distortion of the high-altitude test cycle, and it is difficult to accurately reflect the real emission performance of a vehicle in a high-altitude environment. In addition, the terrain of a high-altitude environment is complex, and the traffic conditions are special. Performing large-scale actual driving emission tests not only has a large technical difficulty, but also has a huge cost, and it is difficult to accumulate a large amount of data in a short period of time. The high test cost and period limit have become an important obstacle to the development of high-altitude emission cycles, and need to be improved. SUMMARY

[0005] The present application provides a method and device for detecting vehicle pollutant emission data in a high-altitude extreme environment, to solve the problems in related technologies that a high-altitude test cycle constructed by directly relying on actual driving emission data of a medium-low altitude environment is likely to cause distortion of the high-altitude test cycle, and it is difficult to accurately reflect the real emission performance of a vehicle in a high-altitude environment. In addition, performing large-scale actual driving emission tests in a high-altitude environment not only has a large technical difficulty, but also has a huge cost, and it is difficult to accumulate a large amount of data in a short period of time, which has become an important obstacle to the development of high-altitude emission cycles.

[0006] The first aspect embodiment of the application provides a detection method for vehicle pollutant emission data in a high-altitude extreme environment, comprising the following steps: obtaining actual driving data of at least one vehicle in different driving states in a high-altitude extreme environment, and calculating motion characteristic data and / or environmental characteristic data of the at least one vehicle in at least one target driving segment based on the actual driving data; inputting the motion characteristic data and the environmental characteristic data into a pre-constructed pollutant emission classification and identification model to output emission characteristic data corresponding to the motion characteristic data and the environmental characteristic data and meeting a preset emission amount; determining boundary conditions of the at least one vehicle in a rolling test working condition based on the emission characteristic data, and constructing a rolling test working condition of the corresponding vehicle by using the boundary conditions, the motion characteristic data, the environmental characteristic data, and the emission characteristic data, so as to determine emission data of at least one pollutant of the vehicle in the corresponding driving state by using the rolling test working condition.

[0007] Through the above technical solution, the motion characteristic data and the environmental characteristic data in different target driving segments can be calculated by using the actual driving data of the vehicle in the high-altitude extreme environment in different driving states, and then the motion characteristic data and the environmental characteristic data are input into the pre-constructed pollutant emission classification and identification model to obtain the emission characteristic data meeting a certain emission amount, so as to determine the boundary conditions in the rolling test working condition, and then construct the rolling test working condition of the corresponding vehicle, and determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the rolling test working condition. Based on the limited high-altitude actual driving emission data, the rolling test working condition meeting the high emission characteristics can be quickly established by using the scientific classification and screening method, which significantly reduces the dependence and cost of large-scale road testing in the plateau region, improves the authenticity and reproducibility of the test working condition in the high-altitude environment, and provides reliable data support for the development and supervision of vehicle emission control technology.

[0008] Optionally, in an embodiment of the application, the construction of the rolling test working condition of the corresponding vehicle by using the boundary conditions, the motion characteristic data, the environmental characteristic data, and the emission characteristic data comprises: screening the motion characteristic data, the environmental characteristic data, and the emission characteristic data to obtain motion characteristic data, environmental characteristic data, and emission characteristic data meeting the boundary conditions; constructing an initial rolling test working condition of the corresponding vehicle based on the motion characteristic data, the environmental characteristic data, and the emission characteristic data meeting the boundary conditions; performing interpolation processing on slope data in the initial rolling test working condition until slope data meeting a preset slope condition is obtained; and constructing a rolling test working condition meeting a preset working condition based on the slope data meeting the preset slope condition and the initial rolling test working condition.

[0009] By the technical solution, the motion characteristic data, the environment characteristic data and the emission characteristic data can be screened, and then the initial drum test working condition is constructed, and the slope data in the initial drum test working condition is subjected to interpolation processing, and then the drum test working condition meeting certain working condition conditions is constructed. Through the multi-dimensional data screening mechanism, the authenticity of the drum test working condition simulation is significantly improved, the test efficiency is improved, and through different slope conditions, the extreme working condition under a high-altitude limit environment can be successfully reproduced, and the extreme working condition coverage capability is improved.

[0010] Optionally, in an embodiment of the present application, before the motion characteristic data and / or the environment characteristic data are input into the pre-constructed pollutant emission classification identification model, the method further comprises: acquiring test motion characteristic data, test environment characteristic data and test emission characteristic data of at least one test vehicle in at least one driving segment in a high-altitude limit environment; determining a driving state of the at least one test vehicle in the corresponding driving segment based on the test motion characteristic data; constructing input data of the pollutant emission classification identification model under the different driving states based on the test motion characteristic data and the test environment characteristic data, and constructing output data of the pollutant emission classification identification model under the different driving states based on the test emission characteristic data; determining architecture information of the pollutant emission classification identification model based on the input data and the output data; training the pollutant emission classification identification model containing the architecture information by using the input data and the output data until a preset training condition is met, ending the training, and constructing a pollutant emission classification identification model of at least one pollutant of the at least one test vehicle under the different driving states.

[0011] Through the technical solution, the driving state of the test vehicle is determined through the test motion characteristic data, the input data is constructed through the test motion characteristic data and the test environment characteristic data, the output data is constructed through the test emission characteristic data, and then the architecture information of the pollutant emission classification identification model is determined, and the pollutant emission classification identification model is trained until a certain training condition is met, and the pollutant emission classification identification model is constructed. By acquiring multi-dimensional data of the test vehicle in a real driving segment, the model has scene self-adaptation capability. By associating the input data and the output data, the model's ability to capture key emission influencing factors is improved, the model training parameter amount is reduced, the emission classification accuracy is improved, and the model's generalization capability is improved.

[0012] Optionally, in an embodiment of the present application, the constructing the output data of the pollutant emission classification identification model under the different driving states based on the test emission characteristic data comprises: screening first test emission characteristic data satisfying the preset emission amount and second test emission characteristic data not satisfying the preset emission amount based on pollutant emission factors of the test emission characteristic data; establishing a first label and a second label corresponding to the output data based on the first test emission characteristic data and the second test emission characteristic data respectively; and constructing the output data based on the first test emission characteristic data, the second test emission characteristic data, the first label and the second label.

[0013] Through the above technical solution, the first test emission characteristic data and the second test emission characteristic data can be screened based on the pollutant emission factors of the test emission characteristic data, and then the corresponding first label and second label are established to construct the output data, so as to realize the fine stratification of the emission characteristics by setting a certain emission amount, and improve the recognition accuracy.

[0014] Optionally, in an embodiment of the present application, the calculating the motion characteristic data and / or the environment characteristic data of the at least one vehicle in at least one target driving segment based on the actual driving data comprises: performing cutting processing on the actual driving data to obtain actual driving data of the at least one vehicle in at least one target driving segment; and calculating the actual driving data in different target driving segments respectively to obtain the motion characteristic data and / or the environment characteristic data of the at least one vehicle in the different target driving segments.

[0015] Through the above technical solution, the actual driving data can be cut and processed to obtain the actual driving data of the vehicle in different target driving segments, and the motion characteristic data and the environment characteristic data in different target driving segments are calculated, the continuous actual driving data is decomposed into discrete target driving segments through cutting processing, the fine analysis of the driving behavior is realized, the calculation efficiency is improved, the characteristic data of different target segments is calculated, the key indicators are extracted, the optimization of different characteristic data is realized, and the adaptability of the model is expanded.

[0016] The second aspect embodiment of the application provides a device for detecting vehicle pollutant emission data in a high-altitude extreme environment, comprising: a calculation module configured to obtain actual driving data of at least one vehicle in different driving states in a high-altitude extreme environment, and calculate motion feature data and / or environmental feature data of the at least one vehicle in at least one target driving segment based on the actual driving data; an output module configured to input the motion feature data and the environmental feature data into a pre-constructed pollutant emission classification and identification model to output emission feature data corresponding to the motion feature data and the environmental feature data and meeting a preset emission amount; and a first determination module configured to determine boundary conditions of the at least one vehicle in a dynamometer test condition based on the emission feature data, and construct a dynamometer test condition of the corresponding vehicle by using the boundary conditions, the motion feature data, the environmental feature data, and the emission feature data, so as to determine emission data of at least one pollutant of the vehicle in the corresponding driving state by using the dynamometer test condition.

[0017] By the above technical solution, the motion feature data and the environmental feature data in different target driving segments can be calculated by using the actual driving data of the vehicle in the high-altitude extreme environment in different driving states, and then the motion feature data and the environmental feature data are input into the pre-constructed pollutant emission classification and identification model to obtain the emission feature data meeting a certain emission amount, so as to determine the boundary conditions in the dynamometer test condition, and then construct the dynamometer test condition of the corresponding vehicle, and determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the dynamometer test condition. Based on the limited high-altitude actual driving emission data, the dynamometer test condition meeting the high emission feature can be quickly established by using a scientific classification and screening method, which significantly reduces the dependence on large-scale road testing in the plateau region and the cost, improves the authenticity and reproducibility of the test condition in the high-altitude environment, and provides reliable data support for the development and supervision of vehicle emission control technology.

[0018] Optionally, in an embodiment of the application, the first determination module comprises: a screening unit configured to screen the motion feature data, the environmental feature data, and the emission feature data to obtain motion feature data, environmental feature data, and emission feature data meeting the boundary conditions; a first construction unit configured to construct an initial dynamometer test condition of the corresponding vehicle based on the motion feature data, the environmental feature data, and the emission feature data meeting the boundary conditions; an interpolation processing unit configured to perform interpolation processing on slope data in the initial dynamometer test condition until slope data meeting a preset slope condition is obtained; and a second construction unit configured to construct a dynamometer test condition meeting a preset condition based on the slope data meeting the preset slope condition and the initial dynamometer test condition.

[0019] By the technical solution, the motion characteristic data, the environment characteristic data and the emission characteristic data can be screened, and then the initial drum test working condition is constructed, and the slope data in the initial drum test working condition is interpolated, and then the drum test working condition meeting certain working condition conditions is constructed. Through the multi-dimensional data screening mechanism, the authenticity of the drum test working condition simulation is significantly improved, the test efficiency is improved, and through different slope conditions, the extreme working condition under the high-altitude extreme environment can be successfully reproduced, and the extreme working condition coverage capability is improved.

[0020] Optionally, in an embodiment of the present application, further comprising: an acquisition module configured to acquire test motion characteristic data, test environment characteristic data and test emission characteristic data of at least one test vehicle in at least one driving segment in a high-altitude extreme environment; a second determination module configured to determine a driving state of the at least one test vehicle in the corresponding driving segment based on the test motion characteristic data; a first construction module configured to construct input data of a pollutant emission classification identification model under different driving states based on the test motion characteristic data and the test environment characteristic data, and construct output data of the pollutant emission classification identification model under the different driving states based on the test emission characteristic data; a third determination module configured to determine architecture information of the pollutant emission classification identification model based on the input data and the output data; and a second construction module configured to train the pollutant emission classification identification model containing the architecture information using the input data and the output data until a preset training condition is met, end the training, and construct a pollutant emission classification identification model of at least one pollutant of the at least one test vehicle under the different driving states.

[0021] Through the technical solution, the driving state of the test vehicle is determined through the test motion characteristic data, the input data is constructed through the test motion characteristic data and the test environment characteristic data, the output data is constructed through the test emission characteristic data, and then the architecture information of the pollutant emission classification identification model is determined, and the pollutant emission classification identification model is trained until a certain training condition is met, and the pollutant emission classification identification model is constructed. Through acquiring multi-dimensional data of the test vehicle in the real driving segment, the model has scene adaptive capability, through correlating the input data and the output data, the model's capture capability of key emission influencing factors is improved, the model training parameter amount is reduced, the emission classification accuracy is improved, and the model's generalization capability is improved.

[0022] Optionally, in an embodiment of the present application, the first construction module comprises: a third screening unit configured to screen first test emission characteristic data satisfying the preset emission amount and second test emission characteristic data not satisfying the preset emission amount based on pollutant emission factors of the test emission characteristic data; a second construction unit configured to establish first labels and second labels corresponding to the output data based on the first test emission characteristic data and the second test emission characteristic data, respectively; and a third construction unit configured to construct the output data based on the first test emission characteristic data, the second test emission characteristic data, the first labels, and the second labels.

[0023] Through the above technical solution, the first test emission characteristic data and the second test emission characteristic data can be screened based on the pollutant emission factors of the test emission characteristic data, and the corresponding first labels and second labels can be established, so as to construct the output data. By setting a certain emission amount, the fine stratification of emission characteristics is realized, and the recognition accuracy is improved.

[0024] Optionally, in an embodiment of the present application, the calculation module comprises: a processing unit configured to perform cutting processing on the actual driving data to obtain actual driving data of the at least one vehicle in at least one target driving segment; and a calculation unit configured to calculate the actual driving data in different target driving segments to obtain motion characteristic data and / or environment characteristic data of the at least one vehicle in the different target driving segments.

[0025] Through the above technical solution, the actual driving data can be cut and processed to obtain the actual driving data of the vehicle in different target driving segments, and the motion characteristic data and the environment characteristic data in different target driving segments can be calculated. By cutting and processing the continuous actual driving data into discrete target driving segments, the fine analysis of driving behavior is realized, the calculation efficiency is improved, the characteristic data of different target segments is calculated, the key indicators are extracted, the optimization of different characteristic data is realized, and the adaptability of the model is expanded.

[0026] The third aspect embodiment of the present application provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the vehicle pollutant emission data detection method in a high-altitude extreme environment as described in the above embodiments.

[0027] The fourth aspect embodiment of the present application provides a computer readable storage medium storing a computer program. When the program is executed by a processor, the vehicle pollutant emission data detection method in a high-altitude extreme environment as described above is implemented.

[0028] The fifth aspect of the application provides a computer program product, comprising a computer program which, when executed, implements the method for detecting vehicle pollutant emission data in a high-altitude extreme environment as described above.

[0029] The embodiments of the application can calculate the motion feature data and the environment feature data in different target driving segments through the actual driving data of the vehicle in the high-altitude extreme environment under different driving states, and then input the motion feature data and the environment feature data into the pre-constructed pollutant emission classification identification model to obtain the emission feature data that meets a certain emission amount, so as to determine the boundary condition under the dynamometer test working condition, and then construct the dynamometer test working condition corresponding to the vehicle, and determine the emission data of at least one pollutant of the vehicle under the corresponding driving state by using the dynamometer test working condition. Based on limited high-altitude actual driving emission data, the dynamometer test working condition meeting the high emission characteristics can be quickly established by using a scientific classification and screening method, which significantly reduces the dependence on large-scale road testing in the plateau area and the cost, improves the authenticity and reproducibility of the test working condition in the high-altitude environment, and provides reliable data support for the development and supervision of vehicle emission control technology. Thus, the problems in the related art that the high-altitude working condition constructed directly by relying on the actual driving emission data in the low-altitude environment is easy to cause distortion of the high-altitude environment test working condition, and it is difficult to accurately reflect the real emission performance of the vehicle in the high-altitude environment, and in addition, the large-scale actual driving emission test in the high-altitude environment not only has great technical difficulty and high cost, but also is difficult to complete a large amount of data accumulation in a short time, which becomes an important obstacle to the development of the high-altitude environment emission working condition are solved.

[0030] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0031] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, including the appended drawings, wherein:

[0032] Figure 1 A flowchart of a method for detecting vehicle pollutant emission data in a high-altitude extreme environment according to an embodiment of the application is provided.

[0033] Figure 2 A flowchart of constructing a pollutant emission classification identification model according to an embodiment of the application is provided.

[0034] Figure 3 A flowchart of a constructed CO pollutant emission classification identification model according to an embodiment of the application is provided.

[0035] Figure 4A flow chart of a process of constructing a test mode of a rotation drum according to an embodiment of the present application is provided;

[0036] Figure 5 A schematic diagram of a CO high emission test mode of a rotation drum according to an embodiment of the present application is provided;

[0037] Figure 6 A flow chart of a working principle of a method for detecting vehicle pollutant emission data in a high-altitude extreme environment according to an embodiment of the present application is provided;

[0038] Figure 7 A block schematic diagram of a device for detecting vehicle pollutant emission data in a high-altitude extreme environment according to an embodiment of the present application is provided;

[0039] Figure 8 A structural schematic diagram of a vehicle according to an embodiment of the present application is provided.

[0040] Reference signs:

[0041] Wherein, 10 is a device for detecting vehicle pollutant emission data in a high-altitude extreme environment; 100 is a calculation module; 200 is an output module; 300 is a first determination module; 801 is a memory; 802 is a processor; and 803 is a communication interface. DETAILED DESCRIPTION

[0042] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0043] A method and device for detecting vehicle pollutant emission data in a high-altitude extreme environment according to an embodiment of the present application are described below with reference to the accompanying drawings. In view of the fact that the actual driving emission data directly dependent on the medium and low altitude environment is prone to cause distortion of the test working condition in the high-altitude environment and is difficult to accurately reflect the real emission performance of the vehicle in the high-altitude environment, and in view of the fact that large-scale actual driving emission test in the high-altitude environment is not only technically difficult and costly, but also difficult to accumulate a large amount of data in a short time, the present application provides a method for detecting vehicle pollutant emission data in a high-altitude extreme environment. In the method, the motion characteristic data and the environmental characteristic data in different target driving segments can be calculated by the actual driving data of the vehicle in the high-altitude extreme environment under different driving conditions, and then the motion characteristic data and the environmental characteristic data are input into the pre-constructed pollutant emission classification and identification model to obtain the emission characteristic data meeting a certain emission amount, so as to determine the boundary condition of the dynamometer test working condition, and then the dynamometer test working condition corresponding to the vehicle is constructed, and the emission data of at least one pollutant of the vehicle under the corresponding driving condition is determined by using the dynamometer test working condition. Based on limited high-altitude actual driving emission data, the dynamometer test working condition meeting the high emission characteristics can be quickly established by a scientific classification and screening method, which significantly reduces the dependence and cost of large-scale road test in the plateau area, improves the authenticity and reproducibility of the test working condition in the high-altitude environment, and provides reliable data support for the development and supervision of vehicle emission control technology. Thus, the problems in the related art that the high-altitude working condition constructed by directly relying on the actual driving emission data in the medium and low altitude environment is prone to cause distortion of the test working condition in the high-altitude environment, and is difficult to accurately reflect the real emission performance of the vehicle in the high-altitude environment, and the large-scale actual driving emission test in the high-altitude environment is not only technically difficult and costly, but also difficult to accumulate a large amount of data in a short time, which becomes an important obstacle to the development of the high-altitude emission working condition, are solved.

[0044] Specifically, Figure 1 A flowchart of a method for detecting vehicle pollutant emission data in a high-altitude extreme environment according to an embodiment of the present application is shown.

[0045] As Figure 1 shown, the method for detecting vehicle pollutant emission data in a high-altitude extreme environment includes the following steps:

[0046] In step S101, actual driving data of at least one vehicle in a high-altitude extreme environment under different driving conditions is obtained, and motion characteristic data and / or environmental characteristic data of the at least one vehicle in at least one target driving segment are calculated based on the actual driving data.

[0047] It can be understood that in the embodiments of the present application, the high-altitude extreme environment can be understood as an area with an altitude of more than 2500 meters, in which the air density is significantly reduced, the atmospheric pressure is significantly reduced, and the human body is easily hypoxic. Therefore, directly performing large-scale actual driving emission tests in the high-altitude extreme environment not only has great technical difficulty and high cost, but also is difficult to accumulate a large amount of data in a short time. The high test cost and cycle limit become an important obstacle to the development of high-altitude extreme environment emission conditions.

[0048] In addition, in the embodiments of the present application, the actual driving data can include but is not limited to driving time, vehicle speed, slope, altitude, environmental temperature, environmental humidity, etc., and the present application is not limited specifically; the motion characteristic data can include but is not limited to the maximum vehicle speed of the vehicle, the segment RPA (Relative Position Accuracy) value, the average vehicle speed, the maximum acceleration, the maximum deceleration, etc., and the present application is not limited specifically; the environmental characteristic data can include but is not limited to the average temperature, the average humidity, the average altitude, the average uphill slope, the average downhill slope, the average uphill ratio, the average downhill ratio, etc., and the present application is not limited specifically.

[0049] Further, in the embodiments of the present application, different driving states can be determined by the maximum vehicle speed, which can include but is not limited to urban driving state, suburban driving state and high-speed driving state, wherein the urban driving state can be understood as a driving state with a maximum vehicle speed of ≤60km / h; the suburban driving state can be understood as a driving state with a maximum vehicle speed of between 60km / h and 80km / h; the high-speed driving state can be understood as a driving state with a maximum vehicle speed of >80km / h, and the specific value can be set by a person skilled in the art according to the actual situation, and the present application is not limited specifically.

[0050] In some embodiments, the embodiments of the present application can obtain actual driving data of the vehicle in different driving states, and then calculate motion characteristic data and environmental characteristic data of the vehicle in different target driving segments through the actual driving data. The target driving segment can be obtained by short-segment cutting processing of the actual driving data, and the specific content is explained below.

[0051] In some embodiments, the embodiments of the present application can obtain actual driving data of the vehicle in different driving states, and then calculate motion characteristic data of the vehicle in different target driving segments through the actual driving data.

[0052] Exemplarily, the embodiment of the present application can collect 200 vehicles in high-altitude extreme environment, 208 million kilometers of actual driving data, and calculate the motion characteristic data of the vehicles in different target driving segments, such as the maximum vehicle speed, the segment RPA value, the average vehicle speed, the maximum acceleration, the maximum deceleration, etc., and the environmental characteristics, such as the average temperature, the average humidity, the average altitude, the average uphill slope, the average downhill slope, the average uphill proportion, the average downhill proportion, etc., which are not specifically limited by the present application.

[0053] Optionally, in an embodiment of the present application, based on the actual driving data, the motion characteristic data and / or the environmental characteristic data of at least one vehicle in at least one target driving segment are calculated, comprising: performing cutting processing on the actual driving data to obtain the actual driving data of at least one vehicle in at least one target driving segment; and respectively calculating the actual driving data in different target driving segments to obtain the motion characteristic data and / or the environmental characteristic data of at least one vehicle in different target driving segments.

[0054] It can be understood that in the embodiment of the present application, the cutting processing can include but is not limited to short-segment cutting processing, which can be set by those skilled in the art according to actual conditions, and the present application is not specifically limited.

[0055] In actual execution process, the embodiment of the present application can perform short-segment cutting processing on the actual driving data, and then obtain the actual driving data of the vehicle in different target driving segments, and respectively calculate the actual driving data in different target driving segments, and then obtain the motion characteristic data and the environmental characteristic data of the vehicle in different target driving segments.

[0056] Exemplarily, the embodiment of the present application can collect 200 vehicles in high-altitude extreme environment, 208 million kilometers of actual driving data, and perform short-segment cutting processing on the actual driving data, and then calculate the motion characteristic data of the vehicle in different target driving segments, such as the maximum vehicle speed, the segment RPA value, the average vehicle speed, the maximum acceleration, the maximum deceleration, etc., and the environmental characteristics, such as the average temperature, the average humidity, the average altitude, the average uphill slope, the average downhill slope, the average uphill proportion, the average downhill proportion, etc., which are not specifically limited by the present application.

[0057] Optionally, in an embodiment of the present application, before the motion feature data and / or the environment feature data are input into the pre-constructed pollutant emission classification identification model, the method further comprises: acquiring test motion feature data, test environment feature data and test emission feature data of at least one test vehicle in at least one driving segment in a high-altitude extreme environment; determining the driving state of the at least one test vehicle in the corresponding driving segment based on the test motion feature data; constructing input data of the pollutant emission classification identification model under different driving states based on the test motion feature data and the test environment feature data, and constructing output data of the pollutant emission classification identification model under different driving states based on the test emission feature data; determining the architecture information of the pollutant emission classification identification model based on the input data and the output data; training the pollutant emission classification identification model containing the architecture information using the input data and the output data until a preset training condition is met, ending the training, and constructing the pollutant emission classification identification model of at least one pollutant of the at least one test vehicle under different driving states.

[0058] In some embodiments, the process of constructing the pollutant emission classification identification model according to an embodiment of the present application is shown in Figure 2 , and the main content can be:

[0059] Step S201: Real-time collection of actual driving emission data of different test vehicles in a high-altitude extreme environment.

[0060] It can be understood that the actual driving emission data of different test vehicles in a high-altitude extreme environment can be collected in real time according to an embodiment of the present application, which can include but is not limited to driving time, vehicle speed, slope, altitude, environmental temperature, environmental humidity and emission data of pollutants such as CO, NO x , PN, etc., without specific limitation, wherein the unit of CO, NO x pollutant concentration can be g / s, and the unit of PN pollutant concentration can be # / s, without specific limitation.

[0061] For example, actual driving emission tests can be performed on 10 test vehicles such as passenger cars according to an embodiment of the present application, without specific limitation, and a vehicle-mounted emission test system with a data collection frequency of 1 Hz can be used to collect actual driving emission data in real time, and the collected data samples are shown in Table 1. Table 1 is a schematic table of actual driving emission data samples according to an embodiment of the present application.

[0062] Table 1

[0063]

[0064] Step S202: Obtain test motion feature data, test environment feature data and test emission feature data of the test vehicle in at least one driving segment.

[0065] In the embodiment of the present application, the actual driving emission data can be cut into short trips, each of which includes an idle segment and a motion segment, and then the test motion feature data of the test vehicle in each driving segment, such as the maximum vehicle speed, the segment RPA value, the average vehicle speed, the maximum acceleration, the maximum deceleration, etc., can be calculated, which are not limited in the present application; the test environment feature data, such as the average temperature, the average humidity, the average altitude, the average uphill slope, the average downhill slope, the average uphill proportion, the average downhill proportion, etc., which are not limited in the present application; and the test emission feature data, such as the average emission value of CO, NOx, PN, etc., with the unit of g / km, which are not limited in the present application. x

[0066] For example, part of the test motion feature data and test emission feature data obtained by the embodiment of the present application are shown in Table 2. Table 2 is a schematic table of part of the test motion feature data and test emission feature data according to an embodiment of the present application.

[0067] Table 2

[0068]

[0069] Step S203: Determine the driving state of the test vehicle based on the test motion feature data.

[0070] In the embodiment of the present application, the driving state of the test vehicle in different driving segments can be divided into the city driving state, the suburban driving state and the high-speed driving state according to the maximum vehicle speed in the test motion feature data, which are not limited in the present application.

[0071] Further, the driving state of the test vehicle in different driving segments can be determined according to the maximum vehicle speed in the test motion feature data.

[0072] Step S204: Determine the input data and output data of the pollutant emission classification identification model.

[0073] ​In the application, different driving states such as urban driving state, suburban driving state and high-speed driving state are not specifically limited, test motion characteristic data such as maximum vehicle speed, segment RPA value, average vehicle speed, maximum acceleration, maximum deceleration, and the like are not specifically limited, test environment characteristic data such as average temperature, average humidity, average altitude, average uphill slope, average downhill slope, average uphill ratio, average downhill ratio, and the like are not specifically limited, and are taken as input data of the pollutant emission classification identification model; and test emission characteristic data such as CO, NO x , PN emission values are taken as output data of the pollutant emission classification identification model according to high, medium and low.

[0074] Step S205: Determine the architecture information of the pollutant emission classification identification model.

[0075] In the application, the C5.0 decision tree model can automatically learn the relationship between the input data and the emission level, generate a classification rule, and quickly determine which emission category a new driving segment belongs to according to the input data.

[0076] Step S206: Construct a pollutant emission classification identification model for different pollutants.

[0077] In the application, a pollutant emission classification identification model for each of CO, NO x , and PN can be constructed.

[0078] For example, a CO pollutant emission classification identification model constructed by the C5.0 decision tree model in the application is shown in Figure 3 . The main content is as follows:

[0079] Step S301: Obtain test motion characteristic data, test environment characteristic data and test emission characteristic data in the corresponding driving segment.

[0080] In the application, the test motion characteristic data and the test environment characteristic data can be taken as input data of the C5.0 decision tree model, and the test emission characteristic data can be taken as output data of the C5.0 decision tree model.

[0081] Step S302: The C5.0 decision tree model determines whether the average vehicle speed is greater than a first threshold value.

[0082] In the application, if the average vehicle speed is less than or equal to the first threshold value, step S303 is performed; otherwise, step S307 is performed.

[0083] In addition, the first threshold value can be 50 km / h, which can be set by those skilled in the art according to actual conditions, and the application does not make specific limitations.

[0084] Step S303: The C5.0 decision tree model determines whether the highest speed is greater than a second threshold value.

[0085] In the embodiment of the application, if the highest speed is less than or equal to the second threshold value, step S304 is executed; otherwise, step S305 is executed.

[0086] In addition, the second threshold value can be 14 km / h, which can be set by those skilled in the art according to actual conditions, and the application does not make specific limitations.

[0087] Step S304: The emission characteristic data of the CO pollutant is determined as high emission data.

[0088] Step S305: The C5.0 decision tree model determines whether the RPA is greater than a third threshold value.

[0089] In the embodiment of the application, if the RPA is less than or equal to the second threshold value, step S304 is executed; otherwise, step S306 is executed.

[0090] In addition, the third threshold value can be 0.15 m / s2, which can be set by those skilled in the art according to actual conditions, and the application does not make specific limitations.

[0091] Step S306: The emission characteristic data of the CO pollutant is determined as non-high emission data.

[0092] Step S307: The C5.0 decision tree model determines whether the average uphill slope is greater than a fourth threshold value.

[0093] In the embodiment of the application, if the average uphill slope is less than or equal to the fourth threshold value, step S304 is executed; otherwise, step S306 is executed.

[0094] In addition, the fourth threshold value can be 1.1%, which can be set by those skilled in the art according to actual conditions, and the application does not make specific limitations.

[0095] It should be noted that in the embodiment of the application, the C5.0 decision tree model can generate classification rules by automatically learning the relationship between the test motion characteristic data, the test environment characteristic data and the test emission characteristic data, and can quickly determine whether a driving segment belongs to which emission category, i.e., whether the driving segment is a non-high emission segment or a high emission segment, according to the input characteristics.

[0096] In addition, the C5.0 decision tree model can generate a CO pollutant emission classification identification model in the order of the highest speed, RPA, average uphill slope, and average speed during classification and judgment, and the specific execution order is not limited in the application. Figure 3 This is only one implementation.

[0097] Step S207: training the pollutant emission classification identification model.

[0098] In the embodiments of the application, the C5.0 decision tree model can be trained by using input data and output data until a certain training condition is met, and the training is ended, and then a corresponding pollutant emission classification identification model is constructed. The certain training condition can be set by a person skilled in the art according to the actual situation, and the application does not make specific limitations.

[0099] Optionally, in an embodiment of the application, based on the test emission characteristic data, the output data of the pollutant emission classification identification model under different driving states is constructed, including: based on the pollutant emission factor of the test emission characteristic data, the first test emission characteristic data satisfying the preset emission amount and the second test emission characteristic data not satisfying the preset emission amount are screened; the first label and the second label corresponding to the output data are respectively established based on the first test emission characteristic data and the second test emission characteristic data; and the output data is constructed based on the first test emission characteristic data, the second test emission characteristic data, the first label and the second label.

[0100] In some embodiments, the embodiments of the application can statistically analyze the distribution of different pollutant emission factors, and then screen the first test emission characteristic data satisfying a certain emission amount and the second test emission characteristic data not satisfying the certain emission amount. The certain emission amount can be set by a person skilled in the art according to the actual situation, and the application does not make specific limitations.

[0101] Further, the embodiments of the application can establish the first label and the second label of the output data according to the first test emission characteristic data and the second test emission characteristic data, and then construct the output data.

[0102] For example, the embodiments of the application can statistically analyze the distribution of different pollutant emission factors for different driving states, and then determine the 90% quantile value of each pollutant emission factor as the high emission segment judgment threshold, for example, the data with a pollutant emission factor higher than the 90% quantile value of the corresponding pollutant is defined as the first test emission characteristic data, i.e. high emission data, and the remaining data is defined as the second test emission characteristic data, i.e. non-high emission data, so as to respectively establish the first label / second label of CO, NO x , PN and other pollutants, i.e. high emission classification label / non-high emission classification label, and then obtain the corresponding output data.

[0103] In step S102, the motion feature data and the environment feature data are input into the pre-constructed pollutant emission classification and identification model to output the emission feature data corresponding to the motion feature data and the environment feature data and satisfying the preset emission amount.

[0104] In actual implementation, the motion feature data and the environment feature data can be input into the pre-constructed pollutant emission classification and identification model, and then the emission feature data of CO, NO x , PN and the like satisfying a certain emission amount can be obtained. The certain emission amount can be set by a person skilled in the art according to actual conditions, and the application does not make specific limitations.

[0105] For example, based on the pre-constructed pollutant emission classification and identification model, the application embodiment can classify the driving states of CO, NO x , PN and the like respectively, extract the emission feature data of each pollutant satisfying a certain emission amount, such as high emission feature data, and classify the identified high emission feature data into the high emission feature database of CO, NO x , PN and the like corresponding to different driving states, such as urban driving state, suburban driving state, high-speed driving state and the like.

[0106] In step S103, based on the emission feature data, the boundary conditions of at least one vehicle in the drum test working condition are determined, and the drum test working condition corresponding to the vehicle is constructed by using the boundary conditions, the motion feature data, the environment feature data and the emission feature data, so as to determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the drum test working condition.

[0107] It can be understood that the application embodiment can determine the boundary conditions of the drum test working condition through the emission feature data.

[0108] For example, the application embodiment can calculate the average temperature, the average humidity and the average altitude in different driving states respectively for the high emission feature database of different pollutants, such as CO, NO x , PN and the like, and then determine the environmental boundary conditions of the drum test working condition corresponding to different pollutants, as shown in Table 3. Table 3 is a schematic table of the environmental boundary conditions of the drum test working condition according to an embodiment of the application.

[0109] Table 3

[0110]

[0111] Further, the embodiment of the present application can construct the drum test working condition of the corresponding vehicle according to the boundary condition, the motion characteristic data, the environment characteristic data and the emission characteristic data, and then determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the drum test working condition.

[0112] Optionally, in an embodiment of the present application, the drum test working condition of the corresponding vehicle is constructed by using the boundary condition, the motion characteristic data, the environment characteristic data and the emission characteristic data, which includes: screening the motion characteristic data, the environment characteristic data and the emission characteristic data to obtain the motion characteristic data, the environment characteristic data and the emission characteristic data satisfying the boundary condition; constructing the initial drum test working condition of the corresponding vehicle based on the motion characteristic data, the environment characteristic data and the emission characteristic data of the boundary condition; performing interpolation processing on the slope data in the initial drum test working condition until the slope data satisfying the preset slope condition is obtained; and constructing the drum test working condition satisfying the preset working condition based on the slope data satisfying the preset slope condition and the initial drum test working condition.

[0113] In some embodiments, the flowchart of the embodiment of the present application for constructing the drum test working condition is shown in Figure 4 , and the main content is:

[0114] Step S401: characteristic data screening and classification.

[0115] In the embodiment of the present application, the motion characteristic data, the environment characteristic data and the emission characteristic data of several target driving segments satisfying the boundary condition can be screened from the high emission characteristic database of different pollutants such as CO, NO x , PN, etc. according to different driving states such as urban driving state, suburban driving state, high-speed driving state, etc. The characteristic data of each target driving segment contains an idle speed segment and a motion segment, and the speed data and the slope data are included as the basic data for generating the drum test working condition.

[0116] Step S402: random combination of the characteristic data of different target driving segments to construct the initial drum test working condition.

[0117] In the embodiment of the present application, several target driving segments can be randomly extracted from the screened characteristic data, and then sequentially combined to form a complete initial drum test working condition.

[0118] Step S403: idle speed interpolation processing of the slope data of different target driving segments to obtain the slope data satisfying a certain slope condition.

[0119] In the embodiment, the slope data in the idling section in the combined test mode is linearly interpolated by using the slope values at the end point of the previous section and the start point of the next section to generate a continuously changing slope curve, so as to ensure the natural transition of the road slope and avoid sudden change.

[0120] It can be understood that the embodiment can interpolate the slope data of different target driving sections in the initial test mode to obtain the slope data meeting certain slope conditions. The certain slope conditions can be set by the person skilled in the art according to the actual situation, and the application does not make specific limitation.

[0121] Step S404: Test mode verification and adjustment.

[0122] In the embodiment, the initial test mode is verified. The time proportion of the city driving state, the suburb driving state and the high-speed driving state is between 30% and 40%, the overall test mode duration is between 90 and 120 minutes, and the overall average speed is between 15 and 40 km / h. If the certain test conditions are not met, the selected target driving section is adjusted until the certain test conditions are met, and the CO, NOx and PN pollutant high emission test mode is obtained. The certain test conditions can be set by the person skilled in the art according to the actual situation, and the application does not make specific limitation. x

[0123] For example, the CO high emission test mode constructed by the embodiment is shown in FIG. 4. Figure 5

[0124] The working principle of the method for detecting vehicle pollutant emission data in high-altitude extreme environment according to the embodiment will be introduced below with reference to a specific embodiment.

[0125] In the embodiment, the actual driving data can include but is not limited to driving time, speed, slope, altitude, environmental temperature, environmental humidity, etc., and the application does not make specific limitation. Figure 6 FIG. 4 is a flow chart of the working principle of the method for detecting vehicle pollutant emission data in high-altitude extreme environment according to the embodiment.

[0126] Step S601: Obtain the actual driving data of the vehicle in the high-altitude extreme environment.

[0127] In the embodiment, the actual driving data can include but is not limited to driving time, speed, slope, altitude, environmental temperature, environmental humidity, etc., and the application does not make specific limitation.

[0128] Step S602: Cut the actual driving data.

[0129] ​​In the embodiment of the present application, the actual driving data can be cut into short driving segments, and then actual driving data of the vehicle in different target driving segments can be obtained.

[0130] Step S603: determining the driving state.

[0131] In the embodiment of the present application, the driving state can be determined by the maximum speed of the vehicle, which can include but is not limited to the city driving state, the suburban driving state and the high-speed driving state. The city driving state can be understood as the driving state with the maximum speed of ≤60km / h. The suburban driving state can be understood as the driving state with the maximum speed of between 60km / h and 80km / h. The high-speed driving state can be understood as the driving state with the maximum speed of >80km / h. The specific values can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations.

[0132] Step S604: collecting the actual driving emission data of different test vehicles in the high-altitude extreme environment in real time.

[0133] In the embodiment of the present application, the actual driving emission data of different test vehicles in the high-altitude extreme environment can be collected in real time, which can include but is not limited to the driving time, the speed, the slope, the altitude, the environmental temperature, the environmental humidity and the emission data of pollutants such as CO, NO x , PN, etc. The present application does not make specific limitations. Further, the actual driving emission data collected in the embodiment of the present application is shown in Table 1.

[0134] Step S605: cutting the actual driving emission data.

[0135] In the embodiment of the present application, the actual driving emission data can be cut into short driving segments, wherein each short driving segment includes an idle segment and a motion segment, and then the test motion characteristic data of the test vehicle in each driving segment can be calculated, such as the maximum speed, the segment RPA value, the average speed, the maximum acceleration, the maximum deceleration, etc. The present application does not make specific limitations. The test environmental characteristic data can include the average temperature, the average humidity, the average altitude, the average uphill slope, the average downhill slope, the average uphill ratio, the average downhill ratio, etc. The present application does not make specific limitations. The test emission characteristic data can include the average emission values of CO, NO x , PN, etc. The present application does not make specific limitations. Further, part of the test motion characteristic data and the test emission characteristic data obtained in the embodiment of the present application are shown in Table 2.

[0136] Step S606: determining the driving state of the test vehicle.

[0137] In this application embodiment, the driving state of the test vehicle in different driving segments can be divided into urban driving state, suburban driving state, and high-speed driving state based on the highest vehicle speed in the test motion feature data. This application does not impose specific limitations.

[0138] Furthermore, embodiments of this application can determine the driving state of the test vehicle in different driving segments based on the highest vehicle speed in the test motion characteristic data.

[0139] Step S607: Construct a pollutant emission classification and identification model.

[0140] The embodiments of this application can be combined with Figure 3 Construct a CO pollutant emission classification and identification model.

[0141] Step S608: Construct a database of high emission characteristics corresponding to different pollutants.

[0142] In this embodiment, CO and NO can be classified and identified based on a pre-built pollutant emission classification and identification model. x The system categorizes PN pollutants by driving status, extracts emission characteristic data corresponding to certain emission levels for each pollutant, such as high emission characteristic data, and then classifies the identified high emission characteristic data into the high emission characteristic databases corresponding to pollutants such as CO, NOx, and PN according to different driving statuses, such as urban driving status, suburban driving status, and highway driving status.

[0143] Step S609: Determine the boundary conditions for the drum test.

[0144] The embodiments of this application can target different pollutants, such as CO and NO. x The database of high emission characteristics such as PN was used to calculate the average temperature, average humidity, and average altitude under different driving conditions, and then the environmental boundary conditions of the drum test conditions corresponding to different pollutants were determined, as shown in Table 3.

[0145] Step S610: Construct the drum test conditions.

[0146] The embodiments of this application can be combined with Figure 4 The test conditions for constructing the drum are shown.

[0147] Step S611: Determine the pollutant emission data of the vehicle under the corresponding driving conditions.

[0148] In this embodiment, the emission data of pollutants of a vehicle under corresponding driving conditions can be determined by using the drum test condition.

[0149] The detection method of vehicle pollutant emission data in a high-altitude extreme environment according to the embodiment of the present application can calculate the motion feature data and the environment feature data in different target driving segments through the actual driving data of the vehicle in the high-altitude extreme environment under different driving states, and then input the motion feature data and the environment feature data into the pre-constructed pollutant emission classification and identification model to obtain the emission feature data that meets a certain emission amount, so as to determine the boundary condition under the dynamometer test condition, and then construct the dynamometer test condition corresponding to the vehicle, and determine the emission data of at least one pollutant of the vehicle under the corresponding driving state by using the dynamometer test condition. Based on limited high-altitude actual driving emission data, the dynamometer test condition meeting the high emission characteristics can be quickly established by using a scientific classification and screening method, which significantly reduces the dependence on large-scale road testing in the plateau area and the cost, improves the authenticity and reproducibility of the test condition in the high-altitude environment, and provides reliable data support for the development and supervision of vehicle emission control technology. Therefore, the problems in the related art that the high-altitude working condition constructed by directly relying on the actual driving emission data in the low-altitude environment is easy to cause distortion of the high-altitude environment test condition, and it is difficult to accurately reflect the real emission performance of the vehicle in the high-altitude environment, and in addition, the large-scale actual driving emission test in the high-altitude environment is not only technically difficult, but also costly, and it is difficult to accumulate a large amount of data in a short time, which becomes an important obstacle to the development of the high-altitude environment emission condition, and other problems are solved.

[0150] Secondly, the detection device of vehicle pollutant emission data in a high-altitude extreme environment according to the embodiment of the present application is described with reference to the accompanying drawings.

[0151] Figure 7 The block schematic diagram of the detection device of vehicle pollutant emission data in a high-altitude extreme environment according to the embodiment of the present application is provided.

[0152] As shown in Figure 7 The detection device of vehicle pollutant emission data in a high-altitude extreme environment 10 includes a calculation module 100, an output module 200 and a first determination module 300.

[0153] The calculation module 100 is configured to obtain the actual driving data of at least one vehicle in a high-altitude extreme environment under different driving states, and calculate the motion feature data and / or the environment feature data of the at least one vehicle in at least one target driving segment based on the actual driving data.

[0154] The output module 200 is configured to input the motion feature data and the environment feature data into a pre-constructed pollutant emission classification and identification model to output the emission feature data corresponding to the motion feature data and the environment feature data and meeting a preset emission amount.

[0155] The first determining module 300 is configured to determine boundary conditions of at least one vehicle in a drum test working condition based on the emission feature data, and construct a drum test working condition corresponding to the vehicle by using the boundary conditions, the motion feature data, the environmental feature data and the emission feature data, so as to determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the drum test working condition.

[0156] Optionally, in an embodiment of the present application, the first determining module 300 comprises a screening unit, a first constructing unit, an interpolation processing unit and a second constructing unit.

[0157] The screening unit is configured to screen the motion feature data, the environmental feature data and the emission feature data to obtain the motion feature data, the environmental feature data and the emission feature data satisfying the boundary conditions.

[0158] The first constructing unit is configured to construct an initial drum test working condition corresponding to the vehicle based on the motion feature data, the environmental feature data and the emission feature data of the boundary conditions.

[0159] The interpolation processing unit is configured to perform interpolation processing on the slope data in the initial drum test working condition until the slope data satisfying a preset slope condition is obtained.

[0160] The second constructing unit is configured to construct a drum test working condition satisfying a preset working condition based on the slope data satisfying the preset slope condition and the initial drum test working condition.

[0161] Optionally, in an embodiment of the present application, the method further comprises an obtaining module, a second determining module, a first constructing module, a third determining module and a second constructing module.

[0162] The obtaining module is configured to obtain test motion feature data, test environmental feature data and test emission feature data of at least one test vehicle in at least one driving segment in a high-altitude extreme environment.

[0163] The second determining module is configured to determine a driving state of the at least one test vehicle in the corresponding driving segment based on the test motion feature data.

[0164] The first constructing module is configured to construct input data of the pollutant emission classification and identification model in different driving states based on the test motion feature data and the test environmental feature data, and construct output data of the pollutant emission classification and identification model in different driving states based on the test emission feature data.

[0165] The third determining module is configured to determine architecture information of the pollutant emission classification and identification model based on the input data and the output data.

[0166] The second construction module is configured to train the pollutant emission classification identification model containing the architecture information by using the input data and the output data until a preset training condition is met, end the training, and construct the pollutant emission classification identification model of at least one pollutant of at least one test vehicle under different driving states.

[0167] Optionally, in an embodiment of the present application, the first construction module comprises a third screening unit, a second construction unit and a third construction unit.

[0168] The third screening unit is configured to screen, based on the pollutant emission factors of the test emission characteristic data, the first test emission characteristic data satisfying the preset emission amount and the second test emission characteristic data not satisfying the preset emission amount.

[0169] The second construction unit is configured to establish the first label and the second label corresponding to the output data based on the first test emission characteristic data and the second test emission characteristic data respectively.

[0170] The third construction unit is configured to construct the output data based on the first test emission characteristic data, the second test emission characteristic data, the first label and the second label.

[0171] Optionally, in an embodiment of the present application, the calculation module 100 comprises a processing unit and a calculation unit.

[0172] The processing unit is configured to perform cutting processing on the actual driving data to obtain the actual driving data of at least one vehicle in at least one target driving segment.

[0173] The calculation unit is configured to perform calculation on the actual driving data in different target driving segments respectively to obtain the motion characteristic data and / or the environment characteristic data of at least one vehicle in different target driving segments.

[0174] It should be noted that the foregoing explanation and description of the embodiment of the method for detecting vehicle pollutant emission data under high-altitude extreme environment are also applicable to the embodiment of the device for detecting vehicle pollutant emission data under high-altitude extreme environment, which will not be described here again.

[0175] The detection device for vehicle pollutant emission data in a high-altitude extreme environment according to the embodiment of the application can calculate the motion feature data and the environment feature data in different target driving segments through the actual driving data of the vehicle in the high-altitude extreme environment under different driving states, and then input the motion feature data and the environment feature data into the pre-constructed pollutant emission classification and identification model to obtain the emission feature data meeting a certain emission amount, so as to determine the boundary condition under the dynamometer test working condition, and then construct the dynamometer test working condition corresponding to the vehicle, and determine the emission data of at least one pollutant of the vehicle under the corresponding driving state by using the dynamometer test working condition. The emission data of the vehicle under the corresponding driving state can be quickly established by using the scientific classification and screening method based on limited high-altitude actual driving emission data, the dynamometer test working condition meeting the high emission feature is quickly established, the dependence on large-scale road testing in the plateau area and the cost are significantly reduced, the authenticity and reproducibility of the test working condition in the high-altitude environment are improved, and reliable data support is provided for the development and supervision of vehicle emission control technology. Therefore, the problems in the related art that the high-altitude working condition constructed by directly depending on the actual driving emission data in the low-altitude environment is easy to cause distortion of the high-altitude environment test working condition, and it is difficult to accurately reflect the real emission performance of the vehicle in the high-altitude environment, and in addition, the large-scale actual driving emission test in the high-altitude environment is not only difficult in technology, but also costly, and it is difficult to accumulate a large amount of data in a short time, which becomes an important obstacle to the development of the high-altitude environment emission working condition, and other problems are solved.

[0176] Figure 8 A structural schematic diagram of a vehicle according to the embodiment of the application is provided. The vehicle can include:

[0177] The memory 801, the processor 802, and the computer program stored in the memory 801 and executable on the processor 802.

[0178] The processor 802 implements the detection method for vehicle pollutant emission data in a high-altitude extreme environment provided in the above embodiment when executing the program.

[0179] Further, the vehicle further includes:

[0180] The communication interface 803 is used for communication between the memory 801 and the processor 802.

[0181] The memory 801 is used to store the computer program executable on the processor 802.

[0182] The memory 801 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0183] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected with each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 8 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.

[0184] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can complete communication between each other through an internal interface.

[0185] The processor 802 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0186] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the method for detecting vehicle pollution emission data in a high-altitude extreme environment as described above.

[0187] The embodiment of the present application further provides a computer program product, which includes a computer program, and the program is executed to implement the method for detecting vehicle pollution emission data in a high-altitude extreme environment as described above.

[0188] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0189] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features, and do not imply or connote relative importance or a specific order of categorization of the indicated features. Accordingly, features described as "first" or "second" can be explicitly or implicitly included in at least one of the features. In the description of the application, the term "N" means at least two, for example, two, three, etc., unless explicitly stated otherwise.

[0190] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes and methods described can be executed by one or more apparatuses or devices, either directly or after conversion to another language. Alternatively, the processes and methods described herein can be executed by more than one apparatus or device working in concert.

[0191] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of instructions to implement logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a machine-readable storage device (e.g., magnetic, optical or other) a machine-readable storage diskette (e.g., floppy, flexible or other), a machine-readable storage card (e.g., ROM, EEPROM, flash memory or other), a machine- readable storage tape (e.g., magnetic, optical or other), a machine-readable storage medium (e.g., a portable electronic device, a computer diskette, a computer memory stick, a computer hard drive, a computer tape, a computer readable storage medium, or other), or a machine-readable wireless transmission (e.g., a radio frequency signal, an infrared signal, a microwave signal, or other). More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

[0192] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the implementation can be carried out using any or a combination of the following technologies, which are all well known in the art: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0193] Those of skill in the art would understand that the steps carried out by the above-mentioned embodiments of the method can be implemented by programs instructing relevant hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one or a combination of the steps of the method embodiments.

[0194] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0195] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for detecting vehicle pollutant emission data in a high-altitude extreme environment, characterized in that, The method comprises the following steps: acquiring actual driving data of at least one vehicle in different driving states in a high-altitude extreme environment, and calculating motion feature data and / or environment feature data of the at least one vehicle in at least one target driving segment based on the actual driving data; inputting the motion feature data and the environment feature data into a pre-constructed pollutant emission classification identification model to output emission feature data corresponding to the motion feature data and the environment feature data that meets a preset emission amount; determining boundary conditions of the at least one vehicle in a rolling test working condition based on the emission feature data, and constructing a rolling test working condition of a corresponding vehicle by using the boundary conditions, the motion feature data, the environment feature data and the emission feature data, so as to determine emission data of at least one pollutant of the vehicle in a corresponding driving state by using the rolling test working condition.

2. The method of claim 1, wherein, The construction of the rolling test working condition of the corresponding vehicle by using the boundary conditions, the motion feature data, the environment feature data and the emission feature data comprises: screening the motion feature data, the environment feature data and the emission feature data to obtain motion feature data, environment feature data and emission feature data that meet the boundary conditions; constructing an initial rolling test working condition of the corresponding vehicle based on the motion feature data, the environment feature data and the emission feature data of the boundary conditions; performing interpolation processing on slope data in the initial rolling test working condition until slope data that meets a preset slope condition is obtained; constructing a rolling test working condition that meets a preset working condition based on the slope data that meets the preset slope condition and the initial rolling test working condition.

3. The method of claim 1, wherein, Before the motion feature data and / or the environment feature data are inputted into the pre-constructed pollutant emission classification identification model, the method further comprises: acquiring test motion feature data, test environment feature data and test emission feature data of at least one test vehicle in a high-altitude extreme environment in at least one driving segment; determining driving states of the at least one test vehicle in a corresponding driving segment based on the test motion feature data; constructing input data of a pollutant emission classification identification model in different driving states based on the test motion feature data and the test environment feature data, and constructing output data of the pollutant emission classification identification model in the different driving states based on the test emission feature data; determining architecture information of the pollutant emission classification identification model based on the input data and the output data; training the pollutant emission classification identification model containing the architecture information by using the input data and the output data until a preset training condition is met, ending the training, and constructing a pollutant emission classification identification model of at least one pollutant of the at least one test vehicle in the different driving states.

4. The method of claim 3, wherein, The construction of the output data of the pollutant emission classification identification model in the different driving states based on the test emission feature data comprises: screening, based on the pollutant emission factor of the test emission characteristic data, to obtain first test emission characteristic data satisfying the preset emission amount and second test emission characteristic data not satisfying the preset emission amount; establishing, based on the first test emission characteristic data and the second test emission characteristic data respectively, first labels and second labels corresponding to the output data; constructing the output data based on the first test emission characteristic data, the second test emission characteristic data, the first labels and the second labels.

5. The method of claim 1, wherein, The calculation of the motion characteristic data and / or the environment characteristic data of the at least one vehicle in at least one target driving segment based on the actual driving data includes: cutting the actual driving data to obtain actual driving data of the at least one vehicle in at least one target driving segment; calculating the actual driving data in different target driving segments respectively to obtain the motion characteristic data and / or the environment characteristic data of the at least one vehicle in the different target driving segments.

6. A device for detecting vehicle pollutant emission data in high altitude extreme environment, characterized in that, It includes: The calculation module is used to obtain the actual driving data of at least one vehicle in different driving states in a high-altitude extreme environment, and calculate the motion characteristic data and / or the environment characteristic data of the at least one vehicle in at least one target driving segment based on the actual driving data; The output module is used to input the motion characteristic data and the environment characteristic data into a pre-constructed pollutant emission classification and identification model to output emission characteristic data satisfying a preset emission amount corresponding to the motion characteristic data and the environment characteristic data; The determination module is used to determine the boundary condition of the at least one vehicle in the drum test working condition based on the emission characteristic data, and to construct the drum test working condition of the corresponding vehicle by using the boundary condition, the motion characteristic data, the environment characteristic data and the emission characteristic data, so as to determine the emission data of at least one pollutant of the vehicle in the corresponding driving state by using the drum test working condition.

7. The apparatus of claim 6, wherein, The determination module includes: The screening unit is used to screen the motion characteristic data, the environment characteristic data and the emission characteristic data to obtain motion characteristic data, environment characteristic data and emission characteristic data satisfying the boundary condition; The first construction unit is used to construct the initial drum test working condition of the corresponding vehicle based on the motion characteristic data, the environment characteristic data and the emission characteristic data of the boundary condition; The interpolation processing unit is used to perform interpolation processing on the slope data in the initial drum test working condition until the slope data satisfying the preset slope condition is obtained; The second construction unit is used to construct the drum test working condition satisfying the preset working condition based on the slope data satisfying the preset slope condition and the initial drum test working condition.

8. A vehicle characterized by comprising: It includes: A memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the detection method of vehicle pollutant emission data in a high-altitude extreme environment according to any one of claims 1-5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by a processor for implementing the method for detecting vehicle pollutant emission data in high-altitude extreme environment according to any one of claims 1-5.

10. A computer program product, characterised in that, The computer program is executed for implementing the method for detecting vehicle pollutant emission data in high-altitude extreme environment according to any one of claims 1-5.

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