Online evaluation methods, devices, and storage media for vehicle emissions compliance

Abstract

This invention discloses an online evaluation method, apparatus, storage medium, and device for vehicle emission compliance. The evaluation method includes the following steps: acquiring remote monitoring data streams of the vehicle to be evaluated within a preset time period; preprocessing the remote monitoring data streams to generate a valid dataset; identifying and segmenting multiple independent driving cycles from the valid dataset, and selecting independent driving cycles with cold start characteristics and engine cumulative power not less than a preset cycle power threshold as valid driving cycles; dividing the data of each valid driving cycle into multiple preset operating condition zones according to operating characteristics; using a corresponding calculation model for each preset operating condition zone to calculate the pollutant emission results of the target pollutant; and comparing the pollutant emission results of the target pollutant in each zone with the corresponding preset emission limits for determination. This invention achieves low-cost, large-scale, and high-frequency emission supervision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, and in particular to a vehicle emission compliance online evaluation method, device, storage medium and equipment. BACKGROUND

[0002] With the continuous growth of global vehicle ownership, pollutants such as nitrogen oxides (NOx) and particulate matter (PM) from motor vehicle exhaust emissions have become the main source of air pollution. For example, heavy-duty diesel vehicles account for more than 80% of total vehicle emissions, and their precise regulation is urgently needed. In order to truly reflect the actual emission level of vehicles, portable vehicle emission testing systems (PEMS) have been introduced into emission regulations in major global markets, which can directly obtain emission data under multiple road conditions and become the "gold standard" for regulatory compliance verification. However, PEMS testing is costly and complex to operate, and as a mandatory regulatory measure, its testing frequency is low and its coverage is limited, making it difficult to achieve large-scale, normalized full-life-cycle emission monitoring of a large number of in-use vehicles, and unable to meet the high-frequency emission screening and regulatory needs. At the same time, the development of Internet of Vehicles technology enables vehicles to collect and transmit multi-dimensional operating data such as location, engine operating conditions, and aftertreatment systems to remote monitoring platforms, providing a data basis for low-cost, efficient remote emission evaluation.

[0003] Although a large amount of remote monitoring data can be collected in real time, the current field still lacks an effective technical solution to convert these indirect emission level reflecting operating conditions and sensor data into accurate and quantitative vehicle emission evaluation results and to build a standardized online compliance evaluation process. SUMMARY

[0004] The purpose of the present application is to provide a vehicle emission compliance online evaluation method, device, storage medium and equipment, which utilizes vehicle remote monitoring data to establish an automated, standardized and high-precision online evaluation process to solve the problem of high cost and low coverage of traditional emission testing, thereby achieving low-cost, large-scale and high-frequency full-life-cycle emission compliance regulation of in-use vehicles.

[0005] To achieve the purpose of the present application, the technical solution provided by the present application is as follows:

[0006] First aspect

[0007] The present application provides a vehicle emission compliance online evaluation method, comprising the following steps:

[0008] Step S1: Acquire the remote monitoring data stream of the vehicle to be evaluated within a preset time period, preprocess the remote monitoring data stream, and generate a valid dataset; wherein, the remote monitoring data stream includes at least: Vehicle Identification Number (VIN), data acquisition time, vehicle speed, engine speed, engine output torque, engine fuel flow rate, dew point status of the NOx sensor downstream of SCR, NOx concentration downstream of SCR, engine coolant temperature, cumulative mileage, engine rated power, atmospheric temperature, atmospheric pressure, and altitude;

[0009] Step S2: Identify and segment multiple independent driving cycles from the effective dataset, and select independent driving cycles that have cold start characteristics and whose cumulative engine power is not less than a preset cycle power threshold as effective driving cycles;

[0010] Step S3: Divide the data of each effective driving cycle into multiple preset operating condition zones according to the operating characteristics; the preset operating condition zones include at least: cold state operation zone, low power zone, and medium-high power zone; the cold state operation zone is the time period from the start of the driving cycle until the engine's accumulated power reaches a preset benchmark value (such as 1 times the WHTC equivalent cycle power); the low power zone and the medium-high power zone are determined by analyzing the relationship between the average engine power and the preset power percentage threshold (such as 6% of the engine's rated power) through a moving time window analysis;

[0011] Step S4: For each preset operating condition zone, use the corresponding calculation model to calculate the pollutant emission results of the target pollutant; the target pollutant may include, but is not limited to, NOx, particulate matter (PM), ammonia (NH3), etc.

[0012] Step S5: Compare the pollutant emission results of the target pollutant in each zone with the corresponding preset emission limits. If the emission result of any pollutant exceeds its corresponding preset emission limit, the vehicle under evaluation is deemed to be unqualified for that target pollutant emission during this evaluation cycle. If the pollutant emission results of all operating condition zones do not exceed the corresponding preset emission limits, the vehicle is deemed to be qualified.

[0013] Furthermore, in step S1, the remote monitoring data stream is preprocessed, including validity cleaning, outlier handling, and data repair.

[0014] The effective cleaning process involves removing data points from the remote monitoring data stream that do not meet the preset environmental boundary conditions (such as altitude and atmospheric temperature) and data points with obviously abnormal sensor readings (such as negative NOx concentrations or data points that far exceed the physical limit).

[0015] The outlier handling involves using statistical algorithms (such as box plots) or machine learning algorithms (such as Isolation Forest and DBSCAN clustering) to identify and process outliers in key parameters (such as vehicle speed, engine speed, and torque).

[0016] The data repair involves estimating or filling invalid data from post-processing system sensors (such as NOx sensors) before they reach their operating temperature (such as dew point) based on a preset repair model, in order to ensure data integrity.

[0017] Furthermore, step S2 specifically includes:

[0018] Step 2.1: Identify and segment multiple independent driving cycles from the effective dataset. Specifically, based on the time interval between effective data points in the effective dataset, the cumulative mileage, and the vehicle speed and engine speed data within a specific time window, automatically identify and segment multiple independent driving cycles. The start time of each independent driving cycle is the moment when the vehicle to be evaluated restarts driving (e.g., the vehicle speed and engine speed start to increase from zero or extremely low values) after a long period of inactivity (e.g., the time interval is greater than 600 seconds and the mileage change is less than 2km).

[0019] Step 2.2: Based on the identified and segmented independent driving cycles, select independent driving cycles with cold start characteristics. The cold start characteristics are that the after-treatment system has not reached the operating temperature in the initial stage of the independent cycle (e.g., the NOx sensor has not reached the dew point state), the engine coolant temperature is lower than the first preset threshold (e.g., 40°C), and the engine exhaust temperature is lower than the second preset threshold (e.g., 100°C).

[0020] Step 2.3: Based on the selected independent driving cycles with cold start characteristics, calculate the cumulative engine power throughout the entire process of each independent driving cycle, and select independent driving cycles whose cumulative engine power is not less than a preset cycle power threshold; the preset cycle power threshold can be associated with the equivalent cycle power based on a standard test cycle (such as WHTC) and the vehicle engine rated power to ensure that the amount of evaluation data is sufficiently representative.

[0021] The method for calculating the cumulative power of the engine is as follows:

[0022]

[0023] in, The cumulative power output of the engine is expressed in kW·h. T Engine torque, measured in N·m; n Engine speed, in r / min; N The total driving cycle time is expressed in seconds. t This refers to the time sequence number of the data point.

[0024] Furthermore, in step S4: the pollutant emission result is a specific emission value or emission rate;

[0025] For the pollutant emission results of the cold-state operation zone, the following method is used for calculation: calculate the total mass of pollutants emitted in the zone and divide it by the total cumulative power of the engine in the cold-state operation zone to obtain the specific emission value in terms of mass / power (mg / kWh);

[0026] For the pollutant emission results of the medium and high power zones, the following method is used to calculate: sum up the total mass emissions of pollutants and the total cumulative power of the engine in all medium and high power zone windows, and calculate the total specific emission value (mg / kWh).

[0027] For the pollutant emission results of low-power zones, the following method is used for calculation: sum up the total mass emission and total duration of pollutants in all low-power zone windows, and calculate the emission rate in units of mass / time (mg / h).

[0028] Second aspect

[0029] This invention provides an online vehicle emission compliance evaluation device, comprising the following modules: a data acquisition and preprocessing module, an effective driving cycle screening module, an operating condition zoning module, a zoning emission calculation module, and a compliance evaluation module;

[0030] The data acquisition and preprocessing module is used to acquire remote monitoring data streams of the vehicles to be evaluated within a preset time period, preprocess the remote monitoring data streams, and generate valid datasets. The remote monitoring data streams include at least: Vehicle Identification Number (VIN), data acquisition time, vehicle speed, engine speed, engine output torque, engine fuel flow rate, dew point status of the NOx sensor downstream of the SCR, NOx concentration downstream of the SCR, engine coolant temperature, cumulative mileage, engine rated power, atmospheric temperature, atmospheric pressure, and altitude.

[0031] The effective driving cycle screening module is used to identify and segment multiple independent driving cycles from the effective dataset, and screen out independent driving cycles that have cold start characteristics and whose cumulative engine power is not less than a preset cycle power threshold as effective driving cycles.

[0032] The operating condition partitioning module is used to divide the data of each effective driving cycle into multiple preset operating condition partitions according to the operating characteristics. The preset operating condition partitions include at least: cold operating partition, low power partition, and medium-high power partition. The cold operating partition is the time period from the start of the driving cycle until the engine's accumulated power reaches a preset benchmark value. The low power partition and the medium-high power partition are determined by analyzing the relationship between the average engine power and the preset power percentage threshold through a moving time window.

[0033] The zone emission calculation module is used to calculate the pollutant emission results of the target pollutant by using the corresponding calculation model for each preset working condition zone.

[0034] The compliance evaluation module is used to compare the pollutant emission results of the target pollutants in each zone with the corresponding preset emission limits. When the emission result of any pollutant exceeds its corresponding preset emission limit, the vehicle under evaluation is determined to be unqualified for the emission of that target pollutant in this evaluation cycle. If the pollutant emission results of all operating condition zones do not exceed the corresponding preset emission limits, the vehicle is determined to be qualified.

[0035] Furthermore, the preprocessing of the remote monitoring data stream includes validity cleaning, outlier handling, and data repair.

[0036] The effective cleaning process involves removing data points from the remote monitoring data stream that do not meet the preset environmental boundary conditions, as well as data points with obviously abnormal sensor readings.

[0037] The outlier handling involves using statistical or machine learning algorithms to identify and process outliers in key parameters.

[0038] The data repair refers to estimating or filling in invalid data from the post-processing system sensors before they reach operating temperature, based on a preset repair model.

[0039] Furthermore, the effective driving cycle screening module is specifically used to perform the following:

[0040] Step 2.1: Identify and segment multiple independent driving cycles from the effective dataset. Specifically, based on the time interval between effective data points in the effective dataset, the cumulative mileage, and the vehicle speed and engine speed data within a specific time window, automatically identify and segment multiple independent driving cycles. The start time of each independent driving cycle is the moment when the vehicle to be evaluated restarts driving after a long period of inactivity.

[0041] Step 2.2: Based on the identified and segmented independent driving cycles, select independent driving cycles with cold start characteristics. The cold start characteristics are that the after-treatment system has not reached the operating temperature in the initial stage of the independent cycle, and the engine coolant temperature is lower than the first preset threshold and the engine exhaust temperature is lower than the second preset threshold.

[0042] Step 2.3: Based on the selected independent driving cycles with cold start characteristics, calculate the cumulative engine power of each independent driving cycle throughout the entire process, and select independent driving cycles with cumulative engine power not less than the preset cycle power threshold.

[0043] The method for calculating the cumulative power of the engine is as follows:

[0044]

[0045] in, The cumulative power output of the engine is expressed in kW·h. T Engine torque, measured in N·m; n Engine speed, in r / min; N The total driving cycle time is expressed in seconds. t This refers to the time sequence number of the data point.

[0046] Furthermore, the pollutant emission results are specific emission values ​​or emission rates;

[0047] For the pollutant emission results of the cold-state operation zone, the following method is used for calculation: calculate the total mass of pollutants emitted in the zone and divide it by the total cumulative power of the engine in the cold-state operation zone to obtain the emission ratio in terms of mass / power.

[0048] For the pollutant emission results of the medium and high power zones, the following method is used to calculate: sum up the total mass emissions of pollutants and the total cumulative power of the engine in all medium and high power zone windows, and calculate the total specific emission value;

[0049] For the pollutant emission results of low-power zones, the following method is used for calculation: sum up the total mass emission and total duration of pollutants in all low-power zone windows, and calculate the emission rate in units of mass / time.

[0050] Third aspect

[0051] This invention provides a storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the online vehicle emission compliance evaluation method.

[0052] Fourth aspect

[0053] The present invention provides an electronic device (such as a server, cloud platform, etc.), the electronic device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the online vehicle emission compliance evaluation method.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] (1) Low-cost, large-scale, and high-frequency emission regulation is achieved: This invention directly utilizes the data from the remote monitoring terminal that is already installed on the vehicle, without the need to install expensive PEMS equipment, which greatly reduces the cost of testing a single vehicle.

[0056] (2) Improved accuracy and scientific rigor of emission assessment: This invention proposes a systematic data processing and analysis process. Through refined data preprocessing, effective driving cycle screening, innovative operating condition zoning, and different methods for calculating pollutant emissions in different zones, the emission compliance of vehicles under different operating conditions is assessed separately, avoiding the distortion caused by simply averaging all data, and significantly improving the accuracy and reliability of remote online assessment results.

[0057] (3) A standardized and automated online evaluation system has been constructed: This invention solidifies the complex emission data analysis process into a set of clear and repeatable algorithm steps, automating the entire process from data cleaning to final compliance determination, eliminating the differences and subjectivity of human operation. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the online vehicle emission compliance evaluation method provided in an embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0060] Figure 1 This is a flowchart of an online evaluation method for vehicle emission compliance provided in an embodiment of the present invention.

[0061] The online vehicle emission compliance evaluation method provided in this embodiment specifically includes the following steps:

[0062] Step S1: Data Acquisition and Preprocessing

[0063] (1) Data Acquisition: Retrieve all remote monitoring data streams of the specified Vehicle Identification Number (VIN) within a preset time period from the remote monitoring data platform of the vehicle to be evaluated. The data stream is recorded in seconds and includes, but is not limited to, the following key fields: data acquisition time, vehicle speed (km / h), engine speed (r / min), engine output torque (N·m), engine fuel flow rate (kg / h), dew point status of the NOx sensor downstream of SCR, NOx concentration downstream of SCR (ppm), engine coolant temperature (°C), cumulative mileage (km), engine rated power (kW), atmospheric temperature (°C), atmospheric pressure (kPa), and altitude (m).

[0064] (2) Data preprocessing:

[0065] a1. Environmental boundary condition cleaning: Traverse all data points and delete data that meets any of the following conditions: altitude > 2400m; atmospheric temperature < -7℃ or atmospheric temperature > 40℃.

[0066] b1. Sensor data cleaning: For NOx concentration data downstream of SCR, delete points with values ​​>3000ppm (considered as extreme sensor anomalies); uniformly correct all points with values ​​<0ppm to 0ppm.

[0067] c1. Handling outliers of key parameters: For key dynamic data such as vehicle speed, engine speed, and engine output torque, the box plot method (IQR method) is preferred for outlier identification.

[0068] For each parameter, calculate its upper quartile Q3 and lower quartile Q1, and determine the normal range as [Q1 - 1.5 × (Q3 - Q1), Q3 + 1.5 × (Q3 - Q1)]. Identified outliers are marked as missing. For all missing values ​​(including original missing values ​​and marked outliers), forward imputation is used, i.e., filling with valid data from the previous second to ensure the continuity of the time series.

[0069] d1. NOx Sensor Dew Point Data Restoration: This involves identifying data where the NOx sensor dew point status is "dew point not reached" and using a pre-trained Gradient Boosting Decision Tree (GBDT) model to predict and fill in the NOx concentration data for these data segments. The model's training set consists of valid data from when the sensor has reached the dew point. Features (inputs) include engine speed, engine output torque, vehicle speed, and engine coolant temperature. The labels (outputs) are the NOx concentration data downstream of the SCR (Self-Conducting Vehicle). This model can accurately estimate the NOx concentration when the sensor is not functioning properly, based on the vehicle's real-time operating conditions.

[0070] Step S2: Valid driving cycle screening

[0071] (1) Independent Driving Cycle Recognition and Segmentation: Traverse the preprocessed valid dataset to identify the start time of all independent driving cycles. A time point is considered the start time of an independent driving cycle if it simultaneously meets the following four conditions:

[0072] Condition a2: The time difference between this time point and the previous valid data point is >600 seconds;

[0073] Condition b2: The difference in cumulative mileage between this time point and the previous valid data point is less than 2km;

[0074] Condition c2: Within 5 seconds after this point in time, all engine speed data are <1000rpm;

[0075] Condition d2: Within 5 seconds after this time point, all vehicle speed data are <5km / h.

[0076] The data from the start time of each identified independent driving cycle to 1 second before the start time of the next independent driving cycle is defined as a complete driving cycle segment.

[0077] (2) Cold start feature screening: From all independent driving cycles, cycles that meet all of the following conditions are selected as valid independent driving cycles with cold start features:

[0078] Condition a3: For the first 10 seconds after the start of the cycle, the NOx sensor dew point status is "dew point not reached";

[0079] Condition b3: Engine coolant temperature at the initial moment of the cycle ≤ 40℃;

[0080] Condition c3: The SCR inlet exhaust temperature at the initial moment of the cycle is <100℃.

[0081] (3) Effective function screening:

[0082] First, calculate the total cumulative power of each selected effective independent driving cycle with cold start characteristics. W total The method for calculating the total accumulated work is as follows:

[0083]

[0084] in, W total The cumulative power output of the engine during the driving cycle is expressed in kW·h. T Engine torque, N·m; n Engine speed, in r / min; N The total driving cycle time is in seconds. t For data point time sequence numbers;

[0085] Secondly, calculate the WHTC cycle equivalent power of the engine in this driving cycle. W WHTC This value is usually a known parameter or calculated based on engine power. The specific calculation method is as follows:

[0086]

[0087] in, W WHTC The WHTC equivalent cyclic work is expressed in kW·h. P rating The rated power of the engine is kW; f is the conversion factor.

[0088] Then, calculate the preset cycle work threshold. W threshold The specific calculation method is as follows:

[0089]

[0090] In this embodiment, the following is taken m =4:

[0091] W threshold =4× W WHTC

[0092] Finally, only those... W total ≥ W threshold An independent driving cycle is selected as the final effective driving cycle for emissions evaluation.

[0093] Step S3: Run the operating condition partition

[0094] For each valid driving cycle that passes the screening, partition it as follows:

[0095] (1) Cold-state operation zone division: Starting from the beginning of the independent driving cycle, data is extracted until the engine's cumulative power first reaches 1× W WHTC At that moment, this data was divided into a cold-state running partition.

[0096] (2) Low power partition and medium-high power partition: All remaining data after the cold operation partition is used as the data to be partitioned.

[0097] Set a moving time window with a width of 300 seconds, starting from the first second of the data to be divided, and sliding forward second by second. For each window, calculate the average engine power within the window. P window Set power threshold Pthreshold =6%× P rating .if P window > P threshold Then all data points within that 300-second window are classified as medium-high power partitions. If P window ≤ P threshold If so, all data points within that 300-second window are classified as low-power partitions.

[0098] Step S4: Zonal Emission Calculation

[0099] (1) Instantaneous mass emission calculation: First, the NOx concentration (ppm) needs to be converted into the instantaneous mass emission rate. m NOx (mg / s). This needs to be combined with the instantaneous exhaust mass flow rate estimated by a model (such as calculations based on fuel flow rate and air-fuel ratio). G exh (kg / s), and use the following formula:

[0100] m NOx =NOx concentration × G exh × k NOx

[0101] in, k NOx It is a unit conversion factor.

[0102] (2) Emission ratio calculation for each zone:

[0103] a4. NOx emission ratio for cold-state operation zones The calculation method is as follows:

[0104]

[0105] in, The total cumulative work of the cold-running zone (i.e., 1× W WHTC ); Duration of cold-state operation of partitioned data, in seconds; for t Instantaneous emission of pollutants at any given time, mg / s; Δ t The data collection interval is s.

[0106] b4. NOx emissions ratio in medium- and high-power zones:

[0107]

[0108] in, The emission values ​​for pollutants in the medium-to-high power zone are expressed in mg / kW·h. na For the first na Number of moving windows; m na It is the total NOx emission mass of the nath non-idle window; Na This represents the total number of windows moved between the medium and high power zones. W na It is the first na The cumulative work done in each non-idle window, kW·h, is summed and iterated through all non-idle windows.

[0109] c4. NOx emission rate in low-power zones:

[0110]

[0111] in, For low-energy-density emissions of pollutants, mg / h; nb For the first nb Number of moving windows; Nb This represents the total number of windows moved in the low-power partition. For the first nb Pollutant emissions per mobile window, mg;

[0112] Step S5: Compliance Assessment

[0113] The calculated emission results for each zone are compared with the zoning limits stipulated by regulations, and the vehicle's emission compliance is determined based on the comparison results. If any of the three comparisons results in "exceeding the limit," the vehicle's NOx emissions for the current evaluation period are deemed non-compliant. Only when the emission results for all three zones meet the limit requirements is the vehicle deemed compliant.

[0114] Through the above specific implementation methods, the present invention can automatically and in a standardized manner complete the online evaluation of vehicle emissions.

[0115] Corresponding to the above method, this embodiment also provides an online vehicle emission compliance evaluation device, including the following modules: a data acquisition and preprocessing module, an effective driving cycle screening module, an operating condition zoning module, a zoning emission calculation module, and a compliance evaluation module;

[0116] The data acquisition and preprocessing module is used to acquire remote monitoring data streams of the vehicles to be evaluated within a preset time period, preprocess the remote monitoring data streams, and generate valid datasets. The remote monitoring data streams include at least: Vehicle Identification Number (VIN), data acquisition time, vehicle speed, engine speed, engine output torque, engine fuel flow rate, dew point status of the NOx sensor downstream of the SCR, NOx concentration downstream of the SCR, engine coolant temperature, cumulative mileage, engine rated power, atmospheric temperature, atmospheric pressure, and altitude.

[0117] The effective driving cycle screening module is used to identify and segment multiple independent driving cycles from the effective dataset, and screen out independent driving cycles that have cold start characteristics and whose cumulative engine power is not less than a preset cycle power threshold as effective driving cycles.

[0118] The operating condition partitioning module is used to divide the data of each effective driving cycle into multiple preset operating condition partitions according to the operating characteristics. The preset operating condition partitions include at least: cold operating partition, low power partition, and medium-high power partition. The cold operating partition is the time period from the start of the driving cycle until the engine's accumulated power reaches a preset benchmark value. The low power partition and the medium-high power partition are determined by analyzing the relationship between the average engine power and the preset power percentage threshold through a moving time window.

[0119] The zone emission calculation module is used to calculate the pollutant emission results of the target pollutant by using the corresponding calculation model for each preset working condition zone.

[0120] The compliance evaluation module is used to compare the pollutant emission results of the target pollutants in each zone with the corresponding preset emission limits. When the emission result of any pollutant exceeds its corresponding preset emission limit, the vehicle under evaluation is determined to be unqualified for the emission of that target pollutant in this evaluation cycle. If the pollutant emission results of all operating condition zones do not exceed the corresponding preset emission limits, the vehicle is determined to be qualified.

[0121] This embodiment also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or instruction set is loaded and executed by a processor to implement the online vehicle emission compliance evaluation method.

[0122] It should be noted that the computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0123] This embodiment also provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the online vehicle emission compliance evaluation method.

[0124] It should be noted that a processor can be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0125] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the vehicle emissions remote assessment method of any embodiment of the present invention described above, and / or other desired functions. Various contents such as initial extrinsic parameters and thresholds may also be stored in the computer-readable storage medium.

[0126] In addition to the methods, apparatus, and devices described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps of the vehicle emissions remote assessment method provided in any embodiment of the present invention.

[0127] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0128] Finally, it should be noted that the above embodiments are merely illustrative and explanatory of the present invention, and are not intended to limit the present invention to the scope of the described embodiments. Furthermore, those skilled in the art will understand that the present invention is not limited to the above embodiments, and many more variations and modifications can be made based on the teachings of the present invention, all of which fall within the scope of protection claimed by the present invention.

Claims

1. A method for online evaluation of vehicle emission compliance, characterized in that, Includes the following steps: Step S1: Acquire the remote monitoring data stream of the vehicle to be evaluated within a preset time period, preprocess the remote monitoring data stream, and generate a valid dataset; wherein, the remote monitoring data stream includes at least: Vehicle Identification Number (VIN), data acquisition time, vehicle speed, engine speed, engine output torque, engine fuel flow rate, dew point status of the NOx sensor downstream of SCR, NOx concentration downstream of SCR, engine coolant temperature, cumulative mileage, engine rated power, atmospheric temperature, atmospheric pressure, and altitude; Step S2: Identify and segment multiple independent driving cycles from the effective dataset, and select independent driving cycles that have cold start characteristics and whose cumulative engine power is not less than a preset cycle power threshold as effective driving cycles; Step S2 specifically includes: Step 2.1: Identify and segment multiple independent driving cycles from the effective dataset. Specifically, based on the time interval between effective data points in the effective dataset, the cumulative mileage, and the vehicle speed and engine speed data within a specific time window, automatically identify and segment multiple independent driving cycles. The start time of each independent driving cycle is the moment when the vehicle to be evaluated restarts driving after a long period of inactivity. Step 2.2: Based on the identified and segmented independent driving cycles, select independent driving cycles with cold start characteristics. The cold start characteristics are that the after-treatment system has not reached the operating temperature in the initial stage of the independent cycle, and the engine coolant temperature is lower than the first preset threshold and the engine exhaust temperature is lower than the second preset threshold. Step 2.3: Based on the selected independent driving cycles with cold start characteristics, calculate the cumulative engine power of each independent driving cycle throughout the entire process, and select independent driving cycles with cumulative engine power not less than the preset cycle power threshold. The method for calculating the cumulative power of the engine is as follows: ； in, The cumulative power output of the engine is expressed in kW·h. T Engine torque, measured in N·m; n Engine speed, in r / min; N The total driving cycle time is expressed in seconds. t For data point time sequence numbers; Step S3: Divide the data of each effective driving cycle into multiple preset operating condition partitions according to the operating characteristics; the preset operating condition partitions include at least: cold state operation partition, low power partition, and medium-high power partition; the cold state operation partition is the time period from the start of the driving cycle until the engine's accumulated power reaches the preset benchmark value; the low power partition and the medium-high power partition are determined by analyzing the relationship between the average engine power and the preset power percentage threshold through a moving time window analysis. Step S4: For each preset operating condition zone, use the corresponding calculation model to calculate the pollutant emission results of the target pollutant; Step S5: Compare the pollutant emission results of the target pollutant in each zone with the corresponding preset emission limits. If the emission result of any pollutant exceeds its corresponding preset emission limit, the vehicle under evaluation is deemed to be unqualified for that target pollutant emission during this evaluation cycle. If the pollutant emission results of all operating condition zones do not exceed the corresponding preset emission limits, the vehicle is deemed to be qualified.

2. The online evaluation method for vehicle emission compliance according to claim 1, characterized in that, In step S1, the remote monitoring data stream is preprocessed, including validity cleaning, outlier handling, and data repair. The effective cleaning process involves removing data points from the remote monitoring data stream that do not meet the preset environmental boundary conditions, as well as data points with obviously abnormal sensor readings. The outlier handling involves using statistical or machine learning algorithms to identify and process outliers in key parameters. The data repair refers to estimating or filling in invalid data from the post-processing system sensors before they reach operating temperature, based on a preset repair model.

3. The online evaluation method for vehicle emission compliance according to claim 1, characterized in that, In step S4: the pollutant emission result is a specific emission value or emission rate; For the pollutant emission results of the cold-state operation zone, the following method is used for calculation: calculate the total mass of pollutants emitted in the zone and divide it by the total cumulative power of the engine in the cold-state operation zone to obtain the emission ratio in terms of mass / power. For the pollutant emission results of the medium and high power zones, the following method is used to calculate: sum up the total mass emissions of pollutants and the total cumulative power of the engine in all medium and high power zone windows, and calculate the total specific emission value; For the pollutant emission results of low-power zones, the following method is used for calculation: sum up the total mass emission and total duration of pollutants in all low-power zone windows, and calculate the emission rate in units of mass / time.

4. An online vehicle emissions compliance evaluation device, characterized in that, It includes the following modules: data acquisition and preprocessing module, effective driving cycle screening module, operating condition zoning module, zoning emission calculation module, and compliance evaluation module; The data acquisition and preprocessing module is used to acquire remote monitoring data streams of the vehicles to be evaluated within a preset time period, preprocess the remote monitoring data streams, and generate valid datasets. The remote monitoring data streams include at least: Vehicle Identification Number (VIN), data acquisition time, vehicle speed, engine speed, engine output torque, engine fuel flow rate, dew point status of the NOx sensor downstream of the SCR, NOx concentration downstream of the SCR, engine coolant temperature, cumulative mileage, engine rated power, atmospheric temperature, atmospheric pressure, and altitude. The effective driving cycle screening module is used to identify and segment multiple independent driving cycles from the effective dataset, and screen out independent driving cycles that have cold start characteristics and whose cumulative engine power is not less than a preset cycle power threshold as effective driving cycles. The effective driving cycle screening module is specifically used to perform the following: Step 2.1: Identify and segment multiple independent driving cycles from the effective dataset. Specifically, based on the time interval between effective data points in the effective dataset, the cumulative mileage, and the vehicle speed and engine speed data within a specific time window, automatically identify and segment multiple independent driving cycles. The start time of each independent driving cycle is the moment when the vehicle to be evaluated restarts driving after a long period of inactivity. Step 2.2: Based on the identified and segmented independent driving cycles, select independent driving cycles with cold start characteristics. The cold start characteristics are that the after-treatment system has not reached the operating temperature in the initial stage of the independent cycle, and the engine coolant temperature is lower than the first preset threshold and the engine exhaust temperature is lower than the second preset threshold. Step 2.3: Based on the selected independent driving cycles with cold start characteristics, calculate the cumulative engine power of each independent driving cycle throughout the entire process, and select independent driving cycles with cumulative engine power not less than the preset cycle power threshold. The method for calculating the cumulative power of the engine is as follows: ； in, The cumulative power output of the engine is expressed in kW·h. T Engine torque, measured in N·m; n Engine speed, in r / min; N The total driving cycle time is expressed in seconds. t For data point time sequence numbers; The operating condition partitioning module is used to divide the data of each effective driving cycle into multiple preset operating condition partitions according to the operating characteristics. The preset operating condition partitions include at least: cold operating partition, low power partition, and medium-high power partition. The cold operating partition is the time period from the start of the driving cycle until the engine's accumulated power reaches a preset benchmark value. The low power partition and the medium-high power partition are determined by analyzing the relationship between the average engine power and the preset power percentage threshold through a moving time window. The zone emission calculation module is used to calculate the pollutant emission results of the target pollutant by using the corresponding calculation model for each preset working condition zone. The compliance evaluation module is used to compare the pollutant emission results of the target pollutants in each zone with the corresponding preset emission limits. When the emission result of any pollutant exceeds its corresponding preset emission limit, the vehicle under evaluation is determined to be unqualified for the emission of that target pollutant in this evaluation cycle. If the pollutant emission results of all operating condition zones do not exceed the corresponding preset emission limits, the vehicle is determined to be qualified.

5. The online vehicle emission compliance evaluation device according to claim 4, characterized in that, The preprocessing of the remote monitoring data stream includes validity cleaning, outlier handling, and data repair. The effective cleaning process involves removing data points from the remote monitoring data stream that do not meet the preset environmental boundary conditions, as well as data points with obviously abnormal sensor readings. The outlier handling involves using statistical or machine learning algorithms to identify and process outliers in key parameters. The data repair refers to estimating or filling in invalid data from the post-processing system sensors before they reach operating temperature, based on a preset repair model.

6. The online vehicle emission compliance evaluation device according to claim 4, characterized in that, The pollutant emission results are expressed as specific emission values ​​or emission rates. For the pollutant emission results of the cold-state operation zone, the following method is used for calculation: calculate the total mass of pollutants emitted in the zone and divide it by the total cumulative power of the engine in the cold-state operation zone to obtain the emission ratio in terms of mass / power. For the pollutant emission results of the medium and high power zones, the following method is used to calculate: sum up the total mass emissions of pollutants and the total cumulative power of the engine in all medium and high power zone windows, and calculate the total specific emission value; For the pollutant emission results of low-power zones, the following method is used for calculation: sum up the total mass emission and total duration of pollutants in all low-power zone windows, and calculate the emission rate in units of mass / time.

7. A storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or instruction set is loaded and executed by a processor to implement the online vehicle emission compliance evaluation method as described in any one of claims 1-3.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the online vehicle emission compliance evaluation method as described in any one of claims 1-3.

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