Super-emission heavy vehicle remote identification method and device capable of replacing PEMS test

Abstract

The invention relates to the technical field of traffic and transportation, in particular to a remote recognition method and device for a super-drainage heavy vehicle capable of replacing a PEMS test, and the method comprises the steps: obtaining test data of the heavy vehicle and remote transmission data issued by a target platform; calculating a first emission average value of the heavy duty vehicle according to the test data, and calculating a second emission average value of the heavy duty vehicle by using various evaluation methods and remote transmission data; selecting a target evaluation method from a plurality of evaluation methods according to the first emission average value and the second emission average value, evaluating the remote transmission data by using the target evaluation method, and generating a data set according to the evaluated data; and training the evaluation model by using the data set, and evaluating the emission state of the heavy duty vehicle by using the trained evaluation model. Therefore, the problem that large deviation exists in online judgment of the emission state of the heavy duty vehicle due to the fact that different evaluation methods have differences in the window division basis and the emission calculation theory in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation, and in particular to a method and a device for remotely identifying an over-displacement heavy-duty vehicle capable of replacing a PEMS test. Background Art

[0002] With the development of IoT cloud technology and the development of heavy-duty vehicle remote monitoring systems, it has become possible to determine the emission status of heavy-duty vehicles online based on remote data. The heavy-duty vehicle remote monitoring system is a heavy-duty vehicle driving and emission data monitoring system that can collect the actual road driving data and remote data of networked heavy-duty vehicles in real time. Using remote data instead of PEMS (Portable Emissions Measurement System) test data to conduct online determination of the emission status of heavy-duty vehicles, and then online identification of heavy-duty vehicles with excessive emissions, can provide targeted clues of illegal vehicles for heavy-duty vehicle emission supervision in real scenarios, thereby improving the efficiency of regulatory authorities in obtaining heavy-duty vehicles with excessive emissions.

[0003] Due to the parameter limitations of telemetry data, especially the lack of the key parameter of heavy-duty vehicle cycle power in the data, the power-based window method cannot be applied to telemetry data. In order to make up for this deficiency, other evaluation methods can be introduced. Among them, the evaluation method refers to dividing the time-continuous vehicle driving and emission data into different windows through a specific strategy, and performing more detailed analysis and processing of the emission data in each window, so as to obtain the multi-dimensional and comprehensive real level of vehicle emissions, and provide effective technical support for evaluating the quality of heavy-duty vehicle emissions.

[0004] In related technologies, since different evaluation methods differ in terms of window division basis and emission calculation theory, there are large differences in the emission levels of heavy-duty vehicles obtained using different methods. This leads to large deviations in the online determination of the emission status of heavy-duty vehicles, and it is impossible to effectively provide targeted and accurate clues to illegal vehicles. This poses great challenges to the supervision and management of heavy-duty vehicle emissions. Summary of the invention

[0005] The present invention provides a remote identification method, device, equipment, medium and program for over-emission heavy-duty vehicles that can replace the PEMS test, so as to solve the problem that different evaluation methods in the related technology have differences in window division basis and emission calculation theory, resulting in large deviations in the online determination of the emission status of heavy-duty vehicles.

[0006] The first aspect of the present invention provides a remote identification method for heavy-duty vehicles with excessive emissions that can replace the PEMS test, including the following steps: obtaining test data of the heavy-duty vehicle and remote transmission data sent by a target platform; calculating a first emission average value of the heavy-duty vehicle based on the test data, and calculating a second emission average value of the heavy-duty vehicle using a plurality of evaluation methods and the remote transmission data; selecting a target evaluation method from a plurality of evaluation methods based on the first emission average value and the second emission average value, evaluating the remote transmission data using the target evaluation method, and generating a data set based on the evaluated data; training an evaluation model using the data set, and evaluating the emission status of the heavy-duty vehicle using the trained evaluation model.

[0007] Optionally, the use of multiple evaluation methods and the remote transmission data to calculate the second emission average value of heavy-duty vehicles includes: using multiple evaluation methods to divide the remote transmission data into windows to generate remote transmission data of multiple windows; calculating the corresponding NOx emission average value based on the remote transmission data of each window; calculating the second emission average value of heavy-duty vehicles based on the accumulated value of the NOx emission average values ​​of all windows and the number of windows.

[0008] Optionally, based on the first emission average value and the second emission average value, a target assessment method is selected from a plurality of assessment methods, including: calculating an emission assessment equivalence coefficient based on the first emission average value and the second emission average value; and screening a target assessment method based on the emission assessment equivalence coefficient.

[0009] Optionally, the screening of the target assessment method according to the emission assessment equivalent coefficient includes: screening multiple assessment methods corresponding to the emission assessment equivalent coefficients approaching the target threshold; and selecting the assessment method with the smallest absolute value of the emission assessment equivalent coefficient from the multiple assessment methods as the target assessment method.

[0010] Optionally, the evaluation model includes an input layer, a long short-term memory network structure layer and an output layer, wherein the input layer inputs the heavy vehicle window feature value generated according to the remote transmission data, each layer of the long short-term memory network structure includes a forget gate, an input gate and an output gate, and the output layer outputs the vehicle emission status, wherein the forget gate calculates the weight of the heavy vehicle window feature value in the previous time step to obtain the first target information; the input gate calculates the weight of the heavy vehicle window feature value in the current time step to obtain the second target information, and fuses the first target information and the second target information to obtain the target information of the current time step, and the output gate generates the vehicle emission status according to the target information of the current time step.

[0011] Optionally, after the heavy-duty vehicle emission data assessment model outputs the emission status of the heavy-duty vehicle, it includes: if the emission status of the heavy-duty vehicle exceeds the standard, obtaining the actual position and identification code of the heavy-duty vehicle; determining the target heavy-duty vehicle according to the actual position and identification code of the heavy-duty vehicle; identifying the actual emission status of the target heavy-duty vehicle; and reversely updating the assessment model according to the actual emission status.

[0012] The second aspect of the present invention provides a remote identification device for heavy-duty vehicles with excessive emissions that can replace the PEMS test, including: an acquisition module, which acquires the test data of the heavy-duty vehicle and the remote transmission data sent by the target platform; a calculation module, which is used to calculate the first emission average value of the heavy-duty vehicle based on the test data, and calculate the second emission average value of the heavy-duty vehicle using multiple evaluation methods and the remote transmission data; a selection module, which is used to select a target evaluation method from multiple evaluation methods based on the first emission average value and the second emission average value, evaluate the remote transmission data using the target evaluation method, and generate a data set based on the evaluated data; an evaluation module, which is used to train an evaluation model using the data set, and evaluate the emission status of the heavy-duty vehicle using the trained evaluation model.

[0013] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to execute the remote identification method for over-displacement heavy-duty vehicles as described in the above embodiment that can replace the PEMS test.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to execute the remote identification method for heavy-duty vehicles exceeding the emission limit that can replace the PEMS test as described in the above embodiment.

[0015] A fifth aspect of the present invention provides a computer program product, including a computer program or instructions, characterized in that when the computer program or instructions are executed, a remote identification method for over-displacement heavy-duty vehicles that can replace the PEMS test as described in the above embodiment is implemented.

[0016] Therefore, the present invention has at least the following beneficial effects: The embodiment of the present invention can calculate the first emission average value of heavy-duty vehicles based on test data, and calculate the second emission average value of heavy-duty vehicles using multiple evaluation methods and remote transmission data, and screen out a target evaluation method that is equivalent to the power-based window method, thereby solving the problem that remote transmission data cannot be directly applied to regulatory standards due to the lack of cycle power parameters. The target evaluation method is used to evaluate the remote transmission data, and remote transmission data that meets the conditions is screened according to the evaluation results to generate a training set to train the evaluation model, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles. The trained evaluation model is used to evaluate the emission status of heavy-duty vehicles, accurately capturing the dynamic emission laws of vehicles, and solving the defect that traditional static methods cannot associate multi-window time series information, thereby having high applicability.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a method for remotely identifying a heavy-duty vehicle with excessive displacement that can replace a PEMS test according to an embodiment of the present invention; Figure 2 A technical roadmap of a method and device for remotely identifying a heavy-duty vehicle with excessive displacement that can replace a PEMS test according to an embodiment of the present invention; Figure 3 A schematic diagram of a work basis window method according to an embodiment of the present invention; Figure 4 A schematic diagram of emission level calculation values ​​and emission assessment equivalent coefficients according to different assessment methods provided in an embodiment of the present invention; Figure 5 This is an example diagram of a remote identification device for over-displacement heavy-duty vehicles provided according to an embodiment of the present invention that can replace the PEMS test; Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0020] As the impact of heavy-duty vehicle emissions on the environment becomes increasingly serious, it is particularly important for regulatory authorities to obtain heavy-duty vehicles with excessive emissions in a timely and effective manner; currently, the method for determining whether heavy-duty vehicle emissions exceed the standard is mainly carried out in accordance with emission regulations. The specific process is: conduct PEMS tests on the target vehicle, use the power-based window method to process the test data to obtain the target vehicle's emission level, and compare it with the limit value standard in the regulations to determine whether the target vehicle's emissions exceed the standard. Due to the high cost of PEMS testing, it is difficult to conduct PEMS testing on all heavy-duty vehicles, making it difficult to obtain heavy-duty vehicles with excessive emissions in a timely and effective manner.

[0021] Therefore, the present invention proposes a remote identification method for over-emission heavy-duty vehicles that can replace the PEMS test, obtains an evaluation method equivalent to the power-based window method, and introduces a machine learning method to establish a heavy-duty vehicle emission status identification model, thereby realizing accurate online judgment of the emission status of heavy-duty vehicles.

[0022] The following describes, with reference to the accompanying drawings, a method, device, electronic device, storage medium and program for remotely identifying a heavy-duty vehicle with excessive displacement that can replace the PEMS test according to an embodiment of the present invention.

[0023] Specifically, Figure 1 The present invention provides a flowchart of a method for remotely identifying a heavy-duty vehicle with excessive displacement that can replace the PEMS test.

[0024] like Figure 1 As shown, the remote identification method for heavy-duty vehicles with excessive emission that can replace the PEMS test includes the following steps: In step S101, the test data of the heavy-duty vehicle and the telematic data sent by the target platform are obtained.

[0025] It is understandable that the embodiments of the present invention can obtain the test data of the heavy-duty vehicle and the telemetry data sent by the target platform, so as to facilitate the subsequent selection of the target evaluation method and the training data set.

[0026] It should be noted that the present invention selects several test heavy-duty vehicles to carry out PEMS tests, and determines the emission status of the test heavy-duty vehicles according to emission regulations, for example: normal emissions or excessive emissions; and marks the emission status, and simultaneously downloads the remote transmission data of the test heavy-duty vehicles for subsequent optimal method selection and model training.

[0027] The heavy-duty vehicle remote monitoring system is used to remotely download the telemetry data of the test heavy-duty vehicle. The data includes time, vehicle speed, acceleration, engine torque, engine speed, fuel flow, NOx emission rate, atmospheric pressure, ambient temperature, altitude and other parameters. The telemetry data sampling frequency is 1 Hz. The heavy-duty vehicle remote monitoring system here is a heavy-duty vehicle driving and emission data monitoring system, which can collect the actual road driving data of networked heavy-duty vehicles in real time, that is, telemetry data.

[0028] In step S102, a first average emission value of the heavy-duty vehicle is calculated based on the test data, and a second average emission value of the heavy-duty vehicle is calculated using a plurality of evaluation methods and telemetry data.

[0029] It can be understood that the embodiment of the present invention can calculate the first emission average value of the heavy-duty vehicle based on the test data, and calculate the second emission average value of the heavy-duty vehicle using multiple evaluation methods and remote transmission data, so as to facilitate the subsequent selection of the target evaluation method from multiple evaluation methods based on the first emission average value and the second emission average value.

[0030] It should be noted that the first emission average value is calculated based on the power-based window method to process the PEMS test data, and the second emission average value is calculated based on multiple evaluation methods to process the remote transmission data. The evaluation method of the present invention is divided into two steps: window division and emission data evaluation.

[0031] Specifically, the power-based window method is used to process the PEMS test data, and whether the test heavy-duty vehicle exceeds the emission limit standard in the emission regulations is determined, and the emission status of the test heavy-duty vehicle is marked as a heavy-duty vehicle with excessive emissions and a heavy-duty vehicle with normal emissions, and then the emission status marking results of the test heavy-duty vehicle are obtained, specifically: First, the PEMS test data is processed using the work-based window method, and the ratio of the total pollutant emissions to the total engine work in each window is calculated, which is recorded as specific emissions. e NOx Secondly, it will be more e NOx By comparing with the limit standards in the emission regulations, it is determined whether the test heavy-duty vehicle exceeds the emission limit, and the emission status of the test heavy-duty vehicle is marked as a heavy-duty vehicle with excessive emissions and a heavy-duty vehicle with normal emissions, thereby obtaining the emission status marking results of the test heavy-duty vehicle.

[0032] The calculation process of the work-based window method is as follows: Figure 3 As shown, the calculation formula of the power-based window method is as follows (1) and (2): (1) (2) In the formula, Indicates i The end time of the window; Indicates i The start time of a window; Indicates i A moment in a window; W ( t ) indicates from 0 to t The accumulated instantaneous output power of the engine at the time,W ref The work required to complete a WHTC (World Harmonized Transient Cycle) test condition for a heavy-duty vehicle.

[0033] The work-based window method uses the WHTC cycle work of heavy vehicles ( W ref ) is used as a ruler to establish a moving window of the work-based window method, where WHTC cycle work refers to the work that a heavy-duty vehicle needs to do to complete a WHTC test condition; the work-based window method uses the engine torque and engine speed in the PEMS test data to calculate the instantaneous output power of the engine, and then accumulates the instantaneous output power of the engine second by second until it is greater than or equal to the corresponding WHTC cycle work, which is recorded as a window. The moving window ends after covering all PEMS test data of heavy-duty vehicles; in all the windows obtained, the total amount of work done by the vehicle in each window is similar.

[0034] In an embodiment of the present invention, a second emission average value of heavy-duty vehicles is calculated using a variety of evaluation methods and remote data, including: using a variety of evaluation methods to divide the remote data into windows to generate remote data of multiple windows; calculating the corresponding NOx emission average value based on the remote data of each window; calculating the second emission average value of heavy-duty vehicles based on the accumulated value of the NOx emission average values ​​of all windows and the number of windows.

[0035] It can be understood that the embodiments of the present invention can use a variety of evaluation methods to divide the remote transmission data into windows to generate remote transmission data of multiple windows; calculate the corresponding NOx emission average value based on the remote transmission data of each window; calculate the second emission average value of heavy-duty vehicles based on the cumulative value of the NOx emission average values ​​of all windows and the number of windows, so as to comprehensively evaluate the emission level of heavy-duty vehicles, but it is necessary to rely on EEEC (Emissions Evaluation Equivalent Coefficient) to screen the optimal method to ensure that the results are close to regulatory standards.

[0036] It should be noted that the evaluation method refers to dividing the time-continuous vehicle emission data into different windows through a specific strategy, and conducting more detailed analysis and processing of the emission data in each window, so as to obtain the multi-dimensional and comprehensive true level of vehicle emissions, and provide effective technical support for evaluating the pros and cons of heavy-duty vehicle emissions.

[0037] Specifically, the evaluation method of the present invention is described with several commonly used methods, such as: moving window average method, three-zone moving average window method, VSP-Bin-based window division method, data reconstruction PEMS window division method, etc. The calculation process of using the above evaluation methods to process the heavy-duty vehicle remote transmission data to obtain the window and evaluate the heavy-duty vehicle emission level is as follows: (1) The moving window average method divides the window based on the time length, which is usually set to 30 seconds. The moving window average method can divide the telematic data of heavy-duty vehicles into several windows with a time length of 30 seconds. Based on this, the ratio of the cumulative sum of the NOx emission rates at all times in each window to the window time length is calculated as the average NOx emission rate of the corresponding window. The average NOx emission rates of all windows are added up and divided by the number of windows to obtain the heavy-duty vehicle emission level assessment result of the moving window average method.

[0038] (2) The three-zone moving average window method uses 300 seconds of time and the load ratio of heavy vehicles as the scale to divide the heavy-duty vehicle telematic data.

[0039] First, the telemetry data of the heavy-duty vehicle is divided into several segments with a time length of 300 s, and the load ratio of each segment of the heavy-duty vehicle is calculated using equations (3) and (4): At the same time, the ratio of the cumulative sum of the NOx emission rates at all times in the segment to the segment time length is calculated as the average NOx emission rate of the corresponding segment.

[0040] Then, according to the load ratio of heavy vehicles, all segments are divided into three intervals: idling area, low load area and medium and high load area; The segments with a load ratio less than 0.06 are classified into the idle zone, the segments with a load ratio greater than 0.2 are classified into the medium and high load zone, and the remaining segments are classified into the low load zone.

[0041] Then, the ratio of the cumulative sum of the average NOx emission rates of all segments in each interval to the total number of segments in the interval is calculated using equation (5) as the average NOx emission rate of the interval.

[0042] Finally, the heavy-duty vehicle emission level assessment results of the three-zone moving average window method were calculated using equation (6).

[0043] ; (3) ; (4) ; (5) ; (6) Where: The CO2 test results for this vehicle type according to the target requirements; is the rated power of the vehicle’s engine; 300 s; 1 s; Fuel flow for heavy vehicles; is the density of diesel; 44 is the relative molecular mass of CO2; is the specific emission of the zone; a is the emission, NOx; b is the type of zone, which can be the idle zone, low load zone or medium and high load zone; is the number of NOx fragments contained in area b; is the instantaneous mass emission of NOx; is the total number of fragments in the three regions; is the number of fragments in region b, The heavy-duty vehicle emission level assessment results calculated using the three-zone moving average window method; k The first speed in a certain range (idle zone, low load zone or medium-high load zone) k fragments.

[0044] (3) The window partitioning method based on VSP-Bin uses vehicle specific power as the yardstick to divide the telematic data of heavy-duty vehicles. VSP refers to vehicle specific power. The calculation formula is as shown in equation (7). Based on VSP, the interval division can obtain several vehicle specific power intervals. The interval here usually refers to Bin, so the vehicle specific power interval is VSP-Bin.

[0045] First, the vehicle specific power of the heavy-duty vehicle at each moment is calculated using equation (7). Then, 10 vehicle specific power intervals are constructed with 2 kw / ton as the vehicle specific power interval, as shown in Table 1 below, and the telematic data of the heavy-duty vehicle is divided into the corresponding specific power intervals.

[0046] Subsequently, the ratio of the cumulative sum of the NOx emission rates at all times within each vehicle specific power interval to the data volume of the corresponding vehicle specific power interval is calculated as the average value of the NOx emission rate of the corresponding vehicle specific power interval; finally, the heavy-duty vehicle emission level assessment result based on the VSP-Bin window partitioning method is calculated using equation (8).

[0047] (7) (8) Where VSP refers to the vehicle specific power, v is the vehicle speed, a is the acceleration; is the data volume of the i-th power ratio interval; is the total data volume; is the average value of NOx emission rate in the i-th specific power interval, The heavy-duty vehicle emission level assessment results are calculated using the window partitioning method based on VSP-Bin.

[0048] Table 1 Vehicle power ratio division interval table

[0049] (4) The data reconstruction PEMS window division method reconstructs the remote transmission data of heavy-duty vehicles into actual road conditions in several PEMS tests, and evaluates the emission level of heavy-duty vehicles based on this.

[0050] First, the telematic data of the heavy-duty vehicle is cut into several short-trip segments, wherein the vehicle speed at the start time and end time of the short-trip segment is 0, and the vehicle speed within the short-trip segment is greater than 0.

[0051] Then, the average speed of all short trip segments is calculated, and all short trip segments are divided into urban areas (vehicle speed <50 km / h), suburban areas (vehicle speed between 50 and 75 km / h) and high-speed areas (vehicle speed greater than 75 km / h) according to the average speed.

[0052] Subsequently, according to the characteristics of the PEMS test conditions in the standard, the corresponding short-trip segments were selected from the urban section, the urban-suburban section and the high-speed section to obtain the reconstructed actual road conditions. Among them, the selection and combination rules of short-trip segments should meet the following requirements: the reasonable boundary conditions between adjacent segments (the average speed of the previous segment differs from the average speed of the next segment by less than 10 km / h); the mileage of the combined urban section segments should account for 15%~20% of the total mileage, the mileage of the combined urban-suburban section segments should account for 20%~25% of the total mileage, and the mileage of the combined high-speed section segments should account for 50%~55% of the total mileage; the average speed of the combined urban section segments is within the range of 15~30 km / h, and the average speed of the combined urban-suburban section segments is within the range of 45~70 km / h.

[0053] Finally, the ratio of the cumulative sum of the NOx emission rates at all times within the reconstructed actual road condition to the length of the reconstructed condition time is calculated as the heavy-duty vehicle emission level evaluation result of the data reconstruction PEMS window division method.

[0054] In step S103, a target evaluation method is selected from a plurality of evaluation methods according to the first emission average value and the second emission average value, the remote transmission data is evaluated using the target evaluation method, and a data set is generated according to the evaluated data.

[0055] It can be understood that the embodiment of the present invention selects a target evaluation method from a plurality of evaluation methods based on the first emission average value and the second emission average value, evaluates the remote data using the target evaluation method, and generates a data set based on the evaluated data, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles.

[0056] It should be noted that the target evaluation method can be understood as the evaluation method that is closest to the calculation results of the power-based window method among multiple evaluation methods. The optimal evaluation method is used to process the telemetry data of the test heavy-duty vehicle, divide it into several windows, and calculate the characteristic values ​​of each window parameter, including the average vehicle speed, average acceleration, average engine torque, average fuel flow, average NOx emission rate, average atmospheric pressure, average ambient temperature, and average altitude. The window characteristics of the test heavy-duty vehicle are combined with the emission status labeling results of the test heavy-duty vehicle, and then the model training data set is established.

[0057] In an embodiment of the present invention, a target evaluation method is selected from a plurality of evaluation methods based on the first emission average value and the second emission average value, including: calculating an emission evaluation equivalence coefficient based on the first emission average value and the second emission average value; and screening the target evaluation method based on the emission evaluation equivalence coefficient.

[0058] It can be understood that the embodiment of the present invention can calculate the emission evaluation equivalent coefficient based on the first emission average value and the second emission average value; screen the target evaluation method based on the emission evaluation equivalent coefficient, so as to select the evaluation method closest to the calculation result of the power basis window method from multiple evaluation methods as the target evaluation method, so as to improve the accuracy of online remote identification of the emission status of heavy-duty vehicles.

[0059] It should be noted that the calculation method of the emission assessment equivalence coefficient is: (9) In the formula, E j It indicates the heavy-duty vehicle emission level assessment result obtained by processing remote transmission data using a certain assessment method; It represents the average value of heavy-duty vehicle emission levels obtained by processing the heavy-duty vehicle PEMS test data using the power-based window method.

[0060] In order to facilitate the evaluation, the calculation results of the power-based window method have been converted into units. The specific conversion method is: the heavy-duty vehicle PEMS test data is processed using the power-based window method to obtain several windows. On this basis, the ratio of the cumulative sum of NOx emission rates at all times in each window to the window time length is calculated as the average NOx emission rate of the corresponding window; the average NOx emission rates of all windows are added and divided by the number of windows to obtain the heavy-duty vehicle emission level assessment results of the power-based window method.

[0061] In an embodiment of the present invention, the target evaluation method is screened according to the emission evaluation equivalence coefficient, including: screening multiple evaluation methods whose emission evaluation equivalence coefficients are close to the target threshold value; and selecting the evaluation method with the smallest absolute value of the emission evaluation equivalence coefficient from the multiple evaluation methods as the target evaluation method.

[0062] The target threshold may be 0 without any specific limitation.

[0063] It can be understood that the embodiments of the present invention can select the evaluation method whose emission evaluation equivalent coefficient is close to the target threshold as the target evaluation method, thereby selecting the evaluation method that is closest to the calculation result of the power basis window method from multiple evaluation methods as the target evaluation method, thereby improving the consistency of remote monitoring results with regulatory standards, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles.

[0064] It should be noted that the physical meaning of the emission assessment equivalence coefficient EEEC can be explained as follows: When EEEC=0, it means that the vehicle emission level evaluated by the current method is consistent with the vehicle emission level evaluated by the work-based window method, and there is no difference in effect.

[0065] When EEEC>0, it means that the vehicle emission level assessed by the current method is generally higher than the vehicle emission level assessed by the work-based window method, and there is a certain degree of over-estimation.

[0066] When EEEC<0, it means that the vehicle emission level assessed by the current method is generally lower than the vehicle emission level assessed by the power-based window method, and there is a certain underestimation.

[0067] When EEEC is closer to 0, it means that the vehicle emission level evaluated by the current method is closer to the evaluation level of the power-based window method. It also means that the current method is better and closer to the evaluation and judgment effect using the power-based window method in the emission regulations.

[0068] In step S104, the evaluation model is trained using the data set, and the trained evaluation model is used to evaluate the emission status of the heavy-duty vehicle.

[0069] The evaluation model includes an input layer, a long short-term memory network structure layer and an output layer. The input layer inputs the heavy-duty vehicle window feature value generated according to the remote transmission data. Each layer of the long short-term memory network structure contains a forget gate, an input gate and an output gate. The output layer outputs the vehicle emission status. The forget gate calculates the weight of the heavy-duty vehicle window feature value in the previous time step to obtain the first target information; the input gate calculates the weight of the heavy-duty vehicle window feature value in the current time step to obtain the second target information, and fuses the first target information and the second target information to obtain the target information of the current time step. The output gate generates the vehicle emission status according to the target information of the current time step.

[0070] It can be understood that the embodiments of the present invention can use the trained evaluation model to evaluate the emission status of heavy-duty vehicles. Through time series modeling and dynamic optimization, high-precision and real-time emission monitoring is achieved, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles.

[0071] Specifically, based on the model training data set, a heavy-duty vehicle emission status recognition model is constructed using the LSTM neural network method: the model takes the characteristic value of the heavy-duty vehicle window as input; and takes the heavy-duty vehicle emission status labeling result as output.

[0072] All windows of the same car in the model training data set constitute a sample. Each sample enters the model in order for training. The model will calculate all windows of each sample frame by frame in time order. After each frame is calculated, a memory unit and a hidden state will be obtained. Among them, the memory unit is a core long-term memory carrier that can transfer information between the previous frame and the next frame across time steps; the hidden state contains the output results of each frame and the final model output results.

[0073] Model structure: The model contains a 4-layer LSTM network structure, and each layer of the network structure contains a forget gate, an input gate, and an output gate. The forget gate determines which information in the memory unit of the previous frame needs to be retained or discarded to obtain valuable old information. Here, the Sigmoid function is used to output a weight of 0 to 1 to process the memory unit of the previous frame. 0 means complete discard and 1 means complete retention. After the input gate calculates the current window data frame by frame to obtain new information, it integrates the valuable old information transmitted by the forget gate of the previous frame to finally obtain the memory unit of the current frame. The output gate generates the hidden state or the final model output result based on the memory unit of the current frame. The Sigmoid activation function is used to generate the output weight of 0 to 1, which is then multiplied by the memory unit processed by the Tanh activation function to finally obtain the hidden state of the current frame and pass it to the next frame.

[0074] In summary, the heavy-duty vehicle emission state recognition model was successfully constructed using the LSTM neural network method, solving the problem that traditional methods can only solve static prediction problems. This type of problem is usually manifested as: when the characteristic value data of a window is input, the model constructed by the traditional method will calculate a corresponding output value. This calculation method cannot take into account the mutual influence between multiple consecutive windows in the time series, and the changes in the emission law during the vehicle driving process. In the process of constructing the heavy-duty vehicle emission state recognition model, the problem to be solved by the present invention is: to determine whether a vehicle has excessive emissions, that is, it is necessary to use the characteristic values ​​of multiple consecutive windows in the time series as the model input, and obtain a final output value through model calculation, thereby realizing the recognition of whether the target vehicle has excessive emissions; LSTM neural network can capture long-term dependencies in time series data and is suitable for processing remote data with strong time continuity. At the same time, the present invention further optimizes the remote data to obtain multiple consecutive windows and their characteristic values ​​in the time series, which are used to more clearly represent the changes in the emission law of the vehicle during driving, thereby providing strong support for the construction of the subsequent heavy-duty vehicle emission state recognition model and the recognition of whether the vehicle has excessive emissions.

[0075] In an embodiment of the present invention, after the heavy-duty vehicle emission data assessment model outputs the emission status of the heavy-duty vehicle, it includes: if the emission status of the heavy-duty vehicle exceeds the standard, obtaining the actual position and identification code of the heavy-duty vehicle; determining the target heavy-duty vehicle according to the actual position and identification code of the heavy-duty vehicle; identifying the actual emission status of the target heavy-duty vehicle; and reversely updating the assessment model according to the actual emission status.

[0076] It can be understood that the embodiments of the present invention can obtain the actual location and identification code of the heavy-duty vehicle when the emission status of the heavy-duty vehicle exceeds the standard; determine the target heavy-duty vehicle based on the actual location and identification code of the heavy-duty vehicle, so that inspectors can use portable emission detection equipment to conduct on-site inspections, identify the actual emission status of the target heavy-duty vehicle, and verify the accuracy of the model; and reversely update the evaluation model based on the actual emission status to improve the accuracy of the online remote identification of the emission status of the heavy-duty vehicle, and through the closed-loop feedback mechanism of "exceeding standard identification-positioning verification-model update", the precise and intelligent emission supervision of heavy-duty vehicles is achieved.

[0077] Specifically, the heavy-duty vehicle emission status recognition model is connected to the heavy-duty vehicle remote monitoring system to remotely identify heavy-duty vehicles with excessive emissions online and issue early warnings. Based on the feedback from on-site law enforcement, the heavy-duty vehicle emission status recognition model is updated online to adapt to changes in vehicle status and environment.

[0078] (1) First, the heavy-duty vehicle emission status recognition model is connected to the heavy-duty vehicle remote monitoring system, relying on the real-time collection of telemetric data of networked heavy-duty vehicles. The telemetric data of all networked heavy-duty vehicles are processed using the optimal evaluation method to obtain the model prediction data set of networked heavy-duty vehicles. Based on this, the emission status of networked heavy-duty vehicles is identified using the heavy-duty vehicle emission status recognition model, and the emission status of heavy-duty vehicles that exceed the emission standards are identified. Networked heavy-duty vehicles with excessive emissions are marked as target heavy-duty vehicles and early warnings are issued. At the same time, enforcement clues of target heavy-duty vehicles with excessive emissions are provided, including vehicle GPS positioning and VIN (Vehicle Identification Number) codes.

[0079] (2) Based on the enforcement clues of the target heavy-duty vehicle's excessive emissions, the target heavy-duty vehicle can be located and subjected to on-site enforcement, and feedback results of the on-site enforcement can be obtained, including whether the actual emissions of the target heavy-duty vehicle exceed the standard.

[0080] (3) Relying on the on-site law enforcement feedback results, the model prediction data set of the target heavy-duty vehicle and the on-site law enforcement feedback results are integrated as new supplementary model training data to perform online update training on the heavy-duty vehicle emission status recognition model, thereby improving the heavy-duty vehicle emission status recognition model's ability to adapt to vehicle status and environmental changes.

[0081] According to the remote identification method for over-emission heavy-duty vehicles proposed in an embodiment of the present invention, which can replace the PEMS test, the first emission average value of the heavy-duty vehicle is calculated according to the test data, and the second emission average value of the heavy-duty vehicle is calculated by using multiple evaluation methods and remote transmission data to screen out a target evaluation method equivalent to the power-based window method, which solves the problem that the remote transmission data cannot be directly applied to regulatory standards due to the lack of cycle power parameters. The remote transmission data is evaluated by using the target evaluation method, and the remote transmission data that meets the conditions is screened according to the evaluation results to generate a training set to train the evaluation model, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles. The trained evaluation model is used to evaluate the emission status of heavy-duty vehicles, accurately capturing the dynamic emission laws of vehicles, and solving the defect that the traditional static method cannot associate multi-window time series information, and has high applicability.

[0082] The following will describe in detail the method for remotely identifying heavy-duty vehicles with excessive displacement that can replace the PEMS test according to the present invention in conjunction with specific embodiments, as follows: 1. Collected and collated the PEMS test data of 200 test heavy-duty vehicles, determined the emission status of the test heavy-duty vehicles according to emission regulations and marked the emission status, simultaneously downloaded the remote transmission data of the test heavy-duty vehicles, and performed data preprocessing for subsequent optimal method selection and model training.

[0083] (1.1) First, the 200 test heavy-duty vehicles collected and collated all met emission regulations before leaving the factory. At the same time, for the PEMS test data of the 200 test heavy-duty vehicles collected and collated, the mileage of the vehicles before the PEMS test was carried out exceeded 10,000 kilometers, that is, there were no new vehicles in the 200 test heavy-duty vehicles collected and collated.

[0084] (1.2) Determine the emission status of the test heavy-duty vehicle according to emission regulations. The specific steps are: Use the work-based window method to process the PEMS test data, and then calculate the ratio of the total pollutant emissions to the total engine work in each window, recorded as the specific emission eNOx. Finally, compare the specific emission eNOx with the limit standard in the emission regulations to determine whether the test heavy-duty vehicle exceeds the emission limit, and mark the emission status of the test heavy-duty vehicle, marking it as a heavy-duty vehicle with excessive emissions and a heavy-duty vehicle with normal emissions, and then obtain the emission status marking results of the test heavy-duty vehicle. In the end, 54 vehicles were heavy-duty vehicles with excessive emissions, and 146 vehicles were heavy-duty vehicles with normal emissions.

[0085] (1.3) Use the heavy-duty vehicle remote monitoring system to remotely download the telemetry data of the test heavy-duty vehicle. The data includes time, vehicle speed, acceleration, engine torque, engine speed, fuel flow, NOx emission rate, atmospheric pressure, ambient temperature, altitude and other parameters. The sampling frequency of the telemetry data is 1 Hz.

[0086] 2. Define the emission assessment equivalent coefficient to quantify the effect of the evaluation method on the evaluation and judgment of the emission level of heavy-duty vehicles; select the evaluation method with the best emission assessment equivalent coefficient EEEC and suitable for telemetry data to establish a model training data set.

[0087] (2.1) First, the evaluation method refers to dividing the time-continuous vehicle emission data into different windows through a specific strategy, and performing more detailed analysis and processing on the emission data in each window, so as to obtain the multi-dimensional and comprehensive real level of vehicle emissions, and provide effective technical support for evaluating the quality of heavy-duty vehicle emissions. Finally, the ratio of the cumulative sum of the NOx emission rates at all times in the reconstructed actual road conditions to the length of the reconstructed conditions is calculated as the heavy-duty vehicle emission level evaluation result of the data reconstruction PEMS window division method.

[0088] (2.2) Then, define the emission evaluation equivalence coefficient EEEC. The physical meaning of the emission evaluation equivalence coefficient EEEC can be explained as follows: when EEEC=0, it means that the vehicle emission level evaluated by the current method has the same effect as the vehicle emission level evaluated by the power-based window method, and there is no difference in effect. When EEEC>0, it means that the vehicle emission level evaluated by the current method is generally higher than the vehicle emission level evaluated by the power-based window method, and there is a certain degree of overestimation. When EEEC<0, it means that the vehicle emission level evaluated by the current method is generally lower than the vehicle emission level evaluated by the power-based window method, and there is a certain degree of underestimation. The closer EEEC is to 0, the closer the vehicle emission level result evaluated by the current method is to the evaluation level of the power-based window method, which also indicates that the current method is better and closer to the evaluation and judgment effect of the power-based window method in the emission regulations.

[0089] (2.3) Then, based on the PEMS test data and telemetry data of the test heavy-duty vehicle obtained in step 1, the power-based window method is used to process the PEMS test data of the test heavy-duty vehicle to obtain the emission level assessment result of the test heavy-duty vehicle, such as Figure 4 The first column on the left side is about 0.255 g / s. The evaluation methods are used to process the remote transmission data of the heavy-duty vehicle under evaluation, and the emission level evaluation results of the heavy-duty vehicle under different evaluation methods are obtained, such as Figure 4 Columns 2 to 5 in the table and calculate the corresponding emission assessment equivalence coefficient EEEC, such as Figure 4 The middle line. Select the evaluation method with the best emission evaluation equivalent coefficient EEEC and suitable for remote data. Figure 4 The results show that the emission level calculated by the moving window average method is closest to the evaluation effect of the power-based window method, which is 0.261 g / s. At the same time, the emission evaluation equivalent coefficient EEEC value of this method is 0.02, which is the best among all evaluation methods. Therefore, the moving window average method is selected as the processing method for subsequent telemetry data.

[0090] (2.4) Finally, the telemetry data of the test heavy-duty vehicle is processed using the optimal evaluation method (i.e., the moving window average method), divided into several windows, and the characteristic values ​​of each window parameter are calculated, including the average vehicle speed, average acceleration, average engine torque, average fuel flow, average NOx emission rate, average atmospheric pressure, average ambient temperature, and average altitude. The window features of the test heavy-duty vehicle are combined with the emission status labeling results of the test heavy-duty vehicle to establish a model training data set.

[0091] 3. Based on the model training data set, a heavy-duty vehicle emission status recognition model is constructed using the LSTM neural network method.

[0092] Among them, the LSTM neural network method's processing capabilities on time series data are used to train a heavy-duty vehicle emission status recognition model. The model uses all windows of each vehicle in the model training data set as frame-by-frame input, the feature value of each window as input parameter, and the emission status labeling result (i.e., emission exceeding the standard and emission being normal) as the final output of the model. The heavy-duty vehicle emission status recognition model is trained and constructed.

[0093] All windows of the same car in the model training data set constitute a sample. Each sample is entered into the model in order for training. The model will calculate all windows of each sample frame by frame in time order. After each frame is calculated, a memory unit and a hidden state will be obtained. The memory unit is a core long-term memory carrier that can transfer information between the previous frame and the next frame across time steps; the hidden state contains the output results of each frame and the final model output results.

[0094] The model contains a 4-layer LSTM network structure, and each layer of the network structure contains a forget gate, an input gate, and an output gate. The forget gate determines which information in the memory unit of the previous frame needs to be retained or discarded to obtain valuable old information. Here, the Sigmoid function is used to output a weight of 0 to 1 to process the memory unit of the previous frame. 0 means complete discard and 1 means complete retention. After the input gate calculates the current window data frame by frame to obtain new information, it integrates the valuable old information transmitted by the forget gate of the previous frame to finally obtain the memory unit of the current frame. The output gate generates the hidden state or the final model output result based on the memory unit of the current frame. The Sigmoid function is used to generate the output weight of 0 to 1, which is then multiplied by the memory unit processed by the Tanh function to finally obtain the hidden state of the current frame and pass it to the next frame.

[0095] 4. Connect the heavy-duty vehicle emission status recognition model to the heavy-duty vehicle remote monitoring system, remotely identify heavy-duty vehicles with excessive emissions online, and issue early warnings. Based on the feedback from on-site law enforcement, update the heavy-duty vehicle emission status recognition model online to adapt to vehicle status and environmental changes.

[0096] (4.1) First, the heavy-duty vehicle emission status recognition model is connected to the heavy-duty vehicle remote monitoring system, relying on the real-time collection of telemetry data of networked heavy-duty vehicles. Here, in order to verify the recognition effect of the technology of the present invention, 100 networked heavy-duty vehicles are randomly selected in the remote monitoring system, and each networked heavy-duty vehicle downloads telemetry data for one month. The optimal evaluation method obtained in step (2.3) is used to process the telemetry data of all networked heavy-duty vehicles to obtain the model prediction data set of networked heavy-duty vehicles. Based on this, the emission status of networked heavy-duty vehicles is identified using the heavy-duty vehicle emission status recognition model. It is identified that 11 networked heavy-duty vehicles have excessive emissions. The networked heavy-duty vehicles with excessive emissions are marked as target heavy-duty vehicles and warned. At the same time, law enforcement clues for the excessive emissions of target heavy-duty vehicles are provided, including vehicle GPS positioning and VIN codes.

[0097] (4.2) Based on the enforcement clues of the target heavy-duty vehicles exceeding the emission standards, the target heavy-duty vehicles can be located and on-site enforcement can be carried out to obtain on-site enforcement feedback results, including whether the actual emissions of the target heavy-duty vehicles exceed the standards. According to the on-site enforcement feedback results, 10 of the vehicles actually have excessive emissions, and the online recognition accuracy of the technology of the present invention is as high as over 90.9%.

[0098] (4.3) Relying on the on-site enforcement feedback results, the model prediction data set of the target heavy-duty vehicle and the on-site enforcement feedback results are integrated as new supplementary model training data to conduct online update training of the heavy-duty vehicle emission status recognition model, so as to improve the adaptability of the heavy-duty vehicle emission status recognition model to vehicle status and environmental changes.

[0099] Next, a remote identification device for over-displacement heavy-duty vehicles proposed in accordance with an embodiment of the present invention and capable of replacing the PEMS test will be described with reference to the accompanying drawings.

[0100] Figure 5 It is a block diagram of a remote identification device for over-displacement heavy-duty vehicles according to an embodiment of the present invention that can replace the PEMS test.

[0101] like Figure 5 As shown, the remote identification device 10 for over-displacement heavy-duty vehicles that can replace the PEMS test includes: an acquisition module 100, a calculation module 200, a selection module 300 and an evaluation module 400.

[0102] Among them, the acquisition module 100 acquires the test data of the heavy-duty vehicle and the telemetry data sent by the target platform; the calculation module 200 is used to calculate the first emission average value of the heavy-duty vehicle based on the test data, and calculate the second emission average value of the heavy-duty vehicle using a variety of evaluation methods and telemetry data; the selection module 300 is used to select a target evaluation method from a variety of evaluation methods based on the first emission average value and the second emission average value, evaluate the telemetry data using the target evaluation method, and generate a data set based on the evaluated data; the evaluation module 400 is used to train the evaluation model using the data set, and evaluate the emission status of the heavy-duty vehicle using the trained evaluation model.

[0103] It should be noted that the aforementioned explanation of the embodiment of the remote identification method for heavy-duty vehicles with excessive displacement that can replace the PEMS test is also applicable to the remote identification device for heavy-duty vehicles with excessive displacement that can replace the PEMS test of this embodiment, and will not be repeated here.

[0104] According to the embodiment of the present invention, a remote identification device for over-emission heavy-duty vehicles that can replace the PEMS test is proposed. The first emission average value of the heavy-duty vehicle is calculated according to the test data, and the second emission average value of the heavy-duty vehicle is calculated by using a variety of evaluation methods and remote transmission data to screen out a target evaluation method equivalent to the power-based window method, which solves the problem that the remote transmission data cannot be directly applied to regulatory standards due to the lack of cycle power parameters. The remote transmission data is evaluated by the target evaluation method, and the remote transmission data that meets the conditions is screened according to the evaluation results to generate a training set to train the evaluation model, thereby improving the accuracy of online remote identification of the emission status of heavy-duty vehicles. The trained evaluation model is used to evaluate the emission status of heavy-duty vehicles, accurately capturing the dynamic emission laws of vehicles, and solving the defect that the traditional static method cannot associate multi-window time series information, and has high applicability.

[0105] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0106] When the processor 602 executes the program, the remote identification method for heavy-duty vehicles exceeding the emission limit provided in the above embodiment that can replace the PEMS test is implemented.

[0107] Furthermore, the vehicle also includes: The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0108] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0109] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0112] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0113] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the above-mentioned method for remotely identifying heavy-duty vehicles with excessive displacement that can replace the PEMS test is implemented.

[0114] An embodiment of the present invention further provides a computer program product, including a computer program or instructions, characterized in that when the computer program or instructions are executed, the above-mentioned method for remotely identifying heavy-duty vehicles with excessive displacement that can replace the PEMS test is implemented.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0116] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0118] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS testing, characterized in that: The following steps are involved: Obtain test data of heavy-duty vehicles and telemetry data sent by the target platform; Calculate a first emission average value of the heavy-duty vehicle based on the test data, and calculate a second emission average value of the heavy-duty vehicle using a plurality of evaluation methods and the telemetry data; According to the first emission average value and the second emission average value, selecting a target evaluation method from a plurality of evaluation methods, evaluating the telemetry data using the target evaluation method, and generating a data set according to the evaluated data; The data set is used to train the evaluation model, and the trained evaluation model is used to evaluate the emission status of the heavy-duty vehicle.

2. The remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS test according to claim 1 is characterized in that: The method of calculating the second emission average value of the heavy-duty vehicle by using multiple evaluation methods and the remote transmission data includes: Using multiple evaluation methods to divide the telemetry data into windows respectively to generate telemetry data of multiple windows; Calculate the corresponding average NOx emission value based on the remote transmission data of each window; The second emission average value of the heavy-duty vehicle is calculated based on the accumulated NOx emission average values ​​of all windows and the number of windows.

3. The remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS test according to claim 1 is characterized in that: A target evaluation method is selected from a plurality of evaluation methods according to the first emission average value and the second emission average value, including: Calculating an emission assessment equivalence coefficient based on the first emission average value and the second emission average value; The target assessment method is screened according to the emission assessment equivalence coefficient.

4. The remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS test according to claim 3 is characterized in that: The method for screening target assessment according to the emission assessment equivalent coefficient comprises: Screening a plurality of assessment methods corresponding to the emission assessment equivalent coefficient approaching the target threshold; The evaluation method with the smallest absolute value of the emission evaluation equivalence coefficient is selected from multiple evaluation methods as the target evaluation method.

5. The remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS test according to claim 1 is characterized in that: The evaluation model includes an input layer, a long short-term memory network structure layer and an output layer, wherein the input layer inputs the heavy-duty vehicle window feature value generated according to the remote transmission data, each layer of the long short-term memory network structure includes a forget gate, an input gate and an output gate, and the output layer outputs the vehicle emission status, wherein the forget gate calculates the weight of the heavy-duty vehicle window feature value in the previous time step to obtain the first target information; the input gate calculates the weight of the heavy-duty vehicle window feature value in the current time step to obtain the second target information, and fuses the first target information and the second target information to obtain the target information of the current time step, and the output gate generates the vehicle emission status according to the target information of the current time step.

6. The remote identification method for heavy-duty vehicles with excessive displacement that can replace PEMS testing according to claim 5 is characterized in that: After the evaluation model outputs the heavy-duty vehicle emission status, it includes: If the emission status of the heavy-duty vehicle exceeds the standard, obtaining the actual location and identification code of the heavy-duty vehicle; Determining the target heavy vehicle according to the actual position and identification code of the heavy vehicle; Identify the actual emission status of target heavy-duty vehicles; The estimation model is reversely updated according to the actual emission status.

7. A remote identification device for heavy-duty vehicles with excessive displacement that can replace PEMS testing, characterized in that: include: Acquisition module, which acquires the test data of heavy-duty vehicles and the telemetry data sent by the target platform; a calculation module, configured to calculate a first emission average value of the heavy-duty vehicle according to the test data, and to calculate a second emission average value of the heavy-duty vehicle using a plurality of evaluation methods and the telematic data; A selection module, configured to select a target evaluation method from a plurality of evaluation methods according to the first emission average value and the second emission average value, evaluate the telemetry data using the target evaluation method, and generate a data set according to the evaluated data; The evaluation module is used to train the evaluation model using the data set, and evaluate the emission status of the heavy-duty vehicle using the trained evaluation model.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a remote identification method for over-displacement heavy-duty vehicles that can replace the PEMS test as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a remote identification method for over-displacement heavy-duty vehicles that can replace the PEMS test as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, is used to implement the remote identification method for heavy-duty vehicles with excessive displacement that can replace the PEMS test as described in any one of claims 1 to 6.

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