Method, device and medium for identifying high-emission diesel vehicles based on multi-source monitoring data

Abstract

The application discloses a kind of based on the identification method, equipment and medium of high emission diesel vehicle of multi-source monitoring data, comprising: according to the invalid data in exhaust remote sensing detection data is eliminated according to preset requirement, and in the remaining valid data, the single NOx emission over-standard vehicle to be identified is screened out;For the vehicle to be identified, from the time corresponding to the unqualified detection point, one week of OBD remote monitoring original data is intercepted forward and backward, respectively, the data column corresponding to the key parameter is extracted from the intercepted remote monitoring original data, at least including time, speed, SCR upstream NOx concentration and SCR downstream NOx concentration;Based on the data column, the first determination is carried out on the vehicle to be identified;If it meets the first determination condition, second determination is carried out.The single remote sensing data comprehensive remote monitoring data is determined to identify the vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle emission monitoring, in particular to a high-emission diesel vehicle identification method based on multi-source monitoring data, equipment and medium. BACKGROUND

[0002] In recent years, China has continuously increased the intensity of motor vehicle pollution prevention and control, and implemented emission standard upgrading. The existing in-use vehicle emission detection method still mainly relies on traditional methods such as double-idle detection method, operating condition method, free acceleration method, etc. In the face of the current huge number of automobiles and the urgent ecological environment problem, the traditional method cannot meet the needs of development in detection efficiency, environmental protection effect and detection cost. Therefore, the tail gas remote sensing technology is gradually introduced into China. The motor vehicle tail gas remote sensing detection equipment is arranged on both sides of the key road, and the infrared sensor is used to detect the concentration of pollutants emitted by the passing vehicle tail gas and provide the corresponding vehicle information to the traffic police personnel to remind the traffic police personnel to intercept.

[0003] At present, the remote sensing technology is mainly applied to high-pollution vehicle screening, clean vehicle exemption and vehicle operating condition evaluation. Due to the limitation of the stability and accuracy of the remote sensing equipment, the remote sensing detection result is not completely used as the standard for judging the vehicle pollutant emission. Moreover, the current standard has limitations. The detection result of the same kind of pollutant is more than twice the standard specified emission limit value, and the measurement time interval is within 6 natural months, so the vehicle to be detected can be determined as a vehicle to be identified. In addition, the cost of the motor vehicle tail gas remote sensing detection equipment is high, and it is only arranged in a few key roads and checkpoints in the city, which cannot cover the detection of all heavy diesel vehicles.

[0004] Therefore, the present application is proposed. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a high-emission diesel vehicle identification method based on multi-source monitoring data, which improves the identification accuracy and efficiency. The remote monitoring data and remote sensing data are comprehensively compared and judged to realize the comprehensive remote monitoring data judgment of single remote sensing data for the vehicle to be identified, and provide data support for the formulation of regional motor vehicle pollution prevention and control work precise management target.

[0006] The present application provides a high-emission diesel vehicle identification method based on multi-source monitoring data, which comprises the following steps:

[0007] S1, according to the preset requirements, the invalid data in the tail gas remote sensing detection data is removed, and the single NOx emission exceeding standard vehicle to be identified is screened out from the remaining valid data;

[0008] S2, for the vehicle to be identified, one week of OBD remote monitoring original data is intercepted forward and backward from the time corresponding to the unqualified detection point;

[0009] S3, extracting a data column corresponding to a key parameter from the intercepted remote monitoring raw data, the key parameter at least including time, vehicle speed, SCR upstream NOx concentration and SCR downstream NOx concentration;

[0010] S4, performing first determination on the to-be-identified vehicle based on the data column;

[0011] S5, if the first determination condition is met, determining that the vehicle model corresponding to the to-be-identified vehicle has a highest vehicle speed value disclosed on the network;

[0012] S6, determining a vehicle speed interval corresponding to 77% of the highest vehicle speed value and 83% of the highest vehicle speed value;

[0013] S7, extracting at least 9 working condition points meeting the vehicle speed interval and having a relative torque greater than or equal to 90% from the intercepted remote monitoring raw data as NOx calculation working condition points;

[0014] S8, determining an average NOx emission concentration at the calculation working condition points;

[0015] S9, determining whether the to-be-identified vehicle is a high-emission vehicle based on the average NOx emission concentration.

[0016] An electronic device is provided in the embodiment of the application, and the electronic device comprises:

[0017] a processor and a memory;

[0018] The processor is configured to execute the steps of the method for identifying high-emission diesel vehicles based on multi-source monitoring data according to any one of the embodiments by invoking programs or instructions stored in the memory.

[0019] A computer readable storage medium is provided in the embodiment of the application, and the computer readable storage medium stores programs or instructions, which cause a computer to execute the steps of the method for identifying high-emission diesel vehicles based on multi-source monitoring data according to any one of the embodiments.

[0020] The method for identifying high-emission diesel vehicles based on multi-source monitoring data provided in the embodiment of the application determines high-emission vehicles by comprehensively comparing remote monitoring data and remote sensing data, establishes a comprehensive comparison and determination model of high-emission vehicles, and realizes determination of high-emission vehicles by comprehensively comparing remote sensing data and remote monitoring data once. The determination efficiency and accuracy are improved, human behaviors of evading detection are effectively avoided, data support is provided for formulating precise management goals of regional motor vehicle pollution prevention and control, and the method can be used for accurate management and rule screening of in-use diesel vehicles by management departments. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the specific embodiments or the prior art of the present application, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0022] Figure 1 is a flowchart of a high-emission diesel vehicle identification method based on multi-source monitoring data provided by an embodiment of the present application.

[0023] Figure 2 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the scope of protection of the present application.

[0025] Reference Figure 1 The high-emission diesel vehicle identification method based on multi-source monitoring data provided by an embodiment of the present application includes the following steps:

[0026] S1, according to the preset requirements, the invalid data in the tail gas remote sensing detection data is removed, and the single NOx emission exceeding vehicle to be identified is screened out in the remaining valid data.

[0027] Specifically, the tail gas remote sensing detection data includes: monitoring time, location latitude and longitude, lane slope, judgment result, license plate number, fuel type, CO2(%), CO(%), HC(%), NO(ppm), Lengman blackness, vehicle speed(km / h), wind speed(m / s), environment temperature(℃), humidity(%), atmospheric pressure(kPa).

[0028] According to the requirements in “Emission Limit, Measurement Methods and Technical Requirements for Exhaust Pollutants of In-Use Diesel Vehicles (Remote Sensing Detection Method)” (HJ845-2017), the interval time of each vehicle passing through is not less than 1.0s, and the atmospheric environment should meet the following conditions: no rain, fog, snow; no obvious dust; wind speed <5.0m / s; environment temperature-20.0~45.0℃; relative humidity <85.0%; atmospheric pressure 70.0~101.4kPa. According to the above principles, the invalid data is removed, and the single NOx emission exceeding heavy diesel vehicle(limit 1500ppm) is screened out in the valid data as the vehicle to be identified.

[0029] S2. For the vehicle to be identified, starting from the time corresponding to the non-compliance detection point, extract one week's worth of raw OBD remote monitoring data forward and backward.

[0030] The remote monitoring raw data includes: license plate number, vehicle identification number (VIN), acquisition time, creation time, vehicle speed, DPF differential pressure, atmospheric pressure, actual torque percentage, engine fuel flow rate, engine coolant temperature, engine speed, intake air volume, reactant balance, cumulative mileage, friction torque, longitude, latitude, SCR (Selective Catalytic Reduction) inlet temperature, SCR outlet temperature, SCR upstream nitrogen oxide concentration, SCR downstream nitrogen oxide concentration, and fuel tank level.

[0031] S3. Extract the data columns corresponding to key parameters from the intercepted remote monitoring raw data. The key parameters include at least time, vehicle speed, NOx concentration upstream of SCR, and NOx concentration downstream of SCR.

[0032] S4. Based on the data column, make the first determination of the vehicle to be identified.

[0033] For example, S4 includes:

[0034] S41. Using the non-conforming detection point as the starting point, based on the data column, push forward m points from the starting point to point A according to the time dimension, and calculate the average vehicle speed of the vehicle to be identified at the m points between point A and the starting point. If the average vehicle speed is greater than 35km / h, then the m points are determined to meet the requirements; otherwise, update point A as the starting point until m points that meet the requirements are obtained.

[0035] S42. For m points that meet the requirements, determine every n consecutive points as a group, perform moving average processing to obtain multiple groups of NOx concentration averages, where n < m;

[0036] For example, if m is 10, the 10 points are labeled as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 in sequence; if n is 2, then 1 and 2 are grouped together, 2 and 3 are grouped together, 3 and 4 are grouped together, ... 8 and 9 are grouped together, and 9 and 10 are grouped together. The average value of each group is calculated, that is, the moving average is processed sequentially.

[0037] S43. If the average NOx concentration of the multiple groups is greater than or equal to the upper limit threshold, then the first determination condition is met; otherwise, the vehicle to be identified is determined to be a non-high emission vehicle.

[0038] Typically, m is 200, and n is 30. The upper threshold value is 1500.

[0039] S5, if the first determination condition is met, it is determined that the highest speed value of the vehicle type corresponding to the to-be-identified vehicle is disclosed on the network.

[0040] S6, determining the vehicle speed interval corresponding to 77% of the highest speed value and 83% of the highest speed value.

[0041] S7, extracting at least 9 working points meeting the vehicle speed interval and the relative torque ≥ 90% from the intercepted remote monitoring raw data as the calculation working points of NOx.

[0042] S8, determining the average concentration of NOx at the calculation working point.

[0043] Specifically, the average concentration of NOx at the calculation working point is determined based on the following formula:

[0044]

[0045] wherein, the average concentration of NOx, the order of magnitude is 10 -6 , is the NOx measurement concentration of the i-th second, the order of magnitude is 10 -6 , that is, the NOx measurement concentration at the i-th calculation working point, k H (i) is the humidity correction coefficient of the i-th second;

[0046]

[0047] wherein, H is the absolute humidity, unit g water / kg dry air; R a is the relative humidity of the environment air (%); P d is the saturated vapor pressure at the environment temperature, unit kPa; P E is the atmospheric pressure, unit kPa.

[0048] S9, determining whether the to-be-identified vehicle is a high-emission vehicle based on the average concentration of NOx.

[0049] Exemplarily, S9 includes:

[0050] S91, if the average concentration of NOx is greater than or equal to 1500 ppm, it is determined that the to-be-identified vehicle is a high-emission vehicle, otherwise it is determined that the to-be-identified vehicle is a non-high-emission vehicle.

[0051] The method for identifying high-emission diesel vehicles based on multi-source monitoring data provided by the embodiment of the present application can improve the determination efficiency and accuracy, effectively avoid the behavior of human beings evading detection, provide data support for formulating the precise management goal of regional motor vehicle pollution prevention and control, and can be used for the accurate supervision and illegal screening of the management department on the diesel vehicles in use.

[0052] Figure 2 A structural schematic diagram of an electronic device is provided in the embodiment of the present application. As shown in the figure, Figure 2 The electronic device 400 includes one or more processors 401 and memories 402.

[0053] The processor 401 can be a central processing unit (CPU) or other forms of processing units with data processing and / or instruction execution capabilities, and can control other components in the electronic device 400 to perform desired functions.

[0054] The memory 402 can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memories and / or non-volatile memories. The volatile memories may, for example, include random access memories (RAMs) and / or cache memories, etc. The non-volatile memories may, for example, include read-only memories (ROMs), hard disks, flash memories, etc. One or more computer program instructions can be stored on the computer readable storage media, and the processor 401 can run the program instructions to implement the method for identifying high-emission diesel vehicles based on multi-source monitoring data of any embodiment of the present application described above and / or other desired functions. Various contents such as initial extrinsic parameters, threshold values, etc. can also be stored in the computer readable storage media.

[0055] In one example, the electronic device 400 can further include input devices 403 and output devices 404, which are interconnected through a bus system and / or other forms of connection mechanisms (not shown). The input devices 403 can include, for example, keyboards, mice, etc. The output devices 404 can output various information to the outside, including pre-warning prompt information, braking force, etc. The output devices 404 can include, for example, displays, speakers, printers, communication networks and remote output devices connected thereto, etc.

[0056] Of course, in order to simplify, Figure 2Only some of the components of the electronic device 400 related to the present application are shown, and components such as a bus, an input / output interface, and the like are omitted. In addition, the electronic device 400 can include any other appropriate components according to a specific application.

[0057] In addition to the above method and device, an embodiment of the present application can also be a computer program product, which includes computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method for identifying high-emission diesel vehicles based on multi-source monitoring data provided by any embodiment of the present application.

[0058] The computer program product can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, and the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0059] In addition, an embodiment of the present application can also be a computer readable storage medium, which stores computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method for identifying high-emission diesel vehicles based on multi-source monitoring data provided by any embodiment of the present application.

[0060] The computer readable storage medium can employ any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium, for example, can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0061] It should be noted that the terms used in the present application are only intended to describe specific embodiments and are not intended to limit the scope of the present application. As shown in the specification of the present application, unless the context clearly indicates otherwise, the words "one", "a", "an", and / or "the" do not specifically refer to the singular, but can also include the plural. The terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method or device including the element.

[0062] It should also be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. Unless otherwise specified and limited, the terms "mounting", "connection", "connection" and the like should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying high-emission diesel vehicles based on multi-source monitoring data, characterized in that, Includes the following steps: S1. Remove invalid data from the exhaust gas remote sensing detection data according to preset requirements, and screen out the vehicles to be identified from the remaining valid data that exceed the NOx emission standard in a single instance. S2. For the vehicle to be identified, starting from the time corresponding to the non-compliance detection point, extract one week's worth of raw OBD remote monitoring data forward and backward; S3. Extract the data columns corresponding to key parameters from the intercepted remote monitoring raw data. The key parameters include at least time, vehicle speed, NOx concentration upstream of the selective catalytic converter (SCR), and NOx concentration downstream of the selective catalytic converter (SCR). S4. Based on the data column, make the first determination of the vehicle to be identified; S5. If the first determination condition is met, then determine the highest speed value of the vehicle model corresponding to the vehicle to be identified that is publicly available online. S6. Determine the speed ranges corresponding to 77% and 83% of the maximum speed value; S7. Extract at least 9 operating points from the intercepted remote monitoring raw data that meet the vehicle speed range and have a relative torque greater than or equal to 90% as NOx calculation operating points. S8. Determine the average NOx emission concentration at the calculated operating point; S9. Determine whether the vehicle to be identified is a high-emission vehicle based on the average NOx emission concentration; S4 includes: S41. Using the non-conforming detection point as the starting point, based on the data column, push forward m points from the starting point to point A according to the time dimension, and calculate the average vehicle speed of the vehicle to be identified at the m points between point A and the starting point. If the average vehicle speed is greater than 35km / h, then the m points are determined to meet the requirements; otherwise, update point A as the starting point until m points that meet the requirements are obtained. S42. For m points that meet the requirements, determine every n consecutive points as a group, perform moving average processing to obtain multiple groups of NOx concentration averages, where n < m; S43. If the average NOx concentration of the multiple groups is greater than or equal to the upper limit threshold, then the first determination condition is met; otherwise, the vehicle to be identified is determined to be a non-high emission vehicle.

2. The method for identifying high-emission diesel vehicles based on multi-source monitoring data according to claim 1, characterized in that, S8 includes: The average NOx emission concentration at the calculated operating point is determined based on the following formula: ； in, The average concentration of NOx emissions. The NOx concentration measured at the i-th second is the NOx concentration measured at the i-th calculation point. Let be the humidity correction factor for the i-th second; = in, This refers to absolute humidity. The relative humidity of the ambient air; It is the saturated vapor pressure at ambient temperature; Atmospheric pressure.

3. The method for identifying high-emission diesel vehicles based on multi-source monitoring data according to claim 1, characterized in that, S9 includes: S91. If the average NOx emission concentration is greater than or equal to 1500 ppm, the vehicle to be identified is determined to be a high-emission vehicle; otherwise, the vehicle to be identified is determined to be a non-high-emission vehicle.

4. The method for identifying high-emission diesel vehicles based on multi-source monitoring data according to claim 1, characterized in that, The exhaust gas remote sensing data includes: monitoring time, location latitude and longitude, lane slope, judgment result, license plate number, fuel type, CO2, CO, HC, NO, Ringelmann smoke level, vehicle speed, wind speed, ambient temperature, humidity, and atmospheric pressure.

5. The method for identifying high-emission diesel vehicles based on multi-source monitoring data according to claim 1, characterized in that, The remote monitoring raw data includes: license plate number, vehicle identification number (VIN), acquisition time, creation time, vehicle speed, DPF differential pressure, atmospheric pressure, actual torque percentage, engine fuel flow rate, engine coolant temperature, engine speed, intake air volume, reactant balance, cumulative mileage, friction torque, longitude, latitude, SCR inlet temperature, SCR outlet temperature, nitrogen oxide concentration upstream of the selective catalytic converter (SCR), nitrogen oxide concentration downstream of the selective catalytic converter (SCR), and fuel tank level.

6. The method for identifying high-emission diesel vehicles based on multi-source monitoring data according to claim 1, characterized in that, The value of m is 200, and the value of n is 30.

7. An electronic device, characterized in that, The electronic device includes: Processor and memory; The processor executes the steps of the high-emission diesel vehicle identification method based on multi-source monitoring data as described in any one of claims 1 to 6 by calling the program or instructions stored in the memory.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that cause a computer to perform the steps of the method for identifying high-emission diesel vehicles based on multi-source monitoring data as described in any one of claims 1 to 6.

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