PEMS emission analysis method, device, computer equipment and storage medium

Abstract

The present application relates to a PEMS emission analysis method, apparatus, computer equipment, and storage medium. The method comprises: obtaining first remote monitoring data corresponding to a PEMS test condition, and obtaining second remote monitoring data of a vehicle to be analyzed; first, determining the time distribution of the first remote monitoring data in each specific power interval based on each set of vehicle status data in the first remote monitoring data; then, determining the specific emission distribution of the second remote monitoring data in each specific power interval based on each set of vehicle status data in the second remote monitoring data; finally, simulating the specific emission of the vehicle to be analyzed under the PEMS test condition based on the number of specific power intervals, the time distribution, and the specific emission distribution, and obtaining the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition. This method can simulate the specific emission analysis value of a vehicle under the PEMS test condition without PEMS testing equipment, greatly reducing the cost of PEMS emission testing.

Description

Technical Field

[0001] The present application relates to the technical field of automobile noise measurement, and in particular to a PEMS emission analysis method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] With the continuous development of my country's economy and society, motor vehicle exhaust emissions have become a major challenge in environmental governance. The increasing number of pollutant emissions has attracted the attention of national environmental protection departments and researchers. Effectively reducing motor vehicle emissions is a key means of improving urban air quality. As the discrepancy between actual road vehicle emissions and laboratory test results grows, accurately determining vehicle emissions under actual road conditions has become a research priority and a foundation for the development of scientific motor vehicle pollution control measures.

[0003] Current vehicle on-road emissions assessments primarily rely on full-vehicle on-road pollutant emission testing using a portable emission measurement system (PEMS). PEMS, comprised of a series of test instruments, offers high precision and accuracy, making it suitable for scientific research on vehicle on-road emission characteristics and a currently supported on-road testing method by regulatory standards. However, due to the heavy weight and bulk of PEMS equipment, test results are affected by sampling, time, mileage, and complex environments, and cannot be fully tested for emissions levels on all sold vehicles throughout their entire lifecycle.

[0004] The National VI monitoring platform has already accumulated a large amount of driving data from actual roads. It can obtain parameters such as NOx emissions and engine aftertreatment systems, and has gradually become an important supplement to the emission monitoring of in-use diesel vehicles. Existing technologies that use National VI remote monitoring data to simulate PEMS emissions mainly divide driving data into short-trip segments, and then divide the segments into urban, suburban, and highway segments. Then, according to PEMS test rules, the segments are combined according to a certain time ratio, and the power-based window method is used to calculate NOx emissions for the combined segments. However, this method is not universally applicable to users' actual road driving conditions. If the user's actual driving conditions do not cover highway, suburban, and urban conditions, it is impossible to obtain a combined segment that meets the PEMS test conditions, which has certain limitations.

[0005] Currently, the cost of conducting PEMS trials is high. Summary of the Invention

[0006] Based on this, it is necessary to provide a PEMS emission analysis method, device, computer equipment, computer-readable storage medium and computer program product that can reduce the cost of PEMS emission testing in order to address the above technical problems.

[0007] In a first aspect, the present application provides a PEMS emission analysis method. The method comprises:

[0008] Acquire first remote monitoring data corresponding to a PEMS test condition, and acquire second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status of the vehicle per unit time;

[0009] Determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to characterize a time proportion of each specific power interval;

[0010] determining, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to characterize an average specific emission value in each specific power interval;

[0011] According to the number of specific power intervals, time distribution and specific emission distribution, the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions is obtained.

[0012] In one embodiment, obtaining first remote monitoring data corresponding to a PEMS test condition includes:

[0013] Determine the vehicle identification code and test time of the target PEMS test vehicle, and obtain the first original remote monitoring data of the target PEMS test vehicle at the test time according to the vehicle identification code;

[0014] Performing data conversion on the first original remote monitoring data and performing data cleaning according to a preset data precision, a preset offset, and a preset data valid range corresponding to each item of data to obtain first intermediate remote monitoring data;

[0015] According to the preset engine coolant temperature, the first intermediate remote monitoring data is screened to obtain the first remote monitoring data.

[0016] In one embodiment, obtaining second remote monitoring data of a vehicle to be analyzed includes:

[0017] Determining the vehicle identification code of the vehicle to be analyzed, and obtaining the second original remote monitoring data of the vehicle to be analyzed according to the vehicle identification code;

[0018] Performing data conversion on the second original remote monitoring data, and performing data cleaning according to a preset data precision, a preset offset, and a preset data valid range corresponding to each item of data, to obtain second intermediate remote monitoring data;

[0019] According to the preset engine coolant temperature, the second intermediate remote monitoring data is screened to obtain the second remote monitoring data.

[0020] In one embodiment, determining, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval includes:

[0021] Acquire at least one test data set according to the first remote monitoring data, each test data set including multiple sets of vehicle status data;

[0022] According to each set of vehicle state data in any test data set, determining the sub-time distribution of the corresponding test data set in each specific power interval;

[0023] According to the sub-time distribution of each test data set in each specific power interval, the time distribution of the first remote monitoring data in each specific power interval is obtained.

[0024] In one embodiment, determining the specific emission distribution of the second remote monitoring data in each specific power interval based on each set of vehicle status data in the second remote monitoring data includes:

[0025] In the second remote monitoring data, determining the specific power interval to which each set of vehicle status data belongs based on the vehicle speed, engine speed, torque percentage, and specific power of each set of vehicle status data;

[0026] According to each group of vehicle status data included in each specific power interval, an average specific emission value of each specific power interval is obtained as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0027] In one embodiment, obtaining a specific emission analysis value of a vehicle to be analyzed under a PEMS test condition based on the number of specific power intervals, time distribution, and specific emission distribution includes:

[0028] According to the time proportion and average specific emission value of each specific power interval, the specific emission interval prediction value of each specific power interval is obtained;

[0029] According to the number of specific power intervals and the specific emission interval prediction value of each specific power interval, the average specific emission prediction value of all specific power intervals is obtained as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0030] In a second aspect, the present application also provides a PEMS emission analysis device. The device comprises:

[0031] A data acquisition module is configured to acquire first remote monitoring data corresponding to a PEMS test condition and second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status per unit time;

[0032] A first calculation module is used to determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to represent a time proportion of each specific power interval;

[0033] a second calculation module, configured to determine, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, wherein the specific emission distribution is used to represent an average specific emission value in each specific power interval;

[0034] The analysis and prediction module is used to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions based on the number of specific power intervals, time distribution and specific emission distribution.

[0035] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0036] Acquire first remote monitoring data corresponding to a PEMS test condition, and acquire second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status of the vehicle per unit time;

[0037] Determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to characterize a time proportion of each specific power interval;

[0038] determining, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to characterize an average specific emission value in each specific power interval;

[0039] According to the number of specific power intervals, time distribution and specific emission distribution, the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions is obtained.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0041] Acquire first remote monitoring data corresponding to a PEMS test condition, and acquire second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status of the vehicle per unit time;

[0042] Determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to characterize a time proportion of each specific power interval;

[0043] determining, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to characterize an average specific emission value in each specific power interval;

[0044] According to the number of specific power intervals, time distribution and specific emission distribution, the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions is obtained.

[0045] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0046] Acquire first remote monitoring data corresponding to a PEMS test condition, and acquire second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status of the vehicle per unit time;

[0047] Determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to characterize a time proportion of each specific power interval;

[0048] determining, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to characterize an average specific emission value in each specific power interval;

[0049] According to the number of specific power intervals, time distribution and specific emission distribution, the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions is obtained.

[0050] The PEMS emissions analysis method, apparatus, computer device, storage medium, and computer program product described above obtain first remote monitoring data corresponding to a PEMS test condition and second remote monitoring data of the vehicle to be analyzed. First, based on each set of vehicle status data in the first remote monitoring data, the time distribution of the first remote monitoring data in each specific power interval is determined. This time distribution corresponds to the emission characteristics of the PEMS test condition. Then, based on each set of vehicle status data in the second remote monitoring data, the specific emission distribution of the second remote monitoring data in each specific power interval is determined. Finally, based on the number of specific power intervals, the time distribution, and the specific emission distribution, combined with the emission characteristics of the PEMS test condition data, the specific emissions of the vehicle to be analyzed under the PEMS test condition are simulated to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition. This method reduces dependence on PEMS testing equipment and has high universality. Regardless of whether a user's actual driving involves highways, suburban areas, or urban areas, the specific emission analysis value of the vehicle under the PEMS test condition can be simulated without PEMS testing equipment, significantly reducing the cost of PEMS emissions testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of a PEMS emission analysis method according to one embodiment;

[0052] Figure 2 Schematic diagram of the time distribution of each Bin interval under multiple test conditions in one embodiment;

[0053] Figure 3 Schematic diagram of the mean distribution of the time proportion of each Bin interval under multiple test conditions in one embodiment;

[0054] Figure 4 is a logic flow chart of a PEMS emission analysis method according to another embodiment;

[0055] Figure 5 A structural block diagram of a PEMS emission analysis device in one embodiment;

[0056] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] In one embodiment, Figure 1As shown, a PEMS emission analysis method is provided. This embodiment uses the method applied to a computer device as an example for illustration. It is understandable that the computer device can specifically be a terminal or a server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices, portable wearable devices, and Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart medical devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0059] Step 102, obtaining first remote monitoring data corresponding to the PEMS test condition, and obtaining second remote monitoring data of the vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data respectively include multiple groups of vehicle status data, each group of vehicle status data is used to represent the vehicle status of the vehicle per unit time.

[0060] Both the first and second remote monitoring data can use National VI remote monitoring data and Internet of Vehicles data. National VI remote monitoring data refers to data transmitted back to the server via the vehicle's onboard OBD device during driving, including vehicle speed, engine torque, friction torque, engine speed, fuel consumption, SCR downstream NOx sensor output, intake air volume signal, and engine coolant temperature. Internet of Vehicles data refers to data transmitted back to the server via the vehicle's onboard device during driving, including altitude data.

[0061] Optionally, determine the vehicle identification number (VIN) and test time of the target PEMS test vehicle, and download the remote monitoring data of the target PEMS test vehicle within the test time range from the National VI remote monitoring platform according to the VIN of the target PEMS test vehicle, extract the vehicle speed, engine torque percentage, friction torque percentage, engine speed, fuel consumption, SCR downstream NOx sensor output value, intake volume signal, engine coolant temperature field, and extract altitude data from the vehicle network data, and finally perform data conversion, cleaning and screening on the above data to obtain the first remote monitoring data.

[0062] Furthermore, the VIN of the vehicle to be analyzed is determined, and the remote monitoring data of the vehicle to be analyzed for any time period is downloaded from the National VI remote monitoring platform according to the VIN of the vehicle to be analyzed, and the vehicle speed, engine torque percentage, friction torque percentage, engine speed, fuel consumption, SCR downstream NOx sensor output value, intake volume signal, engine coolant temperature field are extracted, and the altitude data is extracted from the Internet of Vehicles data. Finally, the above data are converted, cleaned and filtered to obtain the second remote monitoring data.

[0063] Step 104 : determining the time distribution of the first remote monitoring data in each specific power interval based on each set of vehicle status data in the first remote monitoring data, where the time distribution is used to characterize the time proportion of each specific power interval.

[0064] The specific power bins refer to the VSP bins. Vehicle specific power (VSP) is a widely accepted explanatory variable, defined as the power output per ton of vehicle weight, measured in kW / T, the same unit as horsepower. Vehicle status data is partitioned into 27 bins (see Table 1) based on vehicle speed v, engine speed r, engine output torque percentage T, and specific power VSP. Each set of vehicle status data can then be assigned to a bin according to the bin division rules.

[0065]

[0066] Table 1

[0067] Optionally, in the first remote monitoring data, based on the vehicle speed, engine speed, engine output torque percentage and specific power in each group of vehicle status data, the specific power interval to which each group of vehicle status data belongs is determined. Since each group of vehicle status data represents the vehicle status of the vehicle per unit time, the time proportion of each specific power interval relative to the first remote monitoring data can be calculated based on the number of groups of vehicle status data in each specific power interval and the vehicle running time corresponding to the first remote monitoring data, thereby obtaining the time distribution of the first remote monitoring data in each specific power interval.

[0068] Step 106 : determining the specific emission distribution of the second remote monitoring data in each specific power interval based on each set of vehicle status data in the second remote monitoring data, where the specific emission distribution is used to represent the average specific emission value in each specific power interval.

[0069] Optionally, in the second remote monitoring data, based on the vehicle speed, engine speed, engine output torque percentage and specific power in each set of vehicle status data, the specific power interval to which each set of vehicle status data belongs is determined, and based on all vehicle status data in each specific power interval, the average specific emission value of each specific power interval can be calculated, thereby obtaining the specific emission distribution of the second remote monitoring data in each specific power interval.

[0070] Step 108 : Obtain specific emission analysis values ​​of the vehicle to be analyzed under the PEMS test conditions according to the number of specific power intervals, time distribution, and specific emission distribution.

[0071] Optionally, based on the time proportion and average specific emission value of each specific power interval, the time proportion of each specific power interval is used as the weighted weight of the average specific emission value of the corresponding specific power interval, and the average specific emission values ​​of all specific power intervals are weighted and summed. Then, the average value of the weighted summation results is calculated according to the number of specific power intervals to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0072] In the above-mentioned PEMS emissions analysis method, first remote monitoring data corresponding to a PEMS test condition and second remote monitoring data of the vehicle to be analyzed are obtained. First, based on each set of vehicle status data in the first remote monitoring data, the time distribution of the first remote monitoring data in each specific power interval is determined. This time distribution corresponds to the emission characteristics of the PEMS test condition. Then, based on each set of vehicle status data in the second remote monitoring data, the specific emission distribution of the second remote monitoring data in each specific power interval is determined. Finally, based on the number of specific power intervals, the time distribution, and the specific emission distribution, combined with the emission characteristics of the PEMS test condition data, the specific emissions of the vehicle to be analyzed under the PEMS test condition can be simulated to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition. This method reduces dependence on PEMS testing equipment and has high universality. Regardless of whether the user's actual driving covers highways, suburban areas, or urban areas, the specific emission analysis value of the vehicle under the PEMS test condition can be simulated without PEMS testing equipment, significantly reducing the cost of PEMS emissions testing.

[0073] In one embodiment, obtaining first remote monitoring data corresponding to a PEMS test condition includes: determining a vehicle identification code and a test time of a target PEMS test vehicle, and obtaining first original remote monitoring data of the target PEMS test vehicle at the test time based on the vehicle identification code; performing data conversion on the first original remote monitoring data, and performing data cleaning based on a preset data accuracy, a preset offset, and a preset data valid range corresponding to each data item to obtain first intermediate remote monitoring data; and performing data screening on the first intermediate remote monitoring data based on a preset engine coolant temperature to obtain first remote monitoring data.

[0074] Furthermore, obtaining the second remote monitoring data of the vehicle to be analyzed includes: determining the vehicle identification code of the vehicle to be analyzed, and obtaining the second original remote monitoring data of the vehicle to be analyzed according to the vehicle identification code; performing data conversion on the second original remote monitoring data, and performing data cleaning according to the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain the second intermediate remote monitoring data; performing data screening on the second intermediate remote monitoring data according to the preset engine coolant temperature to obtain the second remote monitoring data.

[0075] Specifically, for vehicles previously tested with a PEMS, remote monitoring data corresponding to the test time and vehicle VIN is downloaded from the China VI remote monitoring platform. The following fields are extracted: vehicle speed, engine torque percentage, friction torque percentage, engine speed, fuel consumption, SCR downstream NOx sensor output, intake air volume signal, and engine coolant temperature. Altitude data is also extracted from the connected vehicle data and converted, cleaned, and filtered. This data is then used to extract PEMS test condition characteristics. The same method is used to extract data for vehicles whose emissions levels are to be analyzed.

[0076] Furthermore, the filtered data is transformed and cleaned. The data transformation rules are as follows:

[0077] S i =S i,0 *P i -b i ,i=1,2,3...,Q,

[0078] R min ≤S i ≤R max ,

[0079] Where S i represents the data value after cleaning, i represents the data item to be cleaned, Q represents the total number of data items to be cleaned, S i,0 Represents the original data value, P i Indicates data accuracy, bi Indicates the offset, R min Indicates the minimum value of the data range, R max Indicates the maximum value of the data range. The data precision, offset and data valid range of each data item are shown in Table 2.

[0080]

[0081]

[0082] Table 2

[0083] Finally, filter out data with engine coolant temperature greater than or equal to 70°C.

[0084] In this embodiment, the vehicle identification code and test time of the target PEMS test vehicle are determined, and the first original remote monitoring data of the target PEMS test vehicle at the test time is obtained based on the vehicle identification code; the first original remote monitoring data is converted, and the data is cleaned according to the preset data accuracy, preset offset, and preset data valid range corresponding to each data item to obtain the first intermediate remote monitoring data; the first intermediate remote monitoring data is filtered according to the preset engine coolant temperature to obtain the first remote monitoring data. The vehicle identification code of the vehicle to be analyzed is determined, and the second original remote monitoring data of the vehicle to be analyzed is obtained based on the vehicle identification code; the second original remote monitoring data is converted, and the data is cleaned according to the preset data accuracy, preset offset, and preset data valid range corresponding to each data item to obtain the second intermediate remote monitoring data; the second intermediate remote monitoring data is filtered according to the preset engine coolant temperature to obtain the second remote monitoring data. Accurate PEMS test operating condition data and normal operating condition data of the vehicle to be analyzed can be obtained.

[0085] In one embodiment, based on each group of vehicle status data in the first remote monitoring data, the time distribution of the first remote monitoring data in each power ratio interval is determined, including: based on the first remote monitoring data, obtaining at least one test data set, each test data set including multiple groups of vehicle status data; based on each group of vehicle status data in any test data set, determining the sub-time distribution of the corresponding test data set in each power ratio interval; based on the sub-time distribution of each test data set in each power ratio interval, obtaining the time distribution of the first remote monitoring data in each power ratio interval.

[0086] Optionally, at least one test data set is obtained based on the first remote monitoring data, and each test data set includes multiple groups of vehicle status data. In any test data set, the specific power interval to which each group of vehicle status data belongs is determined based on the vehicle speed, engine speed, torque percentage, and specific power of each group of vehicle status data; the interval duration of each specific power interval is determined based on the number of groups of vehicle status data contained in each specific power interval; the total test duration corresponding to the corresponding test data set is obtained; based on the interval duration of each specific power interval and the total test duration, the time proportion of each specific power interval is obtained as the sub-time distribution of the corresponding test data set in each specific power interval. The number of tests corresponding to the first remote monitoring data is determined, and the number of tests is the same as the number of test data sets obtained; based on the time proportion of each test data set in each specific power interval, the total time proportion of each specific power interval is obtained, and based on the number of tests, the time proportion of each specific power interval is obtained as the time distribution of the first remote monitoring data in each specific power interval.

[0087] Specifically, the acceleration of each set of vehicle status data in the first remote monitoring data is calculated according to the acceleration calculation formula:

[0088]

[0089] Where a represents acceleration, the unit is m / s 2 , i represents the sampling time, the unit is s, v represents the vehicle speed, the unit is km / h, T represents the total working time, the unit is s;

[0090] Calculate the slope of each set of vehicle status data in the first remote monitoring data according to the slope calculation formula:

[0091]

[0092] Where h is the altitude in meters, s is the mileage in meters, v is the speed in kilometers per hour, and t is the driving time in seconds.

[0093] And the specific power of each set of vehicle status data in the first remote monitoring data is calculated according to the specific power VSP calculation formula:

[0094]

[0095] Where v is the vehicle speed in m / s and a is the acceleration in m / s 2 , grade indicates slope, unit is %, C a represents the rolling resistance coefficient, ρ a Indicates the ambient air density in kg / m 3 , take 1.207Kg / m3 , take C D represents the drag coefficient, m represents the vehicle mass in kg, and A represents the frontal area in m 3 . C a 、C D , m, and A are vehicle parameters, which are obtained according to different vehicle model configuration parameters;

[0096] The vehicle acceleration a, road slope grade, and specific power value VSP are calculated for each set of vehicle status data in the first remote monitoring data. In this way, each set of vehicle status data can be partitioned into bins based on vehicle speed v, engine speed r, engine output torque percentage T, and specific power VSP. The vehicle status data is then assigned to each bin in Table 1 according to the bin division rules.

[0097] Furthermore, the first remote monitoring data is divided into M trials, each trial corresponds to a set of remote monitoring data, and the time proportion value Time of the remote monitoring data of the M trials belonging to each Bin interval is calculated. i :

[0098]

[0099] Where q i Indicates the duration of the i-th Bin interval, in seconds, T sum Indicates the total duration of the test. For example, when M=5, the time proportion of the remote monitoring data of 5 tests belonging to each Bin interval is as follows: Figure 2 shown.

[0100] Furthermore, the mean time proportion of the remote monitoring data divided into Bin intervals under M test conditions is calculated as the time distribution of the first remote monitoring data in each specific power interval:

[0101]

[0102] Among them, Time ave,i Time represents the mean time percentage of the i-th Bin interval in M ​​trials, i,m Time represents the mean time percentage of the ith Bin interval in the mth trial, where M represents the number of trials. ave,i Equivalent to the emission characteristics of the PEMS test condition. Continuing with the previous example, when M=5, the average time proportion of the remote monitoring data of the five tests belonging to each Bin interval is as follows: Figure 3 shown.

[0103] In a feasible implementation, based on each set of vehicle status data in the first remote monitoring data, the mileage ratio of the first remote monitoring data in each specific power interval is determined, and the mileage ratio is used as the emission characteristic of the PEMS test condition.

[0104] In this embodiment, at least one test data set is acquired based on the first remote monitoring data, each test data set including multiple sets of vehicle status data. Based on each set of vehicle status data in any test data set, the sub-time distribution of the corresponding test data set in each specific power interval is determined. Based on the sub-time distribution of each test data set in each specific power interval, the time distribution of the first remote monitoring data in each specific power interval is acquired. This allows calculation of the emission characteristics of the PEMS test operating condition.

[0105] In one embodiment, based on each set of vehicle status data in the second remote monitoring data, the specific emission distribution of the second remote monitoring data in each specific power interval is determined, including: in the second remote monitoring data, based on the vehicle speed, engine speed, torque percentage and specific power of each set of vehicle status data, determining the specific power interval to which each set of vehicle status data belongs; based on each set of vehicle status data contained in each specific power interval, obtaining the average specific emission value of each specific power interval as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0106] Optionally, in the second remote monitoring data, the specific power interval to which each set of vehicle status data belongs is determined based on the vehicle speed, engine speed, torque percentage, and specific power of each set of vehicle status data. Based on each set of vehicle status data included in any specific power interval, the nitrogen oxide emissions and the accumulated engine work for the corresponding specific power interval are obtained; and the ratio of the nitrogen oxide emissions to the accumulated engine work is obtained as the average specific emissions value for the corresponding specific power interval, which serves as the specific emissions distribution of the second remote monitoring data in each specific power interval.

[0107] Specifically, the acceleration of each set of vehicle status data in the second remote monitoring data is calculated according to the acceleration calculation formula, and the specific power of each set of vehicle status data in the second remote monitoring data is calculated according to the specific power calculation formula.

[0108] Calculate the vehicle acceleration and specific power values ​​for each set of vehicle status data in the second remote monitoring data. This allows each set of vehicle status data to be partitioned into bins based on vehicle speed, engine speed, engine output torque percentage, and specific power. The vehicle status data is then assigned to each bin in Table 1 based on the bin division rules.

[0109] Calculate the average specific emission value e of the vehicle status data of the vehicle to be analyzed belonging to each Bin intervalnox, i, obtain the specific emission distribution of the second remote monitoring data in each specific power interval:

[0110]

[0111] Among them, e nox, i represents the average specific emission value of the i-th Bin interval, m nox, i represents the NOx emission mass of the i-th Bin interval, W engine,i It represents the cumulative work done by the engine in the i-th Bin interval, where i represents the Bin interval number.

[0112]

[0113]

[0114] Among them, nox t,i Indicates the NOx emission mass at the tth second in the i-th Bin interval. t,i represents the concentration value of the downstream NOx sensor at the tth second in the i-th Bin interval, air t,i Indicates the intake volume at the tth second in the i-th Bin interval, fuel t,i represents the engine fuel flow rate at the tth second in the i-th Bin interval, ρ diesel Indicates diesel density in kg / L, usually 0.832, u NOx It represents the ratio of the density of nitrogen oxide components in the exhaust gas to the exhaust gas density, which is generally taken as 0.001587. fre Indicates the data transmission frequency, which is 1Hz.

[0115]

[0116]

[0117] Among them, W t,i Indicates the instantaneous power value of the engine at the tth second in the i-th Bin interval, in Kw, T t,i Indicates the percentage of engine output torque at the tth second in the i-th Bin interval, in %, f t,i Indicates the friction torque percentage at the tth second in the i-th Bin interval, in %, T ref Indicates the reference torque value of the engine in N·m, r t,i Indicates the rotation speed at the tth second in the i-th Bin interval, in rad / min.

[0118] In this embodiment, the specific power interval to which each set of vehicle status data belongs is determined based on the vehicle speed, engine speed, torque percentage, and specific power of each set of vehicle status data in the second remote monitoring data. Based on the vehicle status data sets within each specific power interval, the average specific emissions value for each specific power interval is obtained as the specific emissions distribution of the second remote monitoring data within each specific power interval. This allows calculation of the specific emissions of the vehicle under analysis under normal operating conditions.

[0119] In one embodiment, the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition is obtained based on the number of specific power intervals, time distribution and specific emission distribution, including: obtaining the specific emission interval prediction value of each specific power interval based on the time proportion and average specific emission value of each specific power interval; obtaining the average specific emission prediction value of all specific power intervals based on the number of specific power intervals and the specific emission interval prediction value of each specific power interval, as the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition.

[0120] Specifically, the PEMS test condition characteristic value Time calculated based on M tests ave,i And the average specific emission value e of each Bin interval of the vehicle data to be analyzed nox, i, thereby calculating the specific emission analysis value of the vehicle to be analyzed under PEMS working conditions:

[0121]

[0122] Wherein, N represents the number of specific power Bin intervals. In this embodiment, as shown in Table 1, N=27.

[0123] In this embodiment, a predicted specific emission interval value is obtained for each specific power interval based on the time percentage and average specific emission value of each specific power interval. Based on the number of specific power intervals and the predicted specific emission interval value for each specific power interval, the average predicted specific emission value for all specific power intervals is obtained, serving as the specific emission analysis value for the vehicle under PEMS test conditions. This allows simulation of specific emission analysis values ​​for vehicles under PEMS test conditions without PEMS testing equipment, significantly reducing the cost of PEMS emissions testing.

[0124] In one embodiment, a PEMS emission analysis method, such as Figure 4 Shown, including:

[0125] The vehicle identification code and test time of the target PEMS test vehicle are determined, and first original remote monitoring data of the target PEMS test vehicle during the test time is obtained based on the vehicle identification code. The first original remote monitoring data is converted and cleaned according to a preset data accuracy, a preset offset, and a preset data validity range corresponding to each data item to obtain first intermediate remote monitoring data. The first intermediate remote monitoring data is filtered based on a preset engine coolant temperature to obtain first remote monitoring data. The first remote monitoring data includes multiple sets of vehicle status data, each set of vehicle status data being used to represent the vehicle status of the vehicle per unit time.

[0126] The vehicle identification code of the vehicle to be analyzed is determined, and second original remote monitoring data of the vehicle to be analyzed is obtained based on the vehicle identification code; the second original remote monitoring data is converted and cleaned according to a preset data precision, a preset offset, and a preset data valid range corresponding to each data item to obtain second intermediate remote monitoring data; the second intermediate remote monitoring data is filtered based on a preset engine coolant temperature to obtain second remote monitoring data. The second remote monitoring data includes multiple sets of vehicle status data, each set of vehicle status data being used to represent the vehicle status of the vehicle per unit time.

[0127] According to the first remote monitoring data, at least one test data set is obtained, each test data set includes multiple groups of vehicle status data. In any test data set, based on the vehicle speed, engine speed, torque percentage and specific power of each group of vehicle status data, the specific power interval to which each group of vehicle status data belongs is determined; based on the number of groups of vehicle status data contained in each specific power interval, the interval length of each specific power interval is determined; the total test duration corresponding to the corresponding test data set is obtained; based on the interval length of each specific power interval and the total test duration, the time proportion of each specific power interval is obtained as the sub-time distribution of the corresponding test data set in each specific power interval. The number of tests corresponding to the first remote monitoring data is determined, and the number of tests is the same as the number of test data sets obtained; based on the time proportion of each test data set in each specific power interval, the total time proportion of each specific power interval is obtained, and based on the number of tests, the time proportion of each specific power interval is obtained as the time distribution of the first remote monitoring data in each specific power interval.

[0128] In the second remote monitoring data, the specific power interval to which each group of vehicle status data belongs is determined based on the vehicle speed, engine speed, torque percentage and specific power of each group of vehicle status data; the nitrogen oxide emissions and the accumulated engine work of the corresponding specific power interval are obtained based on the groups of vehicle status data contained in any specific power interval; and the ratio of the nitrogen oxide emissions to the accumulated engine work is obtained as the average specific emission value of the corresponding specific power interval, and as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0129] According to the time proportion and average specific emission value of each specific power interval, the specific emission interval prediction value of each specific power interval is obtained; according to the number of specific power intervals and the specific emission interval prediction value of each specific power interval, the average specific emission prediction value of all specific power intervals is obtained as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0130] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0131] Based on the same inventive concept, embodiments of the present application also provide a PEMS emissions analysis device for implementing the aforementioned PEMS emissions analysis method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more PEMS emissions analysis device embodiments provided below can be found in the above-described limitations of the PEMS emissions analysis method and are not further elaborated here.

[0132] In one embodiment, Figure 5 As shown, a PEMS emission analysis device 500 is provided, comprising: a data acquisition module 501, a first calculation module 502, a second calculation module 503 and an analysis and prediction module 504, wherein:

[0133] The data acquisition module 501 is used to acquire first remote monitoring data corresponding to the PEMS test condition and second remote monitoring data of the vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data is used to represent the vehicle status of the vehicle per unit time;

[0134] A first calculation module 502 is configured to determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, where the time distribution is used to represent a time proportion of each specific power interval;

[0135] A second calculation module 503 is configured to determine, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, where the specific emission distribution is used to represent an average specific emission value in each specific power interval;

[0136] The analysis and prediction module 504 is used to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions according to the number of specific power intervals, time distribution and specific emission distribution.

[0137] In one embodiment, the data acquisition module 501 is also used to determine the vehicle identification code and test time of the target PEMS test vehicle, and obtain the first original remote monitoring data of the target PEMS test vehicle at the test time according to the vehicle identification code; perform data conversion on the first original remote monitoring data, and perform data cleaning according to the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain first intermediate remote monitoring data; perform data screening on the first intermediate remote monitoring data according to the preset engine coolant temperature to obtain first remote monitoring data.

[0138] In one embodiment, the data acquisition module 501 is also used to determine the vehicle identification code of the vehicle to be analyzed, and obtain the second original remote monitoring data of the vehicle to be analyzed based on the vehicle identification code; perform data conversion on the second original remote monitoring data, and perform data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain second intermediate remote monitoring data; perform data screening on the second intermediate remote monitoring data based on the preset engine coolant temperature to obtain second remote monitoring data.

[0139] In one embodiment, the first calculation module 502 is also used to obtain at least one test data set based on the first remote monitoring data, each test data set including multiple groups of vehicle status data; determine the sub-time distribution of the corresponding test data set in each power ratio interval based on each group of vehicle status data in any test data set; and obtain the time distribution of the first remote monitoring data in each power ratio interval based on the sub-time distribution of each test data set in each power ratio interval.

[0140] In one embodiment, the second calculation module 503 is also used to determine the specific power interval to which each set of vehicle status data belongs in the second remote monitoring data based on the vehicle speed, engine speed, torque percentage and specific power of each set of vehicle status data; and obtain the average specific emission value of each specific power interval based on each set of vehicle status data contained in each specific power interval as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0141] In one embodiment, the analysis and prediction module 504 is also used to obtain the specific emission interval prediction value of each specific power interval based on the time proportion and average specific emission value of each specific power interval; and to obtain the average specific emission prediction value of all specific power intervals based on the number of specific power intervals and the specific emission interval prediction value of each specific power interval as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0142] Each module in the PEMS emission analysis device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor within a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0143] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store vehicle status data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a PEMS emission analysis method is implemented.

[0144] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining first remote monitoring data corresponding to a PEMS test condition, and obtaining second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data respectively include multiple groups of vehicle status data, and each group of vehicle status data is used to characterize the vehicle status of the vehicle per unit time; based on each group of vehicle status data in the first remote monitoring data, determining the time distribution of the first remote monitoring data in each specific power interval, and the time distribution is used to characterize the time proportion of each specific power interval; based on each group of vehicle status data in the second remote monitoring data, determining the specific emission distribution of the second remote monitoring data in each specific power interval, and the specific emission distribution is used to characterize the average specific emission value of each specific power interval; based on the number of specific power intervals, the time distribution and the specific emission distribution, obtaining the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition.

[0146] In one embodiment, when the processor executes the computer program, it further implements the following steps: determining the vehicle identification code and test time of the target PEMS test vehicle, and obtaining the first original remote monitoring data of the target PEMS test vehicle at the test time based on the vehicle identification code; performing data conversion on the first original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain first intermediate remote monitoring data; and performing data screening on the first intermediate remote monitoring data based on the preset engine coolant temperature to obtain the first remote monitoring data.

[0147] In one embodiment, when the processor executes the computer program, it also implements the following steps: determining the vehicle identification code of the vehicle to be analyzed, and obtaining the second original remote monitoring data of the vehicle to be analyzed based on the vehicle identification code; performing data conversion on the second original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain second intermediate remote monitoring data; performing data screening on the second intermediate remote monitoring data based on the preset engine coolant temperature to obtain second remote monitoring data.

[0148] In one embodiment, when the processor executes the computer program, it also implements the following steps: based on the first remote monitoring data, obtaining at least one test data set, each test data set including multiple groups of vehicle status data; based on each group of vehicle status data in any test data set, determining the sub-time distribution of the corresponding test data set in each power ratio interval; based on the sub-time distribution of each test data set in each power ratio interval, obtaining the time distribution of the first remote monitoring data in each power ratio interval.

[0149] In one embodiment, when the processor executes the computer program, the following steps are also implemented: in the second remote monitoring data, based on the vehicle speed, engine speed, torque percentage and specific power of each group of vehicle status data, the specific power interval to which each group of vehicle status data belongs is determined; based on the groups of vehicle status data contained in each specific power interval, the average specific emission value of each specific power interval is obtained as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0150] In one embodiment, when the processor executes the computer program, it also implements the following steps: obtaining a specific emission interval prediction value for each specific power interval based on the time proportion and average specific emission value of each specific power interval; obtaining an average specific emission prediction value for all specific power intervals based on the number of specific power intervals and the specific emission interval prediction value of each specific power interval as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0151] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining first remote monitoring data corresponding to a PEMS test condition, and obtaining second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data respectively include multiple groups of vehicle status data, and each group of vehicle status data is used to characterize the vehicle status of the vehicle per unit time; based on each group of vehicle status data in the first remote monitoring data, determining the time distribution of the first remote monitoring data in each specific power interval, and the time distribution is used to characterize the time proportion of each specific power interval; based on each group of vehicle status data in the second remote monitoring data, determining the specific emission distribution of the second remote monitoring data in each specific power interval, and the specific emission distribution is used to characterize the average specific emission value of each specific power interval; based on the number of specific power intervals, the time distribution and the specific emission distribution, obtaining the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the vehicle identification code and test time of the target PEMS test vehicle, and obtaining the first original remote monitoring data of the target PEMS test vehicle at the test time based on the vehicle identification code; performing data conversion on the first original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain first intermediate remote monitoring data; and performing data screening on the first intermediate remote monitoring data based on the preset engine coolant temperature to obtain the first remote monitoring data.

[0153] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the vehicle identification code of the vehicle to be analyzed, and obtaining the second original remote monitoring data of the vehicle to be analyzed based on the vehicle identification code; performing data conversion on the second original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain second intermediate remote monitoring data; performing data screening on the second intermediate remote monitoring data based on the preset engine coolant temperature to obtain second remote monitoring data.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the first remote monitoring data, at least one test data set is obtained, each test data set includes multiple groups of vehicle status data; based on each group of vehicle status data in any test data set, the sub-time distribution of the corresponding test data set in each power ratio interval is determined; based on the sub-time distribution of each test data set in each power ratio interval, the time distribution of the first remote monitoring data in each power ratio interval is obtained.

[0155] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: in the second remote monitoring data, based on the vehicle speed, engine speed, torque percentage and specific power of each group of vehicle status data, the specific power interval to which each group of vehicle status data belongs is determined; based on the groups of vehicle status data contained in each specific power interval, the average specific emission value of each specific power interval is obtained as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0156] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the time proportion and average specific emission value of each specific power interval, a specific emission interval prediction value of each specific power interval is obtained; based on the number of specific power intervals and the specific emission interval prediction value of each specific power interval, an average specific emission prediction value of all specific power intervals is obtained as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0157] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: obtaining first remote monitoring data corresponding to a PEMS test condition, and obtaining second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data respectively include multiple groups of vehicle status data, each group of vehicle status data being used to characterize the vehicle status of the vehicle per unit time; determining the time distribution of the first remote monitoring data in each specific power interval based on each group of vehicle status data in the first remote monitoring data, the time distribution being used to characterize the time proportion of each specific power interval; determining the specific emission distribution of the second remote monitoring data in each specific power interval based on each group of vehicle status data in the second remote monitoring data, the specific emission distribution being used to characterize the average specific emission value of each specific power interval; obtaining the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition based on the number of specific power intervals, the time distribution and the specific emission distribution.

[0158] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the vehicle identification code and test time of the target PEMS test vehicle, and obtaining the first original remote monitoring data of the target PEMS test vehicle at the test time based on the vehicle identification code; performing data conversion on the first original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain first intermediate remote monitoring data; and performing data screening on the first intermediate remote monitoring data based on the preset engine coolant temperature to obtain the first remote monitoring data.

[0159] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the vehicle identification code of the vehicle to be analyzed, and obtaining the second original remote monitoring data of the vehicle to be analyzed based on the vehicle identification code; performing data conversion on the second original remote monitoring data, and performing data cleaning based on the preset data accuracy, preset offset and preset data valid range corresponding to each data item to obtain second intermediate remote monitoring data; performing data screening on the second intermediate remote monitoring data based on the preset engine coolant temperature to obtain second remote monitoring data.

[0160] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the first remote monitoring data, at least one test data set is obtained, each test data set includes multiple groups of vehicle status data; based on each group of vehicle status data in any test data set, the sub-time distribution of the corresponding test data set in each power ratio interval is determined; based on the sub-time distribution of each test data set in each power ratio interval, the time distribution of the first remote monitoring data in each power ratio interval is obtained.

[0161] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: in the second remote monitoring data, based on the vehicle speed, engine speed, torque percentage and specific power of each group of vehicle status data, the specific power interval to which each group of vehicle status data belongs is determined; based on the groups of vehicle status data contained in each specific power interval, the average specific emission value of each specific power interval is obtained as the specific emission distribution of the second remote monitoring data in each specific power interval.

[0162] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: based on the time proportion and average specific emission value of each specific power interval, a specific emission interval prediction value of each specific power interval is obtained; based on the number of specific power intervals and the specific emission interval prediction value of each specific power interval, an average specific emission prediction value of all specific power intervals is obtained as the specific emission analysis value of the vehicle to be analyzed under the PEMS test conditions.

[0163] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0164] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0165] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A PEMS emission analysis method, characterized in that: The method comprises: Acquire first remote monitoring data corresponding to a PEMS test condition, and acquire second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each include multiple sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status per unit time; Determining, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, wherein the time distribution is used to characterize a time proportion of each specific power interval; determining, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to represent an average specific emission value in each specific power interval; According to the number of specific power intervals, the time distribution and the specific emission distribution, a specific emission analysis value of the vehicle to be analyzed under the PEMS test condition is obtained.

2. The method according to claim 1, characterized in that The obtaining of first remote monitoring data corresponding to the PEMS test condition includes: Determining a vehicle identification code and a test time of a target PEMS test vehicle, and obtaining first original remote monitoring data of the target PEMS test vehicle at the test time according to the vehicle identification code; Performing data conversion on the first original remote monitoring data and performing data cleaning according to a preset data precision, a preset offset, and a preset data valid range corresponding to each item of data to obtain first intermediate remote monitoring data; The first intermediate remote monitoring data is screened according to a preset engine coolant temperature to obtain the first remote monitoring data.

3. The method according to claim 1, characterized in that The step of obtaining the second remote monitoring data of the vehicle to be analyzed includes: Determining a vehicle identification code of the vehicle to be analyzed, and obtaining second original remote monitoring data of the vehicle to be analyzed according to the vehicle identification code; Performing data conversion on the second original remote monitoring data and performing data cleaning according to a preset data precision, a preset offset, and a preset data valid range corresponding to each item of data to obtain second intermediate remote monitoring data; The second intermediate remote monitoring data is screened according to a preset engine coolant temperature to obtain the second remote monitoring data.

4. The method according to claim 1, wherein The determining, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval includes: acquiring at least one test data set according to the first remote monitoring data, each test data set including multiple sets of vehicle status data; According to each set of vehicle state data in any test data set, determining the sub-time distribution of the corresponding test data set in each specific power interval; According to the sub-time distribution of each test data set in each specific power interval, the time distribution of the first remote monitoring data in each specific power interval is obtained.

5. The method according to claim 1, wherein The determining, based on each set of vehicle status data in the second remote monitoring data, the specific emission distribution of the second remote monitoring data in each specific power interval includes: In the second remote monitoring data, determining the specific power interval to which each set of vehicle status data belongs based on the vehicle speed, engine speed, torque percentage, and specific power of each set of vehicle status data; According to each group of vehicle status data included in each specific power interval, an average specific emission value of each specific power interval is obtained as the specific emission distribution of the second remote monitoring data in each specific power interval.

6. The method according to claim 1, characterized in that The obtaining, based on the number of specific power intervals, the time distribution, and the specific emission distribution, a specific emission analysis value of the vehicle to be analyzed under the PEMS test condition includes: According to the time proportion and average specific emission value of each specific power interval, the specific emission interval prediction value of each specific power interval is obtained; According to the number of specific power intervals and the specific emission interval prediction value of each specific power interval, the average specific emission prediction value of all specific power intervals is obtained as the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition.

7. A PEMS emission analysis device, characterized in that: The device comprises: a data acquisition module, configured to acquire first remote monitoring data corresponding to a PEMS test condition and second remote monitoring data of a vehicle to be analyzed; the first remote monitoring data and the second remote monitoring data each comprising a plurality of sets of vehicle status data, each set of vehicle status data being used to represent a vehicle status per unit time; a first calculation module, configured to determine, based on each set of vehicle status data in the first remote monitoring data, a time distribution of the first remote monitoring data in each specific power interval, wherein the time distribution is used to represent a time proportion of each specific power interval; a second calculation module, configured to determine, based on each set of vehicle status data in the second remote monitoring data, a specific emission distribution of the second remote monitoring data in each specific power interval, the specific emission distribution being used to represent an average specific emission value in each specific power interval; The analysis and prediction module is used to obtain the specific emission analysis value of the vehicle to be analyzed under the PEMS test condition according to the number of specific power intervals, the time distribution and the specific emission distribution.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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