Heavy-duty car over-emission detection method and device based on remote monitoring

Abstract

The invention relates to the technical field of emission detection, in particular to a heavy-duty car over-emission detection method and device based on remote monitoring, and the method comprises the steps: obtaining a mileage fragment set of remote monitoring data of a heavy-duty car; extracting characteristic parameters of each mileage segment in the mileage segment set, and determining a high emission interval of the heavy-duty car according to the characteristic parameters, so as to obtain key emission parameters and frequency distribution of the heavy-duty car based on the high emission interval; and on the basis of the key emission parameters and the frequency distribution, determining an online identification result and an emission field detection working condition of the heavy-duty car so as to obtain an actual over-emission result of the heavy-duty car. Therefore, the problems that the influence of vehicle running working conditions such as vehicle speed and torque on emission of the heavy-duty vehicle is not considered in a method in the related technology, the online recognition accuracy is low, the field detection working condition specification definition is not high, the field detection success rate based on online recognition of violation clues is reduced, and law enforcement resources are wasted to a certain extent are solved.

Description

Technical Field

[0001] The present application relates to the field of emission detection technology, and in particular to a method and device for detecting excessive emission of a heavy-duty vehicle based on remote monitoring. Background Art

[0002] The problem of excessive nitrogen oxide emissions from heavy-duty vehicles (over-emission heavy-duty vehicles) will cause serious harm to the atmospheric environment and human health. Obtaining clues of violations of heavy-duty vehicles with over-emissions and conducting on-site inspections and law enforcement penalties are currently important means to limit excessive nitrogen oxide emissions from heavy-duty vehicles. With the development of remote monitoring technology for heavy-duty vehicles, online and remote identification of over-emission heavy-duty vehicles and providing clues of violations are of great significance for improving the law enforcement efficiency of regulatory authorities and effectively combating the problem of excessive nitrogen oxide emissions from heavy-duty vehicles.

[0003] The online and remote identification methods of heavy-duty vehicles with excessive emissions in related technologies are mainly based on the monitoring methods in local standards (DB11 / 1476-2017 Rapid Detection Method and Emission Limits of Nitrogen Oxides for Heavy-Duty Vehicles), which randomly obtain remote monitoring data in a 2000-meter window and calculate the nitrogen oxides (NO x ) and compare it with the regulatory limit to determine whether the vehicle exceeds the limit and provide regulatory authorities with clues to violations.

[0004] However, in the process of online and remote identification of over-emission heavy-duty vehicles, the methods in the relevant technologies did not take into account the impact of heavy-duty vehicle emissions on vehicle operating conditions such as vehicle speed and torque, resulting in frequent capture of heavy-duty vehicles that were in severe operating conditions with excessive emissions for a short period of time but were in normal driving conditions without exceeding the standards for a long time in a randomly acquired 2000-meter window, and misjudged them as over-emission heavy-duty vehicles, ultimately resulting in reduced online identification accuracy; on the other hand, when conducting on-site inspections of target over-emission heavy-duty vehicles based on violation clues obtained through online identification, they are usually randomly driven for 2000 meters and the emissions are compared with the regulatory limits. There is a problem of unclear on-site inspection conditions, which often results in heavy-duty vehicles that are in normal driving conditions with excessive emissions for a long time being driven under milder conditions during inspection, resulting in reduced vehicle emissions and test results that do not exceed the standards, thereby reducing the success rate of on-site inspections based on online identification of violation clues and wasting the law enforcement resources of regulatory authorities to a certain extent, which needs to be urgently addressed. Summary of the invention

[0005] The present application provides a method and device for detecting excessive emission of heavy-duty vehicles based on remote monitoring, in order to solve the problems that the methods in the related technologies do not take into account the influence of heavy-duty vehicle emission on vehicle operating conditions such as vehicle speed and torque, the misjudgment rate is high, the online recognition accuracy is low, and the on-site detection condition specifications are not clear, which reduces the success rate of on-site detection based on online identification of violation clues and wastes the law enforcement resources of regulatory authorities to a certain extent.

[0006] The first aspect of the present application provides a method for detecting over-emission of heavy-duty vehicles based on remote monitoring, comprising the following steps: obtaining a mileage segment set of remote monitoring data of a heavy-duty vehicle; extracting characteristic parameters of each mileage segment in the mileage segment set, and determining a high emission interval of the heavy-duty vehicle based on the characteristic parameters, so as to obtain key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval; determining an online recognition result and an emission on-site detection condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution, so as to obtain an actual over-emission result of the heavy-duty vehicle based on the online recognition result and the emission on-site detection condition.

[0007] Through the above technical means, the embodiment of the present application can obtain the actual over-emission results of heavy-duty vehicles based on the remote monitoring data of heavy-duty vehicles. Through data processing such as data cleaning and segmentation, as well as comprehensive consideration of online identification results and emission on-site detection conditions, the extraction efficiency of key emission parameters and their frequency distribution results and the analysis efficiency in other utilization processes are improved, effectively ensuring the reliability of the data, thereby improving the accuracy of the obtained heavy-duty vehicle over-emission results, thereby making full use of remote monitoring data to accurately and efficiently identify high-probability NO online, remotely, and efficiently. x Heavy-duty vehicles that exceed emission standards can also be inspected on-site in combination with remote monitoring data, which improves the regulatory authorities' remote supervision capabilities and on-site inspection efficiency for heavy-duty vehicle emissions.

[0008] Optionally, in one embodiment of the present application, the mileage segment set of remote monitoring data of a heavy-duty vehicle is obtained, comprising: obtaining the remote monitoring data of the heavy-duty vehicle for data cleaning to obtain valid data that meets preset conditions; cutting the valid data into multiple mileage segments to generate the mileage segment set based on the multiple mileage segments.

[0009] Through the above-mentioned technical means, the embodiment of the present application can clean and cut the remote monitoring data. The data quality of the remote monitoring data is effectively improved through data cleaning. Data cutting helps to analyze the data and can more clearly display the driving characteristics and emission changes of heavy-duty vehicles, thereby determining whether the heavy-duty vehicles have excessive emission behavior.

[0010] Optionally, in one embodiment of the present application, extracting characteristic parameters of each mileage segment in the mileage segment set, determining the high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtaining the key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval, includes: calculating at least one of the vehicle speed, acceleration, torque, speed, fuel consumption and NO corresponding to the multiple mileage segments. x The average value of the emission rate is used to determine the characteristic parameter; according to the NO xThe high emission interval is determined by the interval corresponding to the mileage segment in which the emission rate is greater than the preset high emission threshold; the vehicle speed, acceleration, torque, speed, fuel consumption and NO are calculated within the high emission interval. x The absolute value of the correlation coefficient of the emission rate is used to determine the key emission parameters and their frequency distribution in combination with the preset emission-related threshold.

[0011] Through the above-mentioned technical means, the embodiments of the present application can determine the high emission range of heavy-duty vehicles and obtain the key emission parameters of heavy-duty vehicles and their frequency distribution, so that the possible over-emission behavior of heavy-duty vehicles can be diagnosed by monitoring the abnormal changes of key emission parameters. By statistically analyzing the frequency distribution of each emission parameter, the emission stability of heavy-duty vehicles under different operating conditions can be evaluated, and the potential over-emission trend of heavy-duty vehicle emissions can be identified.

[0012] Optionally, in one embodiment of the present application, determining the online identification result and emission on-site detection condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution includes: obtaining the main distribution interval of the key emission parameters based on the key emission parameters and the frequency distribution; determining the emission on-site detection condition of the heavy-duty vehicle based on the main distribution interval and the frequency threshold.

[0013] Through the above-mentioned technical means, the embodiments of the present application can determine the main distribution range of key emission parameters and determine the online on-site detection and over-emission results of the inspected heavy-duty vehicles based on this. The main distribution range of key emission parameters is used to determine whether the heavy-duty vehicle is in an effective detection state during online on-site detection, thereby improving the reliability and accuracy of the online on-site detection and over-emission results of the heavy-duty vehicle.

[0014] Optionally, in one embodiment of the present application, determining the online identification result and emission on-site detection condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution also includes: judging whether the heavy-duty vehicle meets the on-site detection requirements; and obtaining the emission on-site detection condition of the heavy-duty vehicle if the heavy-duty vehicle meets the on-site detection requirements.

[0015] Through the above-mentioned technical means, the embodiment of the present application can first determine whether the heavy-duty vehicle meets the on-site inspection requirements based on the online on-site inspection conditions of the heavy-duty vehicle, which greatly improves the effectiveness and reliability of the on-site inspection condition results of the heavy-duty vehicle, thereby effectively ensuring the authenticity, effectiveness and reliability of the actual over-emission results of the heavy-duty vehicle finally obtained.

[0016] The second aspect of the present application provides a heavy-duty vehicle over-emission detection device based on remote monitoring, including: an acquisition module, used to obtain a mileage segment set of remote monitoring data of a heavy-duty vehicle; a determination module, used to extract characteristic parameters of each mileage segment in the mileage segment set, and determine the high emission interval of the heavy-duty vehicle according to the characteristic parameters, so as to obtain the key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval; a detection module, used to determine the online identification result and the emission on-site detection condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution, so as to obtain the actual over-emission result of the heavy-duty vehicle according to the online identification result and the emission on-site detection condition.

[0017] Through the above technical means, the embodiment of the present application can obtain the actual over-emission results of heavy-duty vehicles based on the remote monitoring data of heavy-duty vehicles. Through data processing such as data cleaning and segmentation, the extraction efficiency of key emission parameters and their frequency distribution results and the analysis efficiency in other utilization processes are improved, which effectively guarantees the reliability of the data and further improves the accuracy of the obtained over-emission results of heavy-duty vehicles, thereby making full use of remote monitoring data to accurately and efficiently identify high-probability NOx online, remotely, and efficiently. x Heavy-duty vehicles that exceed emission standards can also be inspected on-site in combination with remote monitoring data, which improves the regulatory authorities' remote supervision capabilities and on-site inspection efficiency for heavy-duty vehicle emissions.

[0018] Optionally, in one embodiment of the present application, the acquisition module includes: a first acquisition unit, used to acquire the remote monitoring data of the heavy-duty vehicle for data cleaning to obtain valid data that meets preset conditions; a cutting unit, used to cut the valid data into multiple mileage segments to generate the mileage segment set based on the multiple mileage segments.

[0019] Through the above-mentioned technical means, the embodiment of the present application can clean and cut the remote monitoring data. The data quality of the remote monitoring data is effectively improved through data cleaning. Data cutting helps to analyze the data and can more clearly display the driving characteristics and emission changes of heavy-duty vehicles, thereby determining whether the heavy-duty vehicles have excessive emission behavior.

[0020] Optionally, in one embodiment of the present application, the determination module includes: a first calculation unit for calculating at least one of the vehicle speed, acceleration, torque, rotation speed, fuel consumption and NO corresponding to the plurality of mileage segments. x The average value of the emission rate is used to determine the characteristic parameter; a comparison unit is used to determine the characteristic parameter according to the NO xThe high emission interval is determined by the interval corresponding to the mileage segment in which the emission rate is greater than the preset high emission threshold; the second calculation unit is used to calculate the vehicle speed, acceleration, torque, speed, fuel consumption and NO in the high emission interval. x The absolute value of the correlation coefficient of the emission rate is used to determine the key emission parameters and their frequency distribution in combination with the preset emission-related threshold.

[0021] Through the above-mentioned technical means, the embodiments of the present application can determine the high emission range of heavy-duty vehicles and obtain the key emission parameters of heavy-duty vehicles and their frequency distribution, so that the possible over-emission behavior of heavy-duty vehicles can be diagnosed by monitoring the abnormal changes of key emission parameters. By statistically analyzing the frequency distribution of each emission parameter, the emission stability of heavy-duty vehicles under different operating conditions can be evaluated, and the potential over-emission trend of heavy-duty vehicle emissions can be identified.

[0022] Optionally, in one embodiment of the present application, the detection module includes: a second acquisition unit, used to obtain the main distribution interval of the key emission parameter based on the key emission parameter and the frequency distribution; a determination unit, used to determine the emission on-site detection condition of the heavy-duty vehicle based on the main distribution interval and the frequency threshold.

[0023] Through the above-mentioned technical means, the embodiments of the present application can determine the main distribution range of key emission parameters and determine the emission on-site detection working conditions of the inspected heavy-duty vehicle based on this. The main distribution range of key emission parameters is used to determine whether the heavy-duty vehicle is in an effective detection condition during online on-site detection, thereby improving the reliability and accuracy of the emission on-site detection working conditions of the heavy-duty vehicle.

[0024] Optionally, in one embodiment of the present application, the detection module further includes: a judgment unit, used to judge whether the heavy-duty vehicle meets the on-site detection requirements; and a third acquisition unit, used to obtain the emission on-site detection working conditions of the heavy-duty vehicle when the heavy-duty vehicle meets the on-site detection requirements.

[0025] Through the above-mentioned technical means, the present application can first determine whether the heavy-duty vehicle meets the on-site inspection requirements based on the heavy-duty vehicle's online on-site inspection conditions, greatly improving the effectiveness and reliability of the on-site inspection condition results of the heavy-duty vehicle, thereby effectively ensuring the authenticity, effectiveness and reliability of the actual over-emission results of the heavy-duty vehicle finally obtained.

[0026] The third aspect of the present application provides a vehicle, 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 implement the heavy-duty vehicle over-emission detection method based on remote monitoring as described in the above embodiment.

[0027] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned heavy-duty vehicle over-displacement detection method based on remote monitoring.

[0028] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned heavy-duty vehicle over-emission detection method based on remote monitoring.

[0029] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 A flowchart of a heavy-duty vehicle over-emission detection method based on remote monitoring according to an embodiment of the present application;

[0032] Figure 2 For one embodiment of this application, x Schematic diagram of high emission interval selection;

[0033] Figure 3 The torque P of one embodiment of the present application is Tq Schematic diagram of the frequency distribution of

[0034] Figure 4 The fuel consumption P of an embodiment of this application is Fuel Schematic diagram of the frequency distribution of

[0035] Figure 5 This is a flow chart of a method for online identification and on-site detection of over-displacement heavy-duty vehicles based on telematic data according to an embodiment of the present application;

[0036] Figure 6 A high probability over-emission heavy-duty vehicle NO in a certain effective judgment segment of an embodiment of the present application x Schematic diagram of the distribution results of emission rate;

[0037] Figure 7 This is a schematic diagram of the analysis results of the emission situation of a heavy-duty vehicle with a high probability of over-emission in an effective determination segment according to an embodiment of the present application;

[0038] Figure 8 It is a structural schematic diagram of a heavy-duty vehicle over-displacement detection device based on remote monitoring according to an embodiment of the present application;

[0039] Fig. 9It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application.

[0040] Reference numerals:

[0041] 10-Heavy-duty vehicle over-displacement detection device based on remote monitoring: 100-acquisition module, 200-determination module and 300-detection module; 901-memory, 902-processor and 903-communication interface. DETAILED DESCRIPTION

[0042] Embodiments of the present application are described in detail below, and examples of the embodiments 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 application, and should not be construed as limiting the present application.

[0043] The following describes the heavy-duty vehicle over-emission detection method and device based on remote monitoring of the embodiment of the present application with reference to the accompanying drawings. The method in the related art mentioned in the above background technology does not take into account the influence of heavy-duty vehicle emissions on vehicle operating conditions such as vehicle speed and torque, the misjudgment rate is high, the online recognition accuracy is low, and the on-site detection condition specification is not clear, which reduces the on-site detection success rate based on online identification of violation clues, and wastes the law enforcement resources of the regulatory department to a certain extent. The present application provides a heavy-duty vehicle over-emission detection method based on remote monitoring, in which the remote monitoring data of the heavy-duty vehicle can be obtained and processed such as cleaning, segmentation, and feature parameter extraction, so as to obtain the key emission parameters and frequency distribution of the heavy-duty vehicle and the actual over-emission results of the heavy-duty vehicle from the on-site detection results. Through data processing such as data cleaning and segmentation, the extraction efficiency of key emission parameters and their frequency distribution results is improved, and at the same time, the analysis efficiency of the data in other utilization processes is improved, the reliability of the data is effectively guaranteed, and then the accuracy of the obtained heavy-duty vehicle over-emission results is improved, thereby realizing the full use of remote monitoring data to identify high-probability NO online, remotely, accurately and efficiently. x Heavy-duty vehicles with excessive emissions can also be tested on-site in combination with remote monitoring data, which improves the regulatory authorities' remote monitoring capabilities and on-site testing efficiency for heavy-duty vehicle emissions. This solves the problem that the methods in the relevant technologies do not consider the impact of heavy-duty vehicle emissions on vehicle operating conditions such as vehicle speed and torque, have a high misjudgment rate, low online recognition accuracy, and unclear on-site testing conditions, which reduces the success rate of on-site testing based on online identification of violation clues and wastes the law enforcement resources of regulatory authorities to a certain extent.

[0044] Specifically, Figure 1 A flowchart of a heavy-duty vehicle over-emission detection method based on remote monitoring provided in an embodiment of the present application.

[0045] like Figure 1 As shown, the heavy-duty vehicle over-displacement detection method based on remote monitoring includes the following steps:

[0046] In step S101 , a mileage segment set of remote monitoring data of a heavy-duty vehicle is obtained.

[0047] It is understood that the remote monitoring data of heavy-duty vehicles here refers to real-time data on the operating status, location information, etc. of heavy-duty vehicles collected and transmitted by various sensors, controllers or communication modules installed on heavy-duty vehicles. For example, the operating status data of heavy-duty vehicles and the driving status data of heavy-duty vehicles.

[0048] In certain embodiments, some heavy-duty vehicles may cause serious pollution to the environment by exceeding emission standards during driving, so it is necessary to detect the emission of heavy-duty vehicles. Remote monitoring data is real-time and continuous, which has great advantages for monitoring the emission of heavy-duty vehicles during driving. Therefore, this application can use the remote monitoring data of heavy-duty vehicles to determine the excessive emission results of heavy-duty vehicles. In order to facilitate the effective and efficient use of remote monitoring data during the driving of heavy-duty vehicles, the embodiments of this application can be, but are not limited to, obtaining a mileage segment set formed by remote monitoring data of heavy-duty vehicles, and performing subsequent calculations and processing on the data in the mileage data set.

[0049] For example, the present application can obtain the required remote monitoring data through the data transmitted to the remote server or cloud platform by the vehicle monitoring system or vehicle communication module (such as T-BOX) of the heavy-duty vehicle. The remote monitoring data of the heavy-duty vehicle includes but is not limited to time, speed, acceleration, torque, speed, fuel consumption, NO x Parameters such as emission rate.

[0050] The embodiment of the present application can obtain a mileage segment set of remote monitoring data of heavy-duty vehicles, which is convenient for real-time viewing, and can timely discover the emission situation of heavy-duty vehicles by analyzing these remote monitoring data, and make timely response when heavy-duty vehicles exceed emission limits.

[0051] Optionally, in one embodiment of the present application, obtaining a mileage segment set of remote monitoring data of a heavy-duty vehicle includes: obtaining the remote monitoring data of the heavy-duty vehicle for data cleaning to obtain valid data that meets preset conditions; cutting the valid data into multiple mileage segments to generate a mileage segment set based on the multiple mileage segments.

[0052] It is understood that the preset conditions here refer to certain standards or criteria that the data must meet before using the data, for example, the time of the data is within the valid acquisition period, and the range of the data is within the valid range. The specific preset conditions can be set or adjusted by professionals in this field according to actual conditions or actual needs, and are only illustrative here without specific limitation.

[0053] During the actual implementation process, when the embodiment of the present application obtains the mileage segment set of the remote monitoring data of the heavy-duty vehicle, it is necessary to first perform data cleaning and segment cutting on the acquired remote monitoring data to obtain valid data that meets certain conditions, and then perform segmentation processing on these valid data to obtain mileage segments, and finally form the mileage segment set of the heavy-duty vehicle from these mileage segments.

[0054] For example, this application can obtain the remote monitoring data of 11 heavy-duty vehicles in any working condition for nearly one month, and perform data cleaning and segmentation to form a heavy-duty vehicle mileage segment set. Among them, the cumulative mileage of heavy-duty vehicles is recommended to exceed 10,000 kilometers.

[0055] The remote monitoring data of heavy-duty vehicles also includes but is not limited to the driving time, speed, acceleration, torque, speed, fuel consumption, NO x Parameters such as emission rate, among which the remote monitoring data sampling frequency can be but is not limited to 1 Hz.

[0056] Then, the remote monitoring data is cleaned, mainly to remove NO x For data with emission rate outside the range of [0,3000] ppm / s and torque outside the range of [0%,100%], NO is retained. x The emission rate is in the range of [0,3000] ppm / s and the torque is in the range of [0%,100%], and effective data that can be used is obtained.

[0057] Finally, the valid data are segmented. The segmentation can be but not limited to two steps: (1) according to the time continuity, the valid data are segmented into several continuous heavy-duty vehicle operation segments; (2) the heavy-duty vehicle operation segments are segmented and obtained by using the 30-second moving window average method, and finally the heavy-duty vehicle mileage segment set is formed by these several 30-second mileage segments.

[0058] The embodiment of the present application can clean and cut the remote monitoring data. The data quality of the remote monitoring data is effectively improved through data cleaning. Data cutting helps to analyze the data and can more clearly display the driving characteristics and emission changes of heavy-duty vehicles, thereby determining whether the heavy-duty vehicles have excessive emission behavior.

[0059] Step S102, extracting characteristic parameters of each mileage segment in the mileage segment set, determining the high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtaining key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval.

[0060] In some embodiments, there are many factors that affect the emissions of heavy-duty vehicles in the mileage segments of heavy-duty vehicles. In order to obtain more accurate emission results of heavy-duty vehicles, key emission parameters of heavy-duty vehicles can be obtained to make decisions. In the process of obtaining key emission parameters, it is necessary to first extract various characteristic parameters of the mileage segments of heavy-duty vehicles, then determine the high emission interval of heavy-duty vehicles, and finally determine the key emission parameters of heavy-duty vehicles and their frequency distribution within this high emission interval.

[0061] For example, in the mileage segments of heavy-duty vehicles, some of them have serious emissions, while others have slight emissions. In order to determine whether a heavy-duty vehicle has excessive emissions, the high emission interval of the heavy-duty vehicle, i.e., the mileage segments with higher emissions, can be determined based on these characteristic parameters of the heavy-duty vehicle, but is not limited to, and then judged and processed. Among them, the emission parameters of heavy-duty vehicles include but are not limited to vehicle speed, torque, NO x Parameters related to emissions from heavy-duty vehicles, such as emission rate.

[0062] If the high emission range is determined, then the NO x The emission rate has the greatest impact on the emissions of heavy-duty vehicles, is most closely related to the emissions of heavy-duty vehicles, and causes the most serious pollution to the environment. x Here, the emission rate can be understood as the key emission parameter of heavy-duty vehicles. Once the key emission parameter is determined, its frequency distribution can be obtained from the mileage segment.

[0063] The embodiment of the present application can extract characteristic parameters of heavy-duty vehicle mileage segments based on the mileage segment set of heavy-duty vehicles to determine the high emission interval of heavy-duty vehicles, and then obtain the key emission parameters of heavy-duty vehicles and their frequency distribution. By real-time monitoring and analysis of the key emission parameters of heavy-duty vehicles and their frequency distribution, it is helpful to timely discover and deal with potential emission problems of heavy-duty vehicles.

[0064] Optionally, in one embodiment of the present application, characteristic parameters of each mileage segment in the mileage segment set are extracted, and a high emission interval of the heavy-duty vehicle is determined according to the characteristic parameters, so as to obtain key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval, including: calculating at least one of the vehicle speed, acceleration, torque, speed, fuel consumption and NO corresponding to the multiple mileage segments; x The average value of the emission rate is used to determine the characteristic parameters; according to NO xThe high emission interval is determined by the interval corresponding to the mileage segment where the emission rate is greater than the preset high emission threshold; the vehicle speed, acceleration, torque, speed, fuel consumption and NO are calculated within the high emission interval. x The absolute value of the correlation coefficient of the emission rate is used to determine the key emission parameters in combination with the preset emission-related thresholds.

[0065] In the actual implementation process, when extracting the characteristic parameters of the heavy-duty vehicle mileage segment based on the mileage segment set, the present application can, but is not limited to, first calculate the speed, acceleration, torque, speed, fuel consumption and NO of each mileage segment in the heavy-duty vehicle mileage segment set. x The average value of emission rate and other parameters are used as characteristic parameters of heavy-duty vehicle mileage segments.

[0066] Then, the embodiment of the present application may, but is not limited to, set the NO x The heavy-duty vehicle mileage segment corresponding to the emission rate is divided into a high emission range for heavy-duty vehicles. The preset high emission threshold here can be understood as a pre-set NO x Emission rate thresholds, e.g. NO x The emission rate is 80%, etc.; in a certain mileage segment, NO x When the emission rate exceeds this threshold, the mileage segment can be regarded as a high-emission interval. Figure 2 For one embodiment of this application, x Schematic diagram of high emission interval selection. Figure 2 As shown, the high emission threshold is the NO x Taking the 80% quantile of emission rate as an example, Figure 2 Shows all heavy vehicle mileage clips NO x Emission rate results, and based on NO x The 80% quantile of emission rate is divided into the high emission range of heavy-duty vehicles.

[0067] Then, in the high emission range, the correlation analysis method is used to calculate the relationship between vehicle speed, acceleration, torque, speed, fuel consumption and NO x The correlation coefficient of the emission rate. The correlation coefficient can be understood here as a measure of the x Characteristic parameters other than emission rate and NO x The coefficient of the correlation of the emission rate is, for example, 0.5, etc.

[0068] Finally, the parameter with the absolute value of the correlation coefficient higher than the preset emission-related threshold is selected as the key emission parameter. The preset emission-related threshold can be understood as the threshold of the pre-set correlation coefficient. xWhen the correlation coefficient of the emission rate is higher than the threshold, the characteristic parameter can be determined as a key emission parameter. Specifically, after the key emission parameter is determined, the frequency distribution of the key emission parameter can be obtained accordingly.

[0069] It should be noted that the specific characteristic parameters, preset high emission thresholds, and preset emission-related thresholds can be set or adjusted by professional and technical personnel in this field according to actual conditions or actual needs. They are only used for illustrative purposes here without specific limitations.

[0070] For example, Table 1 shows the characteristic parameters and NO of an embodiment of the present application. x The calculation result table of the correlation of emission rate can be expressed as follows:

[0071] Table 1

[0072]

[0073] As shown in Table 1, the characteristic parameters and NO x The correlation calculation results of the emission rate are selected, and the parameters with the absolute value of the correlation coefficient greater than a certain emission-related threshold of 0.5 are selected as the key emission parameters, that is, the torque P Tq and fuel consumption Fuel . Figure 3 The torque P of one embodiment of the present application is Tq Schematic diagram of the frequency distribution of Figure 4 The fuel consumption P of an embodiment of this application is Fuel Schematic diagram of the frequency distribution of Figure 3 and Figure 4 As shown, the torque P in one embodiment of the present application is shown. Tq Frequency distribution and fuel consumption P Fuel By monitoring the frequency distribution of emission parameters, the potential over-emission trend of heavy-duty vehicles can be identified. For example, if the frequency distribution of a parameter gradually moves to the right (i.e. the value increases), it may indicate that heavy-duty vehicles are about to exceed emissions.

[0074] The embodiments of the present application can determine the high emission range of heavy-duty vehicles and obtain the key emission parameters of heavy-duty vehicles and their frequency distribution, so that the possible over-emission behavior of heavy-duty vehicles can be diagnosed by monitoring the abnormal changes of key emission parameters. By statistically analyzing the frequency distribution of each emission parameter, the emission stability of heavy-duty vehicles under different operating conditions can be evaluated, and the potential over-emission trend of heavy-duty vehicle emissions can be identified.

[0075] Step S103, based on the key emission parameters and frequency distribution, determine the online identification result and emission on-site detection working condition of the heavy-duty vehicle, so as to obtain the actual over-emission result of the heavy-duty vehicle according to the online identification result and emission on-site detection working condition.

[0076] As a possible implementation method, after obtaining the key emission parameters of heavy-duty vehicles and their frequency distribution, the embodiment of the present application can determine the online identification result of the heavy-duty vehicle based on the key emission parameters of the heavy-duty vehicle and its frequency distribution, that is, determine online whether the heavy-duty vehicle has over-emission through the key emission parameters and its frequency distribution.

[0077] Furthermore, considering that the online identification results of heavy-duty vehicles may have certain errors in some extreme cases due to the driving reasons of the heavy-duty vehicles themselves or other external reasons, in order to ensure the accuracy of the actual over-emission results of the detected heavy-duty vehicles, the embodiments of the present application can also be combined with the on-site emission detection conditions of the heavy-duty vehicles, thereby ultimately determining the actual over-emission results of the heavy-duty vehicles.

[0078] For example, the key emission parameter of heavy-duty vehicles is fuel consumption. The frequency distribution of fuel consumption is 60% maintained at a high level, 20% distributed at a stable level, and 20% distributed at a low level, that is, fuel consumption is mostly maintained at a high level, from which NO x If the emission rate is high, then the online identification result of the heavy-duty vehicle can be judged as the heavy-duty vehicle having excessive emission behavior. Combined with the on-site emission detection of the heavy-duty vehicle, which also shows excessive emission behavior, the actual excessive emission result of the heavy-duty vehicle is that the heavy-duty vehicle is excessively discharged.

[0079] If the online identification result of a heavy-duty vehicle shows that there is excessive emission, but the on-site emission detection condition shows that there is no excessive emission, the actual excessive emission result of the heavy-duty vehicle can be determined by the relevant regulatory department or technical personnel based on the actual situation.

[0080] The embodiment of the present application can make full use of the advantages of remote monitoring data to accurately and efficiently identify high-probability NO x It can also effectively improve the accuracy of the actual emission exceeding the standard results by combining the emission on-site testing conditions, which will help improve the remote supervision capability and efficiency of the regulatory authorities on heavy-duty vehicle emissions.

[0081] Optionally, in one embodiment of the present application, the emission on-site detection operating conditions of heavy-duty vehicles are determined based on key emission parameters and frequency distribution, including: obtaining the main distribution interval of the key emission parameters based on the key emission parameters and frequency distribution; determining the emission on-site detection operating conditions of heavy-duty vehicles based on the main distribution interval and frequency threshold.

[0082] In other embodiments, when the key emission parameters and their frequency distribution are obtained to determine the emission on-site detection working conditions of the heavy-duty vehicle, the emission on-site detection working conditions of the heavy-duty vehicle are determined mainly based on the main distribution interval of the key emission parameters, and then the frequency distribution of the key emission parameters and the size of the frequency threshold are determined within the main distribution interval. The main distribution interval here can be understood as the interval in which the frequency distribution of the key emission parameters in this mileage segment accounts for a proportion higher than a certain frequency threshold.

[0083] For example, the present application can obtain the main distribution interval of the key emission parameters based on the frequency distribution of the key emission parameters [p 1 ,p 2 ]. The frequency threshold can be, but is not limited to, set to 30%, indicating that in a mileage segment of 2000 m, the key emission parameters remain in the interval [p 1 ,p 2 ] must account for at least 30% of the time. Figure 3 and Figure 4 As shown, the torque P can be obtained respectively Tq and fuel consumption Fuel The main distribution ranges of these two key emission parameters are Figure 3 The torque P can be obtained Tq The main distribution range is [48%, 58%]; Figure 4 You can get fuel consumption P Fuel The main distribution range of the key emission parameters is [26,32] L / h. 1 ,p 2 ] and its frequency threshold to determine the emission on-site detection conditions of the inspected heavy-duty vehicle, for example, the heavy-duty vehicle has excessive emission.

[0084] The embodiments of the present application can determine the main distribution range of key emission parameters and determine the emission on-site detection working conditions of the inspected heavy-duty vehicle based on this. The main distribution range of key emission parameters can be used to determine whether the heavy-duty vehicle is in an effective detection condition during online on-site detection, thereby improving the reliability and accuracy of the emission on-site detection working conditions of the heavy-duty vehicle.

[0085] Optionally, in one embodiment of the present application, determining the emission on-site detection operating conditions of a heavy-duty vehicle based on key emission parameters and frequency distribution also includes: judging whether the heavy-duty vehicle meets the on-site detection requirements; and obtaining the emission on-site detection operating conditions of the heavy-duty vehicle if the heavy-duty vehicle meets the on-site detection requirements.

[0086] Based on the relevant descriptions of other embodiments, it can be understood that the present application can comprehensively determine the actual over-emission results of heavy-duty vehicles by combining online identification results and emission on-site detection conditions.

[0087] In some embodiments, after monitoring that some heavy-duty vehicles have the possibility of over-emission, the online identification result is that the heavy-duty vehicle has over-emission behavior. However, the heavy-duty vehicle may deliberately drive at a low speed to reduce emissions during on-site testing. In order to ensure the reliability of the actual over-emission results of the heavy-duty vehicle finally detected, the present application can determine whether the heavy-duty vehicle meets the on-site testing requirements when conducting on-site testing on the heavy-duty vehicle, for example, whether the vehicle speed is normal, and whether there is no deliberate reduction in speed. Only when the heavy-duty vehicle meets the on-site testing requirements can the emission on-site testing conditions of the heavy-duty vehicle be obtained, that is, to ensure that the emission on-site testing conditions of the heavy-duty vehicle obtained are true and valid.

[0088] For example, in the embodiment of the present application, when a heavy-duty vehicle is undergoing on-site testing, it can be, but is not limited to, conducting an online on-site test of the inspected heavy-duty vehicle in accordance with the local standard "DB11 / 1476-2017 Rapid Detection Method and Emission Limits of Nitrogen Oxides for Heavy-Duty Vehicles". The heavy-duty vehicle needs to be driven for at least 2000m, and the key emission parameters are within the range [p 1 ,p 2 ] The time proportion within the detection period should be no less than 30% of the frequency threshold, etc.

[0089] When conducting offline on-site inspections of heavy-duty vehicles, this application can greatly improve the validity and reliability of the obtained emission on-site inspection results of heavy-duty vehicles based on whether the heavy-duty vehicles meet the on-site inspection requirements, thereby effectively ensuring the authenticity, validity and reliability of the actual over-emission results of the heavy-duty vehicles finally obtained.

[0090] The present application is described and verified in detail below with a specific embodiment.

[0091] Figure 5 This is a flow chart of a method for online identification and on-site detection of heavy-duty vehicles with excessive displacement based on telematic data according to an embodiment of the present application. Figure 5 As shown:

[0092] Step S501, obtain heavy-duty vehicle remote monitoring data, and perform data cleaning and segment cutting to form a heavy-duty vehicle mileage segment set.

[0093] Step S502: extract the characteristic parameters of the heavy-duty vehicle mileage segments from the heavy-duty vehicle mileage segment set, determine the high emission interval of the heavy-duty vehicle, and obtain key emission parameters such as torque P Tq and fuel consumption Fuel and its frequency distribution.

[0094] Step S503, determining the on-site emission test conditions of the inspected heavy-duty vehicle based on the key emission parameters and their frequency distribution.

[0095] Step S504, based on the on-site emission test conditions of the inspected heavy-duty vehicles, a remote monitoring system is used to identify heavy-duty vehicles with a high probability of exceeding emission limits.

[0096] Next, the results obtained by this method are verified.

[0097] (1) First, obtain the remote monitoring data of heavy-duty vehicles with high probability of exceeding the emission limit. Then, clean the remote monitoring data of heavy-duty vehicles with high probability of exceeding the emission limit to obtain the valid data of heavy-duty vehicles with high probability of exceeding the emission limit. Finally, cut the valid data into several judgment segments with a mileage length of 2000m. The remote monitoring data contains time, speed, acceleration, torque, speed, fuel consumption, NO x Emission rate and other parameters. The sampling frequency of heavy-duty vehicle remote monitoring data is 1Hz, and the accumulated mileage of data is not less than 80 kilometers.

[0098] (2) In the judgment segment of the heavy-duty vehicle with high probability of over-emission, according to the on-site emission detection working condition of the heavy-duty vehicle with high probability of over-emission, the torque P that satisfies the on-site emission detection working condition of the heavy-duty vehicle under inspection is obtained. Tq and fuel consumption Fuel The decision segments that meet the time proportion requirement are taken as valid decision segments. Finally, 8 valid decision segments are obtained from all 31 time-continuous 2000m decision segments.

[0099] (3) In the eight valid judgment segments of heavy-duty vehicles with high probability of exceeding emission limits, the 30-s moving window average method is used to calculate the NO in each valid judgment segment. x The emission rate average value is used to obtain the over-emission probability Pe of a heavy-duty vehicle with a high probability of over-emission. Here, the calculation method of the over-emission probability Pe of a heavy-duty vehicle with a high probability of over-emission can be expressed as follows:

[0100]

[0101] Among them, Nexcessive means NO x The number of valid judgment segments with an average emission rate higher than 550ppm / s. Nall represents the total number of valid judgment segments for heavy-duty vehicles with a high probability of exceeding the emission limit.

[0102] Figure 6 This is a schematic diagram of the distribution results of the NOx emission rate of a heavy-duty vehicle with a high probability of exceeding the emission limit in a certain effective judgment segment of an embodiment of the present application. Figure 7 This is a schematic diagram of the analysis results of the effective determination of the emission situation of a high probability over-emission heavy-duty vehicle in one embodiment of the present application. Figure 6 As shown, it shows all the window NO in a valid judgment segment of the heavy-duty vehicle to be judged. x The results show that the effective judgment fragment contains NO xThe emission rate exceeds the window of 550ppm / s, so it can be considered that the NO x The average emission rate is higher than 550ppm / s. Figure 7 The results of the analysis of the emission of all valid judgment segments of heavy-duty vehicles with high probability of over-emission are shown in the figure. The results show that all 8 valid judgment segments have over-emission phenomenon, that is, there are NO x The emission rate exceeds the window of 550ppm / s. Therefore, the over-emission probability Pe of a heavy-duty vehicle with a high probability of over-emission calculated according to equation (1) is equal to 100%, which effectively shows the accuracy of the over-emission result of a heavy-duty vehicle obtained by the over-emission detection method of the heavy-duty vehicle in the embodiment of the present application.

[0103] According to the heavy-duty vehicle over-emission detection method based on remote monitoring proposed in the embodiment of the present application, the remote monitoring data of the heavy-duty vehicle can be obtained and processed such as cleaning, segmentation, and extraction of characteristic parameters, so as to obtain the key emission parameters and frequency distribution of the heavy-duty vehicle and the actual over-emission results of the heavy-duty vehicle from the on-site detection results. Through data processing such as data cleaning and segmentation, combined with online identification results and emission on-site detection conditions, the extraction efficiency and accuracy of key emission parameters and their frequency distribution results are greatly improved, while the analysis efficiency of the data in other utilization processes is improved, the reliability of the data is effectively guaranteed, and the accuracy of the obtained heavy-duty vehicle over-emission results is improved, thereby making full use of remote monitoring data to accurately and efficiently identify high-probability NO emissions online, remotely, and efficiently. x Heavy-duty vehicles with excessive emissions can also be tested on-site in combination with remote monitoring data, which improves the regulatory authorities' remote monitoring capabilities and on-site testing efficiency for heavy-duty vehicle emissions. This solves the problem that the methods in the relevant technologies do not consider the impact of heavy-duty vehicle emissions on vehicle operating conditions such as vehicle speed and torque, have a high misjudgment rate, low online recognition accuracy, and unclear on-site testing conditions, which reduces the success rate of on-site testing based on online identification of violation clues and wastes the law enforcement resources of regulatory authorities to a certain extent.

[0104] Next, a heavy-duty vehicle over-displacement detection device based on remote monitoring proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0105] Figure 8 It is a structural schematic diagram of a heavy-duty vehicle over-displacement detection device based on remote monitoring according to an embodiment of the present application.

[0106] like Figure 8 As shown, the heavy-duty vehicle over-displacement detection device 10 based on remote monitoring includes: an acquisition module 100, a calculation module 200 and a detection module 300.

[0107] The acquisition module 100 is used to acquire a mileage segment set of remote monitoring data of a heavy-duty vehicle;

[0108] A determination module 200 is used to extract characteristic parameters of each mileage segment in the mileage segment set, determine a high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtain key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval;

[0109] The detection module 300 is used to determine the online identification result and emission on-site detection conditions of the heavy-duty vehicle based on key emission parameters and frequency distribution, so as to obtain the actual over-emission result of the heavy-duty vehicle according to the online identification result and emission on-site detection conditions.

[0110] Optionally, in one embodiment of the present application, the acquisition module 100 includes: a first acquisition unit and a cutting unit.

[0111] The first acquisition unit is used to acquire the remote monitoring data of the heavy-duty vehicle for data cleaning to obtain valid data that meets the preset conditions;

[0112] The cutting unit is used for cutting the valid data into a plurality of mileage segments to generate a mileage segment set according to the plurality of mileage segments.

[0113] Optionally, in one embodiment of the present application, the determination module 200 includes: a first calculation unit, a comparison unit and a second calculation unit.

[0114] The first calculation unit is used to calculate at least one of the vehicle speed, acceleration, torque, rotation speed, fuel consumption and NO corresponding to the multiple mileage segments. x Average values ​​of emission rates to determine characteristic parameters;

[0115] Determine the unit for use according to NO x The interval corresponding to the mileage segment whose emission rate is greater than the preset high emission threshold value determines the high emission interval;

[0116] The second calculation unit is used to calculate the vehicle speed, acceleration, torque, speed, fuel consumption and NO in the high emission range. x The absolute value of the correlation coefficient of the emission rate is used to determine the key emission parameters and their frequency distribution in combination with the preset emission-related thresholds.

[0117] Optionally, in one embodiment of the present application, the detection module 300 includes: a second acquisition unit and a determination unit.

[0118] The second acquisition unit is used to acquire the main distribution range of the key emission parameters based on the key emission parameters and the frequency distribution.

[0119] A determination unit is used to determine the emission on-site detection working conditions of the heavy-duty vehicle based on the main distribution interval and the frequency threshold.

[0120] Optionally, in one embodiment of the present application, the detection module 300 further includes: a judgment unit and a third acquisition unit.

[0121] Among them, the judgment unit is used to judge whether the heavy-duty vehicle meets the on-site inspection requirements;

[0122] The third acquisition unit is used to acquire the emission on-site detection working conditions of the heavy-duty vehicle when the heavy-duty vehicle meets the on-site detection requirements.

[0123] It should be noted that the aforementioned explanation of the embodiment of the heavy-duty vehicle over-displacement detection method based on remote monitoring is also applicable to the heavy-duty vehicle over-displacement detection device based on remote monitoring in this embodiment, and will not be repeated here.

[0124] According to the heavy-duty vehicle over-emission detection device based on remote monitoring proposed in the embodiment of the present application, the remote monitoring data of the heavy-duty vehicle can be obtained and processed such as cleaning, segmentation, and extraction of characteristic parameters, so as to obtain the key emission parameters and frequency distribution of the heavy-duty vehicle and the actual over-emission results of the heavy-duty vehicle from the on-site detection results. Through data processing such as data cleaning and segmentation, combined with online identification results and emission on-site detection conditions, the extraction efficiency and accuracy of key emission parameters and their frequency distribution results are greatly improved, while the analysis efficiency of the data in other utilization processes is improved, the reliability of the data is effectively guaranteed, and the accuracy of the obtained heavy-duty vehicle over-emission results is improved, thereby making full use of remote monitoring data to accurately and efficiently identify high-probability NO emissions online, remotely, and efficiently. x Heavy-duty vehicles with excessive emissions can also be tested on-site in combination with remote monitoring data, which improves the regulatory authorities' remote monitoring capabilities and on-site testing efficiency for heavy-duty vehicle emissions. This solves the problem that the methods in the relevant technologies do not consider the impact of heavy-duty vehicle emissions on vehicle operating conditions such as vehicle speed and torque, have a high misjudgment rate, low online recognition accuracy, and unclear on-site testing conditions, which reduces the success rate of on-site testing based on online identification of violation clues and wastes the law enforcement resources of regulatory authorities to a certain extent.

[0125] Fig. 9 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0126] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0127] When the processor 902 executes the program, the heavy-duty vehicle over-displacement detection method based on remote monitoring provided in the above embodiment is implemented.

[0128] Furthermore, the vehicle also includes:

[0129] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0130] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0131] The memory 901 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.

[0132] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 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, Fig. 9 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.

[0133] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0134] The processor 902 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 application.

[0135] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned heavy-duty vehicle over-displacement detection method based on remote monitoring.

[0136] The embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the heavy-duty vehicle over-emission detection method based on remote monitoring provided in the embodiment of the present application is implemented.

[0137] 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 application. 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.

[0138] 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 technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0139] 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 application 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 application belong.

[0140] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0141] It should be understood that the various parts of the present application 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. 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.

[0142] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment 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.

[0143] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0144] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A heavy-duty vehicle over-displacement detection method based on remote monitoring, characterized in that: The following steps are involved: Obtaining a set of mileage segments of remote monitoring data of heavy-duty vehicles; Extracting characteristic parameters of each mileage segment in the mileage segment set, determining a high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtaining key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval; Based on the key emission parameters and the frequency distribution, the online identification result and the emission on-site detection working condition of the heavy-duty vehicle are determined, so as to obtain the actual over-emission result of the heavy-duty vehicle according to the online identification result and the emission on-site detection working condition.

2. The method according to claim 1, characterized in that The step of obtaining the mileage segment set of the remote monitoring data of the heavy-duty vehicle includes: Acquire the remote monitoring data of the heavy-duty vehicle to perform data cleaning and obtain valid data that meets preset conditions; The valid data is cut into a plurality of mileage segments to generate the mileage segment set according to the plurality of mileage segments.

3. The method according to claim 2, characterized in that The step of extracting characteristic parameters of each mileage segment in the mileage segment set, determining the high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtaining the key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval includes: Calculate at least one of the vehicle speed, acceleration, torque, rotation speed, fuel consumption and NO corresponding to the multiple mileage segments x an average value of the emission rate to determine said characteristic parameter; According to the NO x The high emission interval is determined as an interval corresponding to a mileage segment in which the emission rate is greater than a preset high emission threshold; Calculate the vehicle speed, acceleration, torque, speed, fuel consumption and NO in the high emission range x The absolute value of the correlation coefficient of the emission rate is used to determine the key emission parameters and their frequency distribution in combination with the preset emission-related threshold.

4. The method according to claim 1, characterized in that: The determining of the online identification result and the emission on-site detection working condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution includes: Based on the key emission parameter and the frequency distribution, obtaining a main distribution interval of the key emission parameter; The emission on-site detection working condition of the heavy-duty vehicle is determined based on the main distribution interval and the frequency threshold.

5. The method according to claim 1, characterized in that The determining of the online identification result and the emission on-site detection working condition of the heavy-duty vehicle based on the key emission parameters and the frequency distribution also includes: Determining whether the heavy-duty vehicle meets the on-site inspection requirements; When the heavy-duty vehicle meets the on-site inspection requirements, the emission on-site inspection working conditions of the heavy-duty vehicle are obtained.

6. A heavy-duty vehicle over-displacement detection device based on remote monitoring, characterized in that: include: An acquisition module, used to acquire a mileage segment set of remote monitoring data of a heavy-duty vehicle; a determination module, configured to extract characteristic parameters of each mileage segment in the mileage segment set, determine a high emission interval of the heavy-duty vehicle according to the characteristic parameters, and obtain key emission parameters of the heavy-duty vehicle and their frequency distribution based on the high emission interval; A detection module is used to determine the online identification result and emission on-site detection conditions of the heavy-duty vehicle based on the key emission parameters and the frequency distribution, so as to obtain the actual over-emission result of the heavy-duty vehicle according to the online identification result and the emission on-site detection conditions.

7. The device according to claim 6, characterized in that The acquisition module comprises: An acquisition unit, used for acquiring the remote monitoring data of the heavy-duty vehicle to perform data cleaning and obtain valid data that meets preset conditions; A generating unit is used to cut the valid data into a plurality of mileage segments to generate the mileage segment set according to the plurality of mileage segments.

8. A vehicle, 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 the heavy-duty vehicle over-emission detection method based on remote monitoring as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the heavy-duty vehicle over-emission detection method based on remote monitoring as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that When the computer program product is executed, it is used to implement the heavy-duty vehicle over-emission detection method based on remote monitoring as described in any one of claims 1 to 5.

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