A motor vehicle pollutant rapid evaluation method, device, medium and product

Abstract

This disclosure relates to a method, apparatus, medium, and product for rapid assessment of motor vehicle pollutants, relating to the field of vehicle testing. It includes: acquiring target vehicle parameters; processing the target vehicle parameters according to a preset detection model to determine detection parameters; acquiring several initial detection data based on the detection parameters; processing the several initial detection data according to a preset data processing algorithm to obtain target detection data; determining particle size correction factors corresponding to several target vehicle parameters according to a preset particle size correction factor lookup table; substituting the particle size correction factors into a particulate matter model to obtain particulate matter particle size data; the particulate matter particle size data including the geometric mean particle size; processing the target detection data and the geometric mean particle size according to a preset PN-PM conversion algorithm to obtain detection evaluation data; and determining whether motor vehicle pollutant emissions exceed standards based on the target detection data and the detection evaluation data. This comprehensively and accurately reflects the status of motor vehicle pollutant emissions.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle testing, and in particular to a method, apparatus, medium, and product for rapid assessment of motor vehicle pollutants. Background Technology

[0002] Vehicle exhaust contains a variety of harmful substances, including particulate matter and harmful gases. More than 90% of the particulate matter emitted by motor vehicles consists of ultrafine particles with a diameter of less than 100 nm, although they account for only 0.1-10% of the total mass. After being released into the air, these particles can undergo chemical reactions or secondary condensation and growth into larger particles under certain conditions, thereby causing an increase in ambient PM2.5.

[0003] There are many types of motor vehicles, each subject to different standards, resulting in significant differences in the quantity and quality of particulate matter in their emissions. For example, gasoline vehicles use premixed combustion, which is more complete and produces very few particulate matter. If equipped with a diesel particulate filter (DPF), a properly functioning DPF can typically achieve a particulate filtration efficiency of over 99%. Therefore, this type of vehicle emits relatively few and few particulate matter. For these vehicles, the sampling requirements during testing are smaller, and a single sample is sufficient to represent the vehicle's pollutant emissions. Diesel fuels, on the other hand, use diffusion combustion, which involves localized high temperatures and oxygen deficiency, instantly producing a large amount of black smoke particles. Testing for these vehicles requires more stringent standards. Therefore, when conducting regular or irregular spot checks on motor vehicles, customized sampling strategies are needed based on the specific circumstances of each vehicle. Summary of the Invention

[0004] This disclosure provides a method, device, medium, and product for rapid assessment of motor vehicle pollutants, enabling customized sampling and testing of motor vehicles.

[0005] To achieve the above objectives, the present disclosure adopts the following technical solution: Firstly, this disclosure provides a rapid assessment method for motor vehicle pollutants, the method comprising: Obtain the target vehicle parameters, including: vehicle brand, vehicle type, pollutant emission standard, vehicle life, fuel type, engine information, vehicle historical inspection status, and DPF status; Based on the preset detection model, the target vehicle parameters are processed to determine the detection parameters, including the number of samplings and the sampling duration. Based on the detection parameters, obtain several initial detection data; Based on a preset data processing algorithm, several initial detection data are processed to obtain target detection data; Based on the preset particle size correction factor lookup table, determine the particle size correction factors corresponding to several target vehicle parameters. Substitute the particle size correction factor into the particulate matter model to obtain particulate matter particle size data; the particulate matter particle size data includes the geometric mean particle size of the particles. Based on the preset PN-PM conversion algorithm, the target detection data and geometric mean particle size are processed to obtain detection evaluation data; Based on the target detection data and the detection evaluation data, determine whether the pollutant emissions of motor vehicles exceed the standards.

[0006] The embodiments provided in this disclosure obtain the core factors affecting vehicle pollutant emissions, namely the target vehicle parameters, and accurately determine the sampling parameters using a detection model. This solution solves the problems of excessively long detection times and wasted resources, or detection biases caused by insufficient sampling, resulting from the use of a single sampling strategy for all types of vehicles in traditional detection methods. It achieves "on-demand sampling," effectively shortening the detection cycle, meeting the actual needs of rapid evaluation, and adapting to large-scale vehicle inspection scenarios. This solution can obtain a relatively large amount of initial detection data within a reasonable sampling time. Through data processing algorithms, the raw data is filtered and aggregated, effectively eliminating abnormal / redundant data and retaining valid detection data. This reduces the impact of random errors and environmental interference on the data during the detection process, ensuring the accuracy and representativeness of the target detection data and providing a solid foundation for subsequent evaluation. In addition, this invention maps the dispersed target vehicle parameters to standardized correction factors through a particle size correction factor lookup table and substitutes them into the particulate matter model, realizing the standardized and quantifiable calculation of the geometric mean particle size of particulate matter. This avoids the subjectivity and arbitrariness of particle size estimation and ensures the consistency and reproducibility of particle size data under different vehicles and different detection scenarios. This invention achieves precise conversion between particulate number concentration and mass concentration using a PN-PM conversion algorithm, ultimately yielding comprehensive detection and evaluation data. It breaks through the limitations of traditional detection methods that focus only on a single emission indicator, providing a more comprehensive and accurate reflection of vehicle pollutant emissions and offering multi-dimensional data support for emission assessment. Finally, based on the target detection data and the evaluation data, it determines whether emissions exceed standards, providing a clear and direct basis for vehicle emission regulation. This invention forms a complete technical closed loop from data acquisition and parameter determination to data processing and evaluation data output. Each step is seamlessly integrated and data is interconnected, requiring minimal human intervention. This improves the automation and intelligence level of vehicle pollutant emission detection and evaluation, reduces human error, and achieves precise control of vehicle pollution.

[0007] Secondly, this disclosure provides a rapid assessment device for motor vehicle pollutants, comprising: The acquisition module is used to acquire target vehicle parameters, which include: vehicle brand, vehicle type, pollutant emission standards, vehicle lifespan, fuel type, engine information, vehicle historical inspection status, and DPF status. The detection parameter determination module is used to process the target vehicle parameters according to a preset detection model and determine the detection parameters, including the number of samplings and the sampling duration. The data acquisition module is used to acquire several initial detection data based on the detection parameters; The data processing module is used to process several initial detection data according to a preset data processing algorithm to obtain target detection data; The particulate matter size module is used to determine the particle size correction factor corresponding to several target vehicle parameters according to a preset particle size correction factor lookup table; the particle size correction factor is substituted into the particulate matter model to obtain particulate matter size data; the particulate matter size data includes the geometric mean particle size of the particles. The evaluation module is used to process target detection data and geometric mean particle size according to the preset PN-PM conversion algorithm to obtain detection evaluation data; The judgment module is used to determine whether motor vehicle pollutant emissions exceed the standards based on target detection data and detection evaluation data.

[0008] Thirdly, this disclosure provides a rapid assessment device for motor vehicle pollutants, which includes a processor and a memory. The memory stores computer program code, including computer instructions. When the processor executes the computer instructions, the rapid assessment device for motor vehicle pollutants performs a rapid assessment method for motor vehicle pollutants as described in the first aspect and any possible implementation thereof.

[0009] Fourthly, this disclosure provides a computer-readable storage medium storing computer instructions that, when executed on a vehicle pollutant rapid assessment device, cause the vehicle pollutant rapid assessment device to perform a vehicle pollutant rapid assessment method as described in the first aspect or any of the possible implementations of the first aspect.

[0010] Fifthly, this disclosure provides a computer program product including computer instructions that, when executed on a vehicle pollutant rapid assessment device, cause the vehicle pollutant rapid assessment device to perform a vehicle pollutant rapid assessment method as described in the first aspect and any possible implementation thereof.

[0011] For a detailed description of aspects two through five and their various implementations in this disclosure, please refer to the detailed description in aspect one and its various implementations; and for a detailed analysis of the beneficial effects of aspects two through five and their various implementations in aspect one and its various implementations, please refer to the beneficial effect analysis in aspect one and its various implementations, which will not be repeated here. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the structure of a rapid assessment device for motor vehicle pollutants provided in an embodiment of the present disclosure; Figure 2 This is one of the flowcharts for a rapid assessment method for motor vehicle pollutants provided in this embodiment of the disclosure; Figure 3 This is a second flowchart of a rapid assessment method for motor vehicle pollutants provided in this embodiment of the present disclosure; Figure 4 This is the third flowchart of a rapid assessment method for motor vehicle pollutants provided in this embodiment of the present disclosure; Figure 5 This is the fourth flowchart of a rapid assessment method for motor vehicle pollutants provided in this embodiment of the disclosure; Figure 6 This is the fifth flowchart of a rapid assessment method for motor vehicle pollutants provided in this embodiment of the disclosure; Figure 7 This is a schematic diagram of the composition of a rapid assessment device for motor vehicle pollutants provided in an embodiment of this disclosure. Detailed Implementation

[0013] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0014] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more. Furthermore, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0015] The rapid assessment method for motor vehicle pollutants provided in this embodiment is implemented by a rapid assessment device for motor vehicle pollutants. This rapid assessment device can be a computer device, which can be an electronic device or a server, or a central processing unit (CPU) within an electronic device or server.

[0016] Figure 1 This is a schematic diagram illustrating the composition of a rapid assessment device for motor vehicle pollutants provided in an embodiment of this disclosure. Figure 1 As shown, the rapid assessment device for motor vehicle pollutants may include at least one processor 11, a memory 12, a communication interface 13, and a communication bus 14.

[0017] The processor 11 is the control center of the rapid assessment device for motor vehicle pollutants. It can be a CPU, a microprocessor unit, or one or more integrated circuits used to control the execution of the program in the embodiments of this disclosure.

[0018] As one embodiment, processor 11 may include one or more CPUs, for example Figure 1 CPU0 and CPU1 are shown in the diagram. Furthermore, as one embodiment, the rapid assessment device for motor vehicle pollutants may include multiple such... Figure 1 The processor 11 shown is an example. Each of these processors can be a single-core processor (Single-CPU) or a multi-core processor (Multi-CPU).

[0019] The memory 12 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 12 may exist independently and be connected to the processor 11 via the communication bus 14. The memory 12 may also be integrated with the processor 11.

[0020] In a specific implementation, memory 12 is used to store the data disclosed herein and execute the software programs disclosed herein. Processor 11 can perform various functions of the rapid assessment device for motor vehicle pollutants by running or executing the software programs stored in memory 12 and by calling the data stored in memory 12.

[0021] Communication interface 13 uses any transceiver-like device for communicating with other devices or communication networks, such as radio access networks (RAN), wireless local area networks (WLAN), etc. Communication interface 13 may include a receiving unit to implement receiving functions and a transmitting unit to implement transmitting functions.

[0022] The communication bus 14 may include a path for transmitting information between the aforementioned components.

[0023] It should be pointed out that, Figure 1 The structure shown does not constitute a limitation on the rapid assessment device for motor vehicle pollutants, except Figure 1 In addition to the components shown, the rapid assessment device for motor vehicle pollutants may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0024] Based on the above description of the structure of the rapid assessment device for motor vehicle pollutants, this disclosure provides a method for rapid assessment of motor vehicle pollutants. For example... Figure 2 As shown, the rapid assessment method for motor vehicle pollutants may include the following steps S1-S8: S1: Obtain target vehicle parameters, including: vehicle brand, vehicle type, pollutant emission standard, vehicle life, fuel type, engine information, vehicle historical inspection status, and DPF status; S2: Based on the preset detection model, process the target vehicle parameters and determine the detection parameters, including the number of samplings and the sampling duration; S3: Based on the detection parameters, obtain several initial detection data; S4: Based on the preset data processing algorithm, process several initial detection data to obtain target detection data; S5: Determine the particle size correction factor corresponding to several target vehicle parameters according to the preset particle size correction factor reference table. S6: Substitute the particle size correction factor into the particulate matter model to obtain particulate matter particle size data; the particulate matter particle size data includes the geometric mean particle size of the particles. S7: Based on the preset PN-PM conversion algorithm, process the target detection data and geometric mean particle size to obtain detection evaluation data; S8: Determine whether motor vehicle pollutant emissions exceed standards based on target detection data and detection evaluation data.

[0025] Understandably, target vehicle parameters refer to information such as the mechanical and physical properties of the vehicle to be tested, specifically including: vehicle brand, vehicle type, pollutant emission standards, vehicle lifespan, fuel type, engine information, historical vehicle inspection status, and DPF status. This information can be determined through the vehicle's nameplate, environmental protection list, electronic tags, etc., and can be obtained directly.

[0026] The target vehicle parameters can be used to represent the nature of pollutant emissions from the vehicle under test. Therefore, relevant parameters in the pollutant emission detection process can be determined based on the target vehicle parameters, including detection coefficients, particle size correction factors, etc.

[0027] After obtaining the target vehicle parameters in step S1, step S2 is executed. Specifically, refer to... Figure 3 As shown, step S2 includes: S21: Determine the detection coefficients corresponding to several target vehicle parameters according to the preset detection coefficient comparison table; S22: Substitute the detection coefficients into the sampling formula of the detection model to determine the detection parameters; the detection parameters include the number of samplings and the sampling duration.

[0028] Understandably, different target vehicle parameters correspond to different detection coefficients. Specifically, the corresponding detection coefficients can be directly obtained through a preset detection coefficient lookup table. Then, the detection coefficients are substituted into the preset sampling formula of the detection model. The sampling formula includes a number of sampling steps formula and a duration formula, which determine the number of sampling steps and the sampling duration, respectively.

[0029] Specifically, the sampling formula includes:

[0030]

[0031] in, N is used to represent the number of samples; t is used to represent the sampling duration; k v Used to represent the vehicle type detection coefficient; k s Used to represent the detection coefficient of pollutant emission standards; k y Used to represent the vehicle life detection coefficient; k e Used to represent the engine detection coefficient; k c Used to represent the detection coefficient of the vehicle's historical detection status; k d Used to represent the DPF state detection coefficient.

[0032] In this model, N represents the number of samples and t represents the sampling duration. That is, for this vehicle to be tested, N samples are needed, each lasting t seconds. The total sampling duration is T = N. t+5 (including 5 seconds of preparation time).

[0033] Among the various detection coefficients, the vehicle type corresponds to the vehicle type detection coefficient k. v Vehicle types mainly include passenger vehicles (Category M), freight vehicles (Category N), and trailers (Category O). Specific inspection coefficients are shown in Table 1 below: Table 1

[0034] Pollutant emission standards corresponding to pollutant emission standard detection coefficients The pollutant emission standards refer to the emission standards for motor vehicles that are subject to testing. Currently, motor vehicle pollutant emission standards are divided into six stages: China I, China II, China III, China IV, China V, and China VI. The China I standard sets clear limits for pollutants such as carbon monoxide, hydrocarbons, and particulate matter emitted by vehicles. For example, carbon monoxide emissions must not exceed 3.16 grams per kilometer. The China II standard further reduces pollutant emissions compared to China I, with carbon monoxide emissions reduced by 30% and hydrocarbon and carbon oxide emissions reduced by half. The China III standard introduces a vehicle self-diagnostic system and upgrades the three-way catalytic converter, resulting in a 40% reduction in total vehicle pollutant emissions compared to China II. The China IV standard improves the after-treatment system based on China III, reducing pollutant emissions by 50% to 60% compared to China III. The China V standard is even stricter, not only reducing nitrogen oxide emissions but also adding limits on non-methane hydrocarbons and PM (particulate matter) emissions. The China VI emission standard is currently the strictest emission standard in my country, raising emission standards by 40%-50%, even surpassing the Euro VI standard. Therefore, the higher the national emission standard for motor vehicles, the more stringent the emission requirements. Consequently, in the pre-set detection model, a pollutant emission standard detection coefficient k is set based on the emission standard. s Specifically, the emission standards from National I to National III are implemented, corresponding to the pollutant emission standard detection coefficient k. s1 =1.2; Implementing the National IV-National V emission standards, the corresponding pollutant emission standard detection coefficient k s2 =1; For emission standards meeting or exceeding National VI emission standards, the corresponding pollutant emission standard detection coefficient k is... s3 =0.7.

[0035] Similarly, vehicle lifespan corresponds to the vehicle lifespan detection coefficient k.y Vehicle lifespan refers to the duration of a vehicle's use. The longer the usage time, the more severe the aging of the vehicle's various structures, and the higher the pollutant emissions. In the testing model, the vehicle lifespan detection coefficient... y represents the vehicle's usage time. The longer the usage period, the larger the coefficient. If the usage period is ≤15 years, it is calculated based on the actual usage time; if the usage period is >15 years, it is calculated based on 15 years.

[0036] Engine information corresponds to engine detection coefficient k e Engine information refers to relevant information about the engine of the vehicle under test, specifically including engine displacement and maximum engine power. As the core power component of a motor vehicle, the engine generates mechanical energy by burning fuel (gasoline or diesel), but this process inevitably produces various harmful pollutants. Generally speaking, the larger the displacement and the higher the power, the greater the overall pollutant emissions, and the greater the fluctuation. Therefore, based on engine information, the engine detection coefficient is used in the testing model. D is used to represent engine displacement (L), and P is used to represent engine maximum power (kW).

[0037] Vehicle historical detection status corresponds to vehicle historical detection status detection coefficient k c The vehicle's historical inspection status refers to its past inspection records. It's understandable that vehicles that are driven normally and regularly maintained should have good inspection results in the past; conversely, vehicles with malfunctions that haven't been repaired or maintained may have records of exceeding standards or other related faults in past inspections. For vehicles with a good historical inspection status, the number of sampling attempts and / or the time spent on this inspection can be appropriately reduced. For vehicles with a poor historical inspection status, sampling should be increased. Therefore, based on the vehicle's historical inspection status, a corresponding vehicle historical inspection status inspection coefficient is established. The setting is as follows: if the vehicle has passed historical inspections and has no emission-related fault codes, it is considered to be in a normal state, and the vehicle historical inspection status detection coefficient k is set. c1 =0.8; If there are slight historical exceedances or occasional fault codes, it is in a warning state, and the vehicle's historical detection status detection coefficient k c2 =1.2; If the historical data exceeds the standard significantly, or there are persistent fault codes or DPF abnormalities, then it is considered an abnormal state, and the vehicle historical detection state detection coefficient k is used. c3 =1.4.

[0038] DPF state corresponds to DPF detection coefficient k dThe DPF status refers to the operational status of the particulate filter installed in a motor vehicle. Particulate filters specifically include diesel engine particulate filters (DPFs) and gasoline engine particulate filters (GPFs). A properly functioning particulate filter typically achieves a particulate filtration efficiency of over 99%. However, if the particulate filter is damaged, aged, not regenerated in a timely manner, or even tampered with or removed, its particulate filtration efficiency will be significantly reduced or even completely lost. Particulate matter generated by motor vehicles is directly emitted into the air, causing serious pollution. Therefore, the DPF status specifically refers to the functional integrity of the DPF. The corresponding DPF detection coefficient k is determined based on whether its function is complete. d The setting is as follows: If the DPF function is complete, then the DPF detection coefficient k is... d1 =0.7; if the DPF function is partially missing or not regenerated, then the DPF detection coefficient k d2 =1.2; If the DPF function is completely missing or not installed, then the DPF detection coefficient k is 1.2. d3 =1.5.

[0039] In summary, by obtaining the target vehicle parameters, the corresponding detection coefficients can be obtained. By substituting the detection coefficients into the detection model, the detection parameters can be determined.

[0040] For example, the vehicle to be inspected is a China VI emission standard M1 category passenger vehicle with a 1.6L engine, 90kW power, manufactured 3 years ago, and has a history of normal and fault-free vehicle inspections, and its DPF function is intact. The corresponding inspection coefficients are as follows: Vehicle type detection coefficient k v =0.7; Pollutant emission standard detection coefficient k s =0.7; Vehicle life testing coefficient =0.89; Engine detection coefficient =0.87; Vehicle historical detection status detection coefficient k c =0.8; DPF detection coefficient k d =0.7; Therefore, the number of sampling times is:

[0041] The sampling duration is:

[0042] Therefore, for this target vehicle, a detection scheme of sampling once and continuously sampling for 15 seconds can be selected during the detection process.

[0043] For example, the vehicle to be inspected is a National V emission standard N2 category freight vehicle with a 3.0L engine displacement, 120kW power output, manufactured 5 years ago, and has a history of normal, fault-free vehicle inspections, and its DPF (Discharge Preventive Power) has not been regenerated. The corresponding inspection coefficients are as follows: Vehicle type detection coefficient k v =1.3; Pollutant emission standard detection coefficient k s =1.0; Vehicle life testing coefficient =0.95; Engine detection coefficient =0.97; Vehicle historical detection status detection coefficient k c =0.8; DPF detection coefficient k d =1.2; Therefore, the number of sampling times is:

[0044] The sampling duration is:

[0045] Therefore, for this target vehicle, a detection scheme of sampling twice, with each sample taken for 25 seconds, can be selected during the detection process.

[0046] The detection model provided in this embodiment covers the core factors affecting motor vehicle pollutant emissions, avoiding unreasonable sampling caused by a single parameter. All variables have clear codes and coefficients, which can be directly embedded into the detection system algorithm. Through weights and function constraints, it avoids excessive sampling (time-consuming) or insufficient sampling (inaccurate), balancing sampling efficiency and accuracy.

[0047] It should be noted that the detection model provided in this solution can adjust the weighting coefficients according to regional emission standards and the accuracy of the detection equipment (for example, for regions with stringent environmental monitoring, the weighting coefficients can be adjusted). s The upper limit is 1.5, in order to adapt to different application scenarios.

[0048] In one optional implementation, the target vehicle parameters also include vehicle brand and fuel type. It should be noted that diesel vehicles are a major source of particulate matter emissions; therefore, when testing motor vehicle emissions, diesel vehicle testing should be strengthened. Firstly, according to statistics, under the premise that other parameters are the same, there are significant differences in particulate matter emission data among different brands of diesel vehicles. A comparison was made between the top ten brands of National V emission standard vehicles in operation in City A, based on the number of tests conducted. The brands with the highest average emissions, from highest to lowest, are: Brand A, Brand B, Brand C, Brand D, Brand E, Brand F, Brand G, Brand H, Brand I, and Brand J. Among them, Brand A vehicles had the highest average particulate matter emissions, reaching 1.13 × 10⁻⁶. 7 pcs / cm 3 Vehicles of brand J had the lowest average particulate matter emissions, at 3.39 × 10⁻⁶. 6 pcs / cm 3 The difference between the maximum and minimum values ​​is more than three times. On the other hand, according to statistics, the average particulate matter number concentrations in pollutants from diesel vehicles with power outputs below 100 kW, 100-200 kW, 200-300 kW, and above 300 kW are 9.27 × 10⁻⁶. 6 pcs / cm 3 6.57×10 6 pcs / cm 3 5.07×10 6 pcs / cm 3 5.01×10 6 pcs / cm 3 Overall, higher engine power corresponds to lower PN values ​​(particulate number concentration), showing a trend contrary to conventional wisdom. Therefore, based on the above embodiments, this embodiment further modifies the detection model for diesel vehicles. Specifically, the detection model is as follows:

[0049]

[0050] in, k b Used to indicate the vehicle brand detection coefficient; k p Used to represent the power detection coefficient.

[0051] First, it should be noted that the target vehicle parameters include fuel type. Fuel type includes gasoline and diesel. When the fuel type is determined to be diesel, the detection model in this embodiment is activated.

[0052] Specifically, in this embodiment, vehicle brands include brands A, B, C, D, E, F, G, H, I, and J. Vehicle brands can be identified through the vehicle nameplate. The vehicle brand detection coefficients are shown in Table 2 below, depending on the brand. Table 2

[0053] It should be noted that the vehicle brand detection coefficients mentioned above are based on the ranking of the number of diesel vehicles in operation in City A. Diesel vehicle brands also include brands K, M, N, etc. In practical applications, further statistics can be compiled based on the diesel vehicle brands in the application scenario, and brand detection coefficients can be set to suit the specific application scenario based on the actual vehicle situation. Furthermore, if the obtained vehicle brand is not among the preset vehicle brands, the vehicle brand detection coefficient k will be set accordingly. b Set to 1 to eliminate the impact of unidentified vehicle brands on the detection model's calculation results.

[0054] On the other hand, the power detection coefficient k p The power is determined by the maximum power of the diesel vehicle. The target vehicle parameters include engine information such as engine displacement and maximum engine power. Based on its maximum engine power, the power detection coefficient k is determined. p Specifically, refer to Table 3 below.

[0055] Table 3

[0056] It is understood that this embodiment introduces the main factors affecting particulate matter emissions from diesel vehicles: brand and power, and further limits the detection model for diesel vehicles, so that the determined detection parameters are more consistent with the emission situation of diesel vehicles, and are more accurate and efficient.

[0057] In step S3, detection is performed based on the sampling number N and sampling duration t obtained in step S2 to acquire several initial detection data. In conventional detection, one data point can be acquired every 1 second or n seconds. Taking acquiring one data point every 1 second as an example, the acquired initial detection data consists of N sets, each set containing t data points.

[0058] After obtaining several initial detection data in step S3, step S4 is executed to process the initial detection data to obtain the target detection data. In this embodiment, both the initial detection data and the target detection data are particulate number concentration values, i.e., PN values. Specifically, in step S4, a data processing algorithm is used:

[0059] in, C nUsed to represent target detection data; C ni Used to represent the initial detection data located in the middle position; C nj Used to represent the initial detection data located at the edge; α is used to represent the median weight; β is used to represent the edge number weight; p is used to represent the total weight.

[0060] It should be noted that this embodiment refers to the dual-idle speed method for particulate matter detection. The dual-idle speed method is a commonly used method for current motor vehicle exhaust emission testing. It refers to the method of detecting the concentration of pollutants emitted when the engine speed is low or high by pressing the accelerator pedal while the vehicle is in neutral. The dual-idle speed method is simple to operate, fast and accurate. Compared with the bench test method, the dual-idle speed method tests the entire vehicle rather than the engine, which can better represent the particulate matter emissions of motor vehicle exhaust. In addition, the operating conditions and running conditions of the dual-idle speed method are relatively fixed, making it suitable for comparing the particulate matter emission characteristics of different types of motor vehicles. This embodiment further improves on the dual-idle speed method by performing particulate matter detection under three idling conditions: low, medium and high. Specifically, the vehicle is started, low idle speed (neutral, accelerator pedal fully released) for t / 3 seconds; medium idle speed (speed 1000 rpm) for t / 3 seconds; and high idle speed (accelerator pedal fully depressed) for t / 3 seconds. In other words, the sampling duration is t seconds, with low, medium, and high idle speed conditions each lasting t / 3 seconds. Therefore, in each set of initial test data, the first 1 / 3 of the data represents low idle speed data, the middle 1 / 3 represents medium idle speed data, and the last 1 / 3 represents high idle speed data. Summarizing N sets of initial test data, there are Nt / 3 data points corresponding to low, medium, and high idle speed conditions (taking one data point collected per second as an example). Arranging the initial test data for each condition from smallest to largest, the data in the middle x% is determined as the initial test data C in the middle position. ni Other initial detection data are identified as initial detection data C located at the edge position. nj The ratio of data at the middle and edge positions can be determined based on the actual situation. For example, the initial detection data at the middle 30% can be defined as the initial detection data C at the middle position. ni Therefore, the initial detection data located in the first 35% and the last 35% are the initial detection data C located at the edge. njBecause the vehicle's operating condition is most stable during the middle period of each detection cycle, the initial detection data at the middle position has a higher weight than the initial detection data at the edge position. For example, the weight of the middle number is α=0.7, and the weight of the edge number is β=0.5. Therefore, the total weight p=α×i+β×j, where i is the number of initial detection data at the middle position and j is the number of initial detection data at the edge position. The data processing algorithm in this embodiment can objectively process a large amount of initial detection data to obtain the most representative actual target detection data under different operating conditions.

[0061] Furthermore, before executing the data processing algorithm, several initial detection data points can be filtered. For example, a preset numerical range can be defined; if any initial detection data point does not belong to this range, it is discarded. Then, the initial detection data falling within the preset numerical range is processed by the data processing algorithm to obtain the target detection data. This avoids the adverse effects of erroneous, abnormal, or other discrete data on the target detection data.

[0062] In existing technologies, the conventional method for detecting particulate matter is the opacity method. The result is the mass concentration value of particulate matter (PM, in mg / m³ or mg / km). However, because the shortest wavelength limit for violet light is 380nm, the scattering efficiency decreases significantly when the particle size is less than 30% of the incident light wavelength. Related research indicates that "the accuracy of current opacity meters is generally ±0.3m⁻¹," essentially reaching the limit of their detection capability, and their sensitivity to small particles is severely insufficient. Therefore, particulate number concentration (PN, in particles / m³ or particles / km) detection has been introduced. Current particulate matter emission standards require the full implementation of the China VI (17691) emission standard from July 1, 2021, and require all vehicles to undergo RDE (Real Driving Emissions) testing before leaving the factory (PN limit should meet 1.2 × 10⁻⁶). 12 (particles / kWh). In current technologies, over 90% of particulate matter emitted by motor vehicles consists of ultrafine particles with a diameter of less than 100 nm. Therefore, using particulate number concentration (PM) is a more accurate measure of particulate matter emissions. However, current methods often use PM2.5 as the core indicator, such as PM2.5. 2.5 PM 10Furthermore, PM (particulate matter mass concentration) values ​​directly reflect the total particulate matter mass load and are strongly correlated with atmospheric visibility, deposition, and total pollution load. Therefore, in this embodiment, both the initial detection data and the target detection data are particulate matter number concentration values, i.e., PN values. Moreover, after obtaining the target detection data expressed as PN values, its PM value should also be determined. In this embodiment, the particulate matter particle size data of the vehicle to be tested can be determined, and then in step S7, the detection evaluation data, where the detection evaluation data is the PM value, can be determined using a PN-PM conversion algorithm.

[0063] It should be noted that the particulate matter size data includes: the geometric mean particle size, the geometric standard deviation of the particles, the particle size probability density probability for any particle size interval, and the normalized probability for any particle size interval.

[0064] The geometric mean diameter of particulate matter is used to indicate the average level of particulate matter size. The geometric standard deviation of particulate matter is used to indicate the degree of dispersion of particulate matter size. The smaller the geometric standard deviation, the more clustered the particle size data, indicating more stable particulate matter emissions; the larger the geometric standard deviation, the more dispersed the particle size data, indicating greater fluctuations in particulate matter emissions and more complex particle size composition.

[0065] Since particulate matter particle sizes include four typical ranges: 7-20 nm (nuclear mode), 20-50 nm (Aigen nucleus), 50-100 nm (accumulation mode), and 100-200 nm (coarse mode), the particle size probability density probability and normalized probability of any particle size range can be further determined. The particle size probability density probability of any particle size range refers to the relative likelihood of a random variable taking a value within a specific range. Normalizing the particle size probability density probability of any particle size range ensures the legitimacy and validity of the probability distribution. Probability determination can pinpoint the main particle size ranges of particulate matter and enable targeted analysis of ultrafine particulate matter emission characteristics. In this embodiment, the particle size interval [D1, D2] is set as follows: when i=1, D1=7, D2=20; when i=2, D1=20, D2=50; when i=3, D1=50, D2=100; when i=4, D1=100, D2=200.

[0066] Specifically, refer to Figure 4 As shown, methods for determining particulate matter size data may include: Step A1: Determine the particle size correction factor corresponding to several target vehicle parameters according to the preset particle size correction factor lookup table; Step A2: Determine the standard deviation coefficients corresponding to several target vehicle parameters according to the preset standard deviation coefficient reference table; Step A3: Substitute the particle size correction factor into the particle size formula of the particulate matter model to obtain the geometric mean particle size of the particulate matter. Step A4: Substitute the standard deviation coefficient into the standard deviation formula of the particulate matter model to obtain the geometric standard deviation of the particulate matter; Step A5: Determine the particle size probability density probability for any particle size range based on the geometric mean particle size and geometric standard deviation of the particles; Step A6: Determine the normalized probability of any particle size interval based on the particle size probability density probability of any given particle size interval.

[0067] Specifically, the particulate matter model includes: Particle size formula: D g =D b ×K s ×K y ×K f Standard deviation formula:

[0068] Particle size probability density probability:

[0069] Normalization process:

[0070] in, D g Used to represent the geometric mean particle size of particulate matter; D b Used to represent the basic value of particulate matter particle size; K s Used to represent the particle size correction factor for pollutant emission standards; K y Used to represent the particle size correction factor for vehicle lifespan; K f Used to represent the particle size correction factor for fuel type; σ g Used to represent the geometric standard deviation of particulate matter; σ g0 Used to represent the reference standard deviation of particulate matter; S S The standard deviation coefficient used to represent pollutant emission standards; S y Used to represent the standard deviation coefficient of vehicle life; S f Used to represent the standard deviation coefficient of fuel type; P i This is used to represent the particle size probability density probability of the i-th particle size range; This is used to represent the normalized probability of the i-th particle size interval.

[0071] Understandably, pollutant emission standards and vehicle lifespan information in the target vehicle parameters will affect the particulate matter emissions of the vehicle under test.

[0072] Specifically, pollutant emission standards are the requirements for mandatory after-treatment technologies and particulate limits for vehicles, and are the core influencing factor on the particle size of vehicle emissions. Vehicles meeting only China III or lower emission standards have no mandatory after-treatment requirements; therefore, the carbon soot generated from combustion is directly emitted, primarily consisting of large particles with a wide particle size distribution of 50-500 nm, and a high proportion of coarse particles (>100 nm). This is particularly true for diesel vehicles, with peak particle sizes of approximately 80-200 nm. China IV and China V standards require some vehicles to be equipped with after-treatment devices. Diesel vehicles are equipped with DOC (Diesel Offset Cancellation), gasoline vehicles with three-way catalytic converters, and some high-end models are beginning to be equipped with GPF / DPF (Gas Per Filter / Power Distribution Filter). After-treatment devices can intercept large particles, leaving mainly ultrafine particles with a peak size of approximately 50-100 nm. China V gasoline direct injection (GDI) begins to exhibit nuclear modes (10–30 nm). China VI standards mandate the installation of after-treatment devices and impose limits on particulate matter (PN). DPF / GPF have a filtration efficiency of 90%+, capable of intercepting almost all particles >100 nm. The remaining particles consist of penetrating nanoscale particles and volatile nucleated modal particles. The particle size distribution has a peak value of 20–60 nm, and particles with a size ≥23 nm are strictly controlled.

[0073] Vehicle lifespan information refers to the number of years a vehicle has been in use. Generally, the longer a vehicle has been in use, the more particulate matter it emits, with a higher proportion of large particles, a wider particle size distribution, and a significant increase in coarse particles / accumulation modes. Specifically, for vehicles with a service life of 0-3 years, the combustion system is clean and atomization is good, with emissions mainly consisting of ultrafine particles of 20-60 nm. For vehicles with a service life of 4-8 years, fuel injector atomization deteriorates, large particles begin to increase, the particle size distribution widens, and particles of 50-100 nm increase significantly. For vehicles with a service life of 9-15 years, engine wear occurs, cylinder pressure decreases, combustion deteriorates, and fuel injector blockage / dripping leads to a large amount of large particulate soot production, with the peak particle size shifting significantly towards larger particles (80-200 nm).

[0074] Fuel types mainly include gasoline and diesel. Gasoline vehicles use spark ignition combustion, where the air-fuel mixture is pre-mixed before combustion (PFI is port mixing, GDI is direct injection), resulting in more complete and uniform combustion with virtually no localized rich areas. Soot production is minimal, and particulate matter primarily originates from the combustion of trace impurities in the fuel and a small amount of lubricating oil. The overall particle size is relatively small, with particles ranging from 7-50 nm accounting for over 80%, while coarse particles typically account for less than 10%. Diesel vehicles use compression ignition combustion, which occurs at the air-fuel interface. The uneven mixing of the air-fuel mixture easily creates localized oxygen-deficient areas, promoting soot particle formation. After collision and aggregation, soot particles continuously increase in size, ultimately resulting in a distribution characterized by a predominantly agglomerated mode and a high proportion of coarse particles. Diesel vehicles emit significantly more particulate matter than gasoline vehicles, and even under high-speed, high-load conditions, although nucleated particles may increase, the proportion of coarse particles larger than 110 nm remains far higher than in gasoline vehicles.

[0075] Therefore, pollutant emission standards, vehicle lifespan, and fuel type are the core factors affecting the particulate matter size of the vehicle being tested. Based on the corresponding particulate matter size distribution patterns and correlations, appropriate correction factors are assigned, as detailed in Table 4 below.

[0076] Table 4

[0077] Specifically, the particulate matter model in this embodiment is applied as follows: based on the target vehicle parameters obtained in step S1, the pollutant emission standards, vehicle lifespan, and fuel type of the vehicle to be tested are determined, and the corresponding correction factors and standard deviation coefficients are determined according to Table 4. The baseline particulate matter size D is... b =40nm, baseline standard deviation σ g0 =1.65. Substituting the correction factors and standard deviation coefficients of the vehicle under test into the formulas for geometric mean particle size and geometric standard deviation, the geometric mean particle size D of the particulate matter from the vehicle under test is obtained. g and geometric standard deviation σ g Then, integrate the probability density over each interval [D1, D2] and normalize it to obtain the probability of occurrence for each interval.

[0078] For example, if the vehicle to be tested is a gasoline car with a pollutant emission standard of China V and a vehicle life of 5 years, then the corresponding correction factor K and standard deviation coefficient S are respectively: K s =1.20, K y =1.15, K f =1.00、S S =1.10、S y =1.08, S f =1.00.

[0079] The geometric mean particle size was calculated as follows: D g =D b ×K s ×K y ×K f =0.04×1.20×1.15×1.00=55.2nm; The geometric standard deviation is calculated as follows: =1.65×1.10×1.08×1.00=1.9602; The particle size probability density probability are respectively:

[0080]

[0081]

[0082]

[0083] Normalization yields:

[0084]

[0085]

[0086]

[0087] In summary, the probability of particulate matter emitted by the vehicle under test having a particle size of 7-20nm is 6.68%, 20-50nm is 38.74%, 50-100nm is 38.06%, and 100-200nm is 16.52%. Therefore, the particle size of the particulate matter emitted by the vehicle under test is concentrated in the 20-100nm range.

[0088] It should be noted that the particulate matter model provided in this embodiment derives the geometric mean particle size and particle size probability density probability of the particulate matter from the target vehicle parameters. The present invention is not limited to this. If the particle size data of particulate matter in the pollutants emitted by the vehicle under test can be directly detected or obtained, it can be directly applied to this evaluation method.

[0089] After determining the particulate matter size data in step S6, step S7 is executed. In step S7, the target detection data obtained in step S4 and the geometric mean particle size determined in step S6 are processed by a preset PN-PM conversion algorithm to obtain detection evaluation data.

[0090] It should be noted that the target detection data is the particulate number concentration value, and the detection evaluation data is the particulate mass concentration value; in the PN-PM conversion algorithm, the relationship between the particulate mass concentration value and the particulate number concentration value is as follows:

[0091] in, C m Used to represent detection and evaluation data, which is the mass concentration value of particulate matter; C n Used to represent target detection data, which is the particulate number concentration value; D g Used to represent the geometric mean particle size of particulate matter.

[0092] It should be noted that the geometric mean particle size D of the particulate matter g The units are: micrometers (μm), particulate number concentration value C. n The unit is: pieces / m 3 The unit for particulate number concentration is μg / m³. 3 Particulate number concentration value C n The particle size D of the particulate matter is obtained from step S4. g Obtained from step S6. 7.85 × 10 -7 This is a constant coefficient. Therefore, the particulate matter mass concentration value, which directly reflects the total mass load of particulate matter, is obtained through target detection data (particulate matter number concentration value) and particulate matter particle size data, i.e., detection and evaluation data.

[0093] The embodiments provided in this disclosure obtain the core factors affecting vehicle pollutant emissions, namely the target vehicle parameters, and accurately determine the sampling parameters using a detection model. This solution solves the problems of excessively long detection times and wasted resources, or detection biases caused by insufficient sampling, resulting from the use of a single sampling strategy for all types of vehicles in traditional detection methods. It achieves "on-demand sampling," effectively shortening the detection cycle, meeting the actual needs of rapid evaluation, and adapting to large-scale vehicle inspection scenarios. This solution can obtain a relatively large amount of initial detection data within a reasonable sampling time. Through data processing algorithms, the raw data is filtered and aggregated, effectively eliminating abnormal / redundant data and retaining valid detection data. This reduces the impact of random errors and environmental interference on the data during the detection process, ensuring the accuracy and representativeness of the target detection data and providing a solid foundation for subsequent evaluation. In addition, this invention maps the dispersed target vehicle parameters to standardized correction factors through a particle size correction factor lookup table and substitutes them into the particle size formula of the particulate matter model. This achieves standardized and quantifiable calculation of the geometric mean particle size of particulate matter, avoiding the subjectivity and arbitrariness of particle size estimation, and ensuring the consistency and reproducibility of particle size data under different vehicles and different detection scenarios. This invention achieves precise conversion between particulate number concentration and mass concentration using a PN-PM conversion algorithm, ultimately yielding comprehensive detection and evaluation data. It breaks through the limitations of traditional detection methods that focus only on a single emission indicator, providing a more comprehensive and accurate reflection of vehicle pollutant emissions and offering multi-dimensional data support for emission assessment. Finally, based on the target detection data and the evaluation data, it determines whether emissions exceed standards, providing a clear and direct basis for vehicle emission regulation. This invention forms a complete technical closed loop from data acquisition and parameter determination to data processing and evaluation data output. Each step is seamlessly integrated and data is interconnected, requiring minimal human intervention. This improves the automation and intelligence level of vehicle pollutant emission detection and evaluation, reduces human error, and achieves precise control of vehicle pollution.

[0094] In one alternative implementation, refer to Figure 5 As shown, in step S8 of the present invention, determining whether motor vehicle pollutant emissions exceed the standard based on the target detection data and the detection evaluation data includes: if the target detection data and / or the detection evaluation data exceed a preset threshold, then it is determined that the motor vehicle pollutant emissions exceed the standard.

[0095] Understandably, after obtaining the target detection data and the detection evaluation data, it is possible to determine whether the emissions of the vehicle under test exceed the standards based on preset thresholds. It should be noted that these thresholds are not singular but are set according to the vehicle type, applicable standards, etc. Furthermore, these thresholds include at least two types, used to characterize the particulate matter (PN) value and the particulate matter (PM) value, respectively. If either the PN value represented by the target detection data or the PM value represented by the detection evaluation data exceeds the threshold, the vehicle under test should be considered to have exceeded the particulate matter emission standards.

[0096] In one alternative implementation, refer to Figure 6 As shown, the present invention further includes steps S9-S10: S9: If it is determined that the motor vehicle pollutant emissions exceed the standard, then according to the preset original emission model, process the target vehicle parameters and the target detection data to determine the original emission data and DPF capture efficiency data; S10: Determine the cause of excessive vehicle pollutant emissions based on the original emission data and DPF capture efficiency data. In this embodiment, if it is determined that the particulate matter emissions of the vehicle under test exceed the standard, the cause of the exceedance is further determined through the original emission model. Specifically, if the DPF capture efficiency data exceeds a preset efficiency threshold, the after-treatment device is determined to be faulty; if the DPF capture efficiency data does not exceed the preset efficiency threshold, the vehicle emission is determined to be faulty or aging. It can be understood that the core reasons for excessive particulate matter emissions include: 1) the vehicle's original emissions (original emissions) themselves exceed the standard limits; 2) the particulate matter trap (DPF) processing efficiency decreases. Among them, excessive original emissions refer to the original amount of particulate matter emitted by the vehicle engine combustion, which is directly related to the vehicle's core parameters and engine operating status. As the core purification device for particulate matter emissions from motor vehicles, the core function of the DPF is to capture and oxidize particulate matter in the original emissions. If its processing efficiency decreases (malfunction, non-regeneration, improper adaptation, etc.), a large amount of unfiltered particulate matter will be directly emitted, resulting in the total amount exceeding the standard. Therefore, it is necessary to input the target detection data into the original emission model to determine the original emission data and DPF capture efficiency data, so as to further determine the cause of the emission exceeding the standard.

[0097] The original row model provided in this embodiment is:

[0098] in, C n(raw) Used to represent the original row data; C n Used to represent target detection data; μ DPF Used to represent DPF capture efficiency data; K raw Used to represent the original row coefficient; x s Used to indicate the original emission factor of pollutant emission standards; x v Used to represent the original row factor of vehicle type; x y Used to represent the original emission factor of vehicle lifespan; x c Used to represent the original emission factor of the vehicle's historical detection status; x dUsed to represent the original excretion factor of DPF; u s Used to indicate the original emission weight of pollutant emission standards; u v Used to indicate the original weight of vehicle type; u y Used to represent the original weight of vehicle lifespan; u c Used to represent the original weight of the vehicle's historical detection status; u d Used to represent the original row weights of DPF.

[0099] It is understandable that the target detection data refers to the actual measured number concentration of particulate matter emitted into the air by the vehicle under test after treatment by a particulate filter (if applicable). Therefore, the original emission data of the vehicle under test is inferred from the original emission model. The DPF (Device Performance Factor) collection efficiency data can then be determined using the original emission data and the target detection data.

[0100] In this embodiment, the core parameters affecting particulate matter emissions include: pollutant emission standards, vehicle type information, vehicle lifespan, engine information, vehicle historical inspection status, and DPF status. Therefore, by determining the weights and influencing factors of different factors, the original emission coefficient can be determined, and thus the original emission data can be determined.

[0101] Specifically, the original emission weights of each factor are as follows: Original emission weight of pollutant emission standards u s =40%, original weight of vehicle type u v =20%, original weight of vehicle lifespan u y =10%, original weight of vehicle historical inspection status u c =10%, DPF original weight u d =20%. The original factors for each factor are shown in Table 5.

[0102] Table 5

[0103] According to the comparison table, the original emission factor of any vehicle to be tested can be identified, and then the original emission data and DPF capture efficiency data can be determined.

[0104] For example, the vehicle to be tested is a Category M1 passenger car that meets the China IV emission standard, is 3 years old, has a normal and fault-free historical inspection record, and has a fully functional DPF. The target inspection data is A.

[0105] The original emission coefficient of the vehicle to be tested is: K raw =20 0.4 ×40 0.2 ×300.1 ×30 0.1 ×40 0.2 =28.62 Therefore, the original data is C. n(raw) =28.62A, DPF trapping efficiency data is μ DPF =96.5%.

[0106] The original emission model can determine the original emission data and DPF capture efficiency data of the vehicle under test, thereby identifying the cause of excessive pollutant emissions. If the original emission data exceeds the standard, further investigation should be conducted to check for possible factors such as engine malfunction or low fuel quality. If the DPF capture efficiency data is too low (generally not lower than 80%), further investigation should be conducted to check for DPF malfunction or whether regeneration is required. The original emission model provided in this embodiment provides direction for subsequent testing, eliminating the need for step-by-step investigation and improving the targeting and efficiency of the testing.

[0107] The foregoing primarily describes the solutions provided by the embodiments of this disclosure from the perspective of the device. It is understood that, in order to achieve the above functions, the device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the algorithm steps of the various examples described in the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0108] Figure 7 A schematic diagram of a possible composition of the rapid assessment device for motor vehicle pollutants involved in the above embodiments is shown, such as... Figure 7 As shown, the rapid vehicle pollutant assessment device 100 may include: a data acquisition module 01, a detection parameter determination module 02, a data acquisition module 03, a data processing module 04, a particulate matter size module 05, and an evaluation module 06. Among these, The acquisition module 01 is used to acquire the target vehicle parameters, which include: vehicle brand, vehicle type, pollutant emission standard, vehicle life, fuel type, engine information, vehicle historical inspection status and DPF status. The detection parameter determination module 02 is used to process the target vehicle parameters according to the preset detection model and determine the detection parameters, including the number of samplings and the sampling duration. Data acquisition module 03 is used to acquire several initial detection data based on the detection parameters; Data processing module 04 is used to process several initial detection data according to a preset data processing algorithm to obtain target detection data; The particulate matter size module 05 is used to determine the particle size correction factor corresponding to several target vehicle parameters according to the preset particle size correction factor lookup table; substitute the particle size correction factor into the particulate matter model to obtain particulate matter size data; the particulate matter size data includes the geometric mean particle size of the particles. Evaluation module 06 is used to process target detection data and geometric mean particle size according to a preset PN-PM conversion algorithm to obtain detection evaluation data; The judgment module 07 is used to determine whether the pollutant emissions of motor vehicles exceed the standards based on the target detection data and the detection evaluation data.

[0109] In another alternative implementation, refer to Figure 7 As shown, the rapid vehicle pollutant assessment device 100 also includes: a source tracing module 08. Among them, The source tracing module 08 is used to determine the original emission data and DPF capture efficiency data by processing the target vehicle parameters and target detection data according to the preset original emission model if it is determined that the pollutant emissions of motor vehicles exceed the standard. Based on the original emission data and DPF capture efficiency data, the reasons for the excessive emissions of pollutants from motor vehicles were determined.

[0110] Of course, the rapid assessment device for motor vehicle pollutants provided in this embodiment includes, but is not limited to, the modules described above.

[0111] This disclosure also provides a rapid vehicle pollutant assessment device, which includes a processor and a memory. The memory stores computer program code, including computer instructions. When the processor executes the computer instructions, the rapid vehicle pollutant assessment device performs each step of the method flow shown in the above-described method embodiments.

[0112] This disclosure also provides a computer-readable storage medium storing computer instructions that, when executed on a vehicle pollutant rapid assessment device, cause the vehicle pollutant rapid assessment device to perform each step of the method flow shown in the above method embodiments.

[0113] This disclosure also provides a computer program product, which includes computer instructions that, when executed on a rapid vehicle pollutant testing device, cause the rapid vehicle pollutant testing device to perform each step of the rapid vehicle pollutant testing device in the method flow shown in the above method embodiments.

[0114] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A rapid assessment method for motor vehicle pollutants, characterized in that, include: Obtain the target vehicle parameters, which include: vehicle brand, vehicle type, pollutant emission standard, vehicle life, fuel type, engine information, vehicle historical inspection status, and DPF status; Based on a preset detection model, the target vehicle parameters are processed to determine the detection parameters, which include the number of samplings and the sampling duration. Based on the detection parameters, several initial detection data are obtained; According to a preset data processing algorithm, several initial detection data are processed to obtain target detection data; Based on a preset particle size correction factor lookup table, determine the particle size correction factors corresponding to several of the target vehicle parameters; Substituting the particle size correction factor into the particulate matter model yields particulate matter particle size data; the particulate matter particle size data includes the geometric mean particle size of the particles. According to the preset PN-PM conversion algorithm, the target detection data and the geometric mean particle size are processed to obtain detection evaluation data; Based on the target detection data and the detection evaluation data, it is determined whether the motor vehicle pollutant emissions exceed the standards.

2. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The step of processing the target vehicle parameters according to the preset detection model and determining the detection parameters includes: Based on a preset detection coefficient reference table, the detection coefficients corresponding to several of the target vehicle parameters are determined. Substitute the detection coefficients into the sampling formula of the detection model to determine the detection parameters; The detection parameters include the number of samples and the sampling duration.

3. The rapid assessment method for motor vehicle pollutants according to claim 2, characterized in that, The sampling formula includes: in, N is used to represent the number of samples; t is used to represent the sampling duration; k v Used to represent the vehicle type detection coefficient; k s Used to represent the detection coefficient of pollutant emission standards; k y Used to represent the vehicle life detection coefficient; k e Used to represent the engine detection coefficient; k c Used to represent the detection coefficient of the vehicle's historical detection status; k d Used to represent the DPF state detection coefficient.

4. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The data processing algorithm is as follows: in, C n Used to represent target detection data; C ni Used to represent the initial detection data located in the middle position; C nj Used to represent the initial detection data located at the edge; α is used to represent the median weight; β is used to represent the edge number weight; p is used to represent the total weight.

5. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The particulate matter model includes: Particle size formula: D g =D b ×K s ×K y ×K f Among them, D g Used to represent the geometric mean particle size of particulate matter; D b Used to represent the basic value of particulate matter particle size; K s Used to represent the particle size correction factor for pollutant emission standards; K y Used to represent the particle size correction factor for vehicle lifespan; K f Used to represent the particle size correction factor for fuel type.

6. The rapid assessment method for motor vehicle pollutants according to claim 5, characterized in that, The particulate matter size data further includes: the geometric standard deviation of the particulate matter, the probability density of the particle size for any particle size interval, and the normalized probability for any particle size interval; the method further includes: Based on a preset standard deviation coefficient lookup table, determine the standard deviation coefficients corresponding to several of the target vehicle parameters; Substituting the standard deviation coefficient into the particulate matter model, we obtain the geometric standard deviation of the particulate matter; Based on the geometric mean particle size and the geometric standard deviation of the particles, determine the particle size probability density probability for any particle size range; The normalized probability of any particle size interval is determined based on the particle size probability density probability of any of the aforementioned particle size intervals.

7. The rapid assessment method for motor vehicle pollutants according to claim 6, characterized in that, The particulate matter model also includes: Standard deviation formula: Particle size probability density probability: Normalization process: in, D g Used to represent the geometric mean particle size of particulate matter; σ g Used to represent the geometric standard deviation of particulate matter; σ g0 Used to represent the reference standard deviation of particulate matter; S S The standard deviation coefficient used to represent pollutant emission standards; S y Used to represent the standard deviation coefficient of vehicle life; S f Used to represent the standard deviation coefficient of fuel type; P i This is used to represent the particle size probability density probability of the i-th particle size range; This is used to represent the normalized probability of the i-th particle size interval.

8. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The PN-PM conversion algorithm includes: in, C m Used to represent detection and evaluation data, which is the mass concentration value of particulate matter; C n Used to represent target detection data, which is the particulate number concentration value; D g Used to represent the geometric mean particle size of particulate matter.

9. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The step of determining whether motor vehicle pollutant emissions exceed standards based on the target detection data and the detection evaluation data includes: If the target detection data and / or the detection evaluation data exceed a preset threshold, then it is determined that the motor vehicle pollutant emissions exceed the standard.

10. The rapid assessment method for motor vehicle pollutants according to claim 1, characterized in that, The method further includes: If it is determined that the vehicle pollutant emissions exceed the standard, the target vehicle parameters and the target detection data are processed according to the preset original emission model to determine the original emission data and DPF capture efficiency data. Based on the original emission data and the DPF capture efficiency data, the reasons for the excessive emissions of pollutants from motor vehicles were determined.

11. The rapid assessment method for motor vehicle pollutants according to claim 10, characterized in that, The original row model is as follows: in, C n(raw) Used to represent the original row data; C n Used to represent target detection data; μ DPF Used to represent DPF capture efficiency data; K raw Used to represent the original row coefficient; x s Used to indicate the original emission factor of pollutant emission standards; x v Used to represent the original row factor of vehicle type; x y Used to represent the original emission factor of vehicle lifespan; x c Used to represent the original emission factor of the vehicle's historical detection status; x d Used to represent the original excretion factor of DPF; u s Used to indicate the original emission weight of pollutant emission standards; u v Used to indicate the original weight of vehicle type; u y Used to represent the original weight of vehicle lifespan; u c Used to represent the original weight of the vehicle's historical detection status; u d Used to represent the original row weights of DPF.

12. A rapid testing device for motor vehicle pollutants, characterized in that, include: The acquisition module is used to acquire target vehicle parameters, which include: vehicle brand, vehicle type, pollutant emission standards, vehicle lifespan, fuel type, engine information, vehicle historical inspection status, and DPF status. The detection parameter determination module is used to process the target vehicle parameters according to a preset detection model and determine the detection parameters, including the number of samplings and the sampling duration. The data acquisition module is used to acquire several initial detection data based on the detection parameters; The data processing module is used to process several initial detection data according to a preset data processing algorithm to obtain target detection data; The particulate matter size module is used to determine the particle size correction factor corresponding to several target vehicle parameters according to a preset particle size correction factor lookup table; substitute the particle size correction factor into the particulate matter model to obtain particulate matter size data; the particulate matter size data includes the geometric mean particle size of the particulate matter. The evaluation module is used to process the target detection data and the geometric mean particle size according to a preset PN-PM conversion algorithm to obtain detection evaluation data; The judgment module is used to determine whether the motor vehicle pollutant emissions exceed the standards based on the target detection data and the detection evaluation data.

13. A rapid testing device for motor vehicle pollutants, characterized in that, It includes a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the vehicle pollutant rapid assessment device performs the vehicle pollutant rapid assessment method as described in any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The device includes computer instructions that, when executed on the vehicle pollutant rapid assessment device, cause the vehicle pollutant rapid assessment device to perform the vehicle pollutant rapid assessment method as described in any one of claims 1-11.

15. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed on the vehicle pollutant rapid assessment device, cause the vehicle pollutant rapid assessment device to perform the vehicle pollutant rapid assessment method as described in any one of claims 1-11.

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