Vehicle pollution emission monitoring method and related device

Abstract

The embodiment of the application provides a kind of vehicle pollution emission supervision method and related equipment, belong to vehicle monitoring technical field.The method is by collecting the monitoring data time series of target vehicle from vehicle online monitoring system, each element of monitoring data time series includes pollution gas emission concentration and position information, according to pollution gas emission concentration in monitoring data time series, determine the pollution gas emission amount of target vehicle in unit time, then according to the position information in monitoring data time series, determine the number of times that target vehicle appears in pollution sensitive area in unit time, by considering the interaction number of target vehicle and pollution sensitive area and pollution gas emission amount, determine the pollution risk of target vehicle, the pollution risk characterizes the pollution degree of vehicle to pollution sensitive area, according to the pollution risk, the target vehicle is supervised, can be to pollution sensitive area influence big vehicle emission supervision, improve the environmental protection effect of pollution sensitive area.

Description

Technical Field

[0001] This application relates to the field of vehicle monitoring technology, and in particular to a method and related equipment for monitoring vehicle pollution emissions. Background Technology

[0002] With the development of information technology, the pollution problem caused by gasoline-powered vehicles has received increasing attention. To achieve online monitoring of vehicle emissions, OBD (On-Board Diagnostics) systems are commonly used. These systems continuously collect engine speed, fuel consumption, NOx / PM emission concentrations, and GPS location information via the vehicle's OBD interface or T-BOX terminal, transmitting this data to a cloud platform. The system then calculates the vehicle's emissions based on the collected pollutant concentrations, thereby enabling the monitoring of vehicle emissions risks. Currently, vehicle emissions can also be monitored using roadside remote sensing systems. These systems utilize technologies such as laser remote sensing and spectral analysis to quickly capture actual vehicle exhaust emission data at road checkpoints and key air pollution emission areas, determining whether a vehicle is a high-risk emission vehicle. However, both OBD online monitoring systems and roadside remote sensing systems focus solely on the vehicle's own emissions to determine high-risk status and issue warnings, without considering the vehicle's environmental impact. This results in a failure to provide timely warnings for vehicles with significant environmental impact. Summary of the Invention

[0003] The main objective of this application is to propose a method and related equipment for monitoring vehicle pollution emissions, aiming to improve the environmental protection effect in areas with a significant impact from pollution.

[0004] To achieve the above objectives, one aspect of this application proposes a method for regulating vehicle pollution emissions, comprising the following steps: The system collects time-series monitoring data of the target vehicle from the vehicle online monitoring system; each element of the monitoring data time series includes pollutant emission concentration and location information. The pollutant emission concentration of the target vehicle per unit time is determined based on the pollutant emission concentration in the time series of the monitoring data. The number of times the target vehicle appears in the pollution-sensitive area per unit time is determined based on the location information in the time series of the monitoring data. The pollution risk of the target vehicle is determined based on the number of times and the amount of pollutant emissions, and the target vehicle is monitored based on the pollution risk.

[0005] In some embodiments, determining the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions includes the following steps: Divide the number of times by the area of ​​the pollution-sensitive area to obtain the degree of pollution caused by the target vehicle to the pollution-sensitive area; The pollution risk of the target vehicle is determined based on the degree of pollution of the target vehicle in multiple pollution-sensitive areas and the amount of pollutant gas emitted.

[0006] In some embodiments, determining the pollution risk of the target vehicle based on the degree of pollution of the target vehicle to multiple pollution-sensitive areas and the amount of pollutant gas emissions includes the following steps: By summing up the pollution levels of the target vehicle on multiple pollution-sensitive areas, the environmental impact value of the target vehicle per unit time is obtained. The pollution risk of the target vehicle is obtained by weighting the environmental impact value and the pollutant gas emissions.

[0007] In some embodiments, each element of the monitoring data time series further includes actual engine operating conditions, fuel flow rate, and engine intake air volume. Determining the pollutant emissions of the target vehicle per unit time based on the pollutant emission concentration in the monitoring data time series includes the following steps: The operating condition compensation factor is calculated based on the actual operating conditions of the engine and the upper limit of the engine operating conditions of the target vehicle. The pollutant emission amount is determined based on the operating condition compensation factor, the fuel flow rate, the engine intake air volume, and the pollutant emission concentration.

[0008] In some embodiments, determining the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions includes the following steps: Based on the vehicle identification number of the target vehicle, query the most recent inspection agency of the target vehicle and identify the inspection agency as the target agency; The pollution risk of the target vehicle is determined based on the detection accuracy of the target institution, the number of detections, and the amount of pollutant gas emitted.

[0009] In some embodiments, the detection accuracy of the target apparatus is determined by the following steps: Collect first vehicle monitoring data from the online vehicle monitoring system and second vehicle monitoring data from the road remote sensing monitoring system; Based on the first vehicle monitoring data and the second vehicle monitoring data, each vehicle is tested for exceeding the standard, and a list of vehicles exceeding the standard is formed; the list of vehicles exceeding the standard includes multiple vehicles exceeding the standard. Search for the last inspection agency corresponding to each vehicle in the list of vehicles exceeding the standards, and generate an agency list; The detection accuracy of the target institution is determined based on the frequency of its appearance in the list of institutions.

[0010] To achieve the above objectives, another aspect of this application proposes a vehicle pollution emission monitoring system, comprising: The first module is used to collect time series monitoring data of the target vehicle from the vehicle online monitoring system; each element of the monitoring data time series includes pollutant emission concentration and location information; The second module is used to determine the amount of pollutant emissions from the target vehicle per unit time based on the pollutant emission concentration in the time series of the monitoring data. The third module is used to determine the number of times the target vehicle appears in the pollution-sensitive area per unit time based on the location information in the time series of the monitoring data. The fourth module is used to determine the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions, and to monitor the target vehicle based on the pollution risk.

[0011] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0012] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0014] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, electronic device, storage medium, and program product for monitoring vehicle pollution emissions. This solution collects time series monitoring data of a target vehicle from an online vehicle monitoring system. Each element of the monitoring data time series includes pollutant emission concentration and location information. Based on the pollutant emission concentration in the monitoring data time series, the pollutant emission amount of the target vehicle per unit time is determined. Then, based on the location information in the monitoring data time series, the number of times the target vehicle appears in a pollution-sensitive area per unit time is determined. By considering the number of interactions between the target vehicle and the pollution-sensitive area and the pollutant emission amount, the pollution risk of the target vehicle is determined. This pollution risk characterizes the degree of pollution of the pollution-sensitive area by the vehicle. By monitoring the target vehicle based on this pollution risk, it is possible to achieve emission monitoring of vehicles that have a significant impact on pollution-sensitive areas, thereby improving the environmental protection effect of pollution-sensitive areas. Attached Figure Description

[0015] Figure 1 This is a flowchart of the vehicle pollution emission monitoring method provided in the embodiments of this application; Figure 2 This is a flowchart of the inspection accuracy analysis of the inspection agency provided in the embodiments of this application; Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0018] With the development of information technology, the pollution problem caused by gasoline-powered vehicles has received increasing attention. To achieve online monitoring of vehicle pollution emissions, OBD (On-Board Diagnostics) online monitoring systems or (motor vehicle) road remote sensing monitoring systems are commonly used. OBD online monitoring systems are generally considered "vehicle" systems, while motor vehicle road remote sensing monitoring systems are considered "sky" systems, as detailed below: Vehicle online monitoring systems (such as diesel vehicle OBD online monitoring systems) are remote exhaust gas diagnostic systems designed for heavy-duty diesel vehicles. Through the original vehicle's OBD diagnostic interface, they collect real-time data from the vehicle's on-board computer, nitrogen oxide and particulate matter sensors, SCR temperature, fuel tank level, and urea tank level. The system monitors the emission concentrations of pollutants such as nitrogen oxides and particulate matter in the exhaust gas. It can analyze the data in real time to determine if vehicle exhaust emissions exceed standards and automatically alarm for abnormal vehicles. This provides environmental regulatory departments with a remote monitoring tool for heavy-duty diesel vehicles, promoting in-depth treatment of high-emission vehicles. In real-time vehicle emission monitoring scenarios, the system continuously collects engine speed, fuel consumption, NOx / PM emission concentrations, and GPS location information through the vehicle's OBD interface or T-BOX terminal, transmitting this data to a cloud platform. The system then calculates the vehicle's pollution emissions based on the collected pollutant emission concentrations, thereby enabling the monitoring of vehicle pollution emission risks.

[0019] The vehicle road remote sensing monitoring system utilizes technologies such as laser remote sensing and spectral analysis to rapidly capture actual vehicle exhaust emission data at road checkpoints and key areas of air pollution emission. It obtains real-time concentrations of pollutants such as nitrogen oxides, opacity, and Ringelmann opacity, automatically identifying high-emission vehicles and vehicles emitting black smoke. This effectively screens for violations such as high-emission vehicles and vehicles emitting black smoke, providing a scientific basis for law enforcement, traffic restrictions, and the phasing out of old vehicles, thus contributing to the refined management of air pollution prevention and control. In vehicle emission monitoring scenarios, it uses laser remote sensing and spectral analysis to rapidly capture actual vehicle exhaust emission data at road checkpoints and key areas of air pollution emission to determine whether a vehicle is a high-emission risk vehicle.

[0020] Currently, whether using OBD online monitoring systems or motor vehicle road remote sensing monitoring systems, the focus is only on the vehicle's own pollution emission concentration to determine whether it is high-risk and to issue warnings, without considering the vehicle's impact on the environment. This results in the inability to respond to and issue warnings in a timely manner for vehicles with a significant environmental impact.

[0021] In view of this, this application provides a method and related equipment for monitoring vehicle pollution emissions. This method collects time series monitoring data of a target vehicle from an online vehicle monitoring system. Each element of the monitoring data time series includes pollutant emission concentration and location information. Based on the pollutant emission concentration in the monitoring data time series, the pollutant emission amount of the target vehicle within a certain time window is determined. Then, based on the location information in the monitoring data time series, the number of times the target vehicle appears in a pollution-sensitive area per unit time is determined. By considering the number of interactions between the target vehicle and the pollution-sensitive area and the pollutant emission amount, the pollution risk of the target vehicle is determined. This pollution risk characterizes the degree of pollution of the pollution-sensitive area by the vehicle. By monitoring the target vehicle based on this pollution risk, it is possible to achieve emission monitoring of vehicles that have a significant impact on pollution-sensitive areas, thereby improving the environmental protection effect of pollution-sensitive areas.

[0022] The vehicle pollution emission monitoring method provided in this application relates to the field of vehicle monitoring technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle pollution emission monitoring method, but is not limited to the above forms.

[0023] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0024] Figure 1 This is an optional flowchart of the vehicle pollution emission monitoring method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0025] Step S101: Collect time series monitoring data of the target vehicle from the vehicle online monitoring system; each element of the monitoring data time series includes pollutant emission concentration and location information; Step S102: Determine the pollutant emissions of the target vehicle per unit time based on the pollutant emission concentration in the monitoring data time series; Step S103: Determine the number of times the target vehicle appears in the pollution-sensitive area per unit time based on the location information in the monitoring data time series; Step S104: Determine the pollution risk of the target vehicle based on the number of times and the amount of pollutant gas emitted, and monitor the target vehicle based on the pollution risk.

[0026] Steps S101 to S104 of this application embodiment involve collecting time series monitoring data of the target vehicle from the vehicle online monitoring system. Each element of the monitoring data time series includes pollutant emission concentration and location information. The pollutant emission concentration in the monitoring data time series is used to determine the pollutant emission amount of the target vehicle per unit time. Then, the location information in the monitoring data time series is used to determine the number of times the target vehicle appears in the pollution-sensitive area per unit time. The pollution risk of the target vehicle is determined by considering the number of interactions between the target vehicle and the pollution-sensitive area and the pollutant emission amount. This pollution risk characterizes the degree of pollution of the pollution-sensitive area by the vehicle. Monitoring the target vehicle based on this pollution risk can achieve emission monitoring of vehicles that have a significant impact on the pollution-sensitive area, thereby improving the environmental protection effect of the pollution-sensitive area.

[0027] In step S101 of some embodiments, a time series of monitoring data of the target vehicle from the vehicle online monitoring system is collected. Each element of the monitoring data time series may include, but is not limited to, the pollutant emission concentration, location information, actual engine operating conditions, fuel flow, and engine intake air volume corresponding to the timestamp. The actual engine operating conditions may include, but are not limited to, engine speed, engine load rate, and current exhaust temperature. Specifically, the monitoring data time series may be an OBD real-time data stream (engine speed, load rate, exhaust temperature, NOx sensor value, etc.). When collecting this data stream, a dynamic emission window can be divided according to the vehicle type (heavy / light) and driving conditions (urban congestion / high-speed cruising / cold start, etc.). The window duration is adaptively adjusted (λ 5s-300s). Transmission delay is eliminated through SAE J1979 protocol parsing and time series alignment technology to ensure the consistency of operating condition characteristics within the same window.

[0028] In step S102 of some embodiments, the monitoring data time series is obtained by continuously collecting OBD data streams. The pollutant emission concentration in the data stream can be used to determine the pollutant emission amount of the target vehicle per unit time. The unit time can be a month or a week, etc., and this application embodiment does not impose specific limitations. For example, the pollutant emission amount can be obtained by multiplying the pollutant emission concentration, fuel flow rate, and engine intake air volume within a time window to obtain the emission amount for that time window, and then summing the emission amounts for consecutive time windows to obtain the pollutant emission amount per unit time. In another example, the pollutant emission amount can be obtained by multiplying the pollutant emission concentration, fuel flow rate, engine intake air volume, and operating condition compensation factor within a time window to obtain the emission amount for that time window, and then summing the emission amounts for consecutive time windows to obtain the pollutant emission amount per unit time. Considering the operating condition compensation factor can improve the accuracy of the pollutant emission calculation.

[0029] In step S103 of some embodiments, the system sets at least one pollution-sensitive area, such as a certain area around a hospital, school, office building, or residential area. The pollution-sensitive area can be marked on a map, and a driving path can be formed on the map by combining the location information of the target vehicle. Based on the map, the number of times the target vehicle appears in each pollution-sensitive area per unit time can be counted. The more times the target vehicle appears in a pollution-sensitive area, the greater its environmental impact on that area.

[0030] In step S104 of some embodiments, the pollution risk of a target vehicle is determined based on the frequency and amount of pollutant emissions. This pollution risk characterizes the vehicle itself and the degree of pollution it causes to pollution-sensitive areas. The target vehicle is then monitored based on this pollution risk. For example, the monitoring method could involve real-time linking of high-pollution-risk vehicles to a monitoring database to extract license plates, owners, maintenance history, and the vehicle's operating unit, and then pushing this information to an application inspection system. Alternatively, vehicles with a pollution risk score >80 could be pushed to an enforcement terminal in real-time to trigger roadside inspections; vehicles with a pollution risk score between 50 and 80 could have maintenance recommendations pushed to their owners and repair shops; and vehicles with a pollution risk score between 30 and 50 could be included in an online monitoring whitelist for enhanced tracking, while simultaneously using window heatmaps to locate high-emission road sections for auxiliary mobile monitoring. By employing monitoring measures for vehicles operating in high-risk areas, environmental protection across the entire region, especially in pollution-sensitive areas, can be improved.

[0031] According to some embodiments of this application, step S104 may include, but is not limited to, the following steps: Step S201: Divide the number of times by the area of ​​the pollution-sensitive area to obtain the degree of pollution of the target vehicle in the pollution-sensitive area; Step S202: Determine the pollution risk of the target vehicle based on the degree of pollution and the amount of pollutant gas emitted by the target vehicle in multiple pollution-sensitive areas.

[0032] In step S201 of some embodiments, the degree of pollution of the pollution-sensitive area by the target vehicle can be calculated using the following formula: ; SDD represents the degree of pollution caused by the target vehicle to a pollution-sensitive area. This indicates the number of times the target vehicle appears in pollution-sensitive area i. This represents the area of ​​pollution-sensitive zone i, in km². 2 By further considering the area of ​​pollution-sensitive zones, the pollution level of vehicles in these zones can be represented more accurately.

[0033] In some embodiments, step S202 may include, but is not limited to, the following steps: Step S301: Accumulate the pollution levels of the target vehicle in multiple pollution-sensitive areas to obtain the environmental impact value of the target vehicle per unit time. Step S302: The environmental impact value and the amount of pollutant gas emissions are weighted and calculated to obtain the pollution risk of the target vehicle.

[0034] In this embodiment, the pollution levels of the target vehicle in multiple pollution-sensitive areas are summed to obtain the environmental impact value of the target vehicle per unit time. The pollution risk of the target vehicle is then calculated by weighting the environmental impact value and the amount of pollutant emissions. By taking into account the weights of the environmental impact value and the amount of pollutant emissions in accordance with actual conditions, the calculated pollution risk can meet the actual assessment and regulatory needs. For example, regulators may be more concerned about the impact of the vehicle on pollution-sensitive areas and can assign a higher weight to the environmental impact value to meet actual needs.

[0035] Furthermore, when assessing the pollution risk of vehicles, the exceedance rate can also be considered. The exceedance rate represents the number of times a vehicle's emissions exceed the standard within each time window per unit of time. For example, the pollution risk of a target vehicle per unit of time (e.g., 30 days) can be calculated as follows: Risk Score = (Frequency Weight × Exceedance Rate) + (Spatial Weight × Environmental Impact Value) + (Total Weight × Pollutant Emissions); or vehicles with exceedance counts > the first threshold (e.g., 5 times / month), exceedance rates > the second threshold (e.g., 15%), or environmental impact values ​​> the third threshold can be identified as high-pollution-risk vehicles.

[0036] According to some embodiments of this application, step S102 may include, but is not limited to, the following steps: Step S401: Calculate the operating condition compensation factor based on the actual engine operating conditions and the upper limit of the engine operating conditions of the target vehicle. Step S402: Determine the pollutant emission amount based on the operating condition compensation factor, fuel flow rate, engine intake air volume, and pollutant emission concentration.

[0037] In this embodiment, taking NOx as the pollutant, to more accurately reflect the difference between vehicle NOx emission characteristics under different driving conditions and standard conditions, a dynamic operating condition compensation factor method is proposed to reduce the error between the model calculation value and the actual operating condition, thereby improving the accuracy of NOx emission calculation. The formula for calculating the dynamic operating condition compensation factor is as follows: ; in, It is a dynamic operating condition compensation factor. Engine speed (unit: rpm); This is the engine's maximum speed (unit: rpm), which can be obtained from the vehicle's engine specifications. This is the current engine load rate (%). This is the engine's maximum load rate, which can be set to 100. This is the current exhaust temperature (unit: °C). It is the highest exhaust temperature that the engine can reach (unit: °C), which can be determined through engine performance data; , , These are the weighting coefficients. It is a constant and can be determined by fitting experimental data of vehicles and vehicle types. At the same time, it must satisfy a+b+c+d=1 so that the compensation factor fluctuates within a reasonable range (i.e., CF is less than 1).

[0038] Within each emission window, the NOx emission (g / s) is calculated using an emission factor inversion algorithm: NOx = k × (fuel flow rate + intake air flow rate) × real-time NOx concentration × operating condition compensation factor, where k is a fixed coefficient representing the conversion of PPM to mg / m3. The emission is compared in real-time with the national standard GB 17691-2018 limits, and windows exceeding these limits are automatically marked as high-emission windows. The NOx emissions from each time window are summed to obtain the NOx emissions per unit time.

[0039] According to some embodiments of this application, step S104 may include, but is not limited to, the following steps: Step S501: Based on the vehicle identification of the target vehicle, query the most recent inspection agency of the target vehicle and determine the inspection agency as the target agency. Step S502: Determine the pollution risk of the target vehicle based on the detection accuracy, frequency, and number of pollutant gas emission inspection agencies of the target agency.

[0040] In this embodiment, a periodic emission inspection system for motor vehicles is used for monitoring vehicle emissions. This system is a platform or institution that conducts periodic inspections of motor vehicle exhaust emissions according to relevant environmental standards, and is generally considered a ground-based system. The inspection process includes vehicle appearance inspection, on-board diagnostic (OBD) system check, and exhaust pollutant detection. High-precision gas analyzers, OBD diagnostic instruments, dynamometers, and other equipment are used to measure pollutants such as carbon monoxide, hydrocarbons, and nitrogen oxides in real time. Vehicle owners need to have their vehicles inspected periodically at inspection agencies (platforms). The inspection agencies upload relevant data to the system. Based on the inspection results, the system indicates whether the vehicle's pollution emissions are acceptable. Vehicles with poor inspection results can be subject to control measures. Therefore, vehicles on the road are generally those that have passed inspection. However, there are currently many inspection agencies, and their inspection results are not reliable. Vehicles with pre-existing problems may be deemed to have passed inspection, allowing high-emission vehicles to operate on the road. Based on this, this embodiment also considers the detection accuracy of the target vehicle's most recent inspection agency (i.e., the target agency) when assessing pollution risk. The detection accuracy is inversely proportional to the pollution risk. If the detection accuracy of the target agency is low, it means that the vehicle is more likely to be a high-emission vehicle, i.e., the pollution risk is higher. This improves the accuracy of vehicle pollution risk assessment and thus improves the environmental protection effect on pollution-sensitive areas.

[0041] According to some embodiments of this application, the detection accuracy of the target mechanism in step S502 can be determined by, but is not limited to, the following steps: Step S601: Collect first vehicle monitoring data from the vehicle online monitoring system and second vehicle monitoring data from the road remote sensing monitoring system; Step S602: Based on the first vehicle monitoring data and the second vehicle monitoring data, conduct excess detection on each vehicle to form an excess list; the excess list includes multiple excess vehicles; Step S603: Query the previous inspection agency corresponding to each vehicle in the list of vehicles exceeding the standards, and generate an agency list; Step S604: Determine the detection accuracy of the target institution based on the frequency of its appearance in the institution list.

[0042] Specifically, this embodiment integrates the OBD online monitoring system, the motor vehicle road remote sensing monitoring system, and the motor vehicle periodic emission inspection system to provide potential clues about illegal activities by inspection agencies and identify those with low monitoring accuracy. Data is collected from the OBD online monitoring system—NOx emission concentration; data is collected from the motor vehicle road remote sensing monitoring system—vehicle NOx emission concentration, Ringelmann smudge test, and opacity; data is collected from the motor vehicle periodic emission inspection system—information on the vehicle's last inspection agency. By outputting polluting vehicle information through the OBD and remote sensing systems, the vehicle's last inspection agency is traced in the annual inspection system, and risk is marked. Frequently appearing inspection agencies are defined as high-risk agencies. Please refer to... Figure 2 The specific implementation method is as follows: Data collection and preliminary screening: The OBD online monitoring system (i.e., the OBD data information system) outputs the vehicle's NOx emission concentration and performs automatic data verification. If the emission exceeds the standard, the system outputs vehicle information; otherwise, the process ends. The motor vehicle road remote sensing monitoring system outputs the vehicle's NOx concentration, Ringelmann opacity, and opacity and performs automatic data verification. If any of these indicators exceeds the standard, the system outputs vehicle information; otherwise, the process ends.

[0043] Pollution vehicle consolidation and deduplication: The information of vehicles exceeding the standards output by the OBD and remote sensing systems is combined and deduplicated using license plate / VIN codes to form a list of vehicles exceeding the standards.

[0044] Inspection agency traceability: The list of vehicles exceeding the standard is entered into the motor vehicle periodic emission inspection system, and the information of the last inspection agency for each vehicle is matched to record the frequency of vehicles exceeding the standard associated with each agency.

[0045] High-risk agency identification: This involves statistically analyzing the frequency of vehicles exceeding standards associated with each inspection agency, i.e., the frequency of each agency's appearance in the agency list. This frequency can be normalized and represented as the agency's inspection accuracy rate. Furthermore, agencies with high-frequency associations (reaching a set threshold) can be identified as high-risk inspection agencies, and their information can be output, enabling simultaneous supervision of high-risk inspection agencies.

[0046] This application also provides a vehicle pollution emission monitoring system, including: The first module is used to collect time series monitoring data of target vehicles from the vehicle online monitoring system; each element of the monitoring data time series includes pollutant emission concentration and location information; The second module is used to determine the amount of pollutant emissions from the target vehicle per unit time based on the pollutant emission concentration in the time series of monitoring data. The third module is used to determine the number of times the target vehicle appears in the pollution-sensitive area per unit time based on the location information in the time series of monitoring data. The fourth module is used to determine the pollution risk of a target vehicle based on the number of times and the amount of pollutant emissions, and to regulate the target vehicle based on the pollution risk.

[0047] It is understood that the methods described in the above method embodiments are applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0048] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers and computers.

[0049] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0050] Please see Figure 3 , Figure 3 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0051] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0052] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0053] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0054] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0055] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0056] The vehicle pollution emission monitoring method and related equipment provided in this application collect time series monitoring data of target vehicles from an online vehicle monitoring system. Each element of the monitoring data time series includes pollutant emission concentration and location information. Based on the pollutant emission concentration in the monitoring data time series, the pollutant emission amount of the target vehicle per unit time is determined. Then, based on the location information in the monitoring data time series, the number of times the target vehicle appears in a pollution-sensitive area per unit time is determined. By considering the number of interactions between the target vehicle and the pollution-sensitive area and the pollutant emission amount, the pollution risk of the target vehicle is determined. This pollution risk characterizes the degree of pollution of the pollution-sensitive area by the vehicle. Monitoring the target vehicle based on this pollution risk can achieve emission monitoring of vehicles that have a significant impact on pollution-sensitive areas, thereby improving the environmental protection effect of pollution-sensitive areas.

[0057] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0058] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0059] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0061] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0062] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0063] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0064] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0066] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for monitoring vehicle pollution emissions, characterized in that, Includes the following steps: The system collects time-series monitoring data of the target vehicle from the vehicle online monitoring system; each element of the monitoring data time series includes pollutant emission concentration and location information. The pollutant emission concentration of the target vehicle per unit time is determined based on the pollutant emission concentration in the time series of the monitoring data. The number of times the target vehicle appears in the pollution-sensitive area per unit time is determined based on the location information in the time series of the monitoring data. The pollution risk of the target vehicle is determined based on the number of times and the amount of pollutant emissions, and the target vehicle is monitored based on the pollution risk. Determining the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions includes the following steps: Divide the number of times by the area of ​​the pollution-sensitive area to obtain the degree of pollution caused by the target vehicle to the pollution-sensitive area; By summing up the pollution levels of the target vehicle on multiple pollution-sensitive areas, the environmental impact value of the target vehicle per unit time is obtained. The pollution risk of the target vehicle is obtained by weighting the environmental impact value and the pollutant gas emissions.

2. The method according to claim 1, characterized in that, Each element of the monitoring data time series also includes the actual engine operating conditions, fuel flow rate, and engine intake air volume. Determining the pollutant emissions of the target vehicle per unit time based on the pollutant emission concentration in the monitoring data time series includes the following steps: The operating condition compensation factor is calculated based on the actual operating conditions of the engine and the upper limit of the engine operating conditions of the target vehicle. The pollutant emission amount is determined based on the operating condition compensation factor, the fuel flow rate, the engine intake air volume, and the pollutant emission concentration.

3. The method according to claim 1, characterized in that, Determining the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions includes the following steps: Based on the vehicle identification number of the target vehicle, query the most recent inspection agency of the target vehicle and identify the inspection agency as the target agency; The pollution risk of the target vehicle is determined based on the detection accuracy of the target institution, the number of detections, and the amount of pollutant gas emitted.

4. The method according to claim 3, characterized in that, The detection accuracy of the target institution is determined through the following steps: Collect first vehicle monitoring data from the online vehicle monitoring system and second vehicle monitoring data from the road remote sensing monitoring system; Based on the first vehicle monitoring data and the second vehicle monitoring data, each vehicle is tested for exceeding the standard, and a list of vehicles exceeding the standard is formed; the list of vehicles exceeding the standard includes multiple vehicles exceeding the standard. Search for the last inspection agency corresponding to each vehicle in the list of vehicles exceeding the standards, and generate an agency list; The detection accuracy of the target institution is determined based on the frequency of its appearance in the list of institutions.

5. A vehicle pollution emission monitoring system, characterized in that, include: The first module is used to collect time series monitoring data of target vehicles from the vehicle online monitoring system; Each element of the monitoring data time series includes pollutant emission concentration and location information; The second module is used to determine the amount of pollutant emissions from the target vehicle per unit time based on the pollutant emission concentration in the time series of the monitoring data. The third module is used to determine the number of times the target vehicle appears in the pollution-sensitive area per unit time based on the location information in the time series of the monitoring data. The fourth module is used to determine the pollution risk of the target vehicle based on the number of times and the amount of pollutant emissions, and to monitor the target vehicle based on the pollution risk. The fourth module is specifically used to perform the following steps: Divide the number of times by the area of ​​the pollution-sensitive area to obtain the degree of pollution caused by the target vehicle to the pollution-sensitive area; By summing up the pollution levels of the target vehicle on multiple pollution-sensitive areas, the environmental impact value of the target vehicle per unit time is obtained. The pollution risk of the target vehicle is obtained by weighting the environmental impact value and the pollutant gas emissions.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 4.

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